Focal plane calibration method and apparatus, computer device, and storage medium

Abstract

The present disclosure relates to the technical field of digital exposure, and provides a focal plane calibration method and apparatus, a computer device, and a storage medium. The focal plane calibration method comprises: carrying out image acquisition on a preset template to obtain a target image, wherein the target image comprises a plurality of first object groups arranged side by side in a first direction, and a plurality of second object groups arranged side by side in a second direction, the preset template comprises a plurality of test areas, and a target object in one test area is obtained by one-time exposure of an exposure device; on the basis of the target image, determining pixel position information corresponding to each target object; on the basis of the pixel position information of each target object, determining that an exposure state of the target object is a normal state or a defocused state, and on the basis of a determination result and a pre-stored exposure height corresponding to each first object group, determining predicted focal plane heights respectively corresponding to different test areas; and on the basis of each predicted focal plane height, determining calibration information of an optimal focal plane of the exposure device.

Description

Focal plane calibration method, device, computer equipment and storage medium Technical Field

[0001] The present disclosure belongs to the field of exposure technology, and particularly relates to a focal plane calibration method, apparatus, computer equipment, and storage medium. Background Art

[0002] Exposure is a critical step in the photolithography process. The exposure machine is equipped with an optimal focal plane, also known as the focus plane. However, as the exposure machine ages and is affected by external factors such as the operating environment, the optimal focal plane of the exposure machine will shift. This can cause the dimensions of the product produced after the exposure process to change, affecting product quality and, in severe cases, affecting the entire product.

[0003] Currently, manual calibration is often used to calibrate the focal plane. However, calibration results vary between engineers, resulting in low accuracy. Furthermore, manual calibration is inefficient, significantly impacting production processes and increasing product manufacturing costs.

[0004] Summary of the Invention

[0005] The present disclosure aims to solve at least one of the technical problems existing in the prior art and provides a focal plane calibration method, apparatus, computer device and storage medium.

[0006] In a first aspect, the technical solution adopted to solve the technical problem of the present disclosure is a focal plane calibration method, comprising:

[0007] Performing image acquisition on a preset template to obtain a target image; the target image includes a plurality of first object groups arranged side by side along a first direction, and a plurality of second object groups arranged side by side along a second direction; the first object groups include a plurality of target objects arranged side by side along the second direction; the second object groups include a plurality of target objects arranged side by side along the first direction; the preset template includes a plurality of test areas, and the target objects in one test area are obtained by a single exposure by an exposure device, while the target objects in different test areas are obtained by different exposures by the exposure device;

[0008] Determining pixel position information corresponding to each target object according to the target image;

[0009] Based on the pixel position information of each target object, determining whether the exposure state of the target object is normal or out of focus, and determining the predicted focal plane heights corresponding to different test areas according to the determination result and the pre-stored exposure heights corresponding to each first object group;

[0010] Based on each of the predicted focal plane heights, calibration information of an optimal focal plane of the exposure device is determined.

[0011] In some embodiments, determining pixel position information corresponding to each target object according to the target image includes:

[0012] Extracting contour features from the target image to determine the center position of the target object;

[0013] Pixel position information of the target object is determined based on the center position of the target object and a preset size range of the target object.

[0014] In some embodiments, determining pixel position information corresponding to each target object according to the target image includes:

[0015] Extracting contour features from the target image, performing binarization processing on the target image to obtain a first binary image, and determining the center position of the target object based on the extracted contour features;

[0016] Pixel position information of the target object is determined based on the center position of the target object and a preset size range of the target object.

[0017] In some embodiments, determining pixel position information corresponding to each target object according to the target image includes:

[0018] Extracting contour features from the target image and performing binarization processing on the target image to obtain a first binary image;

[0019] performing dilation processing on the first binary image to obtain a first intermediate image;

[0020] performing an erosion process on the first intermediate image to obtain a second intermediate image;

[0021] extracting contour features from the second intermediate image to determine the center position of the target object;

[0022] Pixel position information of the target object is determined based on the center position of the target object and a preset size range of the target object.

[0023] In some embodiments, binarizing the target image to obtain a first binary image includes:

[0024] Extracting contour features from the target image and performing binarization processing on the target image to obtain an initial binary image;

[0025] Determining a to-be-cropped area based on the pixel value and position information of each pixel point in the initial binary image; the pixels in the to-be-cropped area are an area in the second direction that contains multiple segments of pixel values ​​that are continuously 0 and is surrounded by multiple segments of pixel values ​​that are continuously 255;

[0026] The portion of the initial binary image located in the to-be-cropped area is cut off to obtain the first binary image.

[0027] In some embodiments, the size range of the target object includes a width range and a height range;

[0028] The determining pixel position information of the target object based on the center position of the target object and a preset size range of the target object includes:

[0029] Taking the center position of the target object as the center, based on a random width within the width range and a random height within the height range, the target object is determined, and pixel position information of the target object is determined.

[0030] In some embodiments, determining whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object includes:

[0031] determining the area within the outline of each target object based on the pixel position information of each target object;

[0032] Based on the comparison result between the areas within the contours of the target objects, selecting a reference object from the target objects and determining pixel position information of the reference object;

[0033] Based on the pixel position information of each target object and the pixel position information of the reference object, it is determined whether the exposure state of the target object is normal or out of focus.

[0034] In some embodiments, the determining, based on the pixel position information of each target object and the pixel position information of the reference object, whether the exposure state of the target object is normal or out of focus includes:

[0035] Determining the similarity between each target object and the reference object using a preset similarity algorithm based on the pixel position information of each target object in the target image and the pixel position information of the reference object;

[0036] Based on a comparison result between the similarity between each target object and the reference object and a first preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

[0037] In some embodiments, the determining, based on the pixel position information of each target object and the pixel position information of the reference object, whether the exposure state of the target object is normal or out of focus includes:

[0038] Determining the similarity between each target object and the reference object using a preset similarity algorithm based on the pixel position information of each target object and the pixel position information of the reference object in the first binary image;

[0039] Based on a comparison result between the similarity between each target object and the reference object and a first preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

[0040] In some embodiments, the determining, based on the pixel position information of each target object and the pixel position information of the reference object, whether the exposure state of the target object is normal or out of focus includes:

[0041] determining, based on pixel position information of each target object and pixel position information of the reference object in the second intermediate image, a similarity between each target object and the reference object using a preset similarity algorithm;

[0042] Based on a comparison result between the similarity between each target object and the reference object and a first preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

[0043] In some embodiments, determining whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object includes:

[0044] intercepting target sub-images corresponding to the target objects from the target image according to pixel position information of the target objects, and determining the area within the outline of the target objects in each target sub-image;

[0045] Based on the comparison results between the areas within the contours of the target objects, a reference object is selected from the target objects, and a reference subgraph of the reference object is determined;

[0046] Binarizing each target sub-image and each reference sub-image to obtain a target binary sub-image and a reference binary sub-image;

[0047] Based on each of the target binary subgraphs and the reference binary subgraph, using a preset similarity algorithm, determining the similarity between each of the target objects and the reference object;

[0048] Based on a comparison result between the similarity between each target object and the reference object and a first preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

[0049] In some embodiments, determining whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object includes:

[0050] intercepting target sub-images corresponding to the target objects from the target image according to pixel position information of the target objects, and determining the area within the outline of the target objects in each target sub-image;

[0051] Based on the comparison results between the areas within the contours of the target objects, selecting a reference object from the target objects and determining a reference subgraph of the reference object;

[0052] Reducing the target sub-image and the reference sub-image according to a preset scaling factor to obtain reduced target sub-images and reduced reference sub-images;

[0053] Binarization is performed on each of the reduced target sub-images and the reduced reference sub-images to obtain each target binary sub-image and reference binary sub-image;

[0054] Determining the Hamming distance of each target binary subgraph based on each target binary subgraph and the reference binary subgraph;

[0055] Based on the comparison result of the Hamming distance of each target binary sub-image and a second preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

[0056] In some embodiments, determining whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object includes:

[0057] intercepting a target sub-image corresponding to the target object from the target image according to pixel position information of the target object;

[0058] Performing binarization processing on each of the target subgraphs to obtain each target binary subgraph;

[0059] Determining the Hamming distance of each target binary subimage based on each target binary subimage and a preset pure color image; the size of the pure color image is the same as the preset size;

[0060] Based on the comparison result of the Hamming distance of each target binary sub-image and a third preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

[0061] In some embodiments, determining the predicted focal plane heights corresponding to different test areas according to the judgment result and the pre-stored exposure heights of the target objects in each column includes:

[0062] For the target objects in each of the second object groups arranged side by side along the second direction, determining pixel position information of a defocus boundary corresponding to each of the second object groups based on an exposure state of each of the target objects and pixel position information of each of the target objects;

[0063] Determining regional defocus boundaries corresponding to each of the test areas based on pixel position information of the defocus boundaries corresponding to each of the second object groups and position information of the test areas;

[0064] For the regional defocus boundaries corresponding to the respective test areas, determining the exposure heights of the regional defocus boundaries based on pixel position information of the regional defocus boundaries and pre-stored exposure heights corresponding to the respective first object groups;

[0065] For the regional defocus boundaries respectively corresponding to the test areas, the predicted focal plane height of the test area is determined based on the exposure height of the regional defocus boundaries.

[0066] In some embodiments, the regional out-of-focus boundary includes a first boundary and a second boundary;

[0067] The step of determining the predicted focal plane height of the test area based on the exposure height of the defocus boundary of the area includes:

[0068] The average of the exposure height of the first boundary and the exposure height of the second boundary is used as the predicted focal plane height of the test area.

[0069] In some embodiments, the calibration information of the optimal focal plane of the exposure device includes the height and tilt of the optimal focal plane; the test area includes two;

[0070] The step of determining calibration information of an optimal focal plane of the exposure device based on each of the predicted focal plane heights includes:

[0071] Taking the average of the predicted focal plane heights as the height of the optimal focal plane;

[0072] The difference between the two predicted focal plane heights is used as the tilt of the optimal focal plane.

[0073] In a second aspect, an embodiment of the present disclosure further provides a focal plane calibration device, including an image acquisition module, an image processing module, a prediction module, and a calibration information determination module:

[0074] The image acquisition module is configured to acquire an image of a preset template to obtain a target image; the target image includes a plurality of first object groups arranged side by side along a first direction, and a plurality of second object groups arranged side by side along a second direction; the first object groups include a plurality of target objects arranged side by side along the second direction; the second object groups include a plurality of target objects arranged side by side along the first direction; the preset template includes a plurality of test areas, wherein a target object in one test area is obtained by a single exposure, and target objects in different test areas are obtained by different exposures;

[0075] The image processing module is configured to determine pixel position information corresponding to each target object based on the target image;

[0076] The prediction module is configured to determine whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object, and determine the predicted focal plane heights corresponding to different test areas according to the judgment result and the pre-stored exposure heights corresponding to each first object group;

[0077] The calibration information determination module is configured to determine the calibration information of the optimal focal plane of the exposure device based on each of the predicted focal plane heights.

[0078] In a third aspect, an embodiment of the present disclosure further provides a computer device, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the focal plane calibration method as described in any one of the first aspects are performed.

[0079] In a fourth aspect, an embodiment of the present disclosure further provides a computer non-volatile readable storage medium, wherein a computer program is stored on the computer non-volatile readable storage medium, and when the computer program is executed by a processor, the steps of the focal plane calibration method as described in any one of the first aspects are executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] FIG1 is a flow chart of a focal plane calibration method provided by an embodiment of the present disclosure;

[0081] FIG2 is a schematic diagram of an exemplary preset template after exposure provided by an embodiment of the present disclosure;

[0082] FIG3 is a specific flow chart of an exemplary focal plane calibration method provided by an embodiment of the present disclosure;

[0083] FIG4 is a schematic diagram of Laplacian-based boundary recognition provided by an embodiment of the present disclosure;

[0084] FIG5a is a schematic diagram of boundary recognition in the X direction based on Sobel provided by an embodiment of the present disclosure;

[0085] FIG5 b is a schematic diagram of boundary recognition in the Y direction based on Sobel provided by an embodiment of the present disclosure;

[0086] FIG6 is a schematic diagram of Candy-based boundary recognition provided by an embodiment of the present disclosure;

[0087] FIG7 is a schematic diagram of a first binary image provided by an embodiment of the present disclosure;

[0088] FIG8 is a schematic diagram of the first binary image after redundant images are removed from FIG7 ;

[0089] FIG9 is a schematic diagram of a first intermediate image obtained by dilating a first binary image according to an embodiment of the present disclosure;

[0090] FIG10 is a schematic diagram of a second intermediate image obtained by performing an erosion process on the first intermediate image according to an embodiment of the present disclosure;

[0091] FIG11 is a schematic diagram of a sub-image corresponding to the target object shown in FIG2 ;

[0092] FIG12 is a schematic diagram of a sub-image corresponding to the target object shown in FIG8 ;

[0093] FIG13 is a schematic diagram of a sub-image corresponding to the target object shown in FIG10 ;

[0094] FIG14 is a schematic diagram of converting a target sub-image into a reference sub-image according to an embodiment of the present disclosure;

[0095] FIG15 is a schematic diagram of a defocus boundary corresponding to a second object group provided by an embodiment of the present disclosure;

[0096] FIG16 is a schematic diagram of an out-of-focus boundary of a region on a target image provided by an embodiment of the present disclosure;

[0097] FIG17 is a schematic diagram of a focal plane calibration device provided by an embodiment of the present disclosure;

[0098] FIG18 is a schematic structural diagram of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0099] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the disclosure for which protection is sought, but merely represents selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present disclosure.

[0100] Unless otherwise defined, the technical or scientific terms used in this disclosure should have the usual meanings understood by people with ordinary skills in the field to which this disclosure belongs. The words "first", "second" and similar words used in this disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one", "an" or "the" do not indicate a quantity limitation, but rather indicate the existence of at least one. Words such as "include" or "comprise" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0101] In this disclosure, "multiple or several" refers to two or more. "And / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0102] Before describing the embodiments of the present disclosure, some special terms provided in the embodiments of the present disclosure are further explained.

[0103] 1. Optical Critical Dimension (OCD) is a commonly used measurement device in electronics manufacturing. Its principle is based on the phenomenon of optical reflection. In this device, a beam of light passes through the surface of the object being measured and is affected by the surface topography and reflected. Ultimately, the object's topography is measured by detecting the change in the optical path. Therefore, OCD devices can accurately measure the geometric topography of tiny structures in semiconductor chips, such as line width, line spacing, and radius, to meet the manufacturing precision requirements of advanced manufacturing technologies.

[0104] 2. The Focus Energy Matrix (FEM) is a set of data obtained by varying the focus value in fixed steps in one direction and the exposure energy in fixed steps in the other direction during exposure. This refers to the focus-energy matrix setting. However, in the disclosed embodiments, the FEM matrix is ​​represented as target objects arranged in an array within a preset template, or as target objects within a target image.

[0105] 3. The Laplacian operator is a second-order differential operator in n-dimensional Euclidean space, defined as the divergence div of the gradient grad.

[0106] 4. The Sobel operator is an important processing method in the field of computer vision. It is mainly used to obtain the first-order gradient of digital images. Its common application and physical significance is edge detection.

[0107] 5. The Canny edge detection operator is a multi-level edge detection algorithm.

[0108] 6. The Hamming distance is used in data transmission error control coding. It represents the number of different characters in corresponding positions of two strings (of the same length). We use d(x,y) to represent the Hamming distance between two characters x and y. The Hamming distance is calculated by performing an XOR operation on the two strings and counting the number of 1s in the result.

[0109] In related technologies, exposure machine focal plane calibration often relies on manual calibration. However, different personnel often produce inconsistent and often significant results for the same exposure sample, resulting in low manual calibration accuracy. Furthermore, the subsequent processing and judgment of different calculation results, and finding a more accurate optimal focal plane, is a long and inefficient manual calibration process, significantly impacting production line processes and increasing product manufacturing costs.

[0110] In view of this, the embodiments of the present disclosure provide a focal plane calibration method, apparatus, computer equipment, and storage medium, which can automatically and accurately calculate the calibration information of the optimal focal plane of the exposure device, thereby improving calibration efficiency.

[0111] The focal plane calibration method provided by the embodiment of the present disclosure performs image acquisition on a preset template to obtain a target image; the target image includes multiple groups of first objects arranged side by side along a first direction, and multiple groups of second objects arranged side by side along a second direction; the first object group includes multiple target objects arranged side by side along the second direction; the second object group includes multiple target objects arranged side by side along the first direction; the preset template includes multiple test areas, and the target objects in one test area are obtained by a single exposure of the exposure device, and the target objects in different test areas are obtained by different exposures of the exposure device; according to the target image, pixel position information corresponding to each target object is determined; based on the pixel position information of each target object, the exposure state of the target object is judged to be normal or out of focus, and the predicted focal plane heights corresponding to the different test areas are determined according to the judgment result and the pre-stored exposure heights corresponding to each first object group; based on each predicted focal plane height, calibration information of the optimal focal plane of the exposure device is determined.

[0112] The disclosed embodiment first captures images of a preset template with multiple target objects, then applies image processing technology to the identification and calibration of the focal plane, achieving automated detection and improving calibration efficiency. Simultaneously, based on the pixel position information of each target object, the exposure status of each target object is refined to prepare for subsequent improvements in calibration accuracy. By determining the exposure status of each target object and the exposure heights corresponding to each target object pre-stored in groups, the predicted focal plane heights corresponding to different test areas can be determined. Since different test areas correspond to different exposures of the same exposure device, accurate calibration information for the optimal focal plane can be obtained based on the different prediction results.

[0113] The specific process of a focal plane calibration method provided by an embodiment of the present disclosure is described in detail below.

[0114] FIG1 is a flow chart of a focal plane calibration method provided by an embodiment of the present disclosure. As shown in FIG1 , the method includes steps S11 to S14, wherein:

[0115] S11. Capture an image of a preset template to obtain a target image.

[0116] The target image includes multiple first object groups 31 arranged side by side along a first direction X, and multiple second object groups 32 arranged side by side along a second direction Y; the first object group 31 includes multiple target objects arranged side by side along the second direction Y; the second object group 32 includes multiple target objects arranged side by side along the first direction X.

[0117] The preset template includes multiple test areas, and the target object in one test area is obtained by a single exposure of the exposure device, while the target objects in different test areas are obtained by different exposures of the exposure device. The target image is an image obtained by capturing the preset template, and the content of the target image is therefore the information of the preset template. The preset template is an exposed template, and different test areas are exposed different times to form multiple target objects. The multiple target objects are specifically arranged in the preset template as follows: multiple groups of first objects 31 are arranged side by side along a first direction X, each group of first objects 31 including multiple target objects arranged side by side along a second direction Y; and multiple groups of second objects 32 are arranged side by side along the second direction Y, each group of second objects 32 including multiple target objects arranged side by side along the first direction X (as shown in A in FIG2 ).

[0118] During exposure, different first object groups 31 correspond to different exposure heights. FIG2 is a schematic diagram of an exemplary preset template after exposure provided by an embodiment of the present disclosure. As shown in FIG2 , the preset template is divided into two test areas, which are respectively recorded as a first test area 201 and a second test area 202. Using the same exposure device to be calibrated, the two test areas are exposed separately to form a plurality of target objects arranged in an array. The first direction X is, for example, a row direction, and the second direction Y is, for example, a column direction. Different first object groups 31 correspond to different exposure heights. Taking a test area as an example, a test area includes 25 groups of first object groups 31. During exposure, the exposure height (which can also be understood as the focal plane height or focus height) of the exposure device is adjusted to scan in sequence. For example, along the positive direction of the first direction X, the exposure height corresponding to each group of first object groups 31 decreases in sequence, and the exposure height difference between two adjacent columns of first object groups 31 is the same, for example, p. For example, 2n first object groups 31 arranged side by side along a first direction X are designated as the first first object group 31, the second first object group 31, ..., and the 2nth first object group 31. Based on the currently stored optimal focal plane of 0 μm for the exposure device, the exposure height of the nth group is set as the current optimal focal plane of the exposure device. Specifically, the exposure height corresponding to the first first object group 31 is x μm, the exposure height corresponding to the second first object group 31 is xp, the exposure height corresponding to the third first object group 31 is x-2p, ..., the exposure height corresponding to the nth first object group 31 is 0 μm, the exposure height corresponding to the n+1th first object group 31 is -p, ..., and the exposure height corresponding to the 2nth first object group 31 is -x. The exposure heights corresponding to each first object group 31 are stored for subsequent algorithm recall.

[0119] It should be noted that the exposure process described above to obtain the preset template is a test process for the exposure device. Therefore, for the exposure process of a test area, the exposure height of the exposure device can be adjusted for scanning different columns to collect multiple first object groups 31 corresponding to different exposure heights. However, in actual application, the exposure height of the exposure device is not adjusted during the exposure process. In other words, in actual application, the exposure device performs exposure at an optimal exposure height (optimal focal plane).

[0120] The image acquisition process is performed on the preset template. Specifically, the OCD device can be used to locate and photograph the FEM preset template to obtain a target image to acquire an FEM image.

[0121] S12. Determine pixel position information corresponding to each target object according to the target image.

[0122] During specific implementation, a preset algorithm can be used to identify the object features of the target image to determine the target object, and further determine the pixel position information corresponding to the target object. The pixel position information can represent the position of the target object in the target object. Specifically, the pixel position information may include the position of at least part of the pixel points where the target object is located.

[0123] S13 , judging whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object, and determining the predicted focal plane heights corresponding to different test areas according to the judgment result and the pre-stored exposure heights corresponding to each first object group 31 .

[0124] Based on the pixel position information of each target object, its morphology can be determined, and its exposure status can be determined based on the morphology. For example, the exposure status of a target object with a regular morphology can be determined to be normal, while the exposure status of an irregular morphology can be determined to be defocused. Defocus refers to the deviation of the focal plane of the exposure device from the optimal focal plane.

[0125] Afterwards, based on the judgment results of the exposure status of each target object, for each group of second object groups 32, it is possible to determine the target object closest to the defocused state among the multiple target objects in the normal state, or to determine the target object closest to the normal state among the multiple target objects in the defocused state, thereby determining the defocus boundary corresponding to each group of second object groups 32.

[0126] Next, the predicted focal plane heights corresponding to different test areas are determined based on the defocus boundaries corresponding to each second object group 32. Taking a single test area as an example, the defocus boundaries corresponding to each second object group 32 within the test area are integrated to determine the defocus boundary corresponding to the test area. The predicted focal plane height corresponding to the test area is then determined based on the pre-stored exposure heights corresponding to each first object group 31.

[0127] S14. Determine calibration information of the optimal focal plane of the exposure device based on the predicted focal plane heights.

[0128] The calibration information of the optimal focal plane includes, for example, the height and tilt of the optimal focal plane.

[0129] Specifically, a first preset algorithm can be used to determine the height of the optimal focal plane of the exposure device based on the predicted focal plane heights. A second preset algorithm can be used to determine the tilt of the optimal focal plane of the exposure device based on the predicted focal plane heights. Finally, based on the optimal focal plane height and the tilt of the optimal focal plane, the parameters of the exposure device are adjusted to adjust the exposure device from its current focal plane to the optimal focal plane, thereby achieving automated calibration of the exposure device's focal plane.

[0130] Exemplarily, Figure 3 is a specific flow chart of an exemplary focal plane calibration method provided by an embodiment of the present disclosure. As shown in Figure 3, it includes steps S21 to S25, wherein: S21, using the OCD device to collect FEM images; S22, identifying the target object; S23, determining the defocus boundaries corresponding to each group of second objects 32; S24, determining the defocus boundaries corresponding to each test area; S25, determining the calibration information of the optimal focal plane of the exposure device.

[0131] In some embodiments, with respect to step S12, determining the pixel position information corresponding to each target object specifically includes the following steps S12-1-1 to S12-1-2, wherein:

[0132] S12-1-1. Extract contour features from the target image and determine the center position of the target object.

[0133] A preset algorithm may be used to identify the target image and extract contour features in the target image. The preset algorithm may include, but is not limited to, edge detection algorithms such as Laplacian, Sobel (two-dimensional direction search), and Canny algorithms.

[0134] Afterwards, the extracted contour features may be determined to be the contour of the target object, and the center position of the contour, such as the geometric center position, may be determined and recorded as the center position of the target object.

[0135] Figure 4 is a schematic diagram of Laplacian-based boundary recognition provided by an embodiment of the present disclosure, Figure 5a is a schematic diagram of Sobel-based boundary recognition in the X direction provided by an embodiment of the present disclosure, Figure 5b is a schematic diagram of Sobel-based boundary recognition in the Y direction provided by an embodiment of the present disclosure, and Figure 6 is a schematic diagram of Candy-based boundary recognition provided by an embodiment of the present disclosure.

[0136] As shown in Figures 4, 5a, 5b and 6, for contour features within a certain range, it can be determined that it is the contour of a target object A, and based on the position of the pixel points where the contour of the target object A is located, the center position of the contour of the target object A is determined and recorded as the center position of the target object A.

[0137] For example, a preset algorithm may be used to extract closed contour features in the target image, thereby avoiding the influence of noise such as non-closed contour features on the accuracy of the recognition result.

[0138] S12-1-2. Determine pixel position information of the target object based on the center position of the target object and a preset size range of the target object.

[0139] The preset size range of the target object may be a preset width range and / or width range of the target object, wherein the size range is sufficient to cover the entire target object and does not include adjacent target objects.

[0140] Exemplarily, with the center position of the target object as the center, based on a random width within the width range and a random height within the height range, the target object is determined, and the pixel position information of the target object is determined. For example, the width range is between m1 pixels and m2 pixels; the height range is between m3 pixels and m4 pixels. Among them, m1, m2, m3 and m4 are positive integers, and m2 is greater than m1, and m4 is greater than m3. Randomly select the width of the target object as n1 in the preset width range, m1≤n1≤m2; randomly select the height of the target object as n2 in the preset height range, m3≤n2≤m4, determine a n1×n2 target object, and determine the pixel position information of each pixel point in the n1×n2 area, that is, the pixel position information of the target object.

[0141] This embodiment uses an image recognition algorithm to accurately determine the pixel position information of each target object, thereby improving the accuracy of the subsequent judgment results of the exposure status of the target object, further improving the accuracy of determining the defocus boundary, and ultimately determining more accurate calibration information.

[0142] In some embodiments, with respect to step S12, determining the pixel position information corresponding to each target object specifically includes the following steps S12-2-1 to S12-2-2, wherein:

[0143] S12-2-1. Extract contour features from the target image, perform binarization on the target image to obtain a first binary image, and determine the center position of the target object based on the extracted contour features.

[0144] In one possible implementation, FIG7 is a schematic diagram of a first binary image provided by an embodiment of the present disclosure. As shown in FIG7 , a preset algorithm can be used to identify edge information in the target image and extract contour features therefrom. The preset algorithm, for example, can be a Canny algorithm. During the contour extraction process, the target image is binarized, and the pixel values ​​of the pixels corresponding to the contour features are set to 255, while the pixel values ​​of all other pixels except the contour features are set to 0. Pixels with a pixel value of 255 are displayed as white, and pixels with a pixel value of 0 are displayed as black, resulting in a first binary image.

[0145] In another possible implementation, FIG8 is a schematic diagram of the first binary image after the redundant image is removed from FIG7 . As shown in FIG8 , for the target image, the redundant image can be first removed, and then the target image after the redundant image is removed is converted to obtain the first binary image. The redundant image here can be understood as the interference feature image (features in the area 71 shown in FIG7 ) that is more obvious on the preset template except for the target object when the preset template is captured. The method of removing the redundant image can be, for example, using an image recognition algorithm to identify the redundant target and remove it. Specifically, the corresponding area can be directly cropped, or the pixel value of the pixel point corresponding to the redundant target can be changed. Specifically, the data after the pixel value is changed can be set according to the actual situation, and the embodiment of the present disclosure is not limited to this.

[0146] Afterwards, the extracted contour features may be determined to be the contour of the target object, and the center position of the contour, such as the geometric center position, may be determined and recorded as the center position of the target object.

[0147] As shown in Figure 7 or Figure 8, for the contour features within a certain range, it can be determined that it is the contour of a target object A, and based on the position of the pixel point where the contour of the target object A is located, the center position of the contour of the target object A is determined and recorded as the center position of the target object A.

[0148] For example, a preset algorithm may be used to extract closed contour features in the first binary image, thereby avoiding the influence of noise such as non-closed contour features on the accuracy of the recognition result.

[0149] S12-2-2. Determine pixel position information of the target object based on the center position of the target object and a preset size range of the target object.

[0150] The preset size range of the target object may be a preset width range and / or width range of the target object.

[0151] Exemplarily, with the center position of the target object as the center, based on a random width within the width range and a random height within the height range, the target object is determined, and the pixel position information of the target object is determined. For example, the width range is between m1 pixels and m2 pixels; the height range is between m3 pixels and m4 pixels. Among them, m1, m2, m3 and m4 are positive integers, and m2 is greater than m1, and m4 is greater than m3. Randomly select the width of the target object as n1 in the preset width range, m1≤n1≤m2; randomly select the height of the target object as n2 in the preset height range, m3≤n2≤m4, determine a n1×n2 target object, and determine the pixel position information of each pixel point in the n1×n2 area, that is, the pixel position information of the target object.

[0152] This embodiment converts the target image into a first binary image before performing feature extraction, which improves contour recognition, making it easier to extract the target object's contour features and improving contour recognition efficiency. Furthermore, the image recognition algorithm accurately determines the pixel position information of each target object, thereby improving the accuracy of subsequent determinations of the target object's exposure status, further improving the accuracy of determining the defocus boundary, and ultimately determining more accurate calibration information.

[0153] In some embodiments, with respect to step S12, determining the pixel position information corresponding to each target object specifically includes the following steps S12-3-1 to S12-3-5, wherein:

[0154] S12-3-1. Extract contour features from the target image, perform binarization on the target image, and obtain a first binary image.

[0155] In one possible implementation, as shown in FIG7 , a preset algorithm can be used to identify edge information in a target image and extract contour features therefrom. The preset algorithm can be, for example, the Canny algorithm. During the contour extraction process, the target image is binarized, with the pixel values ​​of pixels corresponding to contour features set to 255 and the pixel values ​​of all pixels other than the contour features set to 0, thereby obtaining a first binary image. Pixels with a pixel value of 255 appear white, while pixels with a pixel value of 0 appear black. In another possible implementation, redundant images can be first removed from the target image, and then the target image after redundant image removal is converted to obtain a first binary image. Redundant images can be understood as images of prominent interfering features other than the target object on a preset template captured during image acquisition. Redundant images can be removed, for example, by using an image recognition algorithm to identify and remove redundant objects. Specifically, the pixel values ​​of pixels corresponding to the redundant objects can be changed. The data resulting from the changed pixel values ​​can be set based on actual circumstances, and this is not limited in the present embodiment.

[0156] S12-3-2. Perform dilation processing on the first binary image to obtain a first intermediate image.

[0157] Figure 9 is a schematic diagram of a first intermediate image after dilation processing of a first binary image, according to an embodiment of the present disclosure. As shown in Figure 9, the first intermediate image can be obtained by dilating the first binary image using an image dilation algorithm. Dilation of the first binary image can increase the contour features of the target object, thereby magnifying the overall contour of the target object and fusing multiple tiny closed contours formed by out-of-focus exposure into a single closed contour.

[0158] S12-3-3. Perform corrosion processing on the first intermediate image to obtain a second intermediate image.

[0159] Figure 10 is a schematic diagram of a second intermediate image obtained by eroding a first intermediate image according to an embodiment of the present disclosure. As shown in Figure 10 , the first intermediate image can be eroded using an image erosion algorithm to obtain the second intermediate image. Erosion of the first intermediate image restores the expanded contour to its original state as closely as possible, allowing for more accurate determination of the center position of the target object.

[0160] In the above steps S12-3-1 to S12-3-3, the edges of the connected white pixels in the first binary image are used as contour feedback, and the contour can be maximized using the dilation and erosion methods.

[0161] S12-3-4. Extract the contour features in the second intermediate image and determine the center position of the target object.

[0162] A preset algorithm can be used to identify the second intermediate image and extract contour features from the second intermediate image. A threshold is then applied to the area of ​​the maximized contour, and contours smaller than the threshold are filtered out, retaining contour information closest to the actual contour. Preset algorithms may include, but are not limited to, edge detection algorithms such as Laplacian, Sobel (two-dimensional directional search), and Canny.

[0163] Afterwards, the extracted contour features may be determined to be the contour of the target object, and the center position of the contour, such as the geometric center position, may be determined and recorded as the center position of the target object.

[0164] As shown in FIG10 , for contour features within a certain range, it can be determined that it is the contour of a target object A. Based on the positions of the pixel points where the contour of the target object A is located, the center of the rectangular frame formed by the boundary of the two pixel points farthest apart in the first direction X along the second direction and the center of the rectangular frame formed by the boundary of the two pixel points farthest apart in the second direction Y along the first direction X is selected as the center position of the contour of the target object A.

[0165] For example, a preset algorithm may be used to extract closed contour features in the second intermediate image, thereby avoiding the influence of noise such as non-closed contour features on the accuracy of the recognition result.

[0166] S12-3-5. Determine pixel position information of the target object based on the center position of the target object and a preset size range of the target object.

[0167] The preset size range of the target object may be a preset width range and / or width range of the target object.

[0168] Exemplarily, with the center position of the target object as the center, based on a random width within the width range and a random height within the height range, the target object is determined, and the pixel position information of the target object is determined. For example, the width range is between m1 pixels and m2 pixels; the height range is between m3 pixels and m4 pixels. Among them, m1, m2, m3 and m4 are positive integers, and m2 is greater than m1, and m4 is greater than m3. The width of the target object is randomly selected as n1 in the preset width range, m1≤n1≤m2; the height of the target object is randomly selected as n2 in the preset height range, m3≤n2≤m4, to determine an n1×n2 target object, and determine the pixel position information of each pixel point in the n1×n2 area, that is, the pixel position information of the target object.

[0169] Since the exposure heights corresponding to the first object groups 31 of each group are different, the target objects in the different first object groups 31 formed by exposure are different. The contour shape of the target object formed by the exposure height closer to the optimal focal plane is more regular; conversely, the contour shape of the target object formed by the exposure height further away from the optimal focal plane is more irregular, and there may be multiple tiny closed contours. If the closed contour features of the first binary image are directly extracted, for some irregular contour shapes, the complete contour features may not be extracted, resulting in inaccurate screening of the target object. Therefore, this embodiment adopts the processing process of the above steps S12-3-5 to S12-3-5, and by adding expansion and corrosion processing, it can eliminate the noise information caused by defocused exposure, improve the accuracy of the edge contour, thereby improving the precise positioning of the target object, and then improve the accuracy of the subsequent judgment result of the exposure state of the target object, improve the accuracy of determining the defocus boundary, and ultimately determine more accurate calibration information.

[0170] In some embodiments, for the above step S12-2-1 or S12-3-1, determining the first binary image may specifically include steps S121 to S123, wherein:

[0171] S121 , extracting contour features from the target image, and performing binarization processing on the target image to obtain an initial binary image.

[0172] As shown in Figure 7, a preset algorithm can be used to identify edge information in the target image and extract contour features. The preset algorithm, for example, can be the Canny algorithm. During the contour extraction process, the target image is binarized, with the pixel values ​​corresponding to the contour features set to 255 and the pixel values ​​of all other pixels except the contour features set to 0. Pixels with a pixel value of 255 appear white, and pixels with a pixel value of 0 appear black, resulting in an initial binary image.

[0173] S122: Determine the area to be cropped based on the pixel value and position information of each pixel point in the initial binary image.

[0174] The pixel points in the area to be cropped are an area in the second direction Y, which contains multiple segments of pixel values ​​that are continuously 0 and is surrounded by multiple segments of pixel values ​​that are continuously 255.

[0175] Specifically, based on the pixel value and position information of each pixel point in the initial binary image, an array obtained by summing along the second direction Y (for example, the column direction) can be determined, and an area containing multiple segments of pixel values ​​continuously equal to 0 and surrounded by multiple segments of pixel points continuously equal to 255 can be determined, as shown in Figure 7, where 71 represents the area to be cropped.

[0176] S123 , cutting out a portion of the initial binary image located in the to-be-cut area to obtain a first binary image.

[0177] As shown in FIG8 , this is the first binary image after eliminating interference features.

[0178] This embodiment further improves the authenticity of the target object's contour and improves the efficiency and accuracy of subsequent contour extraction by eliminating features that significantly interfere with the contour of the target object.

[0179] In some embodiments, step S13 determines whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object, specifically including the following steps S131 to S133, wherein:

[0180] S131 . Determine the area within the outline of each target object based on the pixel position information of each target object.

[0181] Specifically, taking a target object as an example, based on the pixel position information of the target object, the pixel position information here may be the position of the target object's outline, which can save storage space during data storage. Based on the position of the target object's outline, the area of ​​the target object within the outline is determined.

[0182] The area of ​​other target objects within the contour is calculated in the same way as above, and the repeated parts are not repeated here. Finally, the area within the contour of each target object is obtained.

[0183] S132 : Based on the comparison result between the areas within the contours of the target objects, select a reference object from the target objects, and determine pixel position information of the reference object.

[0184] During exposure, the closer the exposure height is to the optimal focal plane, the sharper the image and the smaller the astigmatism, resulting in a relatively smaller image area of ​​the target object. Therefore, we can compare the areas within the outlines of each target object and select the target object with the smallest outline area as the reference object. The pixel position information of the target object with the smallest outline area is then used as the pixel position information of the reference object.

[0185] S133 : Based on the pixel position information of each target object and the pixel position information of the reference object, determine whether the exposure state of the target object is normal or out of focus.

[0186] Specifically, based on the pixel position information of each target object and the pixel position information of a reference object, correlation information or difference information between each target object and the reference object can be determined, with the reference object as the standard. The exposure state is then determined based on whether there is a correlation or difference. For example, if there is a strong correlation between the target object and the reference object, the exposure state of the target object is determined to be normal; conversely, if there is a significant difference between the target object and the reference object, the exposure state of the target object is determined to be out of focus.

[0187] The following describes in detail the different methods for determining the exposure status of a target object provided by the embodiments of the present disclosure.

[0188] In a possible implementation, correlation comparison may be performed directly based on the target image to determine the exposure state of the target object, specifically including the following steps S133-1-1 to S133-1-2, wherein:

[0189] S133-1-1. Based on the pixel position information of each target object in the target image and the pixel position information of the reference object, a preset similarity algorithm is used to determine the similarity between each target object and the reference object.

[0190] The target image in this step, that is, the target image shown in FIG2 , can be understood as the original image before image processing.

[0191] The preset similarity algorithm includes, but is not limited to, a cosine similarity algorithm, an algorithm for calculating similarity using Euclidean distance, an algorithm for calculating similarity using Manhattan distance, and the like.

[0192] Exemplarily, based on the positions of the contour pixels of each target object in the target image and the positions of the contour pixels of the reference object, a preset similarity algorithm is used to determine the similarity between each target object and the reference object.

[0193] Exemplarily, FIG11 is a schematic diagram of the sub-image corresponding to the target object shown in FIG2 . As shown in FIG11 , based on the pixel position information of each target object in the target image, a first sub-image corresponding to the target object is intercepted from the target image. One sub-image includes only one target object, and different sub-images include different target objects. Based on the pixel position information of the reference object in the target image, a second sub-image (i.e., a standard image) corresponding to the reference object is intercepted from the target image. Based on the position of the contour pixel points of the target object in each first sub-image and the position of the contour pixel points of the reference object in the second sub-image, a preset similarity algorithm is used to determine the similarity between each target object and the reference object. Here, by intercepting and saving the sub-images, storage space can be saved.

[0194] S133-1-2. Based on a comparison result of the similarity between each target object and the reference object and a first preset threshold, determine whether the exposure state of the target object is normal or out of focus.

[0195] Taking a target object as an example, determine the comparison result of the similarity between the target object and the reference object and the first preset threshold; if the similarity is greater than the first preset threshold, it can be determined that the exposure state of the target object is normal; conversely, if the similarity is less than or equal to the first preset threshold, it can be determined that the exposure state of the target object is out of focus.

[0196] It should be noted that the first preset threshold in the embodiment of the present disclosure can be set based on experience and actual application, and the embodiment of the present disclosure does not specifically limit it.

[0197] In another possible implementation, correlation comparison may be performed based on the first binary image to determine the exposure state of the target object, specifically including the following steps S133-2-1 to S133-2-2, wherein:

[0198] S133-2-1. Based on the pixel position information of each target object and the pixel position information of the reference object in the first binary image, a preset similarity algorithm is used to determine the similarity between each target object and the reference object.

[0199] The first binary image in this step may be, for example, the first binary image shown in Figure 7 or the first binary image shown in Figure 8. To improve processing efficiency, the present embodiment uses the first binary image shown in Figure 8 as an example for illustration.

[0200] The preset similarity algorithm includes, but is not limited to, a cosine similarity algorithm, an algorithm for calculating similarity using Euclidean distance, an algorithm for calculating similarity using Manhattan distance, and the like.

[0201] Exemplarily, based on the positions of contour pixels of each target object and the positions of contour pixels of the reference object in the first binary image, a preset similarity algorithm is used to determine the similarity between each target object and the reference object.

[0202] Exemplarily, FIG12 is a schematic diagram of the sub-image corresponding to the target object shown in FIG8 . As shown in FIG12 , based on the pixel position information of each target object in the first binary image, a third sub-image corresponding to the target object is intercepted from the first binary image. One sub-image includes only one target object, and different sub-images include different target objects. Based on the pixel position information of the reference object in the first binary image, a fourth sub-image (i.e., a standard image) corresponding to the reference object is intercepted from the first binary image. Based on the position of the contour pixel points of the target object in each third sub-image and the position of the contour pixel points of the reference object in the fourth sub-image, a preset similarity algorithm is used to determine the similarity between each target object and the reference object. Here, by intercepting and saving the sub-images, storage space can be saved.

[0203] S133-2-2. Based on a comparison result of the similarity between each target object and the reference object and a first preset threshold, determine whether the exposure state of the target object is normal or out of focus.

[0204] Taking a target object as an example, determine the comparison result of the similarity between the target object and the reference object and the first preset threshold; if the similarity is greater than the first preset threshold, it can be determined that the exposure state of the target object is normal; conversely, if the similarity is less than or equal to the first preset threshold, it can be determined that the exposure state of the target object is out of focus.

[0205] It should be noted that different correlation algorithms may output results where smaller values ​​indicate greater similarity, or larger values ​​indicate greater similarity. For the disclosed embodiments, the similarity here indicates the degree of similarity between the target object and the reference object. Therefore, a greater similarity, greater than a first preset threshold, indicates a closer match between the target object and the reference object, and the target object's exposure state can be determined to be normal. Conversely, a lower similarity indicates a defocused exposure state.

[0206] The embodiment of the present disclosure can improve the accuracy of the similarity by calculating the similarity between the target object and the reference object on the first binary image, thereby improving the accuracy of the exposure state of the determined target object, and further improving the accuracy of determining the defocus boundary, and ultimately determining more accurate calibration information.

[0207] In another possible implementation, correlation comparison may be performed based on the first binary image to determine the exposure state of the target object, specifically including the following steps S133-3-1 to S133-3-2, wherein:

[0208] S133-3-1. Based on the pixel position information of each target object in the second intermediate image and the pixel position information of the reference object, a preset similarity algorithm is used to determine the similarity between each target object and the reference object.

[0209] The second intermediate image in this step may be, for example, the second intermediate image shown in FIG. 10 .

[0210] The preset similarity algorithm includes, but is not limited to, a cosine similarity algorithm, an algorithm for calculating similarity using Euclidean distance, an algorithm for calculating similarity using Manhattan distance, and the like.

[0211] Exemplarily, based on the positions of the contour pixel points of each target object and the positions of the contour pixel points of the reference object in the second intermediate image, a preset similarity algorithm is used to determine the similarity between each target object and the reference object.

[0212] Exemplarily, FIG13 is a schematic diagram of the sub-image corresponding to the target object shown in FIG10 . As shown in FIG13 , based on the pixel position information of each target object in the second intermediate image, a fifth sub-image corresponding to the target object is intercepted from the second intermediate image. A sub-image includes only one target object, and different sub-images include different target objects. Based on the pixel position information of the reference object in the second intermediate image, a sixth sub-image (i.e., a standard image) corresponding to the reference object is intercepted from the second intermediate image. Based on the position of the contour pixel points of the target object in each fifth sub-image and the position of the contour pixel points of the reference object in the sixth sub-image, a preset similarity algorithm is used to determine the similarity between each target object and the reference object. Here, by intercepting and saving the sub-images, storage space can be saved.

[0213] S133-3-2. Based on a comparison result of the similarity between each target object and the reference object and a first preset threshold, determine whether the exposure state of the target object is normal or out of focus.

[0214] Taking a target object as an example, determine the comparison result of the similarity between the target object and the reference object and the first preset threshold; if the similarity is greater than the first preset threshold, it can be determined that the exposure state of the target object is normal; conversely, if the similarity is less than or equal to the first preset threshold, it can be determined that the exposure state of the target object is out of focus.

[0215] The embodiment of the present disclosure can further improve the accuracy of the similarity by calculating the similarity between the target object and the reference object on the image after corrosion and expansion (i.e., the second intermediate image), thereby improving the accuracy of the exposure state of the determined target object, and further improving the accuracy of determining the defocus boundary, and ultimately determining more accurate calibration information.

[0216] In another possible implementation, correlation comparison may be performed based on the target binary sub-image to determine the exposure state of the target object, which specifically includes the following steps S13-1-1 to S13-1-5, wherein:

[0217] S13-1-1. According to the pixel position information of the target object, intercept the target sub-image corresponding to the target object from the target image, and determine the area within the outline of the target object in each target sub-image.

[0218] The pixel position information of the target object may be the position of the outline of the target object. According to the position of the outline of the target object, the target sub-image corresponding to the target object can be intercepted from the target image; at the same time, according to the position of the outline of the target object, the area of ​​the target object within the outline can also be determined.

[0219] S13-1-2. Based on the comparison result between the areas within the contours of the target objects, select a reference object from the target objects and determine a reference subgraph of the reference object.

[0220] The areas within the contours of the target objects can be compared to select the target object with the smallest contour area as the reference object. The target subgraph corresponding to the target object with the smallest contour area is used as the reference subgraph of the reference object.

[0221] S13-1-3. Binarize each target sub-image and reference sub-image respectively to obtain each target binary sub-image and reference binary sub-image.

[0222] FIG14 is a schematic diagram of converting a target sub-image into a reference sub-image according to an embodiment of the present disclosure. As shown in FIG14 , binarization processing is directly performed based on the pixel values ​​in the target sub-image. For example, the comparison result between the pixel value of each pixel point in the target sub-image and the fourth preset threshold value is determined; when the pixel value of a pixel point is greater than the fourth preset threshold value, the pixel value of the pixel point is updated to the second set value; when the pixel value of a pixel point is less than or equal to the fourth preset threshold value, the pixel value of the pixel point is updated to the first set value. For example, when converting the target image into a black and white image, the value range of the fourth preset threshold value can be between 100 and 200; the first set value can be set to 0 and the second set value can be set to 255. A pixel point with a pixel value of 255 is a pixel point that displays white, and a pixel point with a pixel value of 0 is a pixel point that displays black.

[0223] S13-1-4. Based on each target binary subgraph and the reference binary subgraph, a preset similarity algorithm is used to determine the similarity between each target object and the reference object.

[0224] For example, as shown in FIG14 , taking the similarity between a target object and a reference object as an example, the similarity between the target object and the reference object is determined using a preset similarity algorithm based on the pixel values ​​of each pixel in the target binary subimage and the pixel values ​​of each pixel in the reference binary subimage. Examples of the preset similarity algorithm include, but are not limited to, a cosine similarity algorithm, an algorithm for calculating similarity using Euclidean distance, an algorithm for calculating similarity using Manhattan distance, and the like.

[0225] S133-4-5. Based on a comparison result of the similarity between each target object and the reference object and a first preset threshold, determine whether the exposure state of the target object is normal or out of focus.

[0226] Taking a target object as an example, determine the comparison result of the similarity between the target object and the reference object and the first preset threshold; if the similarity is greater than the first preset threshold, it can be determined that the exposure state of the target object is normal; conversely, if the similarity is less than or equal to the first preset threshold, it can be determined that the exposure state of the target object is out of focus.

[0227] The disclosed embodiment can improve the accuracy of the similarity by calculating the similarity between the target object and the reference object in the target binary sub-image, thereby improving the accuracy of the exposure state of the determined target object, and further improving the accuracy of determining the defocus boundary, and ultimately determining more accurate calibration information.

[0228] In another possible implementation, correlation comparison can be performed based on the target binary sub-image to determine the exposure state of the target object, which specifically includes the following steps S13-2-1 to S13-2-6, wherein:

[0229] S13-2-1. According to the pixel position information of the target object, intercept the target sub-image corresponding to the target object from the target image, and determine the area within the outline of the target object in each target sub-image.

[0230] The implementation principle of this step is the same as that of step S13-1-1, and the repeated parts will not be repeated.

[0231] S13-2-2. Based on the comparison result between the areas within the contours of the target objects, a reference object is selected from the target objects, and a reference subgraph of the reference object is determined.

[0232] The implementation principle of this step is the same as that of step S13-1-2, and the repeated parts will not be repeated.

[0233] S13-2-3. Reduce the target sub-image and the reference sub-image according to a preset scale factor to obtain a reduced target sub-image and a reduced reference sub-image.

[0234] The preset scaling factor can be understood as a reduction scaling factor pre-set for the target object, which can be set based on experience or actual scenarios, and the present disclosure does not limit the specific data. Here, reducing the target sub-image and the reference sub-image can reduce the amount of data processing and improve efficiency.

[0235] S13-2-4. Binarization is performed on each of the reduced target sub-images and the reduced reference sub-images to obtain each target binary sub-image and reference binary sub-image.

[0236] The principle of binarization in this step is the same as the principle of binarization in step S13-1-3, and the repeated parts will not be repeated.

[0237] S13-2-5. Determine the Hamming distance of each target binary subgraph based on each target binary subgraph and the reference binary subgraph.

[0238] Taking a target binary subgraph as an example, the Hamming distance algorithm is used to determine the number of operations required to convert the target binary subgraph into a reference binary subgraph, which is the Hamming distance. The Hamming distance reflects the degree of difference between the reduced-size target object in the target binary subgraph and the reduced-size reference object in the reference binary subgraph. For example, a larger Hamming distance indicates a greater difference.

[0239] S13-2-6. Based on the comparison result of the Hamming distance of each target binary sub-image and the second preset threshold, determine whether the exposure state of the target object is normal or out of focus.

[0240] Taking a target binary sub-image as an example, determine the comparison result of the Hamming distance of the target binary sub-image and the second preset threshold; if the Hamming distance is less than the second preset threshold, it can be determined that the exposure state of the target object is normal; conversely, if the Hamming distance is greater than or equal to the second preset threshold, it can be determined that the exposure state of the target object is out of focus.

[0241] In another possible implementation, correlation comparison can be performed based on the target binary sub-image to determine the exposure state of the target object, which specifically includes the following steps S13-3-1 to S13-3-4, wherein:

[0242] S13-3-1. According to the pixel position information of the target object, a target sub-image corresponding to the target object is intercepted from the target image.

[0243] The implementation principle of this step is the same as that of step S13-1-1, and the repeated parts will not be repeated.

[0244] S13-3-2, performing binarization processing on each target sub-graph to obtain each target binary sub-graph;

[0245] The principle of binarization in this step is the same as the principle of binarization in step S13-1-3, and the repeated parts will not be repeated.

[0246] S13-3-3. Based on each target binary sub-image and a preset pure color image, determine the Hamming distance of each target binary sub-image.

[0247] The size of the solid color image is the same as the preset size.

[0248] The size of the pure-color image is the same as the preset size. The pixel values ​​of each pixel in the pure-color image are the same as the pixel values ​​of the non-target object in the target binary subimage, that is, the pixel values ​​of the target object are opposite. The pixel value of the target object is 255, and the pixel value of each pixel in the pure-color image is 0, that is, the pure-color image is a pure black image.

[0249] Specifically, using a binary sub-image of a target as an example, the Hamming distance algorithm is used to determine the Hamming distance required to convert the target binary sub-image into a pure color image. The calculated Hamming distance can be understood as the number or area of ​​white pixels in the target binary sub-image. The greater the number of white pixels or the larger the area of ​​white pixels, the greater the Hamming distance. A larger Hamming distance indicates a more abnormal exposure state, which means a greater possibility of defocus.

[0250] S13-3-4. Based on the comparison result of the Hamming distance of each target binary sub-image and the third preset threshold, determine whether the exposure state of the target object is normal or out of focus.

[0251] Taking a target binary sub-image as an example, determine the comparison result between the difference between the Hamming distance of the target binary sub-image and the Hamming distance of the reference binary sub-image converted into a pure color image and a third preset threshold; if the difference is less than the third preset threshold, it can be determined that the exposure state of the target object is normal; conversely, if the difference is greater than or equal to the third preset threshold, it can be determined that the exposure state of the target object is out of focus.

[0252] In some embodiments, with respect to step S12, determining the pixel position information corresponding to each target object specifically includes the following steps S12-4-1 to S12-4-3, wherein:

[0253] S12-4-1. Binarize the target image to obtain a second binary image.

[0254] In one possible implementation, as shown in FIG14 , taking a target object as an example, the target image can be converted to directly obtain a second binary image. Exemplarily, the comparison result between the pixel value of each pixel in the target image and the fourth preset threshold is determined; when the pixel value of a pixel is greater than the fourth preset threshold, the pixel value of the pixel is updated to the second set value; when the pixel value of a pixel is less than or equal to the fourth preset threshold, the pixel value of the pixel is updated to the first set value. For example, when converting the target image to a black and white image, the value range of the fourth preset threshold can be between 100 and 200; the first set value can be set to 0 and the second set value can be set to 255. A pixel with a pixel value of 255 is a pixel that displays white, and a pixel with a pixel value of 0 is a pixel that displays black.

[0255] In another possible implementation, for the target image, redundant images can be first eliminated, and then the target image after the redundant images are eliminated can be converted to obtain a second binary image. The redundant image here can be understood as an image of a preset template that is captured when the preset template is imaged, and that has relatively obvious interference features other than the target object. For example, the method of eliminating redundant images can be to use an image recognition algorithm to identify redundant targets and eliminate them. Specifically, the pixel values ​​of the pixels corresponding to the redundant targets can be changed. Specifically, the data after the pixel values ​​are changed can be set according to actual conditions, and this is not limited in the embodiments of the present disclosure.

[0256] S12-4-2. Extract contour features from the second binary image and determine the center position of the target object.

[0257] A preset algorithm can be used to identify the second binary image and extract contour features from the second binary image. The preset algorithm can be, for example, a Canny algorithm, which uses the edges of connected white pixels in the second binary image as contour feedback. Dilation and erosion methods are used to maximize the contour, and a threshold is applied to the area contained in the contour. Contours smaller than the set area threshold are filtered out, retaining contour information closest to the actual contour.

[0258] Afterwards, the extracted contour features may be determined to be the contour of the target object, and the center position of the contour, such as the geometric center position, may be determined and recorded as the center position of the target object.

[0259] For contour features within a certain range, it can be determined that it is the contour of a target object A. Based on the positions of the pixel points where the contour of the target object A is located, the center of the rectangular box formed by the boundary of the two pixel points farthest apart in the first direction X along the second direction and the two pixel points farthest apart in the second direction Y along the first direction X as the boundary is selected as the center position of the contour of the target object A, and recorded as the center position of the target object A.

[0260] For example, a preset algorithm may be used to extract closed contour features in the first binary image, thereby avoiding the influence of noise such as non-closed contour features on the accuracy of the recognition result.

[0261] S12-4-3. Determine pixel position information of the target object based on the center position of the target object and a preset size range of the target object.

[0262] The preset size range of the target object may be a preset width range and / or width range of the target object.

[0263] Exemplarily, with the center position of the target object as the center, based on a random width within the width range and a random height within the height range, the target object is determined, and the pixel position information of the target object is determined. For example, the width range is between m1 pixels and m2 pixels; the height range is between m3 pixels and m4 pixels. Among them, m1, m2, m3 and m4 are positive integers, and m2 is greater than m1, and m4 is greater than m3. Randomly select the width of the target object as n1 in the preset width range, m1≤n1≤m2; randomly select the height of the target object as n2 in the preset height range, m3≤n2≤m4, determine a n1×n2 target object, and determine the pixel position information of each pixel point in the n1×n2 area, that is, the pixel position information of the target object.

[0264] This embodiment converts the target image into a second binary image before feature extraction, which improves contour recognition, making it easier to extract the target object's contour features and improving contour recognition efficiency. Furthermore, the image recognition algorithm accurately determines the pixel position information of each target object, thereby improving the accuracy of subsequent determinations of the target object's exposure status, further improving the accuracy of determining the defocus boundary, and ultimately determining more accurate calibration information.

[0265] In some embodiments, correlation comparison can be performed based on the second binary image to determine the exposure state of the target object, specifically including the following steps S13-4-1 to S13-4-4, wherein:

[0266] S13-4-1. Determine the area within the outline of each target object based on the pixel position information of each target object in the second binary image.

[0267] The implementation principle of this step is the same as that of step S131, and the repeated parts will not be repeated.

[0268] S13-4-2. Based on the comparison result between the areas within the contours of the target objects, select a reference object from the target objects and determine the pixel position information of the reference object.

[0269] The implementation principle of this step is the same as that of step S132, and the repeated parts will not be repeated.

[0270] S13-4-3. Based on the pixel position information of each target object and the pixel position information of the reference object in the second binary image, a preset similarity algorithm is used to determine the similarity between each target object and the reference object.

[0271] The preset similarity algorithm includes, but is not limited to, a cosine similarity algorithm, an algorithm for calculating similarity using Euclidean distance, an algorithm for calculating similarity using Manhattan distance, and the like.

[0272] Exemplarily, as shown in FIG14 , based on the positions of the contour pixel points of each target object and the positions of the contour pixel points of the reference object in the second binary image, a preset similarity algorithm is used to determine the similarity between each target object and the reference object.

[0273] S13-4-4. Based on a comparison result of the similarity between each target object and the reference object and a first preset threshold, determine whether the exposure state of the target object is normal or out of focus.

[0274] Taking a target object as an example, determine the comparison result of the similarity between the target object and the reference object and the first preset threshold; if the similarity is greater than the first preset threshold, it can be determined that the exposure state of the target object is normal; conversely, if the similarity is less than or equal to the first preset threshold, it can be determined that the exposure state of the target object is out of focus.

[0275] The embodiment of the present disclosure can improve the accuracy of the similarity by calculating the similarity between the target object and the reference object on the second binary image, thereby improving the accuracy of the exposure state of the determined target object, and further improving the accuracy of determining the defocus boundary, and ultimately determining more accurate calibration information.

[0276] In some embodiments, correlation comparison can be performed based on the second binary image to determine the exposure state of the target object, which specifically includes the following steps S13-5-1 to S13-5-6, wherein:

[0277] S13-5-1. Determine the area within the outline of each target object based on the pixel position information of each target object in the second binary image.

[0278] The implementation principle of this step is the same as that of step S131, and the repeated parts will not be repeated.

[0279] S13-5-2. Based on the comparison result between the areas within the contours of the target objects, select a reference object from the target objects and determine the pixel position information of the reference object.

[0280] The implementation principle of this step is the same as that of step S132, and the repeated parts will not be repeated.

[0281] S13-5-3. Based on the pixel position information of each target object and the pixel position information of the reference object in the second binary image, the target object and the reference object are reduced according to a preset scale factor to obtain reduced target objects and reduced reference objects.

[0282] The second binary image in this step can be shown in FIG14 , which is a sub-image corresponding to the target object in the second binary image.

[0283] The preset scaling factor can be understood as a pre-set reduction coefficient for the target object. This can be determined based on experience or actual scenarios, and the present disclosure does not limit the specific data. For example, based on the positions of the contour pixels of each target object and the positions of the contour pixels of the reference object in the second binary image, the target object and the reference object are scaled down according to the preset scaling factor to obtain the scaled-down target object and the scaled-down reference object. Here, scaling down the target object and the reference object can reduce the amount of data processing and improve efficiency.

[0284] S13-5-4. Based on the pixel position information of each reduced target object and the pixel position information of the reduced reference object, according to a preset size, a first sub-image corresponding to the reduced target object is cut out from the second binary image, and a second sub-image corresponding to the reduced reference object is cut out from the second binary image.

[0285] The preset size may be pre-set based on experience or actual application scenarios, and the specific data is not specifically limited in the embodiments of this disclosure.

[0286] A first subgraph includes only one target object, and different first subgraphs include different target objects.

[0287] S13-5-5. Based on each first subgraph and the second subgraph, determine the Hamming distance of each first subgraph.

[0288] Taking a first sub-image as an example, the Hamming distance algorithm is used to determine the number of operations required to convert the first sub-image into the second sub-image. This is the Hamming distance. The Hamming distance reflects the degree of difference between the reduced-size target object in the first sub-image and the reduced-size reference object in the second sub-image. For example, a larger Hamming distance indicates a greater difference.

[0289] S13-5-6. Based on the comparison result of the Hamming distance of each first sub-image and the second preset threshold, determine whether the exposure state of the target object is normal or out of focus.

[0290] Taking a first sub-image as an example, determine the comparison result of the Hamming distance of the first sub-image and the second preset threshold; if the Hamming distance is less than the second preset threshold, it can be determined that the exposure state of the target object is normal; conversely, if the Hamming distance is greater than or equal to the second preset threshold, it can be determined that the exposure state of the target object is out of focus.

[0291] In some embodiments, correlation comparison can be performed based on the second binary image to determine the exposure state of the target object, specifically including the following steps S13-6-1 to S13-6-3, wherein:

[0292] S13-6-1. Based on the pixel position information of each target object in the second binary image, cut out the third sub-image corresponding to each target object from the second binary image according to a preset size.

[0293] The second binary image in this step is shown in FIG14 .

[0294] The preset size may be pre-set based on experience or actual application scenarios, and the specific data is not specifically limited in the embodiments of this disclosure.

[0295] A third subgraph includes only one target object, and different third subgraphs include different target objects.

[0296] S13-6-2. Determine the Hamming distance of each third sub-image based on each third sub-image and a preset pure color image.

[0297] The size of the solid-color image is the same as the preset size. The pixel values ​​of each pixel in the solid-color image are the same as the pixel values ​​of the non-target object in the third sub-image, that is, the pixel values ​​of the target object are opposite. The pixel value of the target object is 255, and the pixel value of each pixel in the solid-color image is 0, that is, the solid-color image is a pure black image.

[0298] Specifically, using the third sub-image as an example, the Hamming distance algorithm is used to determine the Hamming distance required to convert the third sub-image into a pure color image. The calculated Hamming distance can be understood as the number or area of ​​white pixels in the third sub-image. A greater number of white pixels or a larger area of ​​white pixels indicates a greater Hamming distance. A larger Hamming distance indicates a more abnormal exposure state, meaning a greater likelihood of defocus.

[0299] S13-6-3. Based on the comparison result of the Hamming distance of each third sub-image and the third preset threshold, determine whether the exposure state of the target object is normal or out of focus.

[0300] Taking a target binary sub-image as an example, determine the comparison result between the difference between the Hamming distance of the target binary sub-image and the Hamming distance of the reference binary sub-image converted into a pure color image and a third preset threshold; if the difference is less than the third preset threshold, it can be determined that the exposure state of the target object is normal; conversely, if the difference is greater than or equal to the third preset threshold, it can be determined that the exposure state of the target object is out of focus.

[0301] In some embodiments, step S13 determines the predicted focal plane heights corresponding to different test areas based on the judgment result and the pre-stored exposure heights corresponding to each first object group 31, specifically including the following steps S134 to S137, wherein:

[0302] S134 , for the target objects in the second object groups 32 arranged side by side along the second direction Y, determine the pixel position information of the defocus boundary corresponding to each second object group 32 based on the exposure status of each target object and the pixel position information of each target object.

[0303] In one possible implementation, FIG15 is a schematic diagram of a defocus boundary corresponding to a group of second object groups provided in an embodiment of the present disclosure. As shown in FIG15 , for a group of second object groups 32, based on the exposure status and pixel position information of each target object in the second object group 32, a target object 322 (for ease of understanding, this target object is referred to as the first target object 322) that is adjacent to the target object 321 with a defocused exposure state and has a normal exposure state is determined. The position of the first target object 322 is determined to be the defocus boundary corresponding to the second object group 32. For example, based on the pixel position information of the first target object, the position of the pixel point in the first target object that is closest to the defocused target object is used as the position of the defocus boundary; alternatively, the position of the geometric center point of the first target object is used as the position of the defocus boundary.

[0304] Since the exposure state of the first target object is close to being defocused, this embodiment uses the position of the first target object closest to the defocused state as the defocus boundary, which can improve the accuracy of subsequent prediction of the optimal focal plane exposure height.

[0305] It should be noted that, as shown in FIG15 , since the preset template sets the exposure height of the 13th group of first object groups 31 as the optimal focal plane during the exposure stage (the optimal focal plane may deviate and is the focal plane to be calibrated), the exposure heights of the other first object groups 31 except the 13th group of first object groups 31 deviate and are symmetrical on the left and right, and therefore a group of second object groups 32 includes two defocus boundaries 323.

[0306] Similarly, the method for determining the defocus boundary corresponding to each second object group 32 is the same as above, and the repeated parts are not repeated here.

[0307] In another possible embodiment, for a second object group 32, based on the exposure status and pixel position information of each target object in the second object group 32, a target object adjacent to the target object with a defocused exposure state and a normal exposure state is determined (for ease of understanding, this target object is referred to as the first target object 322), and a target object adjacent to the first target object with a defocused exposure state is determined (for ease of understanding, this target object is referred to as the second target object 321). The center position between the first target object 322 and the second target object 321 is determined as the defocus boundary corresponding to the second object group 32. The pixel position information of the defocus boundary can then be determined based on the pixel position information of the first target object and the pixel position information of the second target object. For example, the midpoint of the line connecting the geometric center point of the first target object and the geometric center point of the second target object is recorded as the position of the defocus boundary.

[0308] S135 : Determine the regional defocus boundaries corresponding to each test area based on the pixel position information of the defocus boundaries corresponding to each second object group 32 and the position information of the test area.

[0309] Specifically, it is known that the preset template includes multiple test areas, and the positions of the test areas are fixed, so each second object group 32 in each test area can be determined.

[0310] Exemplarily, since each second object group 32 includes two oppositely disposed boundaries, for the convenience of distinction, they are respectively recorded as the first sub-boundary and the second sub-boundary. The regional defocus boundary includes two oppositely disposed boundaries, for the convenience of distinction, they are respectively recorded as the first boundary and the second boundary. Figure 16 is a schematic diagram of the regional defocus boundary on the target image provided by an embodiment of the present disclosure. As shown in Figure 16, 331 represents the first boundary and 332 represents the second boundary. Figure 16 shows two test areas (referred to as the first test area 201 and the second test area 202 for the convenience of description). Taking the determination of the first boundary 331 in one test area as an example, based on the position of the first sub-boundary corresponding to each second object group 32 in the test area, the average value of each first sub-boundary in the first direction X is determined, that is, the position of the first boundary in the first direction X. Similarly, based on the position of the second sub-boundary corresponding to each second object group 32 in the test area, the average value of each second sub-boundary in the first direction X is determined, that is, the position of the second boundary in the first direction X. The first boundary 331 and the second boundary 332 constitute the regional defocus boundary of the test area.

[0311] The method for determining the regional defocus boundaries corresponding to other test areas is similar, and the repeated parts will not be repeated.

[0312] S136 , for the regional defocus boundaries corresponding to the respective test areas, determine the exposure heights of the regional defocus boundaries based on the pixel position information of the regional defocus boundaries and the pre-stored exposure heights corresponding to the respective first object groups 31 .

[0313] The regional out-of-focus boundary includes a first boundary 331 and a second boundary 332. If first boundary 331 is located in first object group 31A (not shown in the figure), the exposure height of first boundary 331 is determined to be the exposure height corresponding to first object group 31A. If second boundary 332 is located in first object group 31B (not shown in the figure), the exposure height of second boundary 332 is determined to be the exposure height corresponding to first object group 31B. If first boundary 331 is not located in first object group 31, first object group 31C (not shown in the figure) closest to first boundary 331 is determined, and the exposure height of first boundary 331 is determined to be the exposure height corresponding to first object group 31C. If second boundary 332 is not located in first object group 31, first object group 31D (not shown in the figure) closest to second boundary 332 is determined, and the exposure height of second boundary 332 is determined to be the exposure height corresponding to first object group 31D.

[0314] S137 . For the regional defocus boundaries corresponding to the respective test regions, determine the predicted focal plane height of the test region based on the exposure height of the regional defocus boundaries.

[0315] Specifically, the average of the exposure height of the first boundary 331 and the exposure height of the second boundary 332 may be used as the predicted focal plane height of the test area.

[0316] In some embodiments, the calibration information for the optimal focal plane of the exposure device includes the optimal focal plane height and tilt; the test area includes two (referred to as a first test area 201 and a second test area 202 for ease of description). Specifically, the first preset algorithm described above is an algorithm for calculating the average of the predicted focal plane heights; the second preset algorithm is an algorithm for calculating the difference between the two predicted focal plane heights. The average of the predicted focal plane heights can be used as the optimal focal plane height. The difference between the two predicted focal plane heights can be used as the tilt of the optimal focal plane.

[0317] Exemplarily, the predicted focal plane height UpBest of the first test area 201 is determined using the following formula:

[0318] UpBest=(UL+UR) / 2, where UL represents the exposure height of the first boundary of the first test area 201 , and UR represents the exposure height of the second boundary of the first test area 201 .

[0319] Exemplarily, the predicted focal plane height DownBest of the second test area 202 is determined using the following formula:

[0320] DownBest=(DL+DR) / 2, where DL represents the exposure height of the first border of the second test area 202 , and DR represents the exposure height of the second border of the second test area 202 .

[0321] For example, the height of the optimal focal plane Best is determined by the following formula:

[0322] Best = (UpBest + DownBest) / 2

[0323] For example, the tilt of the optimal focal plane is determined by the following formula:

[0324] Tilt=UpBest-DownBest

[0325] Those skilled in the art will understand that in the above-mentioned method of the specific implementation method, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0326] In addition, an embodiment of the present disclosure also provides a focal plane calibration device corresponding to the focal plane calibration method. Since the principle of solving the problem by the device in the embodiment of the present disclosure is similar to the above-mentioned focal plane calibration method in the embodiment of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0327] FIG17 is a schematic diagram of a focal plane calibration device provided by an embodiment of the present disclosure, as shown in FIG17 , comprising an image acquisition module 21, an image processing module 22, a prediction module 23 and a calibration information determination module 24.

[0328] The image acquisition module 21 is configured to acquire an image of a preset template to obtain a target image; the target image includes multiple groups of first objects arranged side by side along a first direction, and multiple groups of second objects arranged side by side along a second direction; the first object group includes multiple target objects arranged side by side along the second direction; the second object group includes multiple target objects arranged side by side along the first direction; the preset template includes multiple test areas, and the target objects in one test area are obtained by a single exposure, while the target objects in different test areas are obtained by different exposures.

[0329] It should be noted that the image acquisition module 21 in the embodiment of the present disclosure is configured to execute step S11 in the above-mentioned focal plane calibration method.

[0330] The image processing module 22 is configured to determine pixel position information corresponding to each target object according to the target image.

[0331] It should be noted that the image processing module 22 in the embodiment of the present disclosure is configured to execute step S12 in the above-mentioned focal plane calibration method.

[0332] The prediction module 23 is configured to determine whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object, and determine the predicted focal plane heights corresponding to different test areas according to the judgment result and the pre-stored exposure heights corresponding to each first object group.

[0333] It should be noted that the prediction module 23 in the embodiment of the present disclosure is configured to execute step S13 in the above-mentioned focal plane calibration method.

[0334] The calibration information determination module 24 is configured to determine calibration information of the optimal focal plane of the exposure device based on each predicted focal plane height.

[0335] It should be noted that the calibration information determination module 24 in the embodiment of the present disclosure is configured to execute step S14 in the above-mentioned focal plane calibration method.

[0336] In some embodiments, the image processing module 22 is specifically configured to extract contour features in the target image and determine the center position of the target object; based on the center position of the target object and a preset size range of the target object, determine the pixel position information of the target object.

[0337] It should be noted that the image processing module 22 in the embodiment of the present disclosure is specifically configured to execute steps S12 - 1 - 1 to S12 - 1 - 2 in the above-mentioned focal plane calibration method.

[0338] In some embodiments, the image processing module 22 is specifically configured to extract contour features in the target image, and perform binarization processing on the target image to obtain a first binary image, and determine the center position of the target object based on the extracted contour features; based on the center position of the target object and the pre-set size range of the target object, determine the pixel position information of the target object.

[0339] It should be noted that the image processing module 22 in the embodiment of the present disclosure is specifically configured to execute steps S12-2-1 to S12-2-2 in the above-mentioned focal plane calibration method.

[0340] In some embodiments, the image processing module 22 is specifically configured to extract contour features in the target image, and perform binarization processing on the target image to obtain a first binary image; perform dilation processing on the first binary image to obtain a first intermediate image; perform erosion processing on the first intermediate image to obtain a second intermediate image; extract contour features in the second intermediate image to determine the center position of the target object; determine the pixel position information of the target object based on the center position of the target object and the pre-set size range of the target object.

[0341] It should be noted that the image processing module 22 in the embodiment of the present disclosure is specifically configured to execute steps S12-3-1 to S12-3-2 in the above-mentioned focal plane calibration method.

[0342] In some embodiments, the image processing module 22 performs binarization processing on the target image to obtain a first binary image, including: extracting contour features in the target image, and binarizing the target image to obtain an initial binary image; determining the area to be cropped based on the pixel value and position information of each pixel point in the initial binary image; the pixel points in the area to be cropped are an area in the second direction that contains multiple segments of pixel values ​​that are continuously 0 and is surrounded by multiple segments of pixel values ​​that are continuously 255; and cutting out the portion of the image in the initial binary image that is located in the area to be cropped to obtain a first binary image.

[0343] It should be noted that the image processing module 22 in the embodiment of the present disclosure is specifically configured to execute the specific implementation process of step S12-2-1 or S12-3-1 in the above-mentioned focal plane calibration method.

[0344] In some embodiments, the size range of the target object includes a width range and a height range;

[0345] The image processing module 22 determines the pixel position information of the target object based on the center position of the target object and the preset size range of the target object, including:

[0346] Taking the center position of the target object as the center, based on a random width within a width range and a random height within a height range, the target object is determined, and pixel position information of the target object is determined.

[0347] It should be noted that the image processing module 22 in the embodiment of the present disclosure is specifically configured to execute the specific implementation process of step S12-2-3 or S12-3-5 in the above-mentioned focal plane calibration method.

[0348] In some embodiments, the prediction module 23 includes a judgment unit 231 and a prediction unit 232; wherein the judgment unit 231 is configured to determine the area within the outline of each target object based on the pixel position information of each target object; based on the comparison results between the areas within the outlines of each target object, filter out a reference object from each target object and determine the pixel position information of the reference object; based on the pixel position information of each target object and the pixel position information of the reference object, judge whether the exposure status of the target object is normal or defocused.

[0349] It should be noted that the determination unit 231 in the embodiment of the present disclosure is configured to execute steps S131 to S133 in the above-mentioned focal plane calibration method.

[0350] In some embodiments, the judgment unit 231 is specifically configured to determine the similarity between each target object and the reference object based on the pixel position information of each target object in the target image and the pixel position information of the reference object, using a preset similarity algorithm; and based on the comparison result of the similarity between each target object and the reference object and the first preset threshold, judge whether the exposure status of the target object is normal or defocused.

[0351] It should be noted that the judgment unit 231 in the embodiment of the present disclosure is specifically configured to execute steps S133 - 1 - 1 to S133 - 1 - 2 in the above-mentioned focal plane calibration method.

[0352] In some embodiments, the judgment unit 231 is specifically configured to determine the similarity between each target object and the reference object based on the pixel position information of each target object in the first binary image and the pixel position information of the reference object, using a preset similarity algorithm; and based on the comparison result of the similarity between each target object and the reference object and the first preset threshold, judge whether the exposure status of the target object is normal or defocused.

[0353] It should be noted that the judgment unit 231 in the embodiment of the present disclosure is specifically configured to execute steps S133-2-1 to S133-2-2 in the above-mentioned focal plane calibration method.

[0354] In some embodiments, the judgment unit 231 is specifically configured to determine the similarity between each target object and the reference object based on the pixel position information of each target object in the second intermediate image and the pixel position information of the reference object, using a preset similarity algorithm; and based on the comparison result of the similarity between each target object and the reference object and the first preset threshold, judge whether the exposure status of the target object is normal or defocused.

[0355] It should be noted that the judgment unit 231 in the embodiment of the present disclosure is specifically configured to execute steps S133 - 3 - 1 to S133 - 3 - 2 in the above-mentioned focal plane calibration method.

[0356] In some embodiments, the judgment unit 231 is specifically configured to intercept the target sub-image corresponding to the target object from the target image according to the pixel position information of the target object, and determine the area within the outline of the target object in each target sub-image; based on the comparison result between the areas within the outline of each target object, screen out the reference object from each target object, and determine the reference sub-image of the reference object; binarize each target sub-image and the reference sub-image respectively to obtain each target binary sub-image and a reference binary sub-image; based on each target binary sub-image and the reference binary sub-image, use a preset similarity algorithm to determine the similarity between each target object and the reference object; based on the comparison result of the similarity between each target object and the reference object and the first preset threshold, judge whether the exposure status of the target object is normal or defocused.

[0357] It should be noted that the judgment unit 231 in the embodiment of the present disclosure is specifically configured to execute steps S13 - 1 - 1 to S13 - 1 - 6 in the above-mentioned focal plane calibration method.

[0358] In some embodiments, the judgment unit 231 is specifically configured to intercept the target sub-image corresponding to the target object from the target image according to the pixel position information of the target object, and determine the area within the outline of the target object in each target sub-image; based on the comparison result between the areas within the outline of each target object, screen out the reference object from each target object, and determine the reference sub-image of the reference object; according to the preset scale factor, reduce the target sub-image and the reference sub-image to obtain each reduced target sub-image and the reduced reference sub-image; binarize each reduced target sub-image and the reduced reference sub-image respectively to obtain each target binary sub-image and the reference binary sub-image; determine the Hamming distance of each target binary sub-image based on each target binary sub-image and the reference binary sub-image; based on the comparison result of the Hamming distance of each target binary sub-image and the second preset threshold, judge whether the exposure status of the target object is normal or defocused.

[0359] It should be noted that the judgment unit 231 in the embodiment of the present disclosure is specifically configured to execute steps S13-2-1 to S13-2-6 in the above-mentioned focal plane calibration method.

[0360] In some embodiments, the judgment unit 231 is specifically configured to intercept a target sub-image corresponding to the target object from the target image based on the pixel position information of the target object; perform binarization processing on each target sub-image to obtain each target binary sub-image; determine the Hamming distance of each target binary sub-image based on each target binary sub-image and a preset solid color image; the size of the solid color image is the same as the preset size; based on the comparison result of the Hamming distance of each target binary sub-image and a third preset threshold, judge whether the exposure status of the target object is normal or defocused.

[0361] It should be noted that the judgment unit 231 in the embodiment of the present disclosure is specifically configured to execute steps S13-3-1 to S13-3-6 in the above-mentioned focal plane calibration method.

[0362] In some embodiments, the image processing module 22 is specifically configured to perform binarization processing on the target image to obtain a second binary image; extract contour features in the second binary image to determine the center position of the target object; and determine the pixel position information of the target object based on the center position of the target object and a pre-set size range of the target object.

[0363] It should be noted that the image processing module 22 in the embodiment of the present disclosure is specifically configured to execute steps S12-4-1 to S12-4-3 in the above-mentioned focal plane calibration method.

[0364] In some embodiments, the judgment unit 231 is specifically configured to determine the area within the outline of each target object based on the pixel position information of each target object in the second binary image; based on the comparison result between the areas within the outline of each target object, screen out a reference object from each target object and determine the pixel position information of the reference object; based on the pixel position information of each target object in the second binary image and the pixel position information of the reference object, use a preset similarity algorithm to determine the similarity between each target object and the reference object; based on the comparison result of the similarity between each target object and the reference object and the first preset threshold, judge whether the exposure status of the target object is normal or defocused.

[0365] It should be noted that the judgment unit 231 in the embodiment of the present disclosure is specifically configured to execute steps S13 - 1 - 1 to S13 - 1 - 4 in the above-mentioned focal plane calibration method.

[0366] In some embodiments, the judgment unit 231 is specifically configured to determine the area within the outline of each target object based on the pixel position information of each target object in the second binary image; based on the comparison result between the areas within the outlines of each target object, screen out a reference object from each target object and determine the pixel position information of the reference object; based on the pixel position information of each target object in the second binary image and the pixel position information of the reference object, reduce the target object and the reference object according to a preset scale factor to obtain each reduced target object and the reduced reference object; based on the pixel position information of each reduced target object and the pixel position information of the reduced reference object, according to a preset size, cut out a first sub-image corresponding to the reduced target object from the second binary image, and cut out a second sub-image corresponding to the reduced target object from the second binary image; based on each first sub-image and the second sub-image, determine the Hamming distance of each first sub-image; based on the comparison result of the Hamming distance of each first sub-image and the second preset threshold, judge whether the exposure status of the target object is normal or defocused.

[0367] It should be noted that the judgment unit 231 in the embodiment of the present disclosure is specifically configured to execute steps S13-2-1 to S13-2-6 in the above-mentioned focal plane calibration method.

[0368] In some embodiments, the judgment unit 231 is specifically configured to, based on the pixel position information of each target object in the second binary image, intercept the third sub-image corresponding to each target object from the second binary image according to a preset size; determine the Hamming distance of each third sub-image based on each third sub-image and a preset solid color image; the size of the solid color image is the same as the preset size; based on the comparison result of the Hamming distance of each third sub-image and the third preset threshold, judge whether the exposure status of the target object is normal or defocused.

[0369] It should be noted that the judgment unit 231 in the embodiment of the present disclosure is specifically configured to execute steps S13-3-1 to S13-3-3 in the above-mentioned focal plane calibration method.

[0370] In some embodiments, the prediction unit 232 is configured to determine, for the target objects in each second object group arranged side by side along the second direction, the pixel position information of the defocus boundary corresponding to each second object group based on the exposure status of each target object and the pixel position information of each target object; determine the regional defocus boundary corresponding to each test area based on the pixel position information of the defocus boundary corresponding to each second object group and the position information of the test area; determine the exposure height of the regional defocus boundary corresponding to each test area based on the pixel position information of the regional defocus boundary and the pre-stored exposure height corresponding to each first object group; and determine the predicted focal plane height of the test area based on the exposure height of the regional defocus boundary for the regional defocus boundary corresponding to each test area.

[0371] It should be noted that the prediction unit 232 in the embodiment of the present disclosure is specifically configured to execute steps S134 to S137 in the above-mentioned focal plane calibration method.

[0372] In some embodiments, the regional defocus boundary includes a first boundary and a second boundary; the prediction unit 232 determines the predicted focal plane height of the test area based on the exposure height of the regional defocus boundary, including: taking the average between the exposure height of the first boundary and the exposure height of the second boundary as the predicted focal plane height of the test area.

[0373] It should be noted that the prediction unit 232 in the embodiment of the present disclosure is specifically configured to perform the specific implementation process of step S137 in the above-mentioned focal plane calibration method.

[0374] In some embodiments, the calibration information of the optimal focal plane of the exposure device includes the height and tilt of the optimal focal plane; the test area includes two;

[0375] The calibration information determination module 24 is specifically configured to use the average of the predicted focal plane heights as the height of the optimal focal plane; and use the difference between the two predicted focal plane heights as the tilt of the optimal focal plane.

[0376] It should be noted that the calibration information determination module 24 in the embodiment of the present disclosure is configured to execute the specific implementation process in step S14 in the above-mentioned focal plane calibration method.

[0377] Figure 18 is a schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure. As shown in Figure 18, the computer device provided in an embodiment of the present disclosure includes: one or more processors 301, memory 302, and one or more I / O interfaces 303. Memory 302 stores one or more programs. When executed by the one or more processors, the one or more processors implement the focal plane calibration method described in any of the above embodiments. One or more I / O interfaces 303 are connected between the processors and memory and are configured to facilitate information exchange between the processors and memory.

[0378] Among them, the processor 301 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 302 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read-write interface) 303 is connected between the processor 301 and the memory 302, and can realize information interaction between the processor 301 and the memory 302, including but not limited to a data bus (Bus), etc.

[0379] In some embodiments, the processor 301 , the memory 302 , and the I / O interface 303 are connected to each other via a bus 304 , and further connected to other components of the computing device.

[0380] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium is further provided. The non-transitory computer-readable storage medium stores a computer program, wherein when the program is executed by a processor, the steps of the focal plane calibration method according to any of the above embodiments are implemented.

[0381] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the system of the present disclosure are executed.

[0382] It should be noted that the computer non-transitory readable medium shown in the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any non-transitory computer-readable storage medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the non-transitory computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination thereof.

[0383] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architectures, functions and operations of the devices, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two connected boxes can actually represent execution in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0384] It is understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present disclosure, and the present disclosure is not limited thereto. Those skilled in the art may make various modifications and improvements without departing from the spirit and substance of the present disclosure, and such modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A focal plane calibration method, wherein: include: Capturing an image of a preset template to obtain a target image; the target image includes a plurality of first object groups arranged side by side along a first direction, and a plurality of second object groups arranged side by side along a second direction; the first object group includes a plurality of target objects arranged side by side along the second direction; the second object group includes a plurality of target objects arranged side by side along the first direction; the preset template includes a plurality of test areas, and a target object in one of the test areas is obtained by one exposure of an exposure device, and target objects in different test areas are obtained by different exposures of the exposure device; Determining pixel position information corresponding to each target object according to the target image; Based on the pixel position information of each of the target objects, determining whether the exposure state of the target object is normal or out of focus, and determining the predicted focal plane heights corresponding to the different test areas according to the determination result and the pre-stored exposure heights corresponding to each of the first object groups; Based on each of the predicted focal plane heights, calibration information of an optimal focal plane of the exposure device is determined.

2. The focal plane calibration method according to claim 1, wherein: Determining pixel position information corresponding to each target object according to the target image includes: Extracting contour features in the target image to determine the center position of the target object; Based on the center position of the target object and a preset size range of the target object, pixel position information of the target object is determined.

3. The focal plane calibration method according to claim 1, wherein: Determining pixel position information corresponding to each target object according to the target image includes: Extracting contour features from the target image, performing binarization processing on the target image to obtain a first binary image, and determining the center position of the target object according to the extracted contour features; Based on the center position of the target object and a preset size range of the target object, pixel position information of the target object is determined.

4. The focal plane calibration method according to claim 1, wherein: Determining pixel position information corresponding to each target object according to the target image includes: Extracting contour features from the target image, and performing binarization processing on the target image to obtain a first binary image; Performing dilation processing on the first binary image to obtain a first intermediate image; performing corrosion processing on the first intermediate image to obtain a second intermediate image; Extracting contour features in the second intermediate image to determine the center position of the target object; Based on the center position of the target object and a preset size range of the target object, pixel position information of the target object is determined.

5. The focal plane calibration method according to claim 3 or 4, wherein: The step of performing binarization processing on the target image to obtain a first binary image includes: Extracting contour features from the target image, and performing binarization processing on the target image to obtain an initial binary image; Based on the pixel value and position information of each pixel point in the initial binary image, determining the area to be cropped; the pixel points in the area to be cropped are an area in the second direction that contains multiple segments of pixel values ​​that are continuously 0 and is surrounded by multiple segments of pixel values ​​that are continuously 255; The portion of the initial binary image located in the area to be cropped is cut off to obtain the first binary image.

6. The focal plane calibration method according to any one of claims 2 to 4, wherein: The size range of the target object includes a width range and a height range; The determining the pixel position information of the target object based on the center position of the target object and a preset size range of the target object includes: Taking the center position of the target object as the center, based on a random width within the width range and a random height within the height range, the target object is determined, and pixel position information of the target object is determined.

7. The focal plane calibration method according to claim 4, wherein: The determining, based on the pixel position information of each target object, whether the exposure state of the target object is normal or out of focus includes: Determining the area within the outline of each target object based on the pixel position information of each target object; Based on the comparison result between the areas within the contours of the target objects, a reference object is selected from the target objects, and pixel position information of the reference object is determined; Based on the pixel position information of each of the target objects and the pixel position information of the reference object, it is determined whether the exposure state of the target object is normal or out of focus.

8. The focal plane calibration method according to claim 7, wherein: The step of judging whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object and the pixel position information of the reference object includes: Based on the pixel position information of each of the target objects in the target image and the pixel position information of the reference object, using a preset similarity algorithm, determining the similarity between each of the target objects and the reference object; Based on the comparison result between the similarity between each of the target objects and the reference object and a first preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

9. The focal plane calibration method according to claim 7, wherein: The step of judging whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object and the pixel position information of the reference object includes: Based on the pixel position information of each of the target objects in the first binary image and the pixel position information of the reference object, using a preset similarity algorithm, determining the similarity between each of the target objects and the reference object; Based on the comparison result between the similarity between each of the target objects and the reference object and a first preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

10. The focal plane calibration method according to claim 7, wherein: The step of judging whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object and the pixel position information of the reference object includes: Based on the pixel position information of each of the target objects in the second intermediate image and the pixel position information of the reference object, using a preset similarity algorithm, determining the similarity between each of the target objects and the reference object; Based on the comparison result between the similarity between each of the target objects and the reference object and a first preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

11. The focal plane calibration method according to claim 4, wherein: The step of judging whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object includes: According to the pixel position information of the target object, intercepting the target sub-image corresponding to the target object from the target image, and determining the area within the outline of the target object in each of the target sub-images; Based on the comparison results between the areas within the contours of the target objects, a reference object is selected from the target objects, and a reference subgraph of the reference object is determined; Binarization is performed on each of the target sub-images and the reference sub-images to obtain each target binary sub-image and reference binary sub-image; Based on each of the target binary subgraphs and the reference binary subgraphs, using a preset similarity algorithm, determining the similarity between each of the target objects and the reference object; Based on the comparison result between the similarity between each of the target objects and the reference object and a first preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

12. The focal plane calibration method according to claim 4, wherein: The step of judging whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object includes: According to the pixel position information of the target object, intercepting the target sub-image corresponding to the target object from the target image, and determining the area within the outline of the target object in each of the target sub-images; Based on the comparison results between the areas within the contours of the target objects, a reference object is selected from the target objects, and a reference subgraph of the reference object is determined; According to a preset scaling factor, the target sub-image and the reference sub-image are reduced to obtain the reduced target sub-image and the reduced reference sub-image; Binarization is performed on each of the reduced target sub-images and the reduced reference sub-images to obtain each target binary sub-image and reference binary sub-image; Based on each of the target binary subgraphs and the reference binary subgraph, determining a Hamming distance of each of the target binary subgraphs; Based on the comparison result of the Hamming distance of each of the target binary sub-images and the second preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

13. The focal plane calibration method according to claim 4, wherein: The step of judging whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object includes: According to the pixel position information of the target object, intercepting a target sub-image corresponding to the target object from the target image; Binarization is performed on each of the target subgraphs to obtain each target binary subgraph; Determining the Hamming distance of each of the target binary sub-images based on each of the target binary sub-images and a preset pure color image; the size of the pure color image is the same as the preset size; Based on the comparison result of the Hamming distance of each of the target binary sub-images and the third preset threshold, it is determined whether the exposure state of the target object is normal or out of focus.

14. The focal plane calibration method according to any one of claims 7 to 13, wherein: Determining predicted focal plane heights corresponding to different test areas respectively according to the judgment result and the pre-stored exposure heights of the target objects in each column includes: For the target objects in each of the second object groups arranged side by side along the second direction, based on the exposure status of each of the target objects and the pixel position information of each of the target objects, determining the pixel position information of the defocus boundary corresponding to each of the second object groups; Determining regional defocus boundaries corresponding to each of the test areas based on pixel position information of the defocus boundaries corresponding to each of the second object groups and position information of the test area; For the regional defocus boundaries respectively corresponding to the test areas, based on the pixel position information of the regional defocus boundaries and the pre-stored exposure heights corresponding to the first object groups, determining the exposure heights of the regional defocus boundaries; For the regional defocus boundaries respectively corresponding to the test areas, the predicted focal plane height of the test area is determined based on the exposure height of the regional defocus boundaries.

15. The focal plane calibration method according to claim 14, wherein: The regional defocus boundary includes a first boundary and a second boundary; The step of determining the predicted focal plane height of the test area based on the exposure height of the defocus boundary of the area includes: The average of the exposure height of the first boundary and the exposure height of the second boundary is used as the predicted focal plane height of the test area.

16. The focal plane calibration method according to claim 1, wherein: The calibration information of the best focal plane of the exposure device includes the height and the tilt of the best focal plane; the test area includes two; The step of determining calibration information of an optimal focal plane of the exposure device based on each of the predicted focal plane heights comprises: Taking the average of the predicted focal plane heights as the height of the optimal focal plane; The difference between the two predicted focal plane heights is used as the tilt of the optimal focal plane.

17. A focal plane calibration device, wherein: It includes image acquisition module, image processing module, prediction module and calibration information determination module: The image acquisition module is configured to acquire an image of a preset template to obtain a target image; the target image includes a plurality of first object groups arranged side by side along a first direction, and a plurality of second object groups arranged side by side along a second direction; the first object group includes a plurality of target objects arranged side by side along the second direction; the second object group includes a plurality of target objects arranged side by side along the first direction; the preset template includes a plurality of test areas, and a target object in one of the test areas is obtained by one exposure, and target objects in different test areas are obtained by different exposures; The image processing module is configured to determine pixel position information corresponding to each target object according to the target image; The prediction module is configured to judge whether the exposure state of the target object is normal or out of focus based on the pixel position information of each target object, and determine the predicted focal plane heights corresponding to different test areas according to the judgment result and the pre-stored exposure heights corresponding to each of the first object groups; The calibration information determination module is configured to determine the calibration information of the optimal focal plane of the exposure device based on each of the predicted focal plane heights.

18. A computer device, wherein: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the focal plane calibration method according to any one of claims 1 to 16 are performed.

19. A computer non-transitory readable storage medium, wherein: The computer non-transitory readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the focal plane calibration method according to any one of claims 1 to 16 are executed.

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