Detection method and detection system

Abstract

The present invention provides a detection method and a detection system. Among them, the method includes: obtaining an image of interest according to the image to be processed, the image of interest having a second direction that extends from the target image to the non-target image; obtaining a gray curve of pixel positions in the second direction according to the pixel gray values of the image of interest; performing a partitioning process on the image of interest to obtain an edge area of the target image; performing an edge point acquisition operation on each of one or more edge area images of interest to obtain the position information of the edge points of the target image in the edge area. The detection method can improve the detection accuracy.

Description

Technical Field

[0001] The present invention relates to a detection method and a detection system, in particular to a method and a system for detecting a target to be detected through image processing. Background Art

[0002] In semiconductor detection, it is usually necessary to detect the critical dimensions of a target to be detected. Imaging the target to be detected and obtaining the critical dimensions of the target to be detected from the image of the target to be detected are common technical means in the detection field.

[0003] The method adopted for measuring critical dimensions in traditional technologies is as follows: First, use template matching, etc. to find the approximate region of interest of the dimension to be measured, then use an edge extraction algorithm (such as canny, etc.) within the region of interest to calculate the position of the edge, and then use algorithms such as least squares to fit to obtain the critical dimension information.

[0004] However, with the reduction of the critical dimensions of semiconductor devices, the accuracy requirements for critical dimension measurement are continuously increasing, and traditional measurement methods are difficult to meet the requirements of semiconductor detection. The technical solution of the present invention provides a detection method that can improve the detection accuracy. Summary of the Invention

[0005] To solve the above problems, the present invention proposes a detection method that can improve the signal-to-noise ratio of an image and thus improve the detection accuracy by performing partition processing on the image.

[0006] The technical solution of the present invention provides a detection method, including: acquiring an image of an object to be measured to obtain an image to be processed, where the surface of the object to be measured has a target to be detected, and the image to be processed includes a target image of the target to be detected and a non-target image outside the target image; acquiring an image of interest according to the image to be processed, where the image of interest has a second direction that extends from the target image to the non-target image; acquiring the correspondence between the pixel position and the gray level in the second direction according to the pixel gray level values of the image of interest to obtain a gray level curve; performing partition processing on the image of interest according to the gray level curve to acquire the edge region of the target image; and respectively performing an edge point acquisition operation on the images of interest in one or more edge regions to acquire the position information of the edge points of the target image in the edge regions.

[0007] Optionally, the steps of the partition processing include: acquiring the initial position information of the edge points of the target image according to the gray level curve; and acquiring the edge region of the target image in the image of interest according to the initial position information of the edge points.

[0008] Optionally, the step of obtaining the edge region of the target image in the image of interest according to the initial position information of the edge points includes: obtaining the initial center of the target image according to the initial position information of the edge points; and obtaining the edge region of the target image in the image of interest according to the preset size of the target image and the initial center point.

[0009] Optionally, the target image is circular, and the preset size is the preset diameter or preset radius of the target image;

[0010] The step of obtaining the edge region of the target image in the image of interest according to the preset size of the target image and the initial center includes: obtaining a region with a predetermined width where a circle with the initial center as the center and the preset radius as the radius is located, to obtain the edge region.

[0011] Optionally, the step of obtaining the initial position information of the edge points of the target image according to the gray curve includes: obtaining the gradient of each pixel of the gray curve; obtaining a gradient curve according to the correspondence between the gradient of each pixel of the gray curve and the position of each pixel; and obtaining the initial position information according to the gradient curve.

[0012] Optionally, the image of interest includes a first number of separated edge points of the target image respectively in a second direction;

[0013] The step of obtaining the initial position information according to the gradient curve includes: obtaining the pixel positions corresponding to the first number of gradients ranked top in the gradients of each pixel, to obtain the initial position information of two edge points; or, performing function fitting on the gradient curve, obtaining the positions corresponding to the second number of gradients ranked top, to obtain the initial position information of the edge points, where the second number is less than or equal to the first number.

[0014] Optionally, the edge point obtaining operation includes: obtaining the gradient of the gray curve of the image of interest in the edge region, to obtain an edge gradient; obtaining a relationship curve between the edge gradient of each pixel in the edge region and the position of the corresponding pixel, to obtain an edge gradient curve; and obtaining the position information of the edge points in the edge region according to the edge gradient curve.

[0015] Optionally, the step of obtaining the position information of the edge points in the edge region according to the edge gradient curve includes: obtaining the pixel position corresponding to the maximum edge gradient in each edge region, to obtain the position information of the edge points; or,

[0016] obtaining the pixel positions corresponding to the third number of edge gradients ranked top in the edge region, to obtain candidate edge points; performing fitting on the gradients of the candidate edge points by using a fitting function, to obtain a fitting curve; and obtaining the position corresponding to the maximum gradient on the fitting curve, to obtain the position information of the edge points.

[0017] Optionally, the fitting function includes: Gaussian, quadratic curve or cosine function.

[0018] Optionally, the number of alternative edge points in each edge region is greater than or equal to 3.

[0019] Optionally, before obtaining the initial position information according to the gradient curve, the step of obtaining the initial position information of the edge points of the target image according to the gray curve further includes: optimizing the gradient curve to increase the signal-to-noise ratio of the gradient curve.

[0020] Optionally, the steps of the optimization process include: obtaining a reference curve, where the reference curve represents the gradient curve of the object to be measured along the second direction; performing a multiplication process on the reference curve and the gradient curve to obtain the optimized gradient curve; the multiplication process includes dot multiplication or convolution, and the reference curve includes: the gradient curve of the reference object or the mirror curve of the gradient curve of the reference object, and the reference object is the designed gradient curve of the object to be measured or the standard object to be measured.

[0021] Optionally, the number of the interesting images is multiple, and there is a preset included angle between the second directions of the multiple interesting images;

[0022] The method further includes: repeating, for the multiple interesting images, the steps of obtaining the gray curve of the pixel positions in the second direction to obtaining the position information of the edge points of the target image according to the gray curve, to obtain the position information of multiple edge points of the target image; obtaining the edge contour of the target image according to the position information of the multiple edge points.

[0023] Optionally, before obtaining the edge contour of the target image according to the position information of the multiple edge points, the method further includes: performing denoising processing on the multiple edge points; the method of the denoising processing includes: random sampling algorithm.

[0024] Optionally, the method further includes: obtaining the target size of the target image according to the edge contour; obtaining the size to be measured of the object to be measured according to the target size and the magnification ratio between the target image and the object to be measured;

[0025] And / or, the method further includes: obtaining the target center of the target image according to the edge contour.

[0026] Optionally, the surface of the object to be measured has multiple objects to be measured, and the multiple objects to be measured include the first object to be measured and the second object to be measured;

[0027] The image to be processed includes multiple target images, and the multiple target images include: a first target image of a first target to be measured and a second target image of the first target to be measured;

[0028] The method further includes: obtaining a target center of the target image according to the edge contour; repeating the step of dividing the image to be processed until the step of obtaining the target center is performed to obtain a first target center of the first target image and a second target center of the second target image; and obtaining an offset between the first target to be measured and the second target to be measured according to the first target center and the second target center.

[0029] Optionally, the step of obtaining the image of interest according to the image to be processed includes: providing a template image, where the template image is a standard image of the target to be measured; using the template image to perform matching on the image to be processed to obtain a matching region; and segmenting the matching region to obtain the image of interest; the image of interest further includes the center of the matching region.

[0030] Optionally, the step of obtaining the gray curve of the pixel positions in the second direction according to the pixel gray values of the image of interest includes: obtaining a corresponding relationship curve between the positions and gray values of the pixels in the second direction of the image of interest to obtain the gray curve; the number of pixels in the direction perpendicular to the second direction of the image of interest is one or more.

[0031] A detection system, characterized by including a processing system for executing the above detection method.

[0032] In the detection method provided by the technical solution of the present invention, the image of interest is partitioned to obtain an edge region of the target image, and according to the image of interest in the edge region, the position information of the edge points of the target image is obtained, which can reduce the interference of the non-edge region image on obtaining the position information of the edge points, thereby improving the detection accuracy.

[0033] Furthermore, performing function fitting on the gray curve or the gradient curve can obtain the gray or gradient at any point, thereby accurately obtaining the position of the point corresponding to the maximum gradient, and further realizing sub-pixel accuracy and improving the accuracy of the obtained initial position information.

[0034] Further, pixels corresponding to the second-largest number of edge gradients in each edge region are obtained to get candidate edge points, and a fitting function is used to fit the gradients of the candidate edge points to obtain a fitting curve; the fitting curve represents the gradient at any point, so that the position of the point corresponding to the maximum gradient can be obtained, and sub-pixel accuracy can be achieved. At the same time, by obtaining the pixels corresponding to the second-largest number of edge gradients to get candidate edge points, the interference of non-edge points can be reduced, the fitting difficulty can be lowered, and the complexity of the algorithm can be decreased.

[0035] Further, the denoising process can eliminate outliers among the edge points, thereby improving the detection accuracy.

[0036] Further, the optimization process can perform a multiplication process on the mirror curve and the gradient curve, which can increase the gradient values of the edge points in the gradient curve, thereby increasing the signal-to-noise ratio of the gradient curve and further increasing the accuracy of the obtained edge region.

[0037] Further, the image of interest includes the center of the matching region, which can reduce the deviation between the obtained initial center and the center of the target image, thereby increasing the detection accuracy. Description of the Drawings

[0038] The present invention will be specifically described below with reference to the drawings and in conjunction with embodiments. The advantages and implementation manners of the present invention will become more obvious. Among them, the content shown in the drawings is only used to explain the present invention and does not constitute any limitation to the present invention in any sense. The drawings are only schematic and are not drawn strictly to scale. In the drawings:

[0039] Figure 1 A flowchart showing the steps of an embodiment of the detection method provided by the technical solution of the present invention is shown;

[0040] Figures 2 to 3 A structural schematic diagram showing the steps of obtaining a grayscale curve in an embodiment of the detection method provided by the technical solution of the present invention is shown.

[0041] Figure 4 A flowchart showing the steps of the partition processing and edge point acquisition operations in an embodiment of the detection method of the present invention;

[0042] Figures 5 to 6 Shown is an embodiment of the detection method provided by the technical solution of the present invention Figure 4 The structural schematic diagram of the steps shown. Detailed Embodiments

[0043] During the detection process, the key dimensions of the object to be measured are often obtained by extracting the edges of the target image. A commonly used method for extracting the edges of the target image includes: first, finding the two-dimensional region of interest (ROI) of the dimension to be measured using template matching or the like; within the two-dimensional ROI, calculating the positions of the edges using edge extraction algorithms (such as Canny, etc.); and then using function fitting to fit the extracted edges to obtain the key dimension information. However, when the background gray level is uneven or the gray level of the background is close to the gray level of the edges of the target image, it is very difficult to extract edge data using edge extraction algorithms.

[0044] The technical solution of the present invention provides a detection method, including: obtaining the correspondence between the pixel positions and gray levels in the second direction according to the pixel gray levels of the ROI image to obtain a gray level curve; performing zoning processing on the ROI image according to the gray level curve to obtain the edge region of the target image; and respectively performing edge point obtaining operations on the ROI images of one or more edge regions to obtain the position information of the edge points of the target image in the edge regions. By performing zoning processing on the ROI image to obtain the edge region and performing edge point obtaining operations on the ROI images of the edge regions, the influence of the regions outside the edge regions on the edge point obtaining operations can be reduced, thereby improving the detection accuracy.

[0045] Figure 1 It is a flowchart of the steps of an embodiment of the detection method provided by the technical solution of the present invention.

[0046] Refer to Figure 1 , the technical solution of the present invention provides a detection method, including the following steps:

[0047] Step S1, obtaining an image of the object to be measured to obtain an image to be processed, where the surface of the object to be measured has a target to be measured, and the image to be processed includes a target image of the target to be measured;

[0048] Step S2, obtaining an ROI image according to the image to be processed, where the ROI image has a second direction that extends from the target image to the non-target image;

[0049] Step S3, obtaining the correspondence between the pixel positions and gray levels in the second direction according to the pixel gray levels of the ROI image to obtain a gray level curve;

[0050] Step S4, performing zoning processing on the ROI image according to the gray level curve to obtain the edge region of the target image;

[0051] Step S5, respectively performing edge point obtaining operations on the ROI images of one or more edge regions to obtain the position information of the edge points of the target image in the edge regions.

[0052] Partition the image of interest to obtain the edge region of the target image. Based on the image of interest in the edge region, obtain the position information of the edge points of the target image, which can reduce the interference of the non-edge region image on obtaining the position information of the edge points, thereby improving the detection accuracy.

[0053] Figures 2 to 3 FIG. shows a schematic structural diagram of each step of obtaining the gray curve in an embodiment of the detection method provided by the technical solution of the present invention.

[0054] The image in this embodiment includes: the position information and gray level of each pixel, the position information and gray level corresponding one by one, and the position information of each pixel on the image corresponding one by one to the position of the surface points of the object to be measured.

[0055] The following will Figures 2 to 3 describe the technical solution of the present invention in detail.

[0056] Refer to Figure 2 , execute step S1 to obtain an image of the object to be measured, and obtain a to-be-processed image 10. The surface of the object to be measured has a target to be measured, and the to-be-processed image 10 includes a target image 12 of the target to be measured.

[0057] In this embodiment, the object to be measured is a patterned wafer or chip. In other embodiments, the object to be measured may be an OLED panel or a mobile phone screen.

[0058] In this embodiment, the surface to be measured of the target to be measured is circular, and correspondingly, the target image 12 is circular. Specifically, the target to be measured is a pad. In other embodiments, the surface to be measured of the target to be measured is polygonal, such as rectangular.

[0059] Continue to refer to Figure 2 , execute step S2 to obtain an image of interest according to the to-be-processed image. The image of interest has a second direction A, and the second direction A extends from the target image to the non-target image.

[0060] In this embodiment, the image of interest is obtained by partitioning the to-be-processed image 10.

[0061] Specifically, the to-be-processed image 10 is partitioned to obtain at least one image of interest 11, and the image of interest 11 includes at least part of the edge of the target image 12.

[0062] The image of interest 11 has one or more pixels in a direction perpendicular to the first direction.

[0063] Specifically, in this embodiment, the image of interest 11 has a plurality of pixels in the first direction.

[0064] The division process can extract the image of interest 11, thereby reducing the interference of the non-image of interest 11.

[0065] The steps of the division process include: providing a template image, which is a standard image of the target to be measured; using the template image to match the image 10 to be processed to obtain a matching region; and segmenting the matching region to obtain the image of interest 11.

[0066] In this embodiment, the image of interest further includes the center of the matching region. By the division process, making the image of interest include the center of the matching region can reduce the deviation between the initial center obtained subsequently and the center of the target image, thereby increasing the detection accuracy.

[0067] Using the template image to match the image 10 to be processed to obtain the region of the image to be measured that meets the threshold condition, and obtaining the matching region; the threshold condition includes: making the difference parameter between the gray levels of each pixel point in the matching region and the corresponding pixel gray level of the template image less than a preset value, the difference parameter is positively correlated with the difference gray level, and the difference gray level is the sum of the absolute values of the differences between the gray levels of each pixel point in the matching region and the corresponding pixel gray level of the template image; specifically, the difference parameter is the sum of the absolute values of the differences between the gray levels of each pixel point in the matching region and the corresponding pixel gray level of the template image, or the difference parameter is the average value of the absolute values of the differences between the gray levels of each pixel point in the matching region and the corresponding pixel gray level of the template image, or the difference parameter is the sum or average value of the squares of the differences between the gray levels of each pixel point in the matching region and the corresponding pixel gray level of the template image.

[0068] In this embodiment, the image of interest has a plurality of pixels in the first direction; specifically, the first direction is a straight line, and the first direction is perpendicular to the second direction A.

[0069] The positions of the points on the edge of the target image 12 in the image of interest 11 are smooth curves or straight lines. If the positions of the points on the edge of the target image 12 are smooth curves or straight lines, then the edges of the target image 12 in the image of interest are all in the same first direction or near the same first direction. Furthermore, after the subsequent compression process on the image of interest 11, the gray level difference between the edge of the target image 12 and the background can be increased, and thus the contrast of the compressed gray image can be further increased.

[0070] If the number of pixels of the image of interest 11 in the first direction is too small, it is not conducive to increasing the image contrast. If the number of pixels of the image of interest 11 in the first direction is too large, it is easy to increase the complexity of the compression process. Specifically, in this embodiment, the number of pixels of the image of interest 11 in the first direction is 10 to 50.

[0071] In this embodiment, the first direction is a straight line, and the first direction is parallel to the row or column of 10 pixels of the image to be processed, which is beneficial to simplify the calculation. In other embodiments, the first direction is a curve. For example, the target image 12 is circular, and the first direction is a plurality of concentric arcs with the center of the target image 12 as the center point.

[0072] When the first direction is a straight line, the second direction A is perpendicular to the first direction.

[0073] When the first direction is a curve, the second direction A is perpendicular to the tangent of the first direction.

[0074] In another embodiment, the target image 12 is a polygon, and the first direction is parallel to the side of the target image 12. Specifically, the target image 12 is a rectangle, and the first direction and the second direction A are straight lines and are respectively parallel to two sides of the target image 12.

[0075] In this embodiment, the region of interest image 11 is rectangular. In other embodiments, the region of interest image can be square or other shapes.

[0076] In this embodiment, the region of interest image 11 includes the center of the matching region. Through the partitioning process, making the region of interest image 11 include the center of the matching region can reduce the deviation between the center point and the center of the target image 12, thereby increasing the detection accuracy. In other embodiments, the region of interest image 11 may not include the center of the matching region.

[0077] In this embodiment, the region of interest image 11 includes at least two edge points at both ends of the second direction A on the edge of the target image 12. Specifically, the region of interest image 11 penetrates the target image 12 in the second direction A. In other embodiments, the region of interest image 11 may only include one edge point or more than two edge points located in the second direction A.

[0078] In this embodiment, the number of the region of interest images 11 is multiple; there is a preset angle between the second directions A of the multiple region of interest images 11, and each region of interest image 11 includes the center of the matching region.

[0079] In this embodiment, the preset angles between different region of interest images are equal. In other embodiments, the preset angles between different region of interest images may not be the same.

[0080] Specifically, in this embodiment, the preset angle is 5° to 45°, such as 15° or 30°.

[0081] In other embodiments, the multiple images of interest 11 are strip-shaped, and the extending direction of each image of interest 11 is parallel to the second direction A, and there is a preset distance between the centers of adjacent images of interest 11 in the first direction.

[0082] Execute step S3. According to the pixel gray values of the image of interest 11, obtain the correspondence between the pixel positions and the gray values in the second direction A to obtain a gray curve.

[0083] When the number of pixels of the image of interest 11 in the first direction is multiple, the image of interest 11 can be compressed.

[0084] In this embodiment, before obtaining the correspondence between the pixel positions and the gray values in the second direction A according to the pixel gray values of the image of interest to obtain a gray curve, the detection method further includes compressing the image of interest in the first direction so that the gray values of multiple pixels of the image of interest in the first direction are compressed into one gray value; obtaining the relationship curve between the positions of each pixel in the second direction A and the gray value to obtain a gray curve. By dividing the image to be processed 10 to obtain the image of interest 11, and compressing the image of interest 11 so that the gray values of multiple pixels of the image of interest 11 in the first direction are compressed into one gray value, the target edge data and the background data can be made uniform, so that the target edge data stands out from the background data, increasing the signal-to-noise ratio of the obtained gray curve a, thereby increasing the position information of the obtained edge points, and further improving the detection accuracy. The steps of compressing the image of interest 11 in the first direction include: selecting any pixel in the second direction A of the image of interest 11 to obtain a reference point; performing a linear combination on the gray values of all pixels of the image of interest 11 in the first direction passing through the reference point to obtain the gray value of the reference point; repeating the steps of obtaining the reference point and the compressed gray value to obtain the gray values of multiple pixels in the second direction A of the image of interest 11 to obtain the gray curve a.

[0085] In this embodiment, the linear combination of gray values includes: summing, averaging, or weighting the gray values of all pixels in the first direction passing through the reference point.

[0086] The linear combination gray level includes: when it is the weighted value of all pixel gray level values on the line segment passing through the reference point and parallel to the first direction, pixels with different positions in the first direction have different weights, and pixels with the same position in the first direction and different positions in the second direction A have the same weight. Specifically, for the interested image 11, the smaller the weight of the pixel is, the greater the distance from the second direction A passing through the center of the interested image 11; or, the interested image is divided into multiple sub-regions along the first direction, pixels in different sub-regions have different weights, and pixels in the same sub-region have the same weight.

[0087] According to the linear combination gray level of all pixel gray level values on the first direction passing through the reference point, the gray level of the reference point is obtained, and the contrast of the compressed gray level image can be obtained.

[0088] It should be noted that in other embodiments, the step of obtaining the gray level curve does not include compression processing. According to the pixel gray level values of the interested image, the step of obtaining the corresponding relationship between the pixel positions and gray levels in the second direction A to obtain the gray level curve includes: obtaining the corresponding relationship curve between the positions and gray level values of each pixel in the second direction A of the interested image to obtain the gray level curve; the number of pixels in the direction perpendicular to the second direction A of the interested image is one or more.

[0089] Combined with reference Figure 4 , perform step S4 to perform partitioning processing on the interested image to obtain the edge region of the target image; perform step S5 to perform edge point acquisition operations on one or more edge region interested images respectively to obtain the position information of the target image edge points in the edge region.

[0090] Figure 4 This is a flowchart of each step in the partitioning processing and edge point acquisition operations in an embodiment of the detection method of the present invention.

[0091] The steps of the partitioning processing include:

[0092] Step S41, obtaining the initial position information of the target image edge points according to the gray level curve;

[0093] Step S42, obtaining the edge region of the target image in the interested image according to the initial position information of the edge points;

[0094] The step of obtaining the edge region 13 of the target image 12 in the interested image 11 according to the initial position information of the edge points includes: obtaining the initial center of the target image 12 according to the initial position information of the edge points; obtaining the edge region 13 according to the initial center and the preset size of the target image 12.

[0095] The edge point acquisition operation includes:

[0096] Step S51: Obtain the gradient of the gray curve of the image of interest in the edge area to obtain the edge gradient.

[0097] Step S52: Obtain the relationship curve between the edge gradient of each pixel in the edge area and the position of the corresponding pixel to obtain the edge gradient curve.

[0098] Step S53: Obtain the position information of the edge points in the edge area according to the edge gradient curve.

[0099] Figures 5 to 6 It is the structure diagram of each step in the partition processing and edge point acquisition operation in an embodiment of the technical solution of the present invention.

[0100] Execute step S4 to perform partition processing on the image of interest 11 and obtain the edge area of the target image 12.

[0101] Performing partition processing on the image of interest 11 to obtain the edge area 13 of the target image 12, and obtaining the position information of the edge points of the target image 12 according to the image of interest 11 in the edge area 13 can reduce the interference of the non-edge area image on obtaining the position information of the edge points, thereby improving the detection accuracy.

[0102] In this embodiment, the steps of performing partition processing on the image of interest 11 are as Figure 5 shown.

[0103] Refer to Figure 5 , execute step S41 to obtain the initial position information of the edge points of the target image 12 according to the gray curve a;

[0104] In this embodiment, the steps of obtaining the initial position information of the edge points of the target image 12 according to the gray curve a include: obtaining the gradient of each point of the gray curve a; obtaining the gradient curve b according to the corresponding relationship between the gradient of each pixel of the gray curve a and the position of each pixel; obtaining the initial position information according to the gradient curve b.

[0105] In this embodiment, before obtaining the initial position information according to the gradient curve b, the steps of obtaining the initial position information of the edge points of the target image 12 according to the gray curve a further include: performing optimization processing on the gradient curve b to increase the signal-to-noise ratio of the gradient curve b. In this embodiment, according to the optimized gradient curve b, the initial position information of the edge points of the target image is obtained.

[0106] Specifically, in this embodiment, the image of interest includes two edge points at both ends of the target image in the second direction A. According to the optimized gradient curve b, the initial position information of the two edge points at both ends of the target image 12 in the second direction A is obtained.

[0107] Specifically, in this embodiment, the steps of the optimization process include: obtaining a reference curve, where the reference curve represents the gradient curve b of the object to be measured; performing a multiplication process on the reference curve and the gradient curve b to obtain the optimized gradient curve b.

[0108] The multiplication process includes dot product or convolution. The reference curve includes: the gradient curve b of the reference object or the mirror curve of the gradient curve b of the reference object. The reference object is the designed gradient curve b of the object to be measured or the standard object to be measured.

[0109] The optimization process can increase the gradient value of the edge points in the gradient curve b by multiplying the mirror curve and the gradient curve b, thereby increasing the signal-to-noise ratio of the gradient curve b, and further increasing the accuracy of the obtained edge region 13.

[0110] Specifically, in this embodiment, the optimization of the gradient curve b is achieved by convolving the gradient curve b with the mirror curve. In other embodiments, the optimization process may not be performed.

[0111] In this embodiment, the image of interest includes a first number of separated edge points of the target image located in the second direction A respectively.

[0112] The step of obtaining the initial position information according to the gradient curve includes: obtaining the pixel positions corresponding to the top second number of gradients in the gradients of each pixel to obtain the initial position information of the edge points. In this embodiment, the second number is equal to the first number and is equal to 2. Obtaining the pixel positions corresponding to the top second number of gradients in the gradients of each pixel includes obtaining the first two maximum gradients, and the first two maximum gradients are respectively the initial position information of the edge points at both ends of the second direction A.

[0113] The top second number of gradients refers to the top second number of gradients when the gradients of each pixel are arranged from large to small.

[0114] In this embodiment, the first number is 2. In other embodiments, the first number is an integer greater than 2.

[0115] In this embodiment, specifically, the step of obtaining the pixel positions corresponding to the top first number of gradients in the gradients of each point to obtain the initial position information of the edge points includes: obtaining the top first number of gradients in the gradients of each point to obtain the initial edge gradients; obtaining the pixel positions corresponding to the initial edge gradients to obtain the initial position information of the edge points.

[0116] In another embodiment, the step of obtaining the initial position information according to the gradient curve includes: performing function fitting on the gradient curve b to obtain the positions corresponding to the second largest number of gradients, so as to obtain the initial position information of the edge points. Performing function fitting on the gradient curve can obtain the gradient at any position on the gradient curve, thereby achieving sub-pixel accuracy.

[0117] Specifically, in this embodiment, perform function fitting on the gradient curve b to obtain a first fitting function; obtain the first two largest gradients of the first fitting function to obtain the initial edge gradient, that is, obtain the first two largest gradients of the first fitting function.

[0118] In other embodiments, the step of obtaining the initial position information according to the gradients of the pixels of the grayscale curve a includes: providing a template image, where the template image is a standard image of the target to be measured; obtaining a matching region by matching the image to be processed 10 with the template grayscale image; obtaining an initial edge region 13 of the target image 12 according to the matching region; respectively obtaining the points corresponding to the maximum gradient values in the initial edge region 13 to obtain the initial position information.

[0119] Among them, the step of obtaining the points corresponding to the maximum gradient values in the initial edge region 13 includes: selecting the pixels corresponding to the maximum value among the gradient values of the pixels in the initial edge region 13 to obtain the initial position information; or, obtaining the fourth largest number of gradients and their corresponding pixel positions among the pixels in the initial edge region 13 to obtain a simulated gradient relationship, performing function fitting on the simulated gradient relationship to obtain a second fitting function, where the fourth largest number is greater than or equal to 3; obtaining the position corresponding to the maximum gradient of the second fitting function to obtain the initial position information.

[0120] The fourth largest number of gradients refers to the fourth largest number of gradients when the gradients of each pixel are arranged from large to small.

[0121] The step of obtaining the matching region and the step of obtaining the matching region in the partitioning process may be the same step.

[0122] The above are the steps of obtaining the initial position information through the gradient curve b. In other embodiments, the step of obtaining the initial position information of the edge points of the target image 12 according to the grayscale curve a includes: obtaining the initial position information of the edge points through an edge detection algorithm, such as the Canny algorithm.

[0123] Reference Figure 5 , execute step S42, and obtain the edge region 13 of the target image 11 in the image of interest 11 according to the initial position information of the edge points.

[0124] The steps of obtaining the edge region 13 of the target image 12 in the image of interest 11 include: obtaining the initial center of the edge points according to the initial position information of the edge points; and obtaining the edge region 13 of the target image 12 in the image of interest 11 according to the preset size of the target image 12 and the initial center.

[0125] Specifically, the steps of obtaining the edge region 13 of the target image 12 in the image of interest 11 according to the preset size of the target image 12 and the initial center point include: determining the center point of the edge points according to the initial position information of the edge points to obtain the initial center of the target image 12; and obtaining the edge region 13 according to the initial center and the preset size of the target image 12. In this embodiment, the center point is used as the initial center. In other embodiments, the initial center can be obtained according to the designed positional relationship between the center of the target image and the center point; the positional relationship between the initial center and the center point is the same as the designed positional relationship.

[0126] Specifically, in this embodiment, the target to be measured is circular, the target image 12 is circular, and the preset size is the preset diameter or preset radius of the target image 12.

[0127] The steps of obtaining the edge region 13 of the target image 12 in the image of interest 11 according to the preset size of the target image 12 and the initial center include: obtaining a region with a predetermined width where a circle with the initial center as the center and the preset radius as the radius is located, to obtain the edge region 13.

[0128] In other embodiments, the target to be measured is rectangular, and the preset size is the side length of the target image 12 along the second direction A. The steps of obtaining the edge region 13 of the target image 12 in the image of interest 11 according to the preset size of the target image 12 and the initial center include: obtaining the endpoints of a line segment that takes the initial center as the center point, is parallel to the second direction A, and has a length equal to the preset size; and obtaining the region with a predetermined width where the endpoints are located, to obtain the edge region 13.

[0129] Continue to refer to Figure 5 , execute step S5, perform an edge point acquisition operation on each of one or more edge regions 13 of the image of interest 11, and obtain the position information of the edge points of the target image 12 in the edge region 13.

[0130] It should be noted that one or more edge regions 13 can be obtained according to the partition processing. Specifically, in this embodiment, the number of the edge regions 13 is multiple. The method includes: performing the edge point acquisition operation on each edge region 13 respectively, and obtaining the position information of one edge point in each edge region 13.

[0131] Perform the edge point acquisition operation on any interesting image 11 as Figure 6 shown.

[0132] Please continue to refer to Figure 6 , perform step S51, obtain the gradient of the gray curve of the interesting image 11 in the edge area 13 to obtain the edge gradient; obtain the relationship curve between the edge gradient of each pixel in the edge area 13 and the position of the corresponding pixel to obtain the edge gradient curve; obtain the position information of the edge points in the edge area 13 according to the edge gradient curve.

[0133] The steps of obtaining the position information of the edge points in the edge area 13 according to the edge gradient curve include:

[0134] Obtain the pixel position information corresponding to the maximum edge gradient in each edge area 13 to obtain the position information of the edge points.

[0135] Alternatively, the steps of obtaining the position information of the edge points in the edge area 13 according to the edge gradient include: obtain the pixels corresponding to the top third number of edge gradients in each edge area 13 to obtain alternative edge points; use a fitting function to fit the gradients of the alternative edge points to obtain a fitting curve; obtain the position information corresponding to the maximum gradient on the fitting curve to obtain the position information of the edge points.

[0136] The top third number of edge gradients refers to the top third number of edge gradients when the gradients of each pixel in the edge area 13 are arranged from large to small.

[0137] Obtain the pixels corresponding to the top third number of edge gradients in each edge area 13 to obtain alternative edge points, and use a fitting function to fit the gradients of the alternative edge points to obtain a fitting curve; the fitting curve represents the gradient at any point, so that the position of the point corresponding to the maximum gradient can be obtained, and thus sub-pixel accuracy can be achieved. At the same time, by obtaining the pixels corresponding to the largest multiple edge gradients to obtain alternative edge points, the interference of non-edge points can be reduced, and the fitting difficulty can be reduced, and the complexity of the algorithm can be reduced.

[0138] In this embodiment, the fitting function includes: Gaussian, quadratic curve or cosine function.

[0139] In this embodiment, the number of alternative edge points in each edge area 13 is greater than or equal to 3.

[0140] Refer to Figure 6, the detection method further includes: repeating the compression process for the multiple images of interest 11 until the position information of the edge points of the target image 12 is obtained according to the gray curve, and obtaining the position information of multiple edge points of the target image 12; obtaining the edge contour 31 of the target image 12 according to the position information of the multiple edge points.

[0141] In this embodiment, the step of obtaining the edge contour 31 of the target image 12 according to the position information of the multiple edge points includes: obtaining the edge contour 31 of the target image 12 by performing shape fitting on the position information of the multiple edge points. The shape fitting includes least squares fitting.

[0142] Before obtaining the edge contour 31 of the target image 12 according to the position information of the multiple edge points, the method further includes: performing denoising processing on the multiple edge points.

[0143] The denoising processing can eliminate outliers among the edge points, thereby reducing and improving the detection accuracy.

[0144] In this embodiment, the method of the denoising processing includes: random sampling algorithm.

[0145] In this embodiment, the method further includes: obtaining the target size of the target image 12 according to the edge contour 31; obtaining the size to be measured of the target to be measured according to the magnification of the target image 12 and the target to be measured and the target size; and / or, the method further includes: obtaining the target center of the target image 12 according to the edge contour 31.

[0146] Obtaining the target size according to the edge contour 31 can increase the accuracy of the obtained target size. In other embodiments, obtaining the position information of the edge points of the target image 12 according to the gray curve a includes: respectively obtaining the edge points located at both ends of the second direction A according to the gray curve a to obtain a first edge point and a second edge point; the target size includes: the width along the second direction A; the detection method may not obtain the edge contour 31, and obtain the width of the target image 12 along the second direction A according to the position information of the first edge point and the second edge point.

[0147] Specifically, in this embodiment, the target to be measured is circular; the size to be measured includes the diameter or radius of the target image to be measured. In other embodiments, the target to be measured is a polygon, and the size to be measured includes the side length of the target to be measured.

[0148] In other embodiments, the surface of the analyte has a plurality of analytes of interest, including a first analyte of interest and a second analyte of interest; specifically, the first analyte of interest and the second analyte of interest are located in different film layers on the surface of the analyte.

[0149] The image to be processed 10 includes a plurality of target images 12, and the plurality of target images 12 include: a first target image 12 of the first analyte of interest and a second target image 12 of the first analyte of interest;

[0150] The method further includes: obtaining the target center of the target image 12 according to the edge contour 31; repeating the step of dividing the image to be processed 10 until the target center is obtained, until the first target center of the first target image 12 and the second target center of the second target image 12 are obtained; obtaining the offset of the first analyte of interest and the second analyte of interest according to the first target center and the second target center.

[0151] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be determined by the scope defined by the claims.

Claims

1. A detection method, characterized in that, Including: Obtain an image of the object to be measured to obtain a to-be-processed image. There is a target to be measured on the surface of the object to be measured. The to-be-processed image includes a target image of the target to be measured and a non-target image outside the target image; Obtain an image of interest according to the to-be-processed image. The image of interest has a second direction, and the second direction extends from the target image to the non-target image; According to the pixel gray values of the image of interest, obtain the correspondence between the pixel positions and gray levels in the second direction to obtain a gray curve; Perform partitioning processing on the image of interest according to the gray curve to obtain the edge region of the target image; Perform edge point acquisition operations on the images of interest in one or more edge regions respectively to obtain the position information of the edge points of the target image in the edge region; The steps of the partitioning processing include: obtaining the initial position information of the edge points of the target image according to the gray curve; according to the initial position information of the edge points, obtaining the edge region of the target image in the image of interest; The step of obtaining the initial position information of the edge points of the target image according to the gray curve includes: obtaining the gradient of each pixel of the gray curve; according to the correspondence between the gradient of each pixel of the gray curve and each pixel position, obtaining a gradient curve; obtaining the initial position information according to the gradient curve.

2. The detection method according to claim 1, characterized in that, The step of obtaining the edge region of the target image in the image of interest according to the initial position information of the edge points includes: obtaining the initial center of the target image according to the initial position information of the edge points; according to the preset size of the target image and the initial center point, obtaining the edge region of the target image in the image of interest.

3. The detection method according to claim 2, characterized in that, The target image is circular, and the preset size is the preset diameter or preset radius of the target image; The step of obtaining the edge region of the target image in the image of interest according to the preset size of the target image and the initial center includes: obtaining a region with a predetermined width where a circle with the initial center as the center and the preset radius as the radius is located to obtain the edge region.

4. The detection method according to claim 1, characterized in that, The image of interest includes a first number of separated edge points of the target image respectively located in the second direction; The step of obtaining the initial position information according to the gradient curve includes: obtaining the pixel positions corresponding to the first number of gradients ranked at the front among the gradients of each pixel to obtain the initial position information of two edge points; Alternatively, perform function fitting on the gradient curve to obtain the positions corresponding to the second number of gradients ranked at the front to obtain the initial position information of the edge points, and the second number is less than or equal to the first number.

5. The detection method according to claim 1, characterized in that, The edge point acquisition operation includes: obtaining the gradient of the gray curve of the image of interest in the edge region to obtain an edge gradient; obtaining a relationship curve between the edge gradient of each pixel in the edge region and the corresponding pixel position to obtain an edge gradient curve; obtaining the position information of the edge points in the edge region according to the edge gradient curve.

6. The detection method according to claim 5, characterized in that, The step of obtaining the position information of the edge points in the edge region according to the edge gradient curve includes: obtaining the pixel position corresponding to the maximum edge gradient in each edge region to obtain the position information of the edge points; or, Obtain the pixel positions corresponding to the top third number of edge gradients in the edge region to obtain candidate edge points; use a fitting function to fit the gradients of the candidate edge points to obtain a fitting curve; obtain the position corresponding to the maximum gradient on the fitting curve to obtain the position information of the edge points.

7. The detection method according to claim 6, characterized in that, The fitting function includes: Gaussian, quadratic curve or cosine function.

8. The detection method according to claim 6, characterized in that, The number of candidate edge points in each edge region is greater than or equal to 3.

9. The detection method according to claim 1, characterized in that, Before obtaining the initial position information according to the gradient curve, the step of obtaining the initial position information of the edge points of the target image according to the gray curve further includes: performing an optimization process on the gradient curve to increase the signal-to-noise ratio of the gradient curve.

10. The detection method according to claim 9, characterized in that, The steps of the optimization process include: obtaining a reference curve, where the reference curve represents the gradient curve of the object to be measured along the second direction; performing a multiplication process on the reference curve and the gradient curve to obtain an optimized gradient curve; the multiplication process includes dot multiplication or convolution, and the reference curve includes: the gradient curve of the reference object or the mirror curve of the gradient curve of the reference object, and the reference object is the designed gradient curve of the object to be measured or the standard object to be measured.

11. The detection method according to claim 1, characterized in that, The number of the images of interest is multiple, and there is a preset angle between the second directions of the multiple images of interest; The method further includes: repeating the steps of obtaining the gray curve of the pixel positions in the second direction for the multiple images of interest to obtaining the position information of the edge points of the target image according to the gray curve, and obtaining the position information of multiple edge points of the target image; obtaining the edge contour of the target image according to the position information of the multiple edge points.

12. The detection method according to claim 11, characterized in that, Before obtaining the edge contour of the target image according to the position information of the multiple edge points, the method further includes: performing a denoising process on the multiple edge points; the method of the denoising process includes: a random sampling algorithm.

13. The detection method according to claim 11, characterized in that, The method further includes: obtaining the target size of the target image according to the edge contour; obtaining the size to be measured of the object to be measured according to the target size and the magnification ratio between the target image and the object to be measured; And / or, the method further includes: obtaining the target center of the target image according to the edge contour.

14. The detection method according to claim 11, characterized in that,There are multiple objects to be measured on the surface of the object to be measured, and the multiple objects to be measured include a first object to be measured and a second object to be measured; The image to be processed includes multiple target images, and the multiple target images include: a first target image of the first object to be measured and a second target image of the first object to be measured; The method further includes: obtaining the target center of the target image according to the edge contour; repeating the steps of dividing the image to be processed to obtaining the target center until obtaining the first target center of the first target image and the second target center of the second target image; obtaining the offset between the first object to be measured and the second object to be measured according to the first target center and the second target center.

15. The detection method according to claim 1, wherein, The steps of obtaining the image of interest according to the image to be processed include: providing a template image, which is a standard image of the target to be measured; using the template image to match the image to be processed to obtain a matching region; segmenting the matching region to obtain the image of interest; the image of interest also includes the center of the matching region.

16. The detection method according to claim 1, wherein, The steps of obtaining the gray curve of the pixel positions in the second direction according to the pixel gray values of the image of interest include: obtaining the corresponding relationship curve of the positions and gray values of the pixels in the second direction of the image of interest to obtain the gray curve; the number of pixels in the direction perpendicular to the second direction of the image of interest is one or more.

17. A detection system, wherein, It includes a processing system for executing the detection method according to any one of claims 1 to 16.

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