A method, device, system and storage medium for detecting appearance of display screen

Abstract

The present application discloses a method, device, system and storage medium for detecting the appearance of a display screen, which is used to improve the detection accuracy of the appearance defects of the display screen. The present application method includes: controlling the light source through the time-sharing stroboscopic method to direct stripe light of different phases to the display screen to be tested, using a line scan camera and adopting a multi-frame acquisition method to scan the display screen to be tested, and generating slice image data; performing phase de-phase processing on the slice image data to generate wrapped phase image data; performing phase unwrapping processing on the wrapped phase image data to generate absolute phase image data; performing image fusion processing on the absolute phase image data and the slice image data according to the defect type to be tested to generate a set of images to be detected; performing defect detection on the set of images to be detected to generate a number of slice data; performing integration processing on a number of slice data to generate target defect data.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of display screen image correction, and in particular, to a method, device, system, and storage medium for detecting the appearance of a display screen. Background Art

[0002] With the rapid development of industrial and Internet technologies, the industry's requirements for industrial product quality are becoming more and more refined and standardized. As far as the display panel industry is concerned, the quality of products directly affects its product competitiveness. Among them, the accuracy of product shape and size specifications is the most basic process indicator of product quality. As one of the display components of the device, the display screen is used in various high-end devices, such as mobile phones, TVs, tablets, etc. As people's requirements for screen display continue to increase, the display screen has gradually become a technologically sophisticated product.

[0003] Especially for flexible display screens, currently, defect detection of flexible display screens is a necessary process for adjustment. Defects of flexible display screens can be divided into display defects and screen body defects. For example, in the production process of flexible display screens, foreign matter, dirt, dust, scratches, and other surface defects are prone to occur, affecting the screen quality and user experience. Display defects refer to the discordant areas that appear in the display screen during the display screen display process. In the prior art, display defects usually require inputting a specific picture on the display screen to be tested, and then collecting the picture through the camera, and then performing image detection to analyze it. Screen body defects refer to areas where damage occurs on the surface or inside of the display screen itself (such as broken corners, foreign matter, etc.). Such defects can be analyzed by collecting the picture with the camera and then performing image detection.

[0004] However, as the application fields of flexible displays become more and more extensive and more and more functions are added, the pixel structure of flexible displays becomes more and more complex. For example, the pixel spacing becomes smaller and smaller, the curvature of flexible displays becomes larger and larger, and circuit areas are provided on flexible displays. These make it more difficult to detect defects in flexible displays, especially since circuit areas are provided on flexible displays, which reduces the detection accuracy of display appearance defects (display defects and screen body defects). Summary of the invention

[0005] The present application discloses a method, device, system and storage medium for detecting appearance of a display screen, which are used to improve the detection accuracy of appearance defects of a display screen.

[0006] The first aspect of the present application discloses a method for detecting the appearance of a display screen, comprising:

[0007] The light source is controlled by the time-sharing stroboscopic method to illuminate the display screen to be tested with stripe lights of different phases, and the display screen to be tested is scanned by a line scan camera and a multi-frame acquisition method to generate slice image data;

[0008] performing phase dephasing on the slice image data to generate wrapped phase image data;

[0009] Performing phase unwrapping processing on the wrapped phase image data to generate absolute phase image data;

[0010] According to the type of defect to be detected, the absolute phase image data and the slice image data are subjected to image fusion processing to generate a set of images to be detected;

[0011] Defect detection is performed on the image set to be inspected, and a number of slice data are generated;

[0012] Integrate and process several slice data to generate target defect data.

[0013] Optionally, several slice data are integrated and processed to generate target defect data, including:

[0014] Arrange the slice data and the image to be detected corresponding to the slice data, and save them in the data accumulation module;

[0015] Perform penetration defect judgment on the slice data in the data accumulation module and the corresponding image to be inspected;

[0016] According to the through-defect judgment result, the slice data in the data accumulation module and the corresponding image to be inspected are combined and processed to generate complete product defect integrated data;

[0017] The defect area feature quantity of the product defect integrated data after splicing and merging is refreshed to generate target defect data.

[0018] Optionally, performing phase unwrapping processing on the wrapped phase image data to generate absolute phase image data includes:

[0019] Calculating first reliability parameter data of pixel points except edges in the wrapped phase image data according to a second-order difference method;

[0020] constructing vertical edges and horizontal edges in the wrapped phase image data, and calculating second reliability parameter data of the vertical edges and the horizontal edges;

[0021] Phase unwrapping processing is performed on the wrapped phase image data according to the first reliability parameter data and the second reliability parameter data to generate absolute phase image data.

[0022] Optionally, after performing image fusion processing on the absolute phase image data and the slice image data according to the defect type to be detected to generate a set of images to be detected, and before performing defect detection on the set of images to be detected and generating a plurality of slice data, the method further includes:

[0023] Perform Gaussian filtering, image segmentation, and morphological processing on the image set to be detected;

[0024] Perform connected domain analysis on the image set to be detected.

[0025] Optionally, after performing phase unwrapping processing on the wrapped phase image data to generate the absolute phase image data, performing image fusion processing on the absolute phase image data and the slice image data according to the defect type to be detected, before generating the image set to be detected, the method further includes:

[0026] The absolute phase image data is subjected to grayscale stretching processing.

[0027] The second aspect of the present application discloses a device for detecting the appearance of a display screen, comprising:

[0028] The first generating unit is used to control the light source to irradiate the stripe light of different phases onto the display screen to be tested by using the time-sharing stroboscopic method, and use the line scanning camera and the multi-frame acquisition method to scan the display screen to be tested to generate slice image data;

[0029] A second generating unit is used to perform phase de-processing on the slice image data to generate wrapped phase image data;

[0030] A third generating unit is used to perform phase unwrapping processing on the wrapped phase image data to generate absolute phase image data;

[0031] A fourth generating unit, configured to perform image fusion processing on the absolute phase image data and the slice image data according to the type of defect to be detected, so as to generate a set of images to be detected;

[0032] A fifth generating unit is used to detect defects on the image set to be inspected and generate a plurality of slice data;

[0033] The sixth generating unit is used to integrate and process a plurality of slice data to generate target defect data.

[0034] Optionally, the sixth generation unit includes:

[0035] Arrange the slice data and the image to be detected corresponding to the slice data, and save them in the data accumulation module;

[0036] Perform penetration defect judgment on the slice data in the data accumulation module and the corresponding image to be inspected;

[0037] According to the through-defect judgment result, the slice data in the data accumulation module and the corresponding image to be inspected are combined and processed to generate complete product defect integrated data;

[0038] The defect area feature quantity of the product defect integrated data after splicing and merging is refreshed to generate target defect data.

[0039] Optionally, the third generation unit includes:

[0040] Calculating first reliability parameter data of pixel points except edges in the wrapped phase image data according to a second-order difference method;

[0041] constructing vertical edges and horizontal edges in the wrapped phase image data, and calculating second reliability parameter data of the vertical edges and the horizontal edges;

[0042] Phase unwrapping processing is performed on the wrapped phase image data according to the first reliability parameter data and the second reliability parameter data to generate absolute phase image data.

[0043] Optionally, after the fourth generating unit and before the fifth generating unit, the device further includes:

[0044] The first processing unit is used to perform Gaussian filtering, image segmentation, and morphological processing on the image set to be detected;

[0045] The second processing unit is used to perform connected domain analysis on the image set to be detected.

[0046] Optionally, after the third generating unit and before the fourth generating unit, the device further includes:

[0047] The stretching unit is used to perform grayscale stretching processing on the absolute phase image data.

[0048] A third aspect of the present application provides a system for detecting the appearance of a display screen, comprising:

[0049] Processor, memory, input-output unit, and bus;

[0050] The processor is connected to the memory, the input and output unit, and the bus;

[0051] The memory stores a program, and the processor calls the program to execute the first aspect and any optional method of the first aspect.

[0052] A fourth aspect of the present application provides a computer-readable storage medium, on which a program is stored. When the program is executed on a computer, the program executes the first aspect and any optional processing method of the first aspect.

[0053] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0054] In this application, first of all, the accuracy of traditional detection methods decreases under complex conditions such as the pixel pitch of the flexible display screen becomes smaller, the curvature increases, and the circuit area is set. The time-sharing stroboscopic method combined with the multi-frame acquisition method of the line scan camera can scan the display screen to be tested with stripe lights of different phases. This multi-dimensional lighting and acquisition method can better highlight display defects and screen defects.

[0055] Secondly, since slice image data can be generated, the detection method can detect both display defects and screen defects at the same time. Whether it is a discordant area in the display screen or a damaged area on the surface or inside the display screen, it can be accurately reflected in the generated slice image data.

[0056] Finally, the phase deflection algorithm can restore surface defects based on the deflection of light on the display surface. Compared with traditional detection methods, it can more accurately depict the shape, size, depth and other characteristics of defects. The phase deflection algorithm is a non-contact detection method that only restores defects by analyzing the deflection of light and does not directly contact the display surface, ensuring that the display will not be damaged during the detection process, thereby ensuring the quality of the display. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0058] Figure 1 A schematic diagram of an embodiment of a display screen appearance detection method of the present application;

[0059] Figure 2 A schematic diagram of an embodiment of a method for generating target defect data for the present application;

[0060] Figure 3 A schematic diagram of another embodiment of a method for generating absolute phase image data for the present application;

[0061] Figure 4 A schematic diagram of another embodiment of the method for preprocessing display screen images of the present application;

[0062] Figure 5 A schematic diagram of another embodiment of the method for processing absolute phase image data of the present application;

[0063] Figure 6 A schematic diagram of an embodiment of a device for detecting the appearance of a display screen according to the present application;

[0064] Figure 7 A schematic diagram of an embodiment of a system for detecting the appearance of a display screen of the present application;

[0065] Figure 8 A schematic diagram of the window of the spatial phase unwrapping algorithm of this application. DETAILED DESCRIPTION

[0066] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0067] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0068] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0069] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0070] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0071] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0072] In the prior art, as the application fields of flexible display screens become more and more extensive and more and more functions are added, the pixel structure of flexible display screens becomes more and more complex. For example, the pixel spacing becomes smaller and smaller, the curvature of the flexible display screen becomes larger and larger, and a circuit area is provided on the flexible display screen. These make it more difficult to detect defects in flexible display screens, especially since the circuit area is provided on the flexible display screen, which reduces the detection accuracy of the appearance defects of the display screen (display defects and screen body defects).

[0073] Based on this, the present application discloses a method, device, system and storage medium for detecting the appearance of a display screen, which are used to improve the detection accuracy of display screen appearance defects.

[0074] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0075] The method of the present application can be applied to a server, a device, a terminal or other devices with logic processing capabilities, and the present application does not limit this. For the convenience of description, the following description is made by taking the execution subject as an example of a terminal.

[0076] See also Figure 1 The present application provides an embodiment of a method for detecting appearance of a display screen, comprising:

[0077] 101. Control the light source by using a time-sharing stroboscopic method to illuminate the display screen to be tested with stripe lights of different phases, use a line scan camera and adopt a multi-frame acquisition method to scan the display screen to be tested, and generate slice image data;

[0078] The collected multi-frame image data is transferred to the computer. In the computer, special image processing software is used for data processing. First, each frame of the image is subjected to noise reduction processing to remove interference caused by ambient light or electronic noise. Then, according to the phase information and acquisition sequence of the streak light, the multi-frame images are synthesized. The pixel information of the same position collected at different phases is fused through an algorithm to generate slice image data.

[0079] 102. Perform phase dephasing on the slice image data to generate wrapped phase image data;

[0080] The collected slice image data are grayscale images, which are obtained through specific optical slice technology. The grayscale value of each pixel represents the optical characteristics of the corresponding position.

[0081] The phase shift method is used to perform phase processing on the slice image data. It is necessary for the display to display multiple images with different initial phases and obtain them by the camera. When sinusoidal fringes are used, assuming that the N-step phase shift method is used, the light intensity received by the camera is expressed as:

[0082]

[0083] In the formula, Phase shift The image intensity at the time step is a known quantity obtained by the camera. represents the background light intensity distribution, Indicates the modulation distribution, phase is the unknown quantity to be solved, so we need to find At least three phase shifts are required, that is, N is at least 3, which can be solved by the least squares method, where The result is:

[0084]

[0085] Since the inverse tangent function has a range of ,so is the absolute phase value folded into The wrapping phase within the interval.

[0086] 103. Perform phase unwrapping processing on the wrapped phase image data to generate absolute phase image data;

[0087] There are many grayscale cutoffs in the folded phase image. The cutoffs are overlapped with the object under test and the defects, and the defects cannot be identified. Therefore, the obtained wrapped phase must be unfolded through the spatial phase unwrapping algorithm. The quality-guided path unwrapping algorithm is a spatial phase unwrapping algorithm, which mainly includes the selection of the reliability function and the design of the unwrapping path, and then completes the phase unwrapping to solve the absolute phase. The reliability function is defined by the second-order difference method. The second-order difference will provide a measure for the degree of convexity of the phase function. The details are as follows: Taking a 3×3 window as an example, let the pixel coordinates of the center point be and , see Figure 8 .

[0088] The calculation formula is as follows:

[0089]

[0090] in,

[0091]

[0092]

[0093]

[0094] is a known correlation coefficient that can be set empirically, and the reliability of a pixel is defined as: , As an intermediate parameter, the wrapped phase is unfolded to obtain the absolute phase image data, that is, the grayscale image.

[0095] 104. Perform image fusion processing on the absolute phase image data and the slice image data according to the defect type to be detected, and generate a set of images to be detected;

[0096] By calculating all the pixels, the fused image data is obtained, which is an image in the set of images to be detected. If you want to perform fusion with different parameters or different processing methods multiple times, you can repeat the above process and add the fused image obtained each time to the set of images to be detected.

[0097] 105. Detect defects on the image set to be inspected and generate a number of slice data;

[0098] By setting multiple grayscale thresholds, the pixels in the image are divided into different categories according to the relationship between their grayscale values ​​and the thresholds. If the grayscale values ​​of pixels in a certain area of ​​the image correspond to different thresholds, they are determined to be different defect areas.

[0099] 106. Integrate and process several slice data to generate target defect data.

[0100] The statistical information in the slice data, such as grayscale statistics, texture statistics, etc., is used for integration. By statistically analyzing the pixel grayscale values ​​in the slice data, and then integrating the slice data according to the relationship between these statistics, the target defect data is generated, which provides an important basis for subsequent defect assessment, repair decision-making or quality control.

[0101] In this embodiment, the accuracy of the traditional detection method is reduced in complex situations such as the pixel pitch of the flexible display screen becomes smaller, the curvature increases, and the circuit area is set. The time-sharing stroboscopic method combined with the multi-frame acquisition method of the line scan camera can scan the display screen to be tested through stripe lights of different phases. This multi-dimensional lighting and acquisition method can better highlight display defects and screen defects.

[0102] Secondly, since slice image data can be generated, the detection method can detect both display defects and screen defects at the same time. Whether it is a discordant area in the display screen or a damaged area on the surface or inside the display screen, it can be accurately reflected in the generated slice image data.

[0103] Finally, the phase deflection algorithm can restore surface defects based on the deflection of light on the display surface. Compared with traditional detection methods, it can more accurately depict the shape, size, depth and other characteristics of defects. The phase deflection algorithm is a non-contact detection method that only restores defects by analyzing the deflection of light and does not directly contact the display surface, ensuring that the display will not be damaged during the detection process, thereby ensuring the quality of the display.

[0104] See also Figure 2 The present application provides an embodiment of a method for generating target defect data, comprising:

[0105] 201. Arrange the slice data and the image to be detected corresponding to the slice data, and save them in a data accumulation module;

[0106] Annotate the slice data and the image to be detected, such as adding the image number, detection time, detection location, etc., and then create an index. The index can be built based on this annotation information to quickly locate and find related data. Add the processed slice data and the image to be detected to the data accumulation module, which is a place for centralized storage and management of data. It can continuously receive new slice data and image data to be detected, and integrate and store these data for a long time.

[0107] 202. Perform through-defect judgment on the slice data in the data accumulation module and the corresponding image to be inspected;

[0108] In order to improve the overall inspection efficiency of the entire inspection process, the entire product is split into multiple slices for synchronous inspection. Therefore, there may be through-type defects at the connection of the slices, and it is necessary to judge the through-defects of the slice data and the image to be inspected.

[0109] First, perform internal integration of the slices, obtain the first slice data, and determine whether there are defects. If there are defects, generate the minimum circumscribed rectangle of all defects, where the coordinates of the upper left corner and the lower right corner of the first defect area are recorded as , , , , the second defect area is recorded as , , , , determine whether the left and right boundaries meet the threshold, and merge them into the same defect if they meet the threshold. The formula is as follows:

[0110] or

[0111] If any of the above conditions are met, the two defects are merged into one defect area, and the defect area is refreshed to regenerate the feature quantity and data. The other slice data are judged in the same way. If there is no defect, all the mark positions are empty.

[0112] Secondly, the size of the slice image is obtained. Taking the first slice image as an example, the penetration mark is determined according to the coordinates of the minimum circumscribed rectangle of each defect, where the upper penetration mark and the lower penetration mark are recorded as , , the formula is as follows:

[0113]

[0114] If any of the above formulas is met, the defect area is recorded as upper penetration or lower penetration, and the same applies to other slice defect areas.

[0115] 203. Perform image stitching and merging processing on the slice data in the data accumulation module and the corresponding image to be inspected according to the through-defect judgment result, so as to generate complete product defect integrated data;

[0116] When the data accumulation module obtains data with more than two slices, the data will be transmitted backward. After obtaining the images of the two slices, they are spliced ​​vertically. Specifically, the two slice images are combined in the vertical direction to ensure that the pixels of the two slice images are aligned in the horizontal direction. In a digital image, each pixel has its specific coordinate position. When splicing vertically, it is necessary to ensure that the pixels on the corresponding horizontal coordinates can be correctly connected. There is another direct splicing based on pixel coordinates. Specifically, the common properties of the two slice images, such as resolution, color mode, etc., are first determined. Then, based on the height information of the image, a new blank image is created, whose width is the same as the slice image and whose height is the sum of the heights of the two slice images. Then, the pixels of the first slice image are copied to the upper half of the new image, and the pixels of the second slice image are copied to the lower half of the new image. The spliced ​​image is continued to be saved in the data accumulation module. By analogy, the images of the subsequent slices are continuously spliced ​​until the complete product image is spliced.

[0117] Merge the defects in the spliced ​​product images, obtain the first slice data, and traverse the penetration flag of each defect. If the slice needs to be spliced ​​downward, it is only necessary to traverse the lower penetration flag of each defect, and find the defects that meet the lower penetration and record them as . Get all the upper penetration marks of the second slice to be spliced, and record the upper penetration defects found as , calculate the pixel-by-pixel Euclidean distance between the lower through-defect of slice 1 and all the upper through-defects of slice 2, and find the distance between the nearest pixels of the two defect areas. The formula is as follows:

[0118]

[0119]

[0120] If the above conditions are met, the defects of the two slices can be integrated together, and all defects that meet the conditions are looped through until all the defects of the two slices are spliced ​​together, and the other slices are processed in the same way.

[0121] 204. Refresh the defect area feature quantity of the combined and spliced ​​product defect integrated data to generate target defect data.

[0122] After the through-defect areas are spliced, the feature quantity of the spliced ​​defect areas needs to be refreshed.

[0123] First, refresh the geometric features, such as area, width, height, etc. The geometric features only need to add the corresponding parts of the two defects, which can be calculated by the following formula:

[0124]

[0125] Non-geometric features, such as average grayscale, etc. The defective area after stitching needs to be re-acquired on the original stitching image.

[0126] Coordinate feature quantity, it is necessary to restore the coordinates of all slice defects to the spliced ​​large image to facilitate subsequent operations. Specifically, re-extract the coordinates of the through-type defect area, such as the coordinates of the corner points of the minimum circumscribed rectangle, the coordinates of the center point, etc. Restore the defect coordinates to the spliced ​​large image according to the slice ID. Take the upper left corner of a defect in slice 2 as the coordinates. Taking the standard as an example, the formula is as follows:

[0127]

[0128] in is the coordinate of the defect area on the stitched image, is the slice image height, For the slice image ID, all defect coordinates are spliced ​​according to the above method to generate the coordinates of the defect area on the spliced ​​large image. The above operation can be repeated to refresh all defect feature quantities and generate target defect data.

[0129] See also Figure 3 The present application provides another embodiment of a method for generating absolute phase image data, comprising:

[0130] 301. Calculate first reliability parameter data of pixel points except edges in the wrapped phase image data according to a second-order difference method;

[0131] The lower the second-order difference of a pixel, the more reliable it is. By calculating the first reliability value for each pixel in the image except for the edge, it helps to better distinguish the target from the background, thereby helping to remove unreliable pixel information, thereby improving the accuracy of the recognition algorithm. The calculation formula of the second-order difference method is shown in step 103.

[0132] 302. Construct vertical edges and horizontal edges in the wrapped phase image data, and calculate second reliability parameter data of the vertical edges and the horizontal edges;

[0133] Construct vertical and horizontal edges in the image, and calculate the reliability value for each edge, that is, the second reliability parameter data. The second reliability parameter data is calculated using the second-order difference method as in step 301 above, which is convenient for subsequent operation processing.

[0134] 303. Perform phase unwrapping processing on the wrapped phase image data according to the first reliability parameter data and the second reliability parameter data to generate absolute phase image data.

[0135] Sort all edges by reliability value and save them into array Y. Set each pixel to not belong to any group. Check the two pixels that form an edge. If both pixels belong to different groups, unwrap the pixels that belong to the smallest group and merge the two groups. If both pixels do not belong to any group, unwrap the two pixels and put them into the same group. If one pixel belongs to a group and the other does not, unwrap the pixel that does not belong to any group and add it to the group where the other pixel belongs. Check array Y for unprocessed edges, and if there are no unprocessed edges, unwrap the image boundary relative to the rest of the image to obtain absolute phase image data.

[0136] In this embodiment, the first reliability parameter data and the second reliability parameter data are combined to perform phase unwrapping processing, which improves the accuracy of phase unwrapping. In addition, in the actual image acquisition and processing process, noise and error are inevitable, and by utilizing the reliability parameter data, noise and error can be effectively suppressed during the phase unwrapping process. The phase unwrapping processing method based on the reliability parameter data also enhances the robustness to noise and error.

[0137] See also Figure 4 The present application provides another embodiment of a method for preprocessing a display screen image, comprising:

[0138] 401. Perform Gaussian filtering, image segmentation, and morphological processing on the image set to be detected;

[0139] By using Gaussian filtering, the detailed information of defects can be better preserved while smoothing the image. The formula is as follows:

[0140]

[0141] In the formula, Represents pixel The result after Gaussian filtering is is the standard deviation.

[0142] The threshold segmentation algorithm is the simplest image segmentation technology, which is mainly applicable to the situation where the difference between the foreground and background is large and the area to be segmented is obvious. The key to threshold segmentation is to select a suitable threshold T to distinguish the foreground from the background. Its expression is as follows:

[0143]

[0144] In the formula, Represents the grayscale value of the pixel in the input image. represents the threshold used to segment the foreground and background, Represents the output image.

[0145] The segmented image is morphologically processed to connect and fill small areas.

[0146] 402. Perform connected domain analysis on the image set to be detected.

[0147] Use the connectivity analysis algorithm to identify and mark each connected region in the binary image. Take the breadth-first search algorithm as an example. Breadth-first search is a graph search algorithm. In the connected region marking of the binary image, the image can be regarded as a graph composed of pixels. Each pixel is a node in the graph, and adjacent pixels are connected by edges. Starting from a starting node, first visit the starting node, then visit all the neighboring nodes of the starting node in turn, then visit the neighboring nodes of these neighboring nodes, and so on, until all nodes connected to the starting node are visited. Take the coordinates of a pixel from the queue, and then check the neighboring pixels of this pixel in four directions. For each neighboring pixel, if its value in the binary image is 1 and its value in the marked image is 0, mark this neighboring pixel as the number of the current connected region and add its coordinates to the queue. Repeat this process until all pixels in the queue have been processed. In this way, the marking of a connected region is completed.

[0148] In this embodiment, Gaussian filtering weights and calculates the pixel values ​​of the neighborhood around each pixel point according to the shape of the Gaussian function, and can better retain the image features such as edges and details while removing noise. Image segmentation can separate the target object in the image from the background. The segmented image makes the image analysis simpler and more targeted. The originally complex entire image is decomposed into regions with different semantics, and feature extraction, analysis and other operations can be performed separately for each region.

[0149] See also Figure 5 The present application provides another embodiment of a method for processing absolute phase image data, comprising:

[0150] 501. Perform grayscale stretching processing on the absolute phase image data.

[0151] The absolute phase obtained is positively correlated with the surface gradient of the object being measured. The gradient at the defect will have a sudden change, so the gradient can be used to characterize the defect. Since the surface of the object being measured is approximately flat, the gradient value obtained is mostly approximately 0. The gradient at the defect will have a sudden change, and the gradient will be greater than 1 at this time. It may even exceed 10 at large local defects. Since the grayscale value range that the human eye can recognize is 0~255, and it is difficult to distinguish when the grayscale value is less than 10, the gradient map cannot be directly processed. The gradient map needs to be stretched to the range of 0~255 first, and then the image fusion is completed to facilitate subsequent operations.

[0152] In this embodiment, grayscale stretching processing is performed on the absolute phase image to make details clearer and improve contrast, so that the target recognition algorithm can more accurately locate and identify defects, thereby improving the efficiency and accuracy of the entire detection process.

[0153] See also Figure 6 The present application provides an embodiment of a device for detecting the appearance of a display screen, comprising:

[0154] The first generating unit 601 is used to control the light source to direct stripe lights of different phases onto the display screen to be tested by using a time-sharing stroboscopic method, and use a line scan camera and a multi-frame acquisition method to scan the display screen to be tested to generate slice image data;

[0155] The second generating unit 602 is used to perform phase de-processing on the slice image data to generate wrapped phase image data;

[0156] The third generating unit 603 is used to perform phase unwrapping processing on the wrapped phase image data to generate absolute phase image data;

[0157] Optionally, the third generating unit 603 includes:

[0158] Calculating first reliability parameter data of pixel points except edges in the wrapped phase image data according to a second-order difference method;

[0159] constructing vertical edges and horizontal edges in the wrapped phase image data, and calculating second reliability parameter data of the vertical edges and the horizontal edges;

[0160] Phase unwrapping processing is performed on the wrapped phase image data according to the first reliability parameter data and the second reliability parameter data to generate absolute phase image data.

[0161] A stretching unit 604, used for performing grayscale stretching processing on the absolute phase image data;

[0162] The fourth generating unit 605 is used to perform image fusion processing on the absolute phase image data and the slice image data according to the defect type to be detected, so as to generate a set of images to be detected;

[0163] The first processing unit 606 is used to perform Gaussian filtering, image segmentation, and morphological processing on the image set to be detected;

[0164] The second processing unit 607 is used to perform connected component analysis on the image set to be detected.

[0165] A fifth generating unit 608 is used to detect defects on the image set to be inspected and generate a plurality of slice data;

[0166] The sixth generating unit 609 is used to integrate a plurality of slice data to generate target defect data.

[0167] Optionally, the sixth generating unit 609 includes:

[0168] Arrange the slice data and the image to be detected corresponding to the slice data, and save them in the data accumulation module;

[0169] Perform penetration defect judgment on the slice data in the data accumulation module and the corresponding image to be inspected;

[0170] According to the through-defect judgment result, the slice data in the data accumulation module and the corresponding image to be inspected are combined and processed to generate complete product defect integrated data;

[0171] The defect area feature quantity of the product defect integrated data after splicing and merging is refreshed to generate target defect data.

[0172] See also Figure 7 The present application provides a system for detecting the appearance of a display screen, comprising:

[0173] Processor 701 , memory 702 , input / output unit 703 , and bus 704 .

[0174] The processor 701 is connected to the memory 702 , the input and output unit 703 , and the bus 704 .

[0175] The memory 702 stores a program, and the processor 701 calls the program to execute the following steps: Figure 1 , Figure 2 and Figure 3 , Figure 4 and Figure 5 The method in .

[0176] The present application provides a computer-readable storage medium, on which a program is stored, and when the program is executed on a computer, the program performs the following steps: Figure 1 , Figure 2 and Figure 3 , Figure 4 and Figure 5 The method in .

[0177] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0178] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0179] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0180] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0181] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk and other media that can store program code.

Claims

1. A method for detecting the appearance of a display screen, characterized in that: include: Controlling the light source by time-sharing stroboscopic method to illuminate the display screen to be tested with stripe lights of different phases, using a line scan camera and adopting a multi-frame acquisition method to scan the display screen to be tested, and generating slice image data; performing phase dephasing on the slice image data to generate wrapped phase image data; Calculating first reliability parameter data of pixel points other than edges in the wrapped phase image data according to a second-order difference method; Constructing vertical edges and horizontal edges in the wrapped phase image data, and calculating second reliability parameter data of the vertical edges and the horizontal edges; Performing phase unwrapping processing on the wrapped phase image data according to the first reliability parameter data and the second reliability parameter data to generate absolute phase image data; Performing image fusion processing on the absolute phase image data and the slice image data according to the defect type to be detected, so as to generate a set of images to be detected; Perform defect detection on the set of images to be detected to generate a plurality of slice data; Arrange the slice data and the image to be detected corresponding to the slice data, and save them in the data accumulation module; Performing through-defect judgment on the slice data in the data accumulation module and the corresponding image to be detected; According to the through-defect judgment result, the slice data in the data accumulation module and the corresponding image to be detected are subjected to image stitching and merging processing to generate complete product defect integrated data; The defect area feature quantity of the product defect integrated data after splicing and merging is refreshed to generate target defect data.

2. The method according to claim 1, characterized in that After performing image fusion processing on the absolute phase image data and the slice image data according to the defect type to be detected to generate a set of images to be detected, and before performing defect detection on the set of images to be detected and generating a plurality of slice data, the method further includes: Performing Gaussian filtering, image segmentation, and morphological processing on the set of images to be detected; Performing connected domain analysis on the set of images to be detected.

3. The method according to any one of claims 1 to 2, characterized in that After performing phase unwrapping processing on the wrapped phase image data to generate absolute phase image data, performing image fusion processing on the absolute phase image data and the slice image data according to the defect type to be detected, before generating the image set to be detected, the method further includes: The absolute phase image data is subjected to grayscale stretching processing.

4. A device for detecting the appearance of a display screen, characterized in that: include: The first generating unit is used to control the light source to irradiate the stripe light of different phases onto the display screen to be tested by using a time-sharing stroboscopic method, and use a line scan camera to scan the display screen to be tested in a multi-frame acquisition manner to generate slice image data; A second generating unit is used to perform phase de-processing on the slice image data to generate wrapped phase image data; A third generating unit, configured to perform phase unwrapping processing on the wrapped phase image data to generate absolute phase image data; The third generation unit includes: Calculating first reliability parameter data of pixel points other than edges in the wrapped phase image data according to a second-order difference method; Constructing vertical edges and horizontal edges in the wrapped phase image data, and calculating second reliability parameter data of the vertical edges and the horizontal edges; Performing phase unwrapping processing on the wrapped phase image data according to the first reliability parameter data and the second reliability parameter data to generate absolute phase image data; A fourth generating unit, configured to perform image fusion processing on the absolute phase image data and the slice image data according to the type of defect to be detected, so as to generate a set of images to be detected; A fifth generating unit, used for performing defect detection on the set of images to be detected to generate a plurality of slice data; A sixth generating unit is used to integrate and process a plurality of slice data to generate target defect data; The sixth generation unit comprises: Arrange the slice data and the image to be detected corresponding to the slice data, and save them in the data accumulation module; Performing through-defect judgment on the slice data in the data accumulation module and the corresponding image to be detected; According to the through-defect judgment result, the slice data in the data accumulation module and the corresponding image to be detected are subjected to image stitching and merging processing to generate complete product defect integrated data; The defect area feature quantity of the product defect integrated data after splicing and merging is refreshed to generate target defect data.

5. A system for detecting the appearance of a display screen, characterized in that: include: Processor, memory, input-output unit, and bus; The processor is connected to the memory, the input and output unit, and the bus; The memory stores a program, and the processor calls the program to execute the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a program stored thereon, wherein the program, when executed on a computer, performs the method according to any one of claims 1 to 3.

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