A method, system, equipment and medium for detecting color leakage defects in panel manufacturing process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHENGDU UNION BIG DATA TECH CO LTD
- Filing Date
- 2023-12-04
- Publication Date
- 2026-06-30
AI Technical Summary
Existing target detection algorithms are unable to effectively identify missing process defects in CF color filters, resulting in a high error rate and high labor costs for manual image interpretation.
By employing template matching and color difference analysis, matching templates are obtained, and similarity matching and color difference comparison are performed to identify process defects in CF color filters.
It enables effective identification of manufacturing defects in CF color filters, reduces the false positive rate and labor costs, and improves detection efficiency.
Smart Images
Figure CN117495842B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of panel manufacturing process defect detection technology, and more specifically, to a method, system, equipment, and medium for detecting panel color manufacturing process defects. Background Technology
[0002] The color filter (CF) is an important component of LCD panels. The ability of a display to display vibrant colors primarily relies on the function of the CF color filter. The CF color filter is formed by the repeated periodic arrangement of the three basic color units (RGB).
[0003] In the production of color filters for color processing, a certain color process may be missed, known as a missed process. A missed process is a serious defect. If subsequent processes are carried out on top of a missed process, the resulting product will become scrap, and the capacity of subsequent processes will be wasted. Therefore, panel factories strictly control missed process defects. The factory uses an automated optical inspection (AOI) machine to photograph the produced panel products, and then manual visual inspection is used to identify whether there are any missed process defects in the images. If a missed process defect is found, the panel product is transported to a repair machine to redo the missed process.
[0004] However, panel production generates tens of thousands of images daily, requiring significant manual image review and incurring substantial labor costs. Furthermore, human review is susceptible to variations in experience and condition, leading to a generally high error rate. Therefore, panel manufacturers have widely adopted algorithm-based automated image review systems to replace manual review. These systems typically employ object detection algorithms as their core detection mechanism. While these algorithms are effective at detecting defects such as foreign objects falling onto the product, missed process defects lack the characteristics of foreign objects, and the missed process sections blend seamlessly with the normal background, making effective detection difficult with conventional object detection algorithms. Summary of the Invention
[0005] This invention provides a method, system, equipment, and medium for detecting missing process defects in panel color filters, in order to solve the problem that target detection algorithms have difficulty identifying missing process defects in CF color filters.
[0006] In a first aspect, embodiments of the present invention provide a method for detecting color leakage process defects in a panel, the method flow being as follows:
[0007] A matching template is obtained based on the original panel image, and the matching template contains all color units;
[0008] Similarity matching is performed on the panel image to be detected based on the matching template to obtain several candidate templates;
[0009] Based on the candidate template, obtain the rectangular area corresponding to all color units, and compare the color difference based on the rectangular area to determine whether there is a color missing process defect.
[0010] In the above embodiments, the present invention first obtains a matching template containing three color units (RGB), then obtains several images of the same size as the matching template through the image of the panel to be detected, and selects images that are similar to or the same as the matching template image through similarity matching; finally, it selects rectangular areas corresponding to the three color units (RGB) from the similar or the same images, and determines whether the color difference between any two rectangular areas is greater than a preset value. If the color difference is small, it means that the colors of any two rectangular areas are similar, and there is a color missing process defect in a certain area. The panel product needs to be transported to the repair machine for redoing the missing process.
[0011] As some optional implementations of this application, the process for obtaining a matching template based on the original panel image is as follows:
[0012] The original panel image is cropped to obtain an initial matching template; the initial matching template contains three color units: RGB.
[0013] The initial matching template is processed by edge contour extraction to obtain the final matching template.
[0014] In the above embodiments, grayscale images are generally used for template matching. However, due to differences in the adjustment of the light source of the automated optical inspection (AOI) machine, the background colors of the acquired images vary greatly. Therefore, directly using grayscale images as templates carries a significant risk of missed matching. If grayscale images are used for matching, a template needs to be maintained for each color, which is too costly. Furthermore, the matching time cost is even higher. Therefore, it is necessary to convert the matching template from a three-channel color image to a single-channel image. That is, the captured matching template is first converted into a grayscale image, and then the initial matching template is processed by edge contour extraction (using the Canny algorithm) to obtain the final matching template.
[0015] As some optional implementations of this application, the process of performing similarity matching on the panel image to be detected based on the matching template is as follows:
[0016] Edge contour extraction is performed on the panel image to be detected, and the top left corner pixel of the panel image to be detected is used as the coordinate of the top left corner point. An initial candidate template is obtained with the coordinate of the top left corner point as the reference point.
[0017] The initial candidate templates are shifted one pixel to the right / up in turn to obtain several candidate templates;
[0018] The similarity between several candidate templates and the matching template is calculated sequentially. If the similarity is greater than the matching threshold, the corresponding candidate template is matched with the matching template to obtain the final candidate template.
[0019] In the above embodiments, an initial candidate template is first obtained by coordinate positioning, and then several templates with the same size as the candidate template can be obtained by translation, so as to facilitate the similarity matching of the templates later.
[0020] As some optional implementations of this application, the similarity is calculated using a cosine similarity calculation method, a hash calculation method, a histogram calculation method, or a Pearson correlation coefficient.
[0021] In the above embodiments, by selecting an appropriate similarity calculation method, candidate templates similar to the matching template can be quickly obtained, so as to facilitate subsequent color difference verification processing on the similar candidate templates.
[0022] As one of the optional implementations of this application, the process for obtaining the rectangular regions corresponding to all color units based on the candidate template is as follows:
[0023] The final matching template is divided into three color unit regions;
[0024] Extract a rectangular region from each of the three color unit regions to obtain the first rectangular region, the second rectangular region, and the third rectangular region corresponding to the three color unit regions.
[0025] In the above embodiments, by cropping a small portion of the three color regions, the calculation difficulty of subsequent color difference comparison can be reduced.
[0026] As some optional implementations of this application, the process for color difference comparison based on a rectangular area is as follows:
[0027] Extract the color values of the RGB three color channels for the first rectangular region, the second rectangular region, and the third rectangular region, and calculate the average value of each channel under the coordinates of the three rectangles;
[0028] Calculate the color difference between any two rectangular regions in the three color unit regions based on the average value of each channel under the above rectangular coordinates;
[0029] If the color difference between any two rectangular areas is less than the color difference threshold, the panel under test is determined to have a color omission defect.
[0030] In the above embodiments, by determining whether the color difference between any two rectangular areas is greater than a preset value, if the color difference is less than the preset value, it means that the colors of any two rectangular areas are similar, and one of them has a color missing process defect, so the panel product needs to be transported to the repair machine for redoing the missing process.
[0031] As some optional embodiments of this application, the formula for calculating the color difference between any two rectangular regions is as follows:
[0032]
[0033] Among them, p' r and p r Let p' and p' represent the average color values of any two rectangular regions in the R channel. g and p g Let p' and p' represent the average color values of any two rectangular regions on the G channel. b and p b These represent the average color values of any two rectangular regions on the B channel.
[0034] In the above embodiments, the color difference between any two rectangular regions is calculated using Euclidean distance, which can quickly detect color leakage process defects.
[0035] In a second aspect, the present invention provides a panel color leakage process defect detection system, the system comprising:
[0036] A template acquisition unit, wherein the template acquisition unit acquires a matching template based on the original panel image, and the matching template contains all color units;
[0037] A similarity matching unit performs similarity matching on the panel image to be detected based on a matching template to obtain several candidate templates;
[0038] A color difference matching unit obtains rectangular areas corresponding to all color units based on a candidate template, and performs color difference comparison based on the rectangular areas to determine whether there is a color missing process defect.
[0039] In a third aspect, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for detecting panel color leakage process defects.
[0040] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the panel color leakage process defect detection method.
[0041] The beneficial effects of this invention are as follows:
[0042] This invention combines template matching and color difference analysis to solve the problem that there are few features for identifying missing process defects in CF color filters, making it difficult for target detection algorithms to detect them effectively, and thus achieves effective detection of missing process defects in CF filters. Attached Figure Description
[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0044] Figure 1 This is a schematic diagram of the computer device structure of the hardware operating environment described in the embodiments of the present invention;
[0045] Figure 2 This is a flowchart illustrating the steps of the panel color leakage process defect detection method according to an embodiment of the present invention;
[0046] Figure 3 This is a schematic diagram of the original panel image described in an embodiment of the present invention;
[0047] Figure 4 This is a schematic diagram of the matching template described in an embodiment of the present invention;
[0048] Figure 5 This is a schematic diagram of the rectangular region color difference analysis described in an embodiment of the present invention;
[0049] Figure 6 This is a system block diagram of the panel color leakage process defect detection system described in an embodiment of the present invention. Detailed Implementation
[0050] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.
[0051] To address the problem that target detection algorithms struggle to identify missing process defects in CF color filters, this application provides a method, system, device, and medium for detecting missing process defects in panel color filters. Before introducing the specific technical solutions of this application, the hardware operating environment involved in the embodiments of this application will be described first.
[0052] Please see Figure 1 , Figure 1 This is a schematic diagram of the computer device structure of the hardware operating environment involved in the embodiments of this application.
[0053] like Figure 1As shown, the computer device may include: a processor, such as a central processing unit (CPU), a communication bus, a user interface, a network interface, and memory. The communication bus is used to enable communication between these components. The user interface may include a display screen and an input unit such as a keyboard; optionally, the user interface may also include a standard wired interface or a wireless interface. The network interface may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive; optionally, the memory may also be a storage device independent of the aforementioned processor.
[0054] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0055] like Figure 1 As shown, a memory, as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a software program storage module.
[0056] exist Figure 1 In the computer device shown, the network interface is mainly used for data communication with the network server; the user interface is mainly used for data interaction with the user; the processor and memory in the computer device of this application can be set in the computer device, and the computer device calls the panel color missing process defect detection system stored in the memory through the processor, and executes the panel color missing process defect detection method provided in the embodiment of this application.
[0057] Based on the hardware environment of the foregoing embodiments, embodiments of this application provide a method for detecting panel color loss process defects. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a flowchart of the panel color leakage process defect detection method, and the method flow is as follows:
[0058] (1) Obtain a matching template based on the original panel image, the matching template containing all color units; the original panel image is a panel inspection image obtained by an automated optical inspection (AOI) machine, preferably an image without color missing process defects, please refer to [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram of the original panel image.
[0059] Specifically, the process of obtaining the matching template based on the original panel image is as follows:
[0060] (1.1) Perform image cropping processing on the original panel image to obtain an initial matching template; wherein the initial matching template contains at least three color units: RGB.
[0061] (1.2) Generally, grayscale images are used for template matching. However, due to differences in the light source adjustment of automated optical inspection (AOI) equipment, the background colors of the acquired images vary significantly. Therefore, directly using grayscale images as templates carries a high risk of missed matches. If grayscale images are used for matching, a template needs to be maintained for each color, which is too costly and increases the matching time cost. Therefore, it is necessary to convert the matching template from a three-channel color image to a single-channel image. That is, the captured matching template is first converted to a grayscale image, and then the initial matching template is processed for edge contour extraction (using the Canny algorithm) to obtain the final matching template. Please refer to [link to relevant documentation]. Figure 4 , Figure 4 This is a schematic diagram of the matching template, where the width and height of the matching template are denoted as w and h, respectively.
[0062] (2) Similarity matching is performed on the panel images to be detected based on the matching templates to obtain several candidate templates;
[0063] Specifically, the process of similarity matching for the panel images to be detected based on the matching template is as follows:
[0064] (2.1) First, edge contour extraction processing is performed on the panel image to be detected (using the Canny algorithm). Based on the edge contour extraction processing, the top left corner pixel of the panel image to be detected is taken as the top left corner coordinate, i.e., the starting coordinate (0,0). A candidate template with the same size as the matching template is obtained with the top left corner coordinate as the reference point. That is, the coordinates of the first candidate template are represented as [0,w,0,h]. At the same time, the bottom left corner pixel, the top right corner pixel, and the bottom right corner pixel of the panel image to be detected can also be taken as the corresponding starting coordinates. This embodiment of the invention does not limit this.
[0065] (2.2) Shift the candidate template one pixel to the right / up in turn to obtain several candidate templates with the same size as the matching template. Assuming that the coordinates of the upper left corner of the current candidate template are (x,y), the coordinates of the current candidate template are represented as [x,y,x+w,y+h].
[0066] (2.3) Calculate the similarity between several candidate templates and the matching template in turn. If the similarity is greater than the matching threshold, the corresponding candidate template matches the matching template. Select the candidate template that matches the matching template as the final candidate template. Therefore, one or more candidate templates that are similar to the matching template can be obtained. Specifically, the matching threshold is a preset value that can be set according to the actual situation.
[0067] Specifically, the similarity is calculated using cosine similarity, hash calculation, histogram calculation, or Pearson correlation coefficient. Preferably, cosine similarity is used to obtain the similarity between the candidate template and the matching template, with the similarity value ranging from 0 to 1, and the higher the matching degree, the closer the similarity is to 1.
[0068] (3) Obtain the rectangular area corresponding to all color units based on the candidate template, and compare the color difference based on the rectangular area to determine whether there is a color missing process defect.
[0069] Specifically, the process of obtaining the rectangular regions corresponding to all color units based on the candidate template is as follows:
[0070] (3.1) Divide the final matching template into three color unit regions, that is, obtain three rectangular regions on one or more candidate templates respectively. Please refer to [link to relevant documentation]. Figure 5 .
[0071] (3.2) Extract a rectangular region from each of the three color unit regions to obtain the first rectangular region, the second rectangular region, and the third rectangular region corresponding to the three color unit regions, where the first rectangular region, the second rectangular region, and the third rectangular region are denoted as [x...]. 11 ,y 11 ,x 12 ,y 12 ],[x 21 ,y 21 ,x 22 ,y 22 ],[x 31 ,y 31 ,x 32 ,y 32 ].
[0072] Specifically, the process for color difference comparison based on a rectangular area is as follows:
[0073] (3.3) Perform RGB three-channel color extraction on the first rectangular region, the second rectangular region, and the third rectangular region, and calculate the mean value of the color of each channel under the coordinates of the three rectangles, denoted as p1 respectively. r p1 g p1 b p2r p2 g p2 b p3 r p3 g p3 b .
[0074] (3.4) Calculate the color difference between any two rectangular regions in the three color unit regions based on the average value of each channel under the above rectangular coordinates. For example, the color difference between the first rectangular region and the second rectangular region is denoted as diff_12. Similarly, the color difference between the first rectangular region and the third rectangular region is denoted as diff_13, and the color difference between the second rectangular region and the third rectangular region is denoted as diff_23.
[0075] (3.5) If the color difference between any two rectangular areas is less than the color difference threshold, it is determined that the panel to be detected has a color omission defect. The color difference threshold is a preset value. By setting a missing process defect detection threshold, when the calculated color difference between any two rectangular areas is less than the color difference threshold, it is considered that two color units have the same color, and the image is determined to have a missing process defect. Specifically, the matching degree threshold is a preset value, which can be set according to the actual situation.
[0076] Specifically, the formula for calculating the color difference between any two rectangular regions is as follows:
[0077]
[0078] Among them, p' r and p r Let p' and p' represent the average color values of any two rectangular regions in the R channel. g and p g Let p' and p' represent the average color values of any two rectangular regions on the G channel. b and p b These represent the average color values of any two rectangular regions on the B channel; for example, for the first and second rectangular regions, the color difference is denoted as:
[0079]
[0080] Similarly, the color difference diff_13 and color difference diff_23 can be obtained through the above formulas. That is, the color difference between any two rectangular areas can be calculated by using Euclidean distance, which can quickly detect color leakage process defects.
[0081] In summary, this invention first obtains a matching template containing RGB color units, then acquires several images of the same size as the matching template from the image of the panel to be detected, and selects images that are similar to or identical to the matching template images through similarity matching. Finally, it selects rectangular regions corresponding to the RGB color units from the similar or identical images, and determines whether the color difference between any two rectangular regions is greater than a preset value. If the color difference is small, it indicates that the colors of any two rectangular regions are similar, and a certain region has a color missing process defect, requiring the panel product to be transported to a repair machine for redoing the missing process. That is, by combining the template matching method and the color difference analysis method, this invention solves the problem that there are few identification features for missing process defects in CF color filters, and the target detection algorithm is difficult to detect effectively, thus achieving effective detection of missing process defects in CF.
[0082] Furthermore, in one embodiment, based on the same inventive concept as the foregoing embodiments, this embodiment of the invention provides a panel color leakage process defect detection system. This system corresponds one-to-one with the method described in Embodiment 1. Please refer to [link / reference]. Figure 6 , Figure 6 This is a structural block diagram of the panel color leakage process defect detection system, the system comprising:
[0083] A template acquisition unit, wherein the template acquisition unit acquires a matching template based on the original panel image, and the matching template contains all color units;
[0084] A similarity matching unit performs similarity matching on the panel image to be detected based on a matching template to obtain several candidate templates;
[0085] A color difference matching unit obtains rectangular areas corresponding to all color units based on a candidate template, and performs color difference comparison based on the rectangular areas to determine whether there is a color missing process defect.
[0086] It should be noted that each unit in the panel color missing process defect detection system in this embodiment corresponds one-to-one with each step in the panel color missing process defect detection method in the aforementioned embodiment. Therefore, the specific implementation method and the technical effects achieved in this embodiment can be referred to the implementation method of the aforementioned panel color missing process defect detection method, and will not be repeated here.
[0087] Furthermore, in one embodiment, this application also provides a computer device, the computer device including a processor, a memory, and a computer program stored in the memory, the computer program being executed by the processor to implement the methods in the foregoing embodiments.
[0088] In addition, in one embodiment, this application also provides a computer storage medium storing a computer program that is executed by a processor to implement the methods described in the foregoing embodiments.
[0089] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.
[0090] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
[0091] As an example, executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborating files (e.g., a file that stores one or more modules, subroutines, or code sections).
[0092] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.
[0093] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0094] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0095] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method for detecting panel color miss process defects, the method comprising: The method flow is as follows: A matching template is obtained based on the original panel image, and the matching template contains all color units; Similarity matching is performed on the panel image to be detected based on the matching template to obtain several candidate templates; Based on the candidate template, obtain the rectangular area corresponding to all color units, and compare the color difference based on the rectangular area to determine whether there is a color missing process defect. The process for obtaining the rectangular regions corresponding to all color units based on the candidate template is as follows: The final candidate template is divided into three color unit regions; Extract a rectangular region from each of the three color unit regions to obtain the first rectangular region, the second rectangular region, and the third rectangular region corresponding to the three color unit regions; The process for color difference comparison based on a rectangular area is as follows: Extract the color values of the RGB three color channels for the first rectangular region, the second rectangular region, and the third rectangular region, and calculate the average value of each channel under the coordinates of the three rectangles; Calculate the color difference between any two rectangular regions in the three color unit regions based on the average value of each channel under the above rectangular coordinates; If the color difference between any two rectangular areas is less than the color difference threshold, the panel under test is determined to have a color omission defect.
2. The method of claim 1, wherein the method further comprises: The process of obtaining the matching template based on the original panel image is as follows: The original panel image is cropped to obtain an initial matching template; the initial matching template contains three color units: RGB. The initial matching template is processed by edge contour extraction to obtain the final matching template.
3. The method of claim 1, wherein the method further comprises: The process of similarity matching of the panel images to be detected based on the matching template is as follows: Edge contour extraction is performed on the panel image to be detected, and the top left corner pixel of the panel image to be detected is used as the coordinate of the top left corner point. A candidate template with the same size as the matching template is obtained by using the coordinate of the top left corner point as a reference point. The candidate templates are shifted one pixel to the right / up in turn to obtain several candidate templates; The similarity between several candidate templates and the matching template is calculated sequentially. If the similarity is greater than the matching threshold, the corresponding candidate template is matched with the matching template to obtain the final candidate template.
4. The method of claim 3, wherein the method further comprises: The similarity is calculated using cosine similarity, hash calculation, histogram calculation, or Pearson correlation coefficient.
5. The method of claim 1, wherein the method further comprises: The formula for calculating the color difference between any two rectangular regions is as follows: ； in, and These represent the average color values of any two rectangular regions in the R channel. and These represent the average color values of any two rectangular regions on the G channel. and These represent the average color values of any two rectangular regions on the B channel.
6. A panel color leakage process defect detection system, characterized in that, The system includes: A template acquisition unit, wherein the template acquisition unit acquires a matching template based on the original panel image, and the matching template contains all color units; A similarity matching unit performs similarity matching on the panel image to be detected based on a matching template to obtain several candidate templates; A color difference matching unit obtains rectangular areas corresponding to all color units based on a candidate template, and performs color difference comparison based on the rectangular areas to determine whether there is a color missing process defect. The process for obtaining the rectangular regions corresponding to all color units based on the candidate template is as follows: The final candidate template is divided into three color unit regions; Extract a rectangular region from each of the three color unit regions to obtain the first rectangular region, the second rectangular region, and the third rectangular region corresponding to the three color unit regions; The process for color difference comparison based on a rectangular area is as follows: Extract the color values of the RGB three color channels for the first rectangular region, the second rectangular region, and the third rectangular region, and calculate the average value of each channel under the coordinates of the three rectangles; Calculate the color difference between any two rectangular regions in the three color unit regions based on the average value of each channel under the above rectangular coordinates; If the color difference between any two rectangular areas is less than the color difference threshold, the panel under test is determined to have a color omission defect.
7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes a computer program, it implements the panel color leakage process defect detection method according to any one of claims 1-5.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the panel color leakage process defect detection method according to any one of claims 1-5.