An AOI detection method, system, electronic device and storage medium for removing edge misjudgment of a display screen

Abstract

The present invention provides a method, system, electronic device and storage medium for AOI detection of a display screen to remove edge misjudgment. By acquiring an AOI detection image of the display screen and extracting a display area; performing a gray-scale outward expansion operation on the edge of the display area; the gray-scale outward expansion operation includes expanding outward according to a preset size to obtain an expanded area; generating a detection area image according to the expanded area and the display area image; performing bright point detection on the detection area image. Compared with the prior art, the difference in gray-scale values between the edge in the display area of the expanded image and the gray-scale values of the expanded area part is very small, so there will no longer be misjudgment of the edge in the display area. Then, the extracted defects are intersected with the original display area before expansion, that is, only the bright point defects in the display area are screened out as the final defective defects, avoiding misjudgment of the edge of the LCD display area.

Description

Technical Field

[0001] The present invention relates to the technical field of display screen detection, and in particular, to an AOI detection method, system, electronic device and storage medium for a display screen. Background Art

[0002] AOI (Automated Optical Inspection) is based on the optical principle to detect common defects encountered in welding production. When automatically detecting, the machine automatically scans the PCB through a camera, collects images, compares the tested solder joints with the qualified parameters in the database, and after image processing, checks for defects on the PCB and displays / marks the defects through a display or an automatic marker for maintenance personnel to repair.

[0003] By using AOI as a tool to reduce defects, errors are found and eliminated at an early stage of the assembly process to achieve good process control. Detecting defects early will prevent sending defective boards to subsequent assembly stages.

[0004] For example, the patent document (CN113052829B) proposes an AOI detection method for a main board based on the Internet of Things. An image of the main board to be detected is obtained through a high-resolution charge-coupled device, and the corresponding image is marked as an image to be detected. Subsequently, image processing is performed on the image to be detected. At the same time, an image of a normally detected main board is obtained through a high-resolution charge-coupled device and marked as a standard image, and the image to be detected is compared with the standard image; the image to be detected and the standard image are subjected to image segmentation, and then the image to be detected and the standard image are compared again, and an image of the main board to be detected is obtained and the obtained image is processed. In the prior art including the patent document CN113052829B, when performing defect detection on the display screen of the AOI, the display area of the screen is first intercepted, and threshold segmentation is performed on the display area through local mean and standard deviation analysis to extract defects. When performing threshold segmentation, the algorithm system defaults the gray value outside the display area to 0. When the gray value at the edge of the display area is relatively large, when performing the algorithm processing on the brightness defect of the display area, the area at the edge of the display area will also be misjudged as a bright point defect. Therefore, there is an urgent need for an AOI detection solution for a display screen that can solve the problem of removing edge misjudgment. Summary of the Invention

[0005] In view of this, the purpose of the embodiments of the present invention is to magnify the image of the LCD display area by means of edge gray scale expansion, and then perform image algorithm processing to extract the defects of the LCD display area, so as to avoid misjudgment at the edge of the LCD display area.

[0006] The first aspect of the present invention provides a method for removing edge misjudgment in AOI detection of a display screen, and the method includes:

[0007] Obtain the AOI detection image of the display screen and extract the display area;

[0008] Perform a gray-scale expansion operation on the edge of the display area; the gray-scale expansion operation includes expanding outward by a preset size to obtain an expanded area;

[0009] Generate a detection area image according to the expanded area and the display area image;

[0010] Perform highlight detection on the detection area image to generate a detection result.

[0011] Further, the gray-scale expansion operation includes expanding outward by a preset size to obtain an expanded area, specifically including:

[0012] Expand the pixels in the horizontal and vertical directions of the display area outward by a first predetermined width respectively;

[0013] Fill the pixels of the first predetermined width with the mean value of the edge pixels of the display area.

[0014] Further, filling the pixels of the predetermined width with the mean value of the edge pixels of the display area includes:

[0015] Obtain the pixels of a second predetermined width inside the edge of the display area, and calculate the mean value of the gray-scale values of the pixels of the second predetermined width; the size of the second predetermined width is equal to the first predetermined width;

[0016] Based on the mean value of the pixel gray-scale values, fill the pixels of the first predetermined width to form an expanded area.

[0017] Further, performing a gray-scale expansion operation on the edge of the display area includes:

[0018] Taking the gray-scale value of the boundary pixel of the display area as the value and copying it to the outside of the display area, thereby copying and generating an expanded area of the first predetermined width;

[0019] Perform mean filtering on the pixels in the expanded area according to a predetermined filtering template to form an expanded area.

[0020] Further, performing highlight detection based on the detection area image includes:

[0021] Perform threshold segmentation processing through local mean and standard deviation analysis according to the detection area image to detect the highlight area;

[0022] Perform an intersection operation on the highlight area and the display area to determine the final detected highlights.

[0023] In addition, the second aspect of the present invention provides a display AOI detection system for removing edge misjudgment, the system comprising an acquisition module, an expansion module, a generation module and a detection module; wherein:

[0024] The acquisition module is configured to acquire a display AOI detection image and extract a display area;

[0025] The expansion module is configured to perform a grayscale expansion operation on the edge of the display area; the grayscale expansion operation includes expanding outward by a preset size to obtain an expanded area;

[0026] The generation module is configured to generate a detection area image according to the expanded area and the display area image;

[0027] The detection module is configured to perform bright point detection on the detection area image to generate a detection result.

[0028] Further, the expansion module is further configured to expand the horizontal and vertical directions of the display area outward by a first predetermined width of pixels respectively; and fill the pixels of the first predetermined width with the mean value of the edge pixels of the display area.

[0029] Further, the expansion module is further configured to acquire pixels of a second predetermined width inside the edge of the display area, calculate the mean value of the grayscale values of the pixels of the second predetermined width; the size of the second predetermined width is equal to the first predetermined width; and fill the pixels of the first predetermined width based on the mean value of the pixel grayscale values.

[0030] Further, the detection module is further configured to perform threshold segmentation processing by performing local mean and standard deviation analysis on the detection area image to detect a bright point area; and perform an intersection operation on the bright point area and the display area to determine a final detected bright point.

[0031] In addition, the third aspect of the present invention provides an electronic device, the electronic device comprising: one or more processors, a memory, the memory being configured to store one or more computer programs; characterized in that the computer programs are configured to be executed by the one or more processors, and the programs include steps for performing the method for detecting a display AOI for removing edge misjudgment as described in the first aspect above.

[0032] In addition, the fourth aspect of the present invention provides a storage medium, the storage medium storing a computer program; the program is loaded and executed by a processor to implement the steps of the method for detecting a display AOI for removing edge misjudgment as described in the first aspect above.

[0033] In the solution of the present invention, an AOI detection image of a display screen is acquired, and a display area is extracted; a gray-scale outer expansion operation is performed on the edge of the display area; the gray-scale outer expansion operation includes expanding outward by a preset size to obtain an expanded area; a detection area image is generated according to the expanded area and the display area image; and a bright point detection is performed on the detection area image. Compared with the prior art, the difference in gray-scale values between the edge in the display area of the expanded image and the gray-scale values of the expanded area part is very small, so there will no longer be misjudgments on the inner edge of the display area. Then, the extracted defects are intersected with the original display area before expansion, that is, only the bright point defects within the display area are screened out as the final defective defects, avoiding misjudgments on the edge of the LCD display area. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 is the gray-scale image to be measured disclosed in the embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of the display area disclosed in the embodiment of the present invention;

[0037] Figure 3 is a schematic diagram of the defective extraction result of edge misjudgment disclosed in the embodiment of the present invention;

[0038] Figure 4 is a schematic flowchart of the AOI detection method for a display screen to remove edge misjudgments disclosed in the embodiment of the present invention;

[0039] Figure 5 is a subjective schematic diagram of the implementation of a gray-scale outer expansion algorithm disclosed in the embodiment of the present invention;

[0040] Figure 6 is a schematic diagram of a partial image before expansion disclosed in the embodiment of the present invention;

[0041] Figure 7 is a schematic diagram of a partial image after expansion disclosed in the embodiment of the present invention;

[0042] Figure 8 is a schematic diagram of the final output defective detection result disclosed in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0044] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of this application. However, those skilled in the art will realize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be employed. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of this application.

[0045] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0046] The flowcharts shown in the drawings are only illustrative and not necessarily include all the content and operations / steps, nor are they necessarily executed in the order described. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0047] It should be noted that: "a plurality" as mentioned herein refers to two or more.

[0048] The implementation details of the technical solutions of the embodiments of this application are elaborated in detail below:

[0049] As Figure 1 shown, Figure 1 is the grayscale picture to be measured, and the bright spots on the image are screen defects. Figure 2 is a schematic diagram of the display area of this embodiment, where the rectangular frame is the display area, also referred to as the region of interest (AA region). Figure 3 is a schematic diagram of the defective extraction result of edge misjudgment.

[0050] Figure 3Among them, while extracting the highlights, edge misjudgments are also extracted, such as the rectangular box 2. This is because when performing defect detection on the display screen image in the AOI, specifically when detecting the display area, generally, threshold segmentation of the AA area is performed through local mean and standard deviation analysis to extract the defective area. When performing threshold segmentation, the algorithm system defaults the gray value outside the AA area to 0. When the gray value at the edge of the AA area is relatively large, when performing threshold segmentation of the AA area based on local mean and standard deviation analysis to determine the brightness defect processing of the AA area, the area at the edge of the AA area will also be misjudged as a defect.

[0051] To solve the problem of edge misjudgment, in this embodiment, the image of the AA area of the LCD is enlarged by the method of edge gray value expansion, and then image algorithm processing is performed to extract the defects in the LCD display area, avoiding edge misjudgment in the LCD display area.

[0052] Please refer to Figure 4 , Figure 4 which is a schematic flowchart of a method for removing edge misjudgment in AOI detection of a display screen disclosed in an embodiment of the present invention. As Figure 4 shown, a method for removing edge misjudgment in AOI detection of a display screen according to an embodiment of the present invention includes:

[0053] S401, obtaining an AOI detection image of the display screen and extracting the display area.

[0054] Specifically, in this embodiment, when performing automatic detection, the AOI detection mechanism automatically scans the PCBA (Printed Circuit Board Assembly) product of the display screen through a high-definition CCD camera, then acquires an image, and determines the acquired image as the AOI detection image of the display screen. Further, the display area of the screen image is determined, intercepted, and used as the region of interest of the image.

[0055] S402, performing a gray value expansion operation on the edge of the display area; the gray value expansion operation includes expanding outward by a preset size to obtain an expanded area;

[0056] Further, in step S402, the gray value expansion operation includes expanding outward by a preset size to obtain an expanded area, specifically including: expanding the horizontal and vertical directions of the display area by a first predetermined width of pixels respectively; filling the pixels of the first predetermined width with the mean value of the edge pixels of the display area.

[0057] In this embodiment, the first predetermined width, that is, the expansion size can be determined according to twice the length of the diameter of the largest possible defect.

[0058] In some embodiments, after extracting the image of the AA region, the edge gray values of the extracted image are used for a gray-scale outward expansion algorithm to expand the extracted image outward by a certain size, and then the expanded part is subjected to a gray-scale mean value processing.

[0059] Further, performing a gray-scale outward expansion operation on the edge of the display region includes: taking the gray value of the boundary pixel of the display region as a value and copying it to the outside of the display region, thereby copying and generating an extended region with a first predetermined width; performing mean filtering on the pixels in the extended region according to a predetermined filtering template to form an extended region.

[0060] In some embodiments, regarding the gray-scale outward expansion algorithm, the gray value of the original image boundary pixel can be used as a value and copied to the outside of the image region, copying and generating an outward expansion region with a certain width, and performing gray-scale mean value processing on the pixels in this part of the expanded region to make the gray scale of this part of the image smooth, and then covering the gray value of this expanded part to the corresponding region of the original image. As Figure 5 The subjective schematic diagram of the implementation of the gray-scale outward expansion algorithm in this embodiment is shown as Figure 5 The gray values corresponding to the pixels in the original image are within the left dark dotted line box, the outer dark square part within the dark dotted line box is the gray value of the original image boundary pixel, the light dotted line box is that two pixels with the same gray value are outwardly expanded and copied for each boundary pixel, that is, the green part is the outward expansion region.

[0061] Further, perform mean filtering with a 2*2 mask width on the left green part of the figure (the filtering direction is from left to right and from top to bottom). The value of each pixel after mean filtering is equal to the mean of the gray values of the 4 pixels within its 2*2 mask range, and the gray values of the pixels at the edge part remain unchanged, obtaining Figure 5 the right figure.

[0062] Further, filling the pixels with the first predetermined width by using the mean value of the edge pixels of the display region includes: obtaining the pixels with a second predetermined width inside the edge of the display region, and calculating the mean value of the gray values of the pixels with the second predetermined width; the size of the second predetermined width is equal to the first predetermined width; based on the mean value of the pixel gray values, filling the pixels with the first predetermined width to form an extended region.

[0063] In some embodiments, regarding the gray-scale outward expansion algorithm, it is also possible to first extract the pixels with a width inward from the edge with the same size as the expansion width, obtain the corresponding gray values, and find their mean values for filling the pixels in the extended region. Generally, the gray value of the filled extended region is very close to the gray value of the inner edge of the AA region, and the gray difference between adjacent pixels is generally within 2. As Figure 6 This is a schematic diagram of a local image before expansion. Here, a local image in the lower right corner is given in this embodiment. Figure 7For the expanded image, here, in this embodiment, a partial image of the lower right corner is given. The area outside rectangle 1 and within rectangle 2 is the expanded area.

[0064] S403. Generate a detection area image according to the expanded area and the image of the display area.

[0065] Specifically, in this embodiment, when performing bright point defect detection on the image subsequently, it is based on the detection area image generated from the above-mentioned expanded area and the image of the display area. That is, local mean and standard deviation analysis is performed on the expanded detection area image for threshold segmentation processing to extract defective bright points.

[0066] Because the difference in gray values between the edge gray values in the AA area of the expanded image and the gray values in the expanded area part is very small, there will no longer be misjudgments at the edge in the AA area.

[0067] S404. Perform bright point detection on the detection area image to generate a detection result.

[0068] Furthermore, in step S404, performing bright point detection on the detection area image to generate a detection result includes: performing local mean and standard deviation analysis for threshold segmentation processing according to the detection area image to detect a bright point area; performing an intersection operation between the bright point area and the display area to determine the final detected bright points.

[0069] In some embodiments, for local mean and standard deviation analysis for threshold segmentation processing, the var_threshold() operator in Halcon can be used. The function var_threshold(Image:Region:MaskWidth,MaskHeight,StdDevScale,AbsThreshold,LightDark:) is used for specific calculations. Among them, ①Image: the input image; ②MaskWidth,MaskHeight: the mask units for filtering and smoothing; ③StdDevscale: the standard deviation multiplier factor (abbreviated as the standard deviation factor); ④AbsThreshold: the set absolute threshold; ⑤LightDark: there are 4 values to choose from: 'light', 'dark', 'equal', 'not equal'.

[0070] Further, for example: var threshold(Image, Region, 4, 4, 0.2, 12, 'dark'); in this program, a 4x4 mask is used to traverse the image pixel by pixel. The grayscale mean of the current pixel in the original image is compared with the grayscale values of the 16 pixels in the corresponding mask to find the dark regions. When the grayscale value of the original image pixel is lower than the grayscale mean of the corresponding mask by (0.2, 12) gray levels, the region is segmented. In this program, StdDevScale = 0.2 and AbsThreshold = 12. The key to the problem is to understand how to determine the threshold for segmentation through StdDevscale and AbsThreshold. Secondly, regarding the parameter StdDevScale (standard deviation factor): 1. When the standard deviation factor StdDevscale >= 0, v(x,y) takes the larger value between (StdDevscale x standard deviation) and AbsThreshold; 2. When the standard deviation factor StdDevScale <= 0, v(x,y) takes the smaller value between (StdDevscale x standard deviation) and AbsThreshold.

[0071] Specifically, in this embodiment, after detecting the bright point region, the intersection process is performed between the bright point region and the original display region before expansion, that is, only the bright point defects within the AA region (the original display region) are screened out as the final defective defects to generate the detection result. As Figure 8 shown in the schematic diagram of the defective detection result finally output in this embodiment.

[0072] Compared with the prior art, in this embodiment, the image of the LCD AA region is enlarged by the method of edge grayscale expansion, and then the image algorithm is processed to extract the defective defects in the LCD display region; based on the fact that the difference between the edge grayscale value in the display region of the expanded image and the grayscale value of the expanded region part is very small, there will be no misjudgment of the edge in the display region. Then, the extracted defects are intersected with the original display region before expansion, that is, only the bright point defects within the display region are screened out as the final defective defects, avoiding misjudgment of the LCD display region edge.

[0073] In addition, the second aspect of this embodiment provides an AOI detection system for a display screen that removes edge misjudgment. The system includes an acquisition module, an expansion module, a generation module, and a detection module; where:

[0074] The acquisition module is used to acquire the AOI detection image of the display screen and extract the display region;

[0075] The expansion module is used to perform a grayscale expansion operation on the edge of the display region; the grayscale expansion operation includes expanding outward according to a preset size to obtain an expanded region;

[0076] A generation module, configured to generate a detection region image according to the extended region and the display region image;

[0077] A detection module, configured to perform highlight detection on the detection region image.

[0078] Furthermore, the expansion module is further configured to expand the display region outward by a first predetermined width of pixels in both the horizontal and vertical directions; and fill the pixels of the first predetermined width with the average value of the edge pixels of the display region.

[0079] Furthermore, the expansion module is further configured to obtain pixels of a second predetermined width inside the edge of the display region, and calculate the average value of the gray values of the pixels of the second predetermined width; the size of the second predetermined width is equal to the first predetermined width; and fill the pixels of the first predetermined width based on the average value of the pixel gray values.

[0080] Furthermore, the detection module is further configured to perform threshold segmentation processing through local mean and standard deviation analysis on the detection region image to detect a highlight region; and perform an intersection operation on the highlight region and the display region to determine the final detected highlight.

[0081] Specifically, the threshold segmentation processing through local mean and standard deviation analysis on the detection region image here can refer to the introduction in the first aspect of this embodiment, and the var_threshold() operator in Halcon is used.

[0082] In addition, an embodiment of the present application also discloses an electronic device, which includes: one or more processors, a memory, and the memory is used to store one or more computer programs; characterized in that the computer program is configured to be executed by the one or more processors, and the program includes steps for performing the display screen AOI detection method for removing edge misjudgments as described in the first aspect above.

[0083] In addition, an embodiment of the present application also provides a storage medium, which stores a computer program; the program is loaded and executed by a processor to implement the steps of the display screen AOI detection method for removing edge misjudgments as described in the first aspect above.

[0084] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0085] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices or units, and can also be electrical, mechanical or other forms of connection.

[0086] The units described as separate components may or may not be physically separated. As units, those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0087] In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0088] When 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 such an understanding, the technical solution of the present invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a grid device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0089] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An AOI detection method for a display screen to remove edge misjudgment, characterized in that, The method includes: Obtain the AOI detection screen of the display screen and intercept the display area of the screen; Perform a gray-scale expansion operation on the edge of the display area; the gray-scale expansion operation includes expanding outward by a preset size to obtain an expanded area; Generate a detection area image based on the expanded area and the display area image; Perform highlight detection on the detection area image to generate a detection result; The performing a gray-scale expansion operation on the edge of the display area specifically includes: Expand the horizontal and vertical directions of the display area outward by a first predetermined width of pixels respectively; Fill the pixels of the first predetermined width with the average value of the edge pixels of the display area; The filling the pixels of the predetermined width with the average value of the edge pixels of the display area includes: Obtain the pixels of a second predetermined width inside the edge of the display area, and calculate the average value of the gray-scale values of the pixels of the second predetermined width; the size of the second predetermined width is equal to the first predetermined width; Based on the average value of the pixel gray-scale values, fill the pixels of the first predetermined width to form an expanded area; The performing highlight detection on the detection area image to generate a detection result includes: Perform threshold segmentation processing through local mean and standard deviation analysis on the detection area image to detect a highlight area; Perform an intersection operation on the highlight area and the display area to determine the final detected highlights.

2. The AOI detection method for a display screen to remove edge misjudgment according to claim 1, wherein, The performing a gray-scale expansion operation on the edge of the display area includes: Use the gray-scale value of the boundary pixels of the display area as the value and copy it to the outside of the display area, so as to copy and generate an expanded area of the first predetermined width; Perform mean filtering on the pixels in the expanded area according to a predetermined filtering template to form an expanded area.

3. An AOI detection system for a display screen to remove edge misjudgment, characterized in that, The system includes an acquisition module, an expansion module, a generation module and a detection module; wherein: The acquisition module is used to acquire the AOI detection image of the display screen and extract the display area; The expansion module is used to perform a gray-scale expansion operation on the edge of the display area; the gray-scale expansion operation includes expanding outward by a preset size to obtain an expanded area; The generation module is used to generate a detection area image based on the expanded area and the display area image; The detection module is used to perform highlight detection on the detection area image to generate a detection result; The expansion module is further used to expand the horizontal and vertical directions of the display area outward by a first predetermined width of pixels respectively; fill the pixels of the first predetermined width with the average value of the edge pixels of the display area; Obtain the pixels of a second predetermined width inside the edge of the display area, calculate the average value of the gray-scale values of the pixels of the second predetermined width; the size of the second predetermined width is equal to the first predetermined width; based on the average value of the pixel gray-scale values, fill the pixels of the first predetermined width to form an expanded area; The detection module is further used to perform threshold segmentation processing through local mean and standard deviation analysis on the detection area image to detect a highlight area; perform an intersection operation on the highlight area and the display area to determine the final detected highlights.

4. An electronic device, the electronic device comprising: One or more processors, a memory for storing one or more computer programs; characterized in that the computer programs are configured to be executed by the one or more processors, and the programs include steps for performing the display screen AOI detection method for removing edge misjudgment as described in any one of claims 1-2.

5. A storage medium storing a computer program; the program is loaded and executed by a processor to implement the steps of the display screen AOI detection method for removing edge misjudgment as described in any one of claims 1-2.

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