Defect detection method and device, readable storage medium and automatic optical detection equipment

Abstract

The invention provides a defect detection method. The defect detection method comprises the following steps: acquiring a color picture of a display picture of a target display panel under a specific gray scale; determining brightness values of a plurality of sub-pixels included in each pixel point in the color picture; calculating a maximum brightness difference value among a plurality of sub-pixels in each pixel point based on the brightness value; based on the maximum brightness difference value, converting the color picture into a black and white picture so as to highlight the display defect; obtaining at least one characteristic quantity of a display defect in the black-and-white picture; a type of the display defect is determined based on the at least one feature quantity. The maximum brightness difference value among the multiple sub-pixels in each pixel point can be calculated by determining the brightness values of the multiple sub-pixels contained in each pixel point in the obtained color picture, the color picture is converted into a black and white picture based on the maximum brightness difference value, and then the type of the display defect can be determined by obtaining the characteristic quantity of the display defect. And furthermore, the display defect existing in the target display panel can be accurately detected.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of display, in particular to a defect detection method and device, readable storage medium and automatic optical inspection equipment. BACKGROUND

[0002] Organic Light Emitting Display (OLED) and flat display devices based on Light Emitting Diode (LED) technology have been widely used in mobile phones, televisions, notebook computers, desktop computers and other consumer electronic products due to their high image quality, power saving, thin body and wide application range, and have become the mainstream of display devices.

[0003] However, the accuracy of detecting display defects in OLED display products still needs to be improved. SUMMARY

[0004] Therefore, the embodiments of the present application provide a defect detection method and device, readable storage medium and automatic optical inspection equipment to solve the problems in the prior art.

[0005] The first aspect of the present application provides a defect detection method, which is used for detecting display defects existing in a target display panel, and includes: obtaining a color picture of a display picture of the target display panel under a specific gray scale, wherein the color picture includes display defects; determining the luminance values of a plurality of sub-pixels contained in each pixel point in the color picture; based on the luminance values, calculating the maximum luminance difference between the plurality of sub-pixels in each pixel point; based on the maximum luminance difference of each pixel point, converting the color picture into a black and white picture to highlight the display defects; obtaining at least one feature quantity of the display defects in the black and white picture; based on the at least one feature quantity, determining the type of the display defects.

[0006] In one embodiment, determining the luminance values of the plurality of sub-pixels contained in each pixel point in the color picture includes: calculating the luminance values of the plurality of sub-pixels contained in each pixel point by using a mixed color conversion algorithm; Preferably, the plurality of sub-pixels includes a first sub-pixel, a second sub-pixel and a third sub-pixel. Preferably, the first sub-pixel is a red sub-pixel, the second sub-pixel is a green sub-pixel, and the third sub-pixel is a blue sub-pixel.

[0007] In one embodiment, the maximum luminance difference between the plurality of sub-pixels in each pixel is calculated based on the luminance values, including: determining a maximum luminance value and a minimum luminance value among the luminance values of the plurality of sub-pixels in each pixel; subtracting the minimum luminance value from the maximum luminance value to obtain the maximum luminance difference.

[0008] In one embodiment, the color picture is converted into a black-and-white picture based on the maximum luminance difference data of each pixel, including: the color picture is converted into a grayscale picture using a difference operation based on the maximum luminance difference of each pixel, and the grayscale value of each pixel in the grayscale picture is proportional to the size of the maximum luminance difference; the grayscale picture is converted into a black-and-white picture.

[0009] In one embodiment, the grayscale picture is converted into a black-and-white picture, including: the grayscale picture is converted into a black-and-white picture using a binaryzation process.

[0010] In one embodiment, the at least one feature quantity includes at least one of length, width, and area; Preferably, the type of display defect is determined based on the at least one feature quantity, including: when the area of the display defect is less than a first preset value, the type of display defect is determined to be a color spot; when the area of the display defect is greater than or equal to the first preset value, the type of display defect is determined to be color unevenness.

[0011] In one embodiment, the color picture of the display picture of the target display panel under a specific gray scale is obtained, including: the display picture of the target display panel under a specific gray scale is photographed using a color camera to obtain the color picture; Preferably, before the display picture of the target display panel under a specific gray scale is photographed using a color camera, it further includes: adjusting the light intake and exposure time of the color camera, so that the light intake is greater than or equal to a preset light intake, and the exposure time is less than or equal to a preset exposure time.

[0012] The second aspect of the present application provides a defect detection device, including: an acquisition module for acquiring a color picture of a display picture of a target display panel under a specific gray scale, the color picture including a display defect and at least one feature quantity of the display defect in a black-and-white picture; a determination module for determining the luminance values of the plurality of sub-pixels contained in each pixel in the color picture and determining the type of display defect based on the at least one feature quantity; The computing module is configured to calculate a maximum luminance difference between the plurality of sub-pixels in each pixel point based on the luminance values. The converting module is configured to convert the color picture into a black-and-white picture based on the maximum luminance difference of each pixel point, so as to highlight the display defects.

[0013] The third aspect of the present application provides a readable storage medium, and the readable storage medium stores computer instructions. When the computer instructions are executed, the defect detection method provided in the first aspect of the present application is executed.

[0014] The fourth aspect of the present application provides an automatic optical inspection device, which comprises an execution module and the readable storage medium provided in the third aspect of the present application. When the execution module is running, the computer instructions stored in the readable storage medium are executed.

[0015] According to the defect detection method provided in the embodiments of the present application, the luminance values of the plurality of sub-pixels contained in each pixel point in the acquired color picture are determined, and the maximum luminance difference between the plurality of sub-pixels in each pixel point can be calculated based on the luminance values. In the normal display area, the maximum luminance difference of each pixel point is equal or has a small difference. In the display defect area, the maximum luminance difference of each pixel point has a large difference with the maximum luminance difference of each pixel point in the normal display area. The color picture is converted into a black-and-white picture based on the maximum luminance difference of each pixel point, so as to highlight the display defects, which facilitates the subsequent identification of the display defects. At least one characteristic quantity of the display defects in the black-and-white picture is acquired, and the type of the display defects can be determined according to the characteristic quantities, and then the display defects existing in the target display panel can be accurately detected. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The main step flowchart of the defect detection method of one embodiment of the present application.

[0017] Figure 2 The partial step flowchart of the defect detection method of one embodiment of the present application.

[0018] Figure 3 The partial step flowchart of the defect detection method of one embodiment of the present application.

[0019] Figure 4 The partial step flowchart of the defect detection method of one embodiment of the present application.

[0020] Figure 5 The schematic diagram of the color picture of one embodiment of the present application.

[0021] Figure 6 The schematic diagram of the gray-scale picture of one embodiment of the present application.

[0022] Figure 7 A schematic diagram of a black and white picture according to an embodiment of the present application.

[0023] Figure 8 A schematic diagram of a defect detection device according to an embodiment of the present application.

[0024] Figure 9 A schematic diagram of an automated optical inspection apparatus according to an embodiment of the present application.

[0025] Explanation of reference signs 100, defect detection device; 101, acquisition module; 102, determination module; 103, calculation module; 104, conversion module; 200, automated optical inspection apparatus; 201, execution module; 202, readable storage medium; mura, display defect. DETAILED DESCRIPTION

[0026] At present, in the process of detecting display screen Mura defects, the automated optical inspection (AFT) device will have a large number of missed detection and overkill situations, and the detection situation is poor. When the AFT device detects the display screen Mura defects, a black and white camera and a color camera are usually used to detect the screen Mura defects. The black and white camera mainly detects screen Mura defects such as black spots and white spots, and the color camera mainly detects Mura defects such as S / G direction Mura, color spots, gray scale unevenness, and color cast. For the detection process of the color camera, the traditional Mura defect detection algorithm directly converts the color picture obtained by the color camera into a black and white picture through color space conversion, and then highlights the defective area through the anti-color pull, but this method is suitable for the case where there is a significant black and white brightness difference between the Mura defect and the normal display area. However, for some Mura defects with color cast and uneven color, the black and white picture converted by the above method does not highlight some Mura defects in the black and white picture, or even some Mura defects are missing (the black and white brightness is consistent with the normal display area), which leads to poor detection effect.

[0027] Therefore, the application provides a defect detection method, which comprises the following steps: obtaining a color picture of a display panel at a specific gray scale, wherein the color picture comprises a display defect; determining luminance values of a plurality of sub-pixels included in each pixel point in the color picture; calculating a maximum luminance difference between the plurality of sub-pixels in each pixel point based on the luminance values; converting the color picture into a black-and-white picture based on the maximum luminance difference of each pixel point, so as to highlight the display defect; obtaining at least one feature quantity of the display defect in the black-and-white picture; and determining a type of the display defect based on the at least one feature quantity. The application determines the luminance values of the plurality of sub-pixels included in each pixel point in the obtained color picture, and calculates the maximum luminance difference between the plurality of sub-pixels in each pixel point based on the luminance values. In a normal display area, the maximum luminance difference of each pixel point is equal or has a small difference; and in a display defect area, the maximum luminance difference of each pixel point has a large difference with the maximum luminance difference of each pixel point in the normal display area. The color picture is converted into the black-and-white picture based on the maximum luminance difference of each pixel point, so as to highlight the display defect. Then, the at least one feature quantity of the display defect in the black-and-white picture is obtained, so as to determine the type of the display defect, and then accurately detect the display defect existing in the target display panel.

[0028] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0029] In the drawings, the sizes of layers and regions can be exaggerated for clarity. It can be understood that when a structure is referred to as being "on" or "under" another structure, it can be directly on or under the other structure, or an intervening structure can also be present. Like reference numerals refer to like structures throughout the specification. The structures mentioned herein include any one of a film layer, an element, a device, a member, and an assembly.

[0030] When a structure is referred to as being "connected" to another structure, it can be directly connected to the other structure, or indirectly connected to the other structure with one or more intervening structures placed therebetween. In addition, the terms "first", "second", "third", and the like are used only for differentiation description, and cannot be understood as indicating or implying relative importance.

[0031] Figure 1 The main step flowchart of the defect detection method of one embodiment of the application. Figure 5 The schematic diagram of the color picture of one embodiment of the application. As shown in Figure 1 and Figure 5As shown, in one aspect, the embodiment of the present application provides a defect detection method for detecting a display defect mura existing in a target display panel, the defect detection method comprising the following steps: Step S100, obtaining a color picture of a display picture of the target display panel at a specific gray scale, the color picture comprising the display defect mura.

[0032] Step S200, determining the luminance values of the multiple sub-pixels contained in each pixel point in the color picture.

[0033] Step S300, based on the luminance values, calculating the maximum luminance difference between the multiple sub-pixels in each pixel point.

[0034] Step S400, based on the maximum luminance difference of each pixel point, converting the color picture into a black-and-white picture to highlight the display defect mura.

[0035] Step S500, obtaining at least one feature quantity of the display defect mura in the black-and-white picture.

[0036] Step S600, based on the at least one feature quantity, determining the type of the display defect mura.

[0037] The defect detection method provided by the embodiment of the present application determines the luminance values of the multiple sub-pixels contained in each pixel point in the obtained color picture, and based on these luminance values, the maximum luminance difference between the multiple sub-pixels in each pixel point can be calculated. In a normal display area, the maximum luminance difference of each pixel point is equal or has a small difference; while in a display defect mura area, the maximum luminance difference of each pixel point has a large difference with the maximum luminance difference of each pixel point in the normal display area. Based on the maximum luminance difference of each pixel point, the color picture is converted into a black-and-white picture, so as to highlight the display defect mura, which is convenient for subsequent identification of the display defect mura. At least one feature quantity of the display defect mura in the black-and-white picture is obtained, and according to these feature quantities, the type of the display defect mura can be determined, and then the display defect mura existing in the target display panel can be accurately detected.

[0038] It should be noted that the multiple sub-pixels contained in each pixel in the target display panel are physical units emitting fixed color light, and the final color of the pixel is "synthesized" by controlling the luminance of each sub-pixel using the principle of additive color mixing. Therefore, in the obtained color picture, the display colors in the normal display area are the same or close, so the maximum luminance difference of each pixel point is equal or has a small difference; while the display color of the display defect mura is obviously different from the display color of the normal display area, so the maximum luminance difference of each pixel point of the display defect mura has a large difference with the maximum luminance difference of each pixel point in the normal display area.

[0039] It is understandable that the color of the display defect mura in the acquired color image may appear greenish (e.g., Figure 5 As shown in the image, it may also have a bluish tint. For example, the color of the display defect mura may differ depending on the target display panel, and the color of the display defect mura may also differ for the same target display panel at different specific gray levels. The specific color should be analyzed based on the actual situation. It should be noted that specific gray levels can be selected from the 0 to 255 gray levels as needed. For example, several gray levels can be selected from low gray levels (0 to 85), medium gray levels (86 to 170), and high gray levels (171 to 255).

[0040] In some implementations, determining the luminance values ​​of multiple sub-pixels contained in each pixel of a color image includes: The brightness values ​​of multiple sub-pixels contained in each pixel are calculated using a color blending transformation algorithm.

[0041] Specifically, the color blending transformation algorithm can comprehensively analyze and calculate the color information of multiple sub-pixels contained in each pixel in a color image, thereby accurately obtaining the brightness value corresponding to each sub-pixel, providing a reliable data foundation for subsequent steps.

[0042] Specifically, in the color blending transformation algorithm, the color image is first converted to a color space that separates brightness and color. The brightness values ​​of the multiple sub-pixels contained in each pixel can then be determined using this color space. Since the principle of the color blending transformation algorithm is existing technology, the implementation method described in this application will not be elaborated upon here.

[0043] In some implementations, multiple sub-pixels include a first sub-pixel, a second sub-pixel, and a third sub-pixel. Specifically, the first sub-pixel is a red sub-pixel, the second sub-pixel is a green sub-pixel, and the third sub-pixel is a blue sub-pixel. After acquiring the color image, for each pixel, the brightness values ​​of its red, green, and blue sub-pixels are obtained separately. In this way, the color composition of each pixel can be comprehensively and meticulously understood, providing sufficient basis for accurately judging display defects (mura) in subsequent implementations.

[0044] Of course, the first sub-pixel can also be a green or blue sub-pixel, the second sub-pixel can also be a red or blue sub-pixel, and the third sub-pixel can also be a red or green sub-pixel, etc. Furthermore, the arrangement of the first, second, and third sub-pixels can be set as needed, and is not limited in the embodiments of this application.

[0045] Figure 2 This is a flowchart of some steps in a defect detection method according to one embodiment of this application. Figure 1 andFigure 2 As shown, in some embodiments, step S300 includes: Step S301: Determine the maximum and minimum brightness values ​​among the brightness values ​​of multiple sub-pixels in each pixel.

[0046] Step S302: Subtract the minimum brightness value from the maximum brightness value to obtain the maximum brightness difference.

[0047] First, determine the maximum and minimum brightness values ​​among the brightness values ​​of multiple sub-pixels in each pixel. The maximum brightness difference can be obtained by subtracting the maximum and minimum brightness values.

[0048] Suppose that the maximum brightness value among the first, second, and third sub-pixels of a pixel is the brightness value of the first sub-pixel, and the minimum brightness value is the brightness value of the third sub-pixel. In this case, the maximum brightness difference of the pixel is equal to the brightness value of the first sub-pixel minus the brightness value of the third sub-pixel.

[0049] In this embodiment, we will take as an example a pixel A containing a red sub-pixel (first sub-pixel) with a brightness value of 150 nits, a green sub-pixel (second sub-pixel) with a brightness value of 180 nits, and a blue sub-pixel (third sub-pixel) with a brightness value of 255 nits. The largest brightness value in pixel A is the blue sub-pixel at 255 nits, and the smallest is the red sub-pixel at 150 nits. The maximum brightness difference in pixel A is 255 nits - 150 nits = 105 nits.

[0050] The maximum brightness difference of the remaining pixels is obtained by the same calculation method as described above, and will not be elaborated further here. Assume that the maximum brightness difference of several pixels B, C, D, E, and F (in addition to the other pixels not shown) is also obtained by the above calculation as follows: 78 nits, 79 nits, 95 nits, 188 nits, and 206 nits, respectively.

[0051] A preliminary analysis of the maximum brightness differences of pixels A, B, C, D, E, and F reveals that pixels B and C have the closest maximum brightness differences, suggesting they are likely pixels within the normal display area. However, the differences between the maximum brightness differences of pixels A, D, E, and F and those of pixels B, C, and D are significant, leading to the preliminary assessment that pixels A, D, E, and F are either within display defects or on the edge lines.

[0052] Figure 3 This is a second flowchart of some steps of a defect detection method according to one embodiment of this application. Figure 6 This is a schematic diagram of a grayscale image according to one embodiment of this application. Figure 7 This is a schematic diagram of a black and white image illustrating one embodiment of this application. (See attached image.)Figure 3 , Figure 6 and Figure 7 As shown, in some implementations, converting a color image to a black and white image based on the maximum brightness difference data of each pixel includes: Step S401: Based on the maximum brightness difference of each pixel, the color image is converted into a grayscale image by differential operation. The grayscale value of each pixel in the grayscale image is proportional to the magnitude of the maximum brightness difference.

[0053] Step S402: Convert the grayscale image to a black and white image.

[0054] In this embodiment, a difference operation is first used to convert the color image into a grayscale image, and then the grayscale image is further converted into a black and white image, ultimately converting the color image into a black and white image, providing a suitable image basis for subsequent possible defect detection and other operations.

[0055] Specifically, in the process of converting a color image to a grayscale image, a first threshold is set. This first threshold is greater than the minimum of the maximum brightness differences among all pixels and less than the maximum of the maximum brightness differences among all pixels. Pixels with a maximum brightness difference less than or equal to the first threshold are converted to black pixels, while pixels with a maximum brightness difference greater than the first threshold are converted to gray pixels. The grayscale value of a gray pixel is directly proportional to the magnitude of the maximum brightness difference; that is, the larger the maximum brightness difference, the larger the grayscale value of the corresponding gray pixel and the closer its color is to white.

[0056] It should be noted here that, because the maximum brightness difference of pixels in the normal display area of ​​a color image is relatively small or equal, while the maximum brightness difference of pixels in the area with display defects (mura) differs significantly from that in the normal display area, the first threshold must be greater than the maximum brightness difference of multiple pixels in the normal display area, but less than the maximum brightness difference of multiple pixels in the area with display defects (mura). This first threshold setting achieves the initial division between the normal display area and the display defect (mura) area, and in the grayscale image, the outline of the display defect (mura) is already partially formed.

[0057] The following explanation uses the maximum brightness difference values ​​of several pixels A, B, C, D, E, and F obtained from the above calculations as an example. Since the maximum brightness differences of pixels B and C are close, they are initially judged to be pixels in the normal display area. Therefore, when setting the first threshold, as many such pixels as possible should be filtered out to reduce interference. Here, the first threshold is set to the maximum value of the maximum brightness difference values ​​of pixels B and C, which is 79 nits. Specifically, we can use a first threshold equal to 79 nits as an example. The maximum brightness differences of pixels A, D, E, and F are all greater than 79 nits. Subtracting the maximum brightness differences of pixels A, D, E, and F from the first threshold yields: 26, 16, 109, and 127, respectively. These values ​​are then used as the grayscale values ​​of pixels A, D, E, and F. Pixels B and C, with a grayscale value of 0, are converted to black. Pixels A, D, E, and F are converted to gray. Pixel D, with the smallest grayscale value (16), has a color closest to black, while pixel F, with the largest grayscale value (127), has a color closest to white. The colors of pixels A and E are between those of pixels D and F, and the color depth of pixel A is greater than that of pixel E. The remaining pixels (not shown) are converted using the same method to obtain the converted grayscale image, as shown below. Figure 6 As shown.

[0058] In some implementations, converting a grayscale image to a black and white image includes: Binarization is used to convert grayscale images into black and white images.

[0059] Specifically, in the process of converting a grayscale image to a black and white image using binarization, a second threshold is set. Pixels with grayscale values ​​greater than the second threshold are converted to white, and pixels with grayscale values ​​less than or equal to the second threshold are converted to black, thereby converting the grayscale image to a black and white image.

[0060] Because some pixels in the normally displayed area may still exist among the gray pixels in the converted grayscale image, causing the display defect mura to be unclear, converting the grayscale image to a black and white image can highlight the display defect mura, making it easier to identify. It should be noted that the second threshold should be greater than the maximum brightness difference between multiple pixels in the normally displayed area, but less than the maximum brightness difference between multiple pixels in the area with the display defect mura. By setting the second threshold, as many pixels as possible in the normally displayed area are converted to black pixels, and as many pixels as possible in the area with the display defect mura are converted to white pixels, thus highlighting the display defect mura in white.

[0061] The grayscale values ​​of pixels A, B, C, D, E, and F in the converted grayscale image are 26, 0, 0, 16, 109, and 127, respectively. Further analysis reveals that the grayscale values ​​of pixels A and D are similar, while the grayscale values ​​of pixels E and F are similar, and the difference between the grayscale values ​​of pixels A and D and pixels E and F is significant. Therefore, pixels E and F are determined to be located within the normal display area or along the edge of the normal display area. Thus, when setting the second threshold, as many of these pixels as possible should be filtered out to reduce interference. Taking a second threshold of 30 as an example, pixels E and F with grayscale values ​​less than 30 are converted to black pixels, and pixels A and D with grayscale values ​​greater than 30 are converted to white pixels. The same method is applied to all other pixels (not shown) to obtain the converted black and white image, as shown below. Figure 7 As shown.

[0062] In some embodiments, at least one feature quantity includes at least one of length, width, and area, but is not limited to this, and is specifically set as needed. For example, in other embodiments, at least one feature quantity may also include shape, etc.

[0063] In some implementations, determining the type of a display defect mura based on at least one characteristic quantity includes: When the area of ​​the displayed defect mura is smaller than the first preset value, the type of the displayed defect mura is determined to be a colored spot.

[0064] When the area of ​​the display defect mura is greater than or equal to the first preset value, the type of the display defect mura is determined to be color unevenness.

[0065] When the area of ​​the display defect mura is smaller than the first preset value, it indicates that the area of ​​the display defect mura is small, and therefore the type of display defect mura is determined to be color spot. When the area of ​​the display defect mura is greater than or equal to the first preset value, it indicates that the area of ​​the display defect mura is large, and the type of display defect mura is determined to be color unevenness.

[0066] Of course, in other embodiments, the type of display defect mura can be determined based on at least one of the feature quantities, such as length and width, or by combining length and width with area, and is not limited to the embodiments of this application.

[0067] like Figure 7As shown, in the converted black and white image, the white area represents the region where the display defect mura is located. The type of display defect mura can be determined by calculating the area of ​​the white area. The first preset value is set by those skilled in the art based on experience, and this application embodiment does not impose specific limitations. For example, assuming the first preset value is equal to 8000 pixels, when the area of ​​the white region (the region where the display defect mura is located) in the converted black and white image contains less than 8000 pixels, the type of display defect mura is determined to be color spot; when the area of ​​the white region (the region where the display defect mura is located) in the converted black and white image contains more than or equal to 8000 pixels, the type of display defect mura is determined to be color unevenness.

[0068] In some implementations, acquiring a color image of the target display panel at a specific grayscale includes: A color camera is used to photograph the display screen of the target display panel at a specific grayscale level to obtain a color image.

[0069] During the acquisition of color images, parameters such as resolution and color reproduction of the color camera need to be set appropriately based on the characteristics of the target display panel and the required detection accuracy. For example, for high-resolution target display panels, a color camera with high pixel count should be selected to ensure that subtle display defects (mura) can be captured; simultaneously, to accurately reflect the true colors of the displayed image, the color camera's color reproduction should also reach a high level. Furthermore, factors such as ambient light and angle during photography can also affect the quality of color images; therefore, these factors must be strictly controlled in practice.

[0070] In some implementations, the process further includes, before taking a picture of the target display panel at a specific grayscale using a color camera: Adjust the amount of light entering the color camera and the exposure time so that the amount of light entering the camera is greater than or equal to the preset amount of light entering the camera, and the exposure time is less than or equal to the preset exposure time.

[0071] This adjustment ensures that a color image with appropriate brightness and clear details is obtained during the shooting process. If the amount of light entering the camera is less than the preset amount, the overall image may be too dark, making it difficult to capture subtle display defects (mura). Conversely, increasing the amount of light while decreasing the exposure time avoids overexposure of the color image, preventing the loss of information in some areas of the display defects (mura). Therefore, properly adjusting the amount of light entering the color camera and the exposure time is crucial for accurately acquiring color images of the target display panel without adversely affecting the production cycle and capacity.

[0072] It should be noted that the preset light intake and preset exposure time can be set as needed, and are not limited in the embodiments of this application.

[0073] In addition to the steps described above, this defect detection method also includes preprocessing the acquired color images. The goal of preprocessing the color images is to improve their image quality, standardize the data, and enhance features, so that subsequent analysis and recognition steps can achieve better results.

[0074] Specifically, preprocessing includes operations such as noise reduction, image enhancement, and sharpening for color images.

[0075] Figure 8 This is a schematic diagram of the defect detection device according to one embodiment of this application. Figure 8 As shown, in another aspect, this application provides a defect detection device, including an acquisition module 101, a determination module 102, a calculation module 103, and a conversion module 104. The acquisition module 101 acquires a color image of the target display panel at a specific grayscale, the color image including a display defect mura, and acquires at least one feature of the display defect mura in the black and white image. The determination module 102 determines the brightness values ​​of multiple sub-pixels contained in each pixel in the color image and determines the type of display defect mura based on at least one feature. The calculation module 103 calculates the maximum brightness difference between multiple sub-pixels in each pixel based on the brightness values. The conversion module 104 converts the color image into a black and white image based on the maximum brightness difference of each pixel to highlight the display defect mura.

[0076] The defect detection apparatus 100 provided in this application determines the brightness values ​​of multiple sub-pixels contained in each pixel of a color image acquired by the acquisition module 101 through the determination module 102. Based on these brightness values, the calculation module 103 can calculate the maximum brightness difference between the multiple sub-pixels in each pixel. Based on the maximum brightness difference of each pixel, the conversion module 104 can convert the color image into a black and white image, thereby highlighting the display defect mura and facilitating subsequent identification of the display defect mura. The acquisition module 101 further acquires at least one feature quantity of the display defect mura in the black and white image. Based on these feature quantities, the determination module 102 can determine the type of display defect mura, thereby accurately detecting the display defect mura present in the target display panel.

[0077] In another aspect, the embodiments of this application also provide a readable storage medium 202, which stores computer instructions. When the computer instructions are executed, the defect detection method provided by the embodiments of this application is executed.

[0078] Since the computer instructions stored in the readable storage medium 202 provided in this application embodiment execute the defect detection method provided in this application embodiment when they are executed, the readable storage medium 202 provided in this application embodiment has the same technical effect as the defect detection method provided in this application embodiment. For details, please refer to the above description. This application embodiment will not be described again here.

[0079] Specifically, the readable storage medium 202 in the embodiments of this application can be a hard disk, USB flash drive, cloud drive, etc., but is not limited to these. Any readable medium that can store the defect detection method of the embodiments of this application is within the protection scope of this application.

[0080] Figure 9 This is a schematic diagram of the structure of an automated optical inspection device according to one embodiment of this application. Figure 9 As shown, in another aspect, this application provides an automatic optical inspection device, including an execution module 201 and a readable storage medium 202 provided in this application, which are connected by communication. When the execution module 201 is running, it executes computer instructions stored in the readable storage medium 202.

[0081] Since the automatic optical inspection device 200 provided in this application includes the readable storage medium 202 provided in this application, the automatic optical inspection device 200 provided in this application has the same technical effect as the defect detection method provided in this application. For details, please refer to the above description. This application will not be described again here.

[0082] In this embodiment, the target display panel can be an Organic Light Emitting Diode (OLED) display panel or a Quantum Dot Light Emitting Diode (QLED) display panel. The target display panel includes a display area and a non-display area. The non-display area includes a border area that surrounds the display area. The display area has a display function. The shape of the display area of ​​the target display panel can be rectangular, square, circular, elliptical, or other shapes.

[0083] The display area includes multiple pixels arranged in a horizontal and vertical direction, with the horizontal direction perpendicular to the vertical direction. Each pixel includes multiple sub-pixels displaying different colors. Each sub-pixel includes a pixel circuit and a light-emitting device driven by the pixel circuit to emit light of the corresponding color. One pixel circuit drives at least one light-emitting device to emit light. For example, the display area includes a normal display area and a light-transmitting display area. The light-transmitting display area is a display area set for a corresponding sensor and has light-transmitting properties, while the normal display area is a display area not set for a corresponding sensor. In the normal display area, one pixel circuit drives one light-emitting device to emit light; in the light-transmitting display area, one pixel circuit drives one or more light-emitting devices to emit light. Further, the pixel circuit includes a driving transistor and a data transistor. The source of the data transistor is connected to a data line providing the data signal "Data," the gate of the data transistor is connected to a scan line providing the scan signal "Scan," the drain of the data transistor is connected to the gate of the driving transistor, the two ends of a storage capacitor are connected to the gate and source of the driving transistor, respectively, and the drain of the driving transistor is connected to the light-emitting device.

[0084] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0085] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A defect detection method, characterized in that, The defect detection method is used to detect display defects present in a target display panel, and the defect detection method includes: Obtain a color image of the target display panel at a specific grayscale, the color image including the display defect; Determine the brightness values ​​of multiple sub-pixels contained in each pixel in the color image; Based on the brightness value, the maximum brightness difference between the plurality of sub-pixels in each pixel is calculated; Based on the maximum brightness difference of each pixel, the color image is converted into a black and white image to highlight the display defect; Obtain at least one feature of the display defect in the black and white image; The type of the display defect is determined based on the at least one feature quantity.

2. The defect detection method according to claim 1, characterized in that, The determination of the brightness values ​​of multiple sub-pixels contained in each pixel in the color image includes: The brightness values ​​of multiple sub-pixels contained in each pixel are calculated using a color blending transformation algorithm; Preferably, the plurality of sub-pixels includes a first sub-pixel, a second sub-pixel, and a third sub-pixel; Preferably, the first sub-pixel is a red sub-pixel, the second sub-pixel is a green sub-pixel, and the third sub-pixel is a blue sub-pixel.

3. The defect detection method according to claim 1, characterized in that, The step of calculating the maximum brightness difference between the plurality of sub-pixels in each pixel based on the brightness value includes: Determine the maximum and minimum brightness values ​​among the brightness values ​​of the plurality of sub-pixels in each pixel point; The maximum brightness difference is obtained by subtracting the minimum brightness value from the maximum brightness value.

4. The defect detection method according to claim 1, characterized in that, Converting the color image to a black and white image based on the maximum brightness difference data of each pixel includes: Based on the maximum brightness difference of each pixel, the color image is converted into a grayscale image using differential operation. The grayscale value of each pixel in the grayscale image is proportional to the magnitude of the maximum brightness difference. Convert the grayscale image to the black and white image.

5. The defect detection method according to claim 4, characterized in that, The step of converting the grayscale image to the black and white image includes: The grayscale image is converted into a black and white image using binarization.

6. The defect detection method according to claim 1, characterized in that, The at least one feature quantity includes at least one of length, width, and area; Preferably, determining the type of the display defect based on the at least one feature quantity includes: When the area of ​​the display defect is smaller than a first preset value, the type of the display defect is determined to be a color spot; When the area of ​​the display defect is greater than or equal to the first preset value, the type of the display defect is determined to be color unevenness.

7. The defect detection method according to claim 1, characterized in that, The process of acquiring a color image of the target display panel at a specific grayscale includes: A color camera is used to take pictures of the display screen of the target display panel at a specific grayscale to obtain the color image; Preferably, before taking a picture of the target display panel at a specific grayscale using a color camera, the method further includes: Adjust the amount of light entering the color camera and the exposure time so that the amount of light entering the camera is greater than or equal to a preset amount of light entering the camera, and the exposure time is less than or equal to a preset exposure time.

8. A defect detection device, characterized in that, include: The acquisition module is used to acquire a color image of the display screen of the target display panel at a specific grayscale, wherein the color image includes display defects and at least one feature of the display defects is acquired from the black and white image; The determination module is used to determine the brightness values ​​of multiple sub-pixels contained in each pixel in the color image and to determine the type of the display defect based on the at least one feature quantity; The calculation module is used to calculate the maximum brightness difference between the plurality of sub-pixels in each pixel based on the brightness value; The conversion module is used to convert the color image into a black and white image based on the maximum brightness difference of each pixel, so as to highlight the display defect.

9. A readable storage medium, characterized in that, The readable storage medium stores computer instructions, which, when executed, perform the defect detection method according to any one of claims 1 to 7.

10. An automatic optical inspection device, characterized in that, The device includes an execution module with a communication connection and a readable storage medium as described in claim 9. When the execution module is running, it executes computer instructions stored in the readable storage medium.

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