Display panel defect detection method, device and equipment based on attention potential energy, medium and product

Abstract

The invention discloses a display panel defect detection method, device and equipment based on attention potential energy, a medium and a product. The method comprises the following steps: acquiring an image set of a multi-scale channel associated with a to-be-detected panel; performing quality assignment on each image pixel in the image set of the multi-scale channel to obtain a point mass body set of the multi-scale channel; constructing an attention potential energy function, and obtaining an attention potential energy vector set of the multi-scale channel according to the point mass body set of the multi-scale channel; and obtaining a defect detection result of the to-be-detected panel based on the attention potential energy value and the attention potential energy range associated with the attention potential energy vector set of the multi-scale channel. According to the invention, the defect detection result and the human eyes have extremely high alignment, so that the distortion of the detection result is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial machine vision defect detection, and in particular to a display panel defect detection method, device, equipment, medium and product based on attention potential. Background Art

[0002] With the development of the general trend of "machines replacing people", the application of machine vision systems in the industrial chain is becoming more and more extensive. In the field of defect detection of luminous panels (including but not limited to LCD, OLED, Micro-OLED, backlight panels and other display devices and their corresponding process products), due to the rising cost of manual inspection, the long training cycle of inspection operators and human eye damage (occupational diseases) caused by inspection operations, various inspection systems based on machine vision technology are also being applied and have achieved certain results. Stable, reliable, and highly consistent inspection results of luminous surface machine vision systems are of great significance to the development of the industry, and the inspection algorithm has core value in the entire inspection system.

[0003] At present, the relevant detection algorithms are mainly divided into three categories: methods based on filtering and image segmentation; methods based on background fitting or modeling; and sample learning methods based on various artificial neural networks. Methods based on filtering and image segmentation mainly achieve the purpose of defect detection through various types of filtering (such as Gabor filtering, "frequency domain contrast sensitivity function filter template", etc.) and segmentation algorithms ("CV model segmentation", "adaptive threshold segmentation", "level set", etc.) and assist other image optimization processing methods. They are classic image algorithms. Methods based on background fitting or modeling mainly obtain the background through various background fitting or modeling methods (such as "using the PCA algorithm to learn a large number of defect-free samples, automatically extracting the difference features between the background and the target, and reconstructing the background image", "double N-order polynomial surface fitting model", "B-spline fitting", etc.), and then achieve the purpose of defect detection through image difference and assisting other image optimization processing methods. The sample learning method based on various artificial neural networks mainly uses various artificial neural networks (such as "PA FPN feature enhancement network", "MobileNetV3 algorithm model", "manually simulate various types of Mura defects using graphics processing methods to form a sample library; train the model through deep neural networks") and assist in some detail processing to train the defect samples and then make predictions to achieve the purpose of defect detection. It belongs to the traditional deep learning type.

[0004] Although the above methods can achieve certain effects in various applications, due to some inherent difficulties in light-emitting panel detection, such as MURA detection, the MURA contrast superimposed on moiré and pixel texture is extremely low, there is no detailed boundary and the morphology changes are diverse, resulting in poor results of the above image processing methods, and it is difficult to effectively segment. Methods based on deep learning often fail to converge or overfit during training, and auxiliary enhancement methods introduce a large number of noise blocks of the same grayscale, resulting in poor prediction results. On the other hand, even if some MURA can be segmented or predicted, its detection effect is poorly aligned with human eye detection, resulting in detection distortion. Summary of the invention

[0005] The present invention provides a display panel defect detection method, device, equipment, medium and product based on attention potential energy to solve the technical problem of defect detection distortion caused by poor alignment between detection effect and human eye detection in the prior art.

[0006] According to one aspect of the present invention, a display panel defect detection method based on attention potential is provided, comprising:

[0007] Acquire an image set of multi-scale channels associated with the panel to be detected;

[0008] Assigning a quality value to each image pixel in the image set of the multi-scale channel to obtain a point quality volume set of the multi-scale channel;

[0009] Constructing an attention potential energy function, and obtaining an attention potential energy vector set of the multi-scale channel according to the point mass volume set of the multi-scale channel;

[0010] The defect detection result of the panel to be inspected is obtained based on the attention potential energy value and the attention potential energy range associated with the attention potential energy vector set of the multi-scale channel.

[0011] According to another aspect of the present invention, there is provided a display panel defect detection device based on attention potential energy, comprising:

[0012] A first acquisition module is used to acquire an image set of multi-scale channels associated with the panel to be detected;

[0013] A first conversion module is used to assign a quality value to each image pixel in the image set of the multi-scale channel to obtain a point quality volume set of the multi-scale channel;

[0014] A second conversion module is used to construct an attention potential energy function and obtain an attention potential energy vector set of the multi-scale channel according to the point mass volume set of the multi-scale channel;

[0015] A detection module is used to obtain a defect detection result of the panel to be detected based on the attention potential energy value and the attention potential energy range associated with the attention potential energy vector set of the multi-scale channel.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the display panel defect detection method based on attention potential as described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the display panel defect detection method based on attention potential as described in any embodiment of the present invention when executed.

[0021] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program, and the computer program, when executed by a processor, implements the display panel defect detection method based on attention potential energy described in any embodiment of the present invention.

[0022] The technical solution of the embodiment of the present invention assigns a mass value to each image pixel contained in each image in the image set of the multi-scale channel associated with the panel to be inspected, thereby obtaining a point mass volume set of the multi-scale channel, and simulating the characteristics of the human eye by constructing an attention potential energy function, and obtaining an attention potential energy vector set of the multi-scale channel based on the point mass volume set of the multi-scale channel, and obtaining the defect detection result of the panel to be inspected based on the attention potential energy value associated with the attention potential energy vector set of the multi-scale channel, so that the defect detection result has a very high alignment with the human eye, thereby reducing the distortion of the detection result.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 is a flow chart of a display panel defect detection method based on attention potential energy provided by an embodiment of the present invention;

[0026] Figure 2 is a flow chart of another display panel defect detection method based on attention potential energy provided by an embodiment of the present invention;

[0027] Figure 3 is a configuration flow chart of a state observation table provided by an embodiment of the present invention;

[0028] Figure 4 is a flow chart for realizing display panel defect detection based on attention potential energy provided by an embodiment of the present invention;

[0029] Figure 5 It is a schematic diagram showing an original image with a strong defect and its attention potential energy vector diagram provided by an embodiment of the present invention;

[0030] Figure 6 It is a schematic diagram showing an original image of a dirt defect and its attention potential energy vector diagram provided by an embodiment of the present invention;

[0031] Figure 7 It is a schematic diagram showing an original image of a vertical MURA defect and its attention potential energy vector diagram provided by an embodiment of the present invention;

[0032] Figure 8 It is a schematic diagram showing an original image of a black GAP defect and its attention potential energy vector diagram provided by an embodiment of the present invention;

[0033] Fig. 9 is a structural schematic diagram of a display panel defect detection device based on attention potential energy provided by an embodiment of the present invention;

[0034] Fig.10 It is a structural block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] In one embodiment, Figure 1 It is a flow chart of a display panel defect detection method based on attention potential provided by an embodiment of the present invention. This embodiment is applicable to the situation where defects of a display panel are detected based on attention potential. The method can be executed by a display panel defect detection device based on attention potential. The display panel defect detection device based on attention potential can be implemented in the form of hardware and / or software. The display panel defect detection device based on attention potential can be configured in an electronic device.

[0038] It should be noted that the direct function of the display light-emitting panel to be tested is to display content for human eyes to see, and its picture quality (especially MURA judgment) standard is essentially based on the sense of human eyes. Therefore, the ultimate and only standard for evaluating the performance of the panel detection machine vision system must be the alignment characteristics with the human eye. It should be noted that the "alignment characteristics with the human eye" specifically refers to the consistency of the inspection results of professionally trained visual manual inspection operators in the process of display light-emitting panels. Such visual manual inspection results themselves have strong standardization and consistency, and are the final inspection standards for product shipments.

[0039] like Figure 1 As shown, the method includes:

[0040] S110 , obtaining an image set of multi-scale channels associated with the panel to be detected.

[0041] In one example, the panel to be inspected refers to a display light-emitting panel that needs to be inspected for defects; the display panel refers to the core component contained in the display screen of each terminal device, which is used for the display function of the image; for example, the terminal device may include but not display one of the following: a computer, a tablet computer, an iPad, a mobile phone, and a television. In one example, a multi-scale channel refers to a plurality of different physical scales; an image set of a multi-scale channel refers to a collection of images of a plurality of different physical scales. In one example, the display panel may include, but is not limited to, one of the following: a liquid crystal display (LCD), an organic light-emitting diode (OLED), a MICRO-OLED (a miniaturized version of an OLED), and the like.

[0042] In the actual operation process, since the physical scales of the specific defects contained in the panel to be detected are different, and the differences in physical scales are relatively large, for example, the specific defect degree may include but is not limited to one of the following: points, lines, spots, MURA and unevenness; its physical scale may include: different levels such as microns, millimeters and centimeters. In an embodiment, the target image associated with the panel to be detected can be scale-separated by methods such as "Laplacian pyramid" to obtain an image set of multi-scale channels, which is conducive to improving the timeliness of subsequent processing steps. Exemplarily, assuming that the multi-scale channel includes three physical scales: microns, millimeters and centimeters, correspondingly, the target image can be scale-separated by the "Laplacian pyramid" method to obtain an image set of three physical scales, that is, the image set includes images of three physical scales. Among them, microscopic and subtle defects can be detected based on micron-level images; slightly larger defects can be detected based on millimeter-level images; larger defects can be detected based on centimeter-level images.

[0043] It should be noted that the image size corresponding to each scale channel image is different, but its coverage is the same as the original image.

[0044] S120 , assigning a quality value to each image pixel in the image set of the multi-scale channel to obtain a point quality volume set of the multi-scale channel.

[0045] The point mass volume set of the multi-scale channel refers to the set of point mass volumes of multiple different physical scales. The point mass volume set refers to the set of point mass volumes of a scale channel, that is, the set of all point mass volumes corresponding to the image of a scale channel. In one example, each image pixel of each image in the image set of the multi-scale channel can be used as a point mass volume with mass, and a mass value is assigned to each point mass volume to obtain the point mass volume set of the multi-scale channel.

[0046] S130. Construct an attention potential energy function, and obtain an attention potential energy vector set of the multi-scale channel based on the point mass volume set of the multi-scale channel.

[0047] In one example, the attention potential energy function is constructed based on a pre-constructed potential energy function and an attention kernel function; the attention potential energy function can convert the point mass volume set of the above-mentioned multi-scale channel into a corresponding attention potential energy vector set. The attention potential energy vector set can use the original pixels of the target image as the basic material, and through the potential energy function and the attention kernel function, it integrates the subjective preference factors of the human eye, and has a strong alignment with the detection results of the person.

[0048] S140. Obtain defect detection results of the panel to be detected based on the attention potential energy value and the attention potential energy range associated with the attention potential energy vector set of the multi-scale channel.

[0049] In one example, the defect detection result is used to characterize whether there are defects on the panel to be detected, as well as descriptions of related information such as the defect type, defect degree, and defect location; whether there are defects is the basic judgment result of the panel to be detected. For example, if there are no defects on the panel to be detected, it is characterized that the panel to be detected is qualified; if there are defects on the panel to be detected, it is characterized that the panel to be detected is unqualified. Of course, if the qualified requirements of the panel to be detected are not high, the defect detection result of the panel to be detected can be further judged based on information such as the defect type, defect degree, and defect location when there are defects in the panel to be detected. For example, if the defect type is a bad pixel, and the defect location is located all around, and the defect degree is that there is a bad pixel, then if the defect detection requirements are not high or the defect parameters are within the detection specification range, the defect detection result can be judged to be qualified.

[0050] In one example, the attention potential energy value can also be called the observation intensity value, which can be used as the final evaluation value of the defect detection result of the panel to be detected; the attention potential energy vector set of each scale channel can be adaptively thresholded to obtain the corresponding attention potential energy value and attention potential energy range. Based on the attention potential energy value and attention potential energy range, the defect detection result of the panel to be detected can be determined.

[0051] The technical solution of this embodiment is to obtain a point mass volume set of the multi-scale channel by assigning a mass value to each image pixel contained in each image in the image set of the multi-scale channel associated with the panel to be inspected, and to simulate the characteristics of the human eye by constructing an attention potential energy function, and to obtain an attention potential energy vector set of the multi-scale channel based on the point mass volume set of the multi-scale channel, and to obtain the defect detection result of the panel to be inspected based on the attention potential energy value associated with the attention potential energy vector set of the multi-scale channel, so that the defect detection result has a very high alignment with the human eye, thereby reducing the distortion of the detection result.

[0052] In one embodiment, Figure 2 This is a flowchart of another display panel defect detection method based on attention potential energy provided by an embodiment of the present invention. This embodiment is based on the above embodiment and further explains the process of acquiring the image set of the multi-scale channel, the process of determining the point mass volume set of the multi-scale channel, the process of determining the attention potential energy vector set of the multi-scale channel, and the process of determining the defect detection result. Figure 2 As shown, the method includes:

[0053] S210: Acquire an original image associated with the panel to be inspected.

[0054] The original image refers to the initial image obtained by the image acquisition device before any defect detection algorithm and modification is performed on the panel to be inspected; generally speaking, the original image can truly reflect the surface appearance and internal structure (within the image presentable range) of the panel to be inspected. Exemplarily, the image acquisition device can be an industrial camera, and can be a black and white camera, that is, the original image can be a black and white image.

[0055] S220: Perform image preprocessing on the original image to obtain a related target image.

[0056] In one example, image preprocessing refers to some necessary denoising, filtering and other operations on the original image; the target image refers to the image obtained after denoising, filtering and other operations on the original image. Image preprocessing of the original image can remove or reduce the pixel texture of the panel to be detected (except the backlight panel) and the moiré pattern generated by its imaging process. Although the above texture has obvious periodicity, considering that the texture changes caused by different products and different camera resolutions are very obvious, the filtering effect based on the frequency domain processing method of Fourier transform is not good, the frequency residue is large, the parameter adjustment is complex, the calculation amount is large and the efficiency is not high. Generally, conventional filtering methods, such as Gaussian filtering, have poor removal effects on the above textures. Therefore, in this solution, a custom convolution operator is used to perform spatial filtering on the original image, which has the characteristics of high computational efficiency and good filtering effect.

[0057] S230 , performing a scale separation operation on the target image to obtain an image set of multi-scale channels.

[0058] The physical scales of the specific defects of the panel to be inspected, such as points, lines, spots, MURA, and unevenness, vary greatly, ranging from microns to centimeters. Therefore, scale separation operations are performed on the target image to obtain an image set of multi-scale channels, which is beneficial to improving the timeliness of subsequent processing steps. Generally, the sampling is performed through methods such as the "Laplacian Pyramid" to obtain images of corresponding scales.

[0059] S240 , obtaining each image pixel of each image in the image set of multi-scale channels.

[0060] In one example, the image pixels contained in each image may be acquired in sequence by traversing pixel by pixel.

[0061] S250, multiply each image pixel by the associated quality weight coefficient to obtain a point mass volume set of a multi-scale channel.

[0062] In one example, each image pixel contained in each image in the above-mentioned multi-scale channel image set can be regarded as a point mass body containing mass and a mass value can be assigned. For example, the point mass body of the pixel point coordinate (i, j) is: g(i, j) = h(i, j)*I(i, j); wherein h(i, j) is a quality weight coefficient that varies according to the position coordinate (i, j), and can be matched and assigned according to the characteristics of different product models and the subjective requirements of quality evaluators. For example, the value range of h(i, j) can be 0-10. Generally, the value of h(i, j) is 1. The multi-scale channel image set is converted into a multi-scale channel point mass body set.

[0063] S260. Construct the potential energy function of a point mass body.

[0064] In one embodiment, the potential energy function includes the following: Among them, G(i,j) represents the point mass body at the pixel coordinate (i,j) within the field radius range k represents the deviation length between i and x-axis; l represents the deviation length between j and y-axis; C represents the disturbance coefficient (used for special case processing, such as the processing of singular points and singular regions described below, and generally takes a value of 0 to 10).

[0065] S270. Apply the potential energy function to the point mass volume set of the multi-scale channel to obtain the potential energy vector set of the multi-scale channel.

[0066] Obviously, the potential energy of the point mass body is inversely proportional to the square of the domain radius and proportional to the mass in the domain. The "condensation" state of the characteristic quantity in the local range is effectively characterized by integration (discrete accumulation), which is very targeted for the expression of MURA-type defects of light-emitting panels. From the obtained MURA image, its grayscale difference is very low (1 to 2 grayscales), there is no boundary contour, and the shape changes randomly. It is almost impossible to effectively process it according to the pixels of the original image, or the processing result is very different from the human eye's sense of dispersion. However, from the perspective of the human eye's observation characteristics, "condensing" the image and shaking and dragging it can effectively perceive the existence of MURA from the background. The potential energy function constructed above effectively simulates this observation characteristic of the human eye for low-contrast MURA through potential energy. Through this step, the point mass set of the above multi-scale channel is converted into the potential energy vector set of the multi-scale channel.

[0067] S280, construct attention kernel function Among them, X is the potential energy vector of the multi-scale channel after the potential energy function conversion, and M is a state observation table that describes the state that meets the subjective requirements of the human eye.

[0068] In one example, the potential energy function constructed above is used to characterize the degree of aggregation of local feature quantities, and needs to be further associated with the human eye attention feature. Let the attention kernel function be Among them, X is the potential energy vector set of the multi-scale channel obtained by converting the potential energy function constructed above; M is a state observation table that describes the subjective requirements of the human eye (such as specific visual inspection specifications for different product models). The state observation table represents subjective preferences such as perspective sensitivity, regional sensitivity, and morphological sensitivity in the form of state coefficients.

[0069] In one embodiment, the display panel defect detection method based on attention potential also includes: obtaining a preferred area in a target image associated with the panel to be detected; automatically generating an associated state observation table based on the preferred area. Wherein, the preferred area refers to an area that is set according to the detection requirements, and the preferred area is a partial area in the target image; the preferred area can be set manually and based on the detection requirements, for example, the preferred area can include but is not limited to one of the following: a key sensitive area, a negligible area, and a gray limit area, etc., and can be located at various positions in the target image, such as surrounding positions and center positions. The state observation table is a state observation table that describes the subjective requirements of the human eye (such as specific visual inspection specifications for panels to be detected for different product models); the state observation table characterizes subjective preferences such as visual sensitivity, regional sensitivity, and morphological sensitivity in the form of state coefficients. Table 1 is a schematic table of a state observation table provided in an embodiment of the present invention. As shown in Table 1, the values ​​in the first three rows and the first three columns in Table 1 represent the weight coefficients of the center position and the four sides and four corners. For example, the weight coefficient of the center position can be 1.00, the weight coefficient of the four corners can be 0.6, and the weight coefficient of the four sides can be 1.5; the values ​​in the first three rows and the 4th to 7th columns in Table 1 represent the number and width of the area to be detected. For example, the area numbers are 101, 102, 103, 104, 105 and 0.00, and the corresponding area widths are 20.00, 20.00, 15.00, 15.00, 0.00 and 0.00, respectively; the values ​​in the 4th to 6th rows and the 1st to 7th columns in Table 1 represent the viewing angle sensitivity angle and the corresponding area number. For example, the viewing angle sensitivity angles are 90.00, 45.00 and 10.00, respectively, and the area width corresponding to 90.00 is 20.00, 20.00, 15.00, 15.00, 0.00 and 0.00, respectively. The domain numbers are 101, 102, 103, 104, 105 and 0.00 respectively, and the area numbers corresponding to 45.00 are 101, 102, 103, 104 and 0.00 respectively; and the area numbers corresponding to 10.00 are 101, 102, 0.00, 0.00, 0.00 and 0.00 respectively; the values ​​located in rows 7-8 and columns 1-7 in Table 1 represent the defect sample type number, neighborhood major axis, neighborhood minor axis and quantity of the display panel. For example, the defect sample type number of the display panel is 11, and its neighborhood major axis, neighborhood minor axis and quantity are 15.00, 15.00 and 3 respectively, indicating that the defect sample type is dark spot interval; the defect sample type number of the display panel is 12, and its neighborhood major axis, neighborhood minor axis and quantity are 20.00, 60.00 and 2.00 respectively, indicating that the defect sample type is vertical MURA.

[0070] Table 1

[0071]

[0072]

[0073] S290. Apply the attention kernel function to the potential energy function to obtain the attention potential energy function.

[0074] In an embodiment, the attention kernel function can be applied to the potential energy function to obtain the attention potential energy function

[0075] S2100. Apply the attention potential energy function to the multi-scale channel potential energy vector set to obtain the attention potential energy vector set of the multi-scale channel.

[0076] In one embodiment, when a target image associated with a panel to be inspected contains singular points and singular regions, the display panel defect detection method based on attention potential energy further includes: obtaining a binary image mask of the singular points and singular regions in the target image;

[0077] Correspondingly, an attention potential energy function is constructed, and the attention potential energy vector set of the multi-scale channel is obtained according to the point mass volume set of the multi-scale channel, which also includes: determining the perturbation coefficients of the image pixels associated with the singular points and singular regions based on the binary image mask and assigning specific values; substituting the perturbation coefficients into the attention potential energy function to obtain the attention potential energy vector set of the multi-scale channel excluding the singular points and singular regions.

[0078] In one example, the singular points and singular regions refer to the positions corresponding to the external foreign objects in the external environment in the target image, and the distribution and occupied areas of the singular points and singular regions on the target image are not fixed.

[0079] In the process of defect detection of the panel to be inspected, it is inevitable that it will be affected by surface foreign matter such as dust in the environment. These external foreign matter are not real defects. For example, the vector prints and labels on the polarizer of the display module also need to be ignored. If the above parts are not processed, after being processed in the aforementioned steps, a very strong attention potential energy response will be formed in the attention potential energy vector set, forming singular points and singular areas. This result itself is reasonable, because the human eye itself responds strongly to these obvious features, but these are external interferences and need to be discarded in the detection results, but cannot be discarded or filtered out in the attention potential energy vector set that has been generated, because singular points and singular areas lead to a strong attention response that is far larger than their own area, and it is necessary to add singular points and singular areas to the aforementioned processing steps.

[0080] After conventional image processing (filtering, dynamic threshold segmentation, morphological post-processing) is performed on the original image of the panel to be inspected (for example, the dust removal image and the label image), multiple binary image masks s0~sn (dust removal mask, label mask, etc.) containing singular points and singular regions in the target image can be obtained. These image masks indicate the position and morphology of the singular points and singular regions in the target image, and further, the perturbation coefficient C in the potential energy function of the above point mass body can be assigned according to the singular position corresponding to the binary image mask. Here, the singular points and singular regions are interference terms, so the perturbation coefficient C is assigned 0, and then the potential energy function excluding the processing of singular points and singular regions is obtained: Correspondingly, we can obtain the potential energy vector set of the multi-scale channel excluding singular points and singular regions.

[0081] In one example, when the target image associated with the panel to be detected does not contain a singular position, an attention potential energy function is constructed based on the attention kernel function and the potential energy function, which is recorded as Among them, att(i,j) means that the pixel coordinates (i,j) are within the field radius range. The attention potential vector within.

[0082] In one example, when the target image associated with the panel to be inspected contains singular positions, multiple binary image masks s0~sn (dust removal masks, label masks, etc.) containing singular points and singular regions in the target image can be obtained. These image masks indicate the positions and shapes of the singular points and singular regions in the target image, and further, the perturbation coefficient C in the potential energy function of the above-mentioned point mass body can be assigned according to the singular positions corresponding to the binary image masks. Here, the singular points and singular regions are interference terms, so the perturbation coefficient C is assigned 0, and the attention potential energy function is constructed based on the attention kernel function and the potential energy function, which is recorded as Among them, att'(i,j) means that the pixel coordinates (i,j) are within the area radius range. The attention potential energy vector within is the attention potential energy vector that excludes the above-mentioned image masks such as dust, foreign matter, vector prints, and labels.

[0083] It can be understood that the attention kernel function is applied to the potential energy function constructed above, that is, the potential energy set vector of the multi-scale channel is converted into the attention potential energy vector set of the multi-scale channel. This attention potential energy vector set uses the original pixels of the image as the basic material, and integrates the subjective preference factors of the human eye through the potential energy function and the attention kernel function, and has a strong alignment with the inspection results of the human eye.

[0084] S2110. Perform adaptive threshold processing on the attention potential energy vector set of the multi-scale channel to obtain the attention potential energy value and attention potential energy range of the multi-scale channel.

[0085] Furthermore, the acquired attention potential energy vector set of the multi-scale channel is subjected to adaptive threshold processing (note that the threshold here is the attention potential energy threshold, not the image grayscale threshold) to obtain a specific attention potential energy value (also called an observation intensity value) and the corresponding attention potential energy range (also called an observation field value). Note that this observation intensity value can be used as the final evaluation value.

[0086] S2120. Perform a local inverse transformation on the attention potential energy value and the attention potential energy range of the multi-scale channel to obtain the physical intensity value and the physical intensity range of the multi-scale channel.

[0087] Since the observed field values ​​are generally quite different from the physical values ​​of actual object defects, it is necessary to perform a local inverse transformation on the aforementioned determination process of the attention potential energy value and the attention potential energy range to obtain the physical intensity value and the physical intensity range in the image, and then obtain the true physical value of the defect.

[0088] S2130: Merge the physical intensity values ​​of the multi-scale channels at the same coordinates, and obtain a defect detection result of the panel to be detected based on the physical intensity range of the multi-scale channels.

[0089] In an embodiment, the attention potential energy values ​​(i.e., physical intensity values) of multi-scale channels of the same coordinates can be aggregated and merged (i.e., accumulated), and the defect detection result of the panel to be inspected can be given as qualified or unqualified based on the set threshold value (observation intensity value and observation field value).

[0090] The technical solution of this embodiment abandons the classic image processing category and the traditional deep learning category through a detection algorithm that can be strongly aligned with the subjective sense of the human eye, and simulates the characteristics of the human eye by constructing an attention potential energy function, so that the detection result has a very high alignment with the human eye.

[0091] In one embodiment, Figure 3 is a configuration flow chart of a state observation table provided by an embodiment of the present invention, such as Figure 3 As shown in the figure, the configuration process of the state observation table includes the following steps:

[0092] S310, obtaining a target image of a limit sample of a panel to be inspected.

[0093] Generally, the number of target images of the limit sample of the panel to be inspected may be 3 to 5.

[0094] S320: Determine a preferred region in the target image.

[0095] Depending on the model of the panel to be inspected, it can be determined whether manual preference settings are required, such as key sensitive areas, ignored areas, grayscale limits, etc.

[0096] S330: Generate a state observation table corresponding to the preferred area.

[0097] In an embodiment, the program automatically generates a status observation table according to the settings of these preference areas.

[0098] In one embodiment, Figure 4 This is a flowchart of an implementation of display panel defect detection based on attention potential provided by an embodiment of the present invention. Based on the above embodiment, this embodiment is taken as a preferred embodiment, and takes the three physical scales including micrometers, millimeters and centimeters as examples to illustrate the display panel defect detection process based on attention potential. Figure 4 As shown, the defect detection process of the display panel based on attention potential in this embodiment includes the following steps:

[0099] Step 1: Obtain the original image of the panel to be inspected.

[0100] Step 2: perform image preprocessing on the original image to obtain the target image.

[0101] Step 3: preprocess the target image to obtain the corresponding image mask.

[0102] In an embodiment, a target image including surface dust removal, labels, etc. for mask processing is acquired; the input images such as dust removal and labels are preprocessed to generate a corresponding image mask.

[0103] Step 4: perform scale separation on the target image to obtain an image set of multi-scale channels.

[0104] In the embodiment, the scale set conversion of the three channels is beneficial to improving the timeliness of subsequent processing steps; it is noted that this step simultaneously performs scale separation on the above-mentioned image mask.

[0105] Step 5: Convert the image set of multi-scale channels into a point mass volume set of corresponding multi-scale channels.

[0106] In one example, image sets of three scale channels are converted into corresponding point mass volume sets, namely point mass volume set 1, point mass volume set 2 and point mass volume set 3.

[0107] In an embodiment, the image set of the multi-scale channel is converted into a point mass volume set of the multi-scale channel: g(i,j)=h(i,j)*I(i,j), where h(i,j) is a quality weight coefficient that varies according to position. In this example, the weight coefficient h(i,j) is taken as a constant h0; note that this step simultaneously converts the corresponding image mask mentioned above.

[0108] Step 6: Obtain the state observation table corresponding to each image.

[0109] Step 7, potential energy processing.

[0110] The potential energy of point mass set 1, point mass set 2 and point mass set 3 are processed in parallel to obtain the corresponding potential energy vector set. In this embodiment, the potential energy function of three different sizes of r0, r1 and r2 described by the above method is Potential energy processing is performed, and the three channels r0, r1, and r2 are processed in parallel to speed up the calculation.

[0111] Step 8: Construct the attention kernel function.

[0112] Based on the potential energy vector set, the attention kernel functions of point mass set 1, point mass set 2 and point mass set 3 are constructed in parallel.

[0113] Step 9, attention potential processing.

[0114] An attention potential energy function is constructed based on the attention kernel function and the potential energy function, and based on the attention potential energy function, the potential energy vector set of each scale channel is converted into an attention potential energy vector set of multi-scale channels in parallel.

[0115] Step 10: Processing of singular points and singular regions.

[0116] This step requires using the image mask generated above to process singular points and singular areas, and adjusting parameters to ensure that the detection results are well aligned with the results observed by the human eye.

[0117] Step 11, inverse transform.

[0118] The acquired attention potential energy vector set is subjected to adaptive threshold processing to obtain a specific attention potential energy value (observation intensity value); at the same time, a local inverse transformation is performed to obtain the domain range value in the image, and then the real physical value of the defect is obtained.

[0119] Step 12: output the defect detection result of the panel to be detected.

[0120] Figure 5 It is a schematic diagram showing an original image with a strong defect and its attention potential energy vector diagram provided by an embodiment of the present invention; Figure 6 It is a schematic diagram showing an original image of a dirt defect and its attention potential energy vector diagram provided by an embodiment of the present invention; Figure 7 8 is a schematic diagram of the display of an original image of a vertical MURA defect and its attention potential energy vector diagram provided by an embodiment of the present invention; 8 is a schematic diagram of the display of an original image of a black GAP defect and its attention potential energy vector diagram provided by an embodiment of the present invention.

[0121] like Figure 5 As shown in the figure, the defect can be clearly seen in the original image of the strong defect (left figure), and in its attention potential energy vector diagram (right figure), red is used to indicate that the stronger the contrast of this area, the more obvious the corresponding defect; Figure 6 , 7 As shown in Figure 8, the original image contains a weak defect (left image). The defect cannot be clearly seen in the original image, but in its corresponding attention potential energy vector diagram (right image), it can be seen Figure 6 The right image in the figure contains relatively weak contrast colors such as orange, and the corresponding defects are not very obvious; Figure 7 The right image in the figure contains a small amount of colors with slightly stronger contrast, such as red, and relatively weaker contrast, such as yellow, and the corresponding defects are not very obvious; Figure 8 The right image in contains only the least contrasting colors, such as cyan, where the defect is least noticeable.

[0122] In one embodiment, Fig. 9 is a schematic diagram of the structure of a display panel defect detection device based on attention potential provided by an embodiment of the present invention. Fig. 9 As shown, the device includes: a first acquisition module 910, a first conversion module 920, a second conversion module 930 and a detection module 940.

[0123] Wherein, the first acquisition module 910 is used to acquire an image set of multi-scale channels associated with the panel to be detected;

[0124] A first conversion module 920 is used to assign a quality value to each image pixel in the image set of the multi-scale channel to obtain a point quality volume set of the multi-scale channel;

[0125] A second conversion module 930 is used to construct an attention potential energy function and obtain an attention potential energy vector set of the multi-scale channel according to the point mass volume set of the multi-scale channel;

[0126] The detection module 940 is used to obtain a defect detection result of the panel to be detected based on the attention potential energy value and the attention potential energy range associated with the attention potential energy vector set of the multi-scale channel.

[0127] The technical solution of this embodiment is to obtain a point mass volume set of the multi-scale channel by assigning a mass value to each image pixel contained in each image in the image set of the multi-scale channel associated with the panel to be inspected, and to simulate the characteristics of the human eye by constructing an attention potential energy function, and to obtain an attention potential energy vector set of the multi-scale channel based on the point mass volume set of the multi-scale channel, and to obtain the defect detection result of the panel to be inspected based on the attention potential energy value associated with the attention potential energy vector set of the multi-scale channel, so that the defect detection result has a very high alignment with the human eye, thereby reducing the distortion of the detection result.

[0128] In one embodiment, the first acquisition module 910 includes:

[0129] A first acquisition unit, used to acquire an original image associated with the panel to be detected;

[0130] An image preprocessing unit, used for performing image preprocessing on the original image to obtain a related target image;

[0131] The scale separation unit is used to perform a scale separation operation on the target image to obtain an image set of multi-scale channels.

[0132] In one embodiment, the first conversion module 920 includes:

[0133] A second acquisition unit is used to acquire each image pixel of each image in the image set of the multi-scale channel;

[0134] The first determination unit is used to multiply each image pixel by the associated quality weight coefficient to obtain a point quality volume set of the multi-scale channel.

[0135] In one embodiment, the second conversion module 930 includes:

[0136] The first function construction unit is used to construct the potential energy function of a point mass body Among them, G(i,j) represents the point mass body at the pixel coordinate (i,j) within the field radius range The potential energy in the inner space; k represents the deviation length between the x-axis direction and i; l represents the deviation length between the y-axis direction and j; c represents the disturbance coefficient;

[0137] A first conversion unit is used to apply a potential energy function to a point mass volume set of a multi-scale channel to obtain a potential energy vector set of the multi-scale channel;

[0138] The second function construction unit is used to construct the attention kernel function Among them, X is the potential energy vector of the multi-scale channel after the potential energy function conversion, and M is a state observation table that describes the state that meets the subjective requirements of the human eye;

[0139] The third function construction unit is used to apply the attention kernel function to the potential energy function to obtain the attention potential energy function.

[0140] The second conversion unit is used to apply the attention potential energy function to the multi-scale channel potential energy vector set to obtain the attention potential energy vector set of the multi-scale channel.

[0141] In one embodiment, the detection module 940 includes:

[0142] A processing unit, used for performing adaptive threshold processing on the attention potential energy vector set of the multi-scale channel to obtain the attention potential energy value and attention potential energy range of the multi-scale channel;

[0143] An inverse transformation unit, used to perform a local inverse transformation on the attention potential energy value and the attention potential energy range of the multi-scale channel to obtain the physical intensity value and the physical intensity range of the multi-scale channel;

[0144] The merging unit is used to merge the physical intensity values ​​of the multi-scale channels of the same coordinates, and obtain the defect detection result of the panel to be detected based on the physical intensity range of the multi-scale channels.

[0145] In one embodiment, when a target image associated with a panel to be inspected contains singular points and singular regions, the display panel defect detection device based on attention potential energy further includes:

[0146] A second acquisition module is used to acquire a binary image mask of a singular position in the target image;

[0147] Correspondingly, the second conversion module 930 further includes:

[0148] A second determination unit is used to determine the disturbance coefficients of the image pixels associated with the singular positions based on the binary image mask and assign specific values;

[0149] The third conversion unit is used to substitute the perturbation coefficient into the attention potential energy function to obtain a set of attention potential energy vectors of the multi-scale channels excluding singular points and singular regions.

[0150] In one embodiment, the display panel defect detection device based on attention potential energy further includes:

[0151] A third acquisition module is used to acquire a preferred area in a target image associated with the panel to be detected;

[0152] A generation module is used to automatically generate an associated state observation table based on the preference area.

[0153] The display panel defect detection device based on attention potential provided in an embodiment of the present invention can execute the display panel defect detection method based on attention potential provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0154] In one embodiment, Fig.10 is a structural block diagram of an electronic device provided by an embodiment of the present invention, such as Fig.10 As shown, a schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0155] like Fig.10 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0156] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0157] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the display panel defect detection method based on attention potential.

[0158] In some embodiments, the display panel defect detection method based on attention potential can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the display panel defect detection method based on attention potential described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the display panel defect detection method based on attention potential in any other appropriate manner (for example, by means of firmware).

[0159] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0160] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0161] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0162] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0163] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0164] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0165] An embodiment of the present invention also provides a computer program product, including a computer program, which, when executed by a processor, can implement a display panel defect detection method based on attention potential as provided in any embodiment of the present application.

[0166] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present application, and the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0167] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0168] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A display panel defect detection method based on attention potential, characterized in that: include: Acquire an image set of multi-scale channels associated with the panel to be detected; Assigning a quality value to each image pixel in the image set of the multi-scale channel to obtain a point quality volume set of the multi-scale channel; Constructing an attention potential energy function, and obtaining an attention potential energy vector set of the multi-scale channel according to the point mass volume set of the multi-scale channel; The defect detection result of the panel to be inspected is obtained based on the attention potential energy value and the attention potential energy range associated with the attention potential energy vector set of the multi-scale channel.

2. The method according to claim 1, characterized in that The step of acquiring an image set of multi-scale channels associated with the panel to be detected includes: Obtaining an original image associated with the panel to be inspected; Performing image preprocessing on the original image to obtain a related target image; A scale separation operation is performed on the target image to obtain an image set of multi-scale channels.

3. The method according to claim 1, characterized in that The step of assigning a quality value to each image pixel in the image set of the multi-scale channel to obtain a point quality volume set of the multi-scale channel includes: Obtain each image pixel of each image in the image set of the multi-scale channel; Each of the image pixels is multiplied by the associated quality weight coefficient to obtain a point mass volume set of a multi-scale channel.

4. The method according to claim 1, characterized in that: Constructing an attention potential energy function, and obtaining an attention potential energy vector set of the multi-scale channel according to the point mass volume set of the multi-scale channel, including: Constructing the potential energy function of a point mass Among them, G(i,j) represents the point mass body at the pixel coordinate (i,j) within the field radius range The potential energy in the inner space; k represents the deviation length between the x-axis direction and i; l represents the deviation length between the y-axis direction and j; C represents the disturbance coefficient; Applying the potential energy function to the point mass volume set of the multi-scale channel to obtain a potential energy vector set of the multi-scale channel; Constructing the attention kernel function Wherein, X is the potential energy vector of the multi-scale channel after the potential energy function is converted, and M is a state observation table that describes the state that meets the subjective requirements of the human eye; Apply the attention kernel function to the potential energy function to obtain the attention potential energy function The attention potential energy function is applied to the multi-scale channel potential energy vector set to obtain the attention potential energy vector set of the multi-scale channel.

5. The method according to claim 1, characterized in that Obtaining a defect detection result of the panel to be detected based on the attention potential energy value and the attention potential energy range associated with the attention potential energy vector set of the multi-scale channel, including: Performing adaptive threshold processing on the attention potential energy vector set of the multi-scale channel to obtain the attention potential energy value and attention potential energy range of the multi-scale channel; Performing a local inverse transformation on the attention potential energy value and the attention potential energy range of the multi-scale channel to obtain the physical intensity value and the physical intensity range of the multi-scale channel; The physical intensity values ​​of the multi-scale channels at the same coordinates are merged, and the defect detection result of the panel to be detected is obtained based on the physical intensity range of the multi-scale channels.

6. The method according to claim 4, characterized in that In the case where the target image associated with the panel to be inspected contains singular points and singular areas, the method further includes: Obtaining a binary image mask of singular points and singular regions in the target image; Correspondingly, the construction of the attention potential energy function and obtaining the attention potential energy vector set of the multi-scale channel according to the point mass volume set of the multi-scale channel also includes: Determine disturbance coefficients of image pixels associated with the singular points and singular regions based on the binary image mask and assign specific values; Substituting the perturbation coefficient into the attention potential energy function, a set of attention potential energy vectors of the multi-scale channels excluding the singular points and singular regions is obtained.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Acquire a preferred area in a target image associated with the panel to be inspected; An associated state observation table is automatically generated based on the preference area.

8. A display panel defect detection device based on attention potential, characterized in that: include: A first acquisition module is used to acquire an image set of multi-scale channels associated with the panel to be detected; A first conversion module is used to assign a quality value to each image pixel in the image set of the multi-scale channel to obtain a point quality volume set of the multi-scale channel; A second conversion module is used to construct an attention potential energy function and obtain an attention potential energy vector set of the multi-scale channel according to the point mass volume set of the multi-scale channel; A detection module is used to obtain a defect detection result of the panel to be detected based on the attention potential energy value and the attention potential energy range associated with the attention potential energy vector set of the multi-scale channel.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the display panel defect detection method based on attention potential as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the display panel defect detection method based on attention potential energy described in any one of claims 1 to 7 when executed.

11. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the display panel defect detection method based on attention potential according to any one of claims 1 to 7.

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