A method, system and electronic device for detecting dead pixels on a mobile phone screen

Abstract

The present application provides a method, system and electronic device for detecting dead pixels on a mobile phone screen, relating to the technical field of visual detection, including: evaluating the importance of detecting dead pixels on the mobile phone screen; driving the first channel for dead pixel detection to detect dead pixels on the protective layer based on the detection severity of the protective layer; detecting dead pixels on the touch layer according to the second channel for dead pixel detection based on the detection severity of the touch layer; detecting dead pixels on the display layer according to the third channel for dead pixel detection based on the detection severity of the display layer; inputting the dead pixel detection results of the protective layer, the touch layer and the display layer into the screen quality adaptation inspection channel; and generating a screen assembly instruction when the screen quality adaptation inspection result is qualified. Through the present application, the technical problem in the prior art that the accuracy of dead pixel detection on the screen is relatively low due to the lack of detection of the multi-layer structure of the screen can be solved, and the accuracy of dead pixel detection on the screen is improved through hierarchical detection.

Description

Technical Field

[0001] This application relates to the field of visual detection technology, and particularly to a method, a system and an electronic device for detecting dead pixels on a mobile phone screen. Background Art

[0002] Dead pixels usually refer to abnormal pixel points on the screen, including bright points (abnormal pixel emission), dark points (non-emitting pixel points), and dead points (completely unresponsive). Currently, methods for detecting dead pixels on a mobile phone screen capture the screen display image through a high-resolution camera or an optical sensor to detect dead pixels, bright points, and light leakage in the display layer. However, most methods only focus on one layer of the screen or take the same detection measures for different layers, which is prone to false detection or missed detection, and it is difficult to comprehensively cover all possible types of screen defects, thus affecting the detection accuracy.

[0003] In summary, in the prior art, there is a technical problem that since the detection is usually carried out for a single layer, the multi-layer structure of the screen is not covered, resulting in some dead pixels being missed, further affecting the accuracy of detecting dead pixels on the mobile phone screen. Summary of the Invention

[0004] The purpose of this application is to provide a method, a system and an electronic device for detecting dead pixels on a mobile phone screen, so as to solve the technical problem in the prior art that since the detection is usually carried out for a single layer, the multi-layer structure of the screen is not covered, resulting in some dead pixels being missed, further affecting the accuracy of detecting dead pixels on the mobile phone screen.

[0005] In view of the above problems, this application provides a method, a system and an electronic device for detecting dead pixels on a mobile phone screen.

[0006] In a first aspect, the present application provides a method for detecting bad pixels on a mobile phone screen. The method for detecting bad pixels on a mobile phone screen is implemented through a system for detecting bad pixels on a mobile phone screen. Among them, the method for detecting bad pixels on a mobile phone screen includes: evaluating the importance of bad pixel detection based on the target mobile phone screen to obtain the detection severity of the protective layer, the detection severity of the touch layer, and the detection severity of the display layer. The target mobile phone screen includes a protective layer, a touch layer, and a display layer; based on the detection severity of the protective layer, driving the first channel for bad pixel detection to perform bad pixel detection on the protective layer to obtain the bad pixel detection result of the protective layer; based on the detection severity of the touch layer, performing bad pixel detection on the touch layer according to the second channel for bad pixel detection to obtain the bad pixel detection result of the touch layer; based on the detection severity of the display layer, performing bad pixel detection on the display layer according to the third channel for bad pixel detection to obtain the bad pixel detection result of the display layer; inputting the bad pixel detection result of the protective layer, the bad pixel detection result of the touch layer, and the bad pixel detection result of the display layer into the screen quality adaptation inspection channel to obtain the screen quality adaptation inspection result; when the screen quality adaptation inspection result is qualified, generating a screen assembly instruction, and performing the assembly of the target mobile phone screen according to the screen assembly instruction.

[0007] In a second aspect, the present application further provides a system for detecting bad pixels on a mobile phone screen, which is used to execute the method for detecting bad pixels on a mobile phone screen as described in the first aspect. Among them, the system for detecting bad pixels on a mobile phone screen includes: an importance evaluation module, which is used to evaluate the importance of bad pixel detection based on the target mobile phone screen to obtain the detection severity of the protective layer, the detection severity of the touch layer, and the detection severity of the display layer. The target mobile phone screen includes a protective layer, a touch layer, and a display layer; a first detection module, which is used to drive the first channel for bad pixel detection to perform bad pixel detection on the protective layer based on the detection severity of the protective layer to obtain the bad pixel detection result of the protective layer; a second detection module, which is used to perform bad pixel detection on the touch layer according to the second channel for bad pixel detection based on the detection severity of the touch layer to obtain the bad pixel detection result of the touch layer; a third detection module, which is used to perform bad pixel detection on the display layer according to the third channel for bad pixel detection based on the detection severity of the display layer to obtain the bad pixel detection result of the display layer; an adaptation verification module, which is used to input the bad pixel detection result of the protective layer, the bad pixel detection result of the touch layer, and the bad pixel detection result of the display layer into the screen quality adaptation inspection channel to obtain the screen quality adaptation inspection result; an assembly instruction generation module, which is used to generate a screen assembly instruction when the screen quality adaptation inspection result is qualified, and perform the assembly of the target mobile phone screen according to the screen assembly instruction.

[0008] In a third aspect, the present application also provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the steps of the method for detecting dead pixels on a mobile phone screen according to any one of the above first aspects.

[0009] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] By evaluating the importance of dead pixel detection for the target mobile phone screen, obtaining the detection severity of the protective layer, the detection severity of the touch layer, and the detection severity of the display layer, where the target mobile phone screen includes a protective layer, a touch layer, and a display layer; based on the detection severity of the protective layer, driving the first dead pixel detection channel to detect dead pixels on the protective layer to obtain the dead pixel detection result of the protective layer; based on the detection severity of the touch layer, detecting dead pixels on the touch layer according to the second dead pixel detection channel to obtain the dead pixel detection result of the touch layer; based on the detection severity of the display layer, detecting dead pixels on the display layer according to the third dead pixel detection channel to obtain the dead pixel detection result of the display layer; inputting the dead pixel detection result of the protective layer, the dead pixel detection result of the touch layer, and the dead pixel detection result of the display layer into the screen quality adaptation inspection channel to obtain the screen quality adaptation inspection result; when the screen quality adaptation inspection result is qualified, generating a screen assembly instruction, and executing the assembly of the target mobile phone screen according to the screen assembly instruction. That is to say, by evaluating the importance of detection for the three levels of the target mobile phone screen, determining the detection severity of each level, detecting dead pixels on the corresponding level using a dedicated detection channel according to the detection severity of each level, inputting the dead pixel detection results of the three levels into a comprehensive inspection channel to obtain the inspection result, and generating a screen assembly instruction to assemble the target mobile phone screen when the inspection result is qualified, the accuracy of dead pixel detection is improved, thereby improving the overall quality of the mobile phone screen.

[0011] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the following specifically gives the specific embodiments of the present application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] To more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are merely exemplary, and for those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0013] Figure 1 It is a schematic flowchart of a method for detecting dead pixels on a mobile phone screen according to the present application;

[0014] Figure 2 It is a schematic structural diagram of a system for detecting dead pixels on a mobile phone screen according to the present application;

[0015] Figure 3 It is a schematic structural diagram of an exemplary electronic device according to the present application.

[0016] Explanation of reference numerals: Importance evaluation module 11, First detection module 12, Second detection module 13, Third detection module 14, Adaptation verification module 15, Assembly instruction generation module 16, Bus 300, Receiver 301, Processor 302, Transmitter 303, Memory 304, Bus interface 305. Detailed implementation manners

[0017] By providing a method, system and electronic device for detecting dead pixels on a mobile phone screen, the present application solves the technical problem in the prior art that due to usually detecting only at a single level, the multi-layer structure of the screen is not covered, resulting in some dead pixels being missed, further affecting the accuracy of detecting dead pixels on the mobile phone screen. By evaluating the detection importance of three levels of the target mobile phone screen, determining the detection severity of each level, using dedicated detection channels to detect dead pixels of the corresponding level according to the detection severity of each level, inputting the dead pixel detection results of the three levels into a comprehensive verification channel to obtain a verification result, and generating a screen assembly instruction to assemble the target mobile phone screen when the verification result is qualified, the accuracy of dead pixel detection is improved, thereby improving the overall quality of the mobile phone screen.

[0018] Next, the technical solutions in the present application will be clearly and completely described with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application. Additionally, it should be noted that for the sake of description, only the parts related to the present application are shown in the drawings rather than all of them.

[0019] Embodiment 1, please refer to the attached Figure 1, this application provides a method for detecting dead pixels on a mobile phone screen. Among them, the method for detecting dead pixels on a mobile phone screen is applied to a system for detecting dead pixels on a mobile phone screen. The method for detecting dead pixels on a mobile phone screen specifically includes the following steps:

[0020] S100: Evaluate the importance of dead pixel detection based on the target mobile phone screen to obtain the detection severity of the protective layer, the detection severity of the touch layer, and the detection severity of the display layer. The target mobile phone screen includes a protective layer, a touch layer, and a display layer.

[0021] Specifically, the target mobile phone screen includes a protective layer, a touch layer, and a display layer. Among them, the protective layer refers to the outermost layer of the screen, which is responsible for resisting external physical damage (such as scratches and impacts) and protecting internal components from physical damage or contamination; the touch layer is located under the protective layer and senses the capacitive signals of user touches and converts them into input instructions; the display layer is located under the touch layer and is used to present visual information (images, text, etc.), usually composed of a liquid crystal or OLED screen. Evaluate the importance of dead pixel detection for the three layers of the target mobile phone screen, usually according to the specific functions and defects of each layer and their impact on the overall quality.

[0022] Evaluate the importance of dead pixel detection based on the degree of damage to the main functions of the screen by dead pixels (such as display effect or touch sensitivity), the probability of dead pixels appearing, the cost of repair or replacement, etc., to obtain the detection severity of each layer. The detection severity of the protective layer refers to the degree of emphasis on the defects of the protective layer during dead pixel detection. The higher the detection severity of the protective layer, the more image data of the protective layer is collected; the detection severity of the touch layer refers to the degree of emphasis on the defects of the touch layer during dead pixel detection. The greater the detection severity of the touch layer, the more touch operations are performed; the detection severity of the display layer refers to the degree of emphasis on the defects of the display layer during dead pixel detection. The greater the detection severity of the display layer, the more image data is collected for the display layer. Through the evaluation of the importance of dead pixel detection, accurately evaluate the overall quality of the mobile phone screen, and conduct targeted dead pixel detection according to the importance of different layers, which helps to improve the accuracy and efficiency of detection.

[0023] S200: Based on the detection severity of the protective layer, drive the first channel of dead pixel detection to detect dead pixels on the protective layer and obtain the dead pixel detection result of the protective layer.

[0024] Furthermore, S200 of this application includes: The first channel of dead pixel detection includes a vision camera, an image filter, and a dead pixel detection model for the protective layer; based on the detection severity of the protective layer, drive the vision camera to collect images of the protective layer to obtain the collected images of the protective layer; input the collected images of the protective layer into the image filter to obtain the enhanced images of the protective layer; input the enhanced images of the protective layer into the dead pixel detection model for the protective layer to generate the dead pixel detection result of the protective layer.

[0025] Specifically, the first channel of dead pixel detection includes a vision camera, an image filter, and a protective layer dead pixel detection model. Among them, the vision camera is a high-resolution image acquisition device used to obtain image data of the mobile phone screen; the image filter is an image processing tool designed to process the acquired raw image to improve image quality and details, making it more suitable for subsequent analysis; the protective layer dead pixel detection model is a model trained based on machine learning or image analysis algorithms, used to analyze the processed image and identify dead pixels therein (such as scratches, cracks, etc.).

[0026] Based on the detection severity of the protective layer, determine how much image acquisition is required for the protective layer. If the detection severity of the protective layer is high, the vision camera will acquire more image data to ensure full coverage of all potential dead pixel areas to capture more details. The vision camera usually uses high-resolution CCD or CMOS sensors, combined with appropriate light sources and shooting angles, to ensure that the details on the surface of the protective layer can be clearly captured. The image acquisition of the protective layer refers to the image data acquired by the vision camera, which reflects the state of the protective layer, including information such as possible defects, scratches, and cracks on the protective layer.

[0027] After the image is acquired, it is input into the image filter for processing. The image filter is used to eliminate noise or unnecessary interference in the image while enhancing dead pixel features. Select a suitable image filter, such as the non-local means filter. Remove noise by comparing the similarity of different regions in the image while retaining more detailed information. Calculate the similarity between different regions in the image and perform weighted average of pixel values according to the similarity, thereby achieving the effects of denoising and smoothing. Adopt the adaptive search window technique to dynamically adjust the window size according to the characteristics of the image region to obtain multiple pixel blocks. For each pixel block, calculate the similarity metric with other pixel blocks, such as the Euclidean distance. According to the similarity, compare the similarity of the current pixel point with other pixel points in its neighborhood, assign different weights and perform weighted average. The more similar pixel blocks have greater weights, and the less similar pixel blocks have smaller weights.

[0028] Take the result of the weighted average as the filtering result of the current pixel, thus achieving the effect of denoising. Since there may be many small scratches, particles, or noise on the protective layer, through the use of non-local means filtering, the details in the image (such as scratches and cracks) are retained, and the noise is effectively removed. For example, the standard deviation of the noise in the original image is 15. After NLM filtering, the standard deviation of the noise is reduced to 5, and the details of the scratches and cracks are more prominent, facilitating subsequent detection. After being processed by the non-local means filter, the enhanced image of the protective layer obtained will be clearer and have less noise than the original image, while retaining the important dead pixel features in the original image.

[0029] Collect a large number of sample data containing images of the protective layer, covering normal areas and defective pixel areas. Each image will be marked with the location or type of defective pixels. Denoise the images, such as using methods like mean filtering, bilateral filtering, or Gaussian filtering to remove background noise and retain the characteristics of defective pixels. Perform operations such as cropping, scaling, or rotating the images to enhance the generalization ability of the model. Convert the images into a format suitable for input to the deep learning model (such as RGB images of 224x224 pixels). Divide the processed image set into a training set and a validation set. Construct a convolutional neural network, including multiple convolutional layers, pooling layers, and fully connected layers. Extract image features through the convolutional layers, use the pooling layers for dimensionality reduction and to reduce the computational burden, and perform classification using the fully connected layers.

[0030] Train the convolutional neural network using the training set, and optimize the network weights using the backpropagation algorithm so that it can accurately identify defective pixels in the images. During the training process, use a loss function (such as cross-entropy loss) to measure the error between the model output and the actual labels, and update the network parameters through an optimization algorithm (such as the Adam optimizer). According to the cross-entropy loss function, calculate the cross-entropy loss and then sum it to obtain the overall loss value. Evaluate the trained model using the validation set to check its performance on unseen data, including accuracy, precision, recall, etc. Use the trained model as a defective pixel detection model for the protective layer of the mobile phone screen to detect defective pixels (such as scratches, cracks, etc.) on the protective layer. By inputting the image and analyzing its features, output whether there are defective pixels in the image and the location or type of the defective pixels.

[0031] Input the enhanced image of the protective layer processed by the graphic filter into the trained defective pixel detection model for the protective layer. Gradually extract the features in the image through multiple convolutional layers and pooling layers, and input the extracted features into the fully connected layer for classification. According to the input image, output the defective pixel detection result. If defective pixels are detected, output the location (such as a coordinate box) and type of the defective pixels; if no defective pixels are detected, output no defective pixels. Dynamically adjust the amount of image data collected according to the detection severity of the protective layer to ensure that high-severity areas obtain sufficient image coverage, thereby effectively improving the comprehensiveness of defective pixel detection. Use an image filter to process the collected images, effectively remove noise and highlight the characteristics of defective pixels. Analyze the enhanced images through the trained defective pixel detection model to automatically identify defective pixels, reduce manual intervention, and improve the detection efficiency and precision.

[0032] S300: Based on the detection severity of the touch layer, perform defective pixel detection on the touch layer according to the second channel of defective pixel detection to obtain the touch layer defective pixel detection result.

[0033] Further, S300 of the present application includes: the second channel for detecting bad pixels includes a touch robot and a touch layer bad pixel recognition model; based on the detection severity of the touch layer, the touch robot performs multiple touch operations on the touch layer to obtain multiple sets of touch response data; the multiple sets of touch response data are input into the touch layer bad pixel recognition model to obtain multiple sets of touch layer bad pixel recognition results; the multiple sets of touch layer bad pixel recognition results are fused to generate the touch layer bad pixel detection result.

[0034] Specifically, the second channel for detecting bad pixels is a channel dedicated to detecting bad pixels on the touch layer, including a touch robot and a touch layer bad pixel recognition model. The touch robot is an automated device used to simulate human touch operations. It can usually precisely perform a series of operations on the touch screen (such as clicking, swiping, pressing, etc.) and collect touch response data. The touch layer bad pixel recognition model is similar to the protective layer bad pixel detection model, both of which are used to detect and identify bad pixels. The difference is that the touch layer bad pixel recognition model is applied to the touch layer and obtains bad pixel recognition results based on touch response data.

[0035] According to the detection severity of the touch layer of the target mobile phone screen, determine how many touch operations need to be performed. The greater the touch layer detection severity, the more potential problems exist in the touch layer or the higher the precision of detection required. Therefore, the touch robot needs to perform more touch operations. The touch robot performs a series of touch operations according to the touch layer detection severity, including clicking, swiping, long pressing, etc. Each touch operation records touch response data, reflecting the reaction of the touch layer. Touch response data refers to the response data collected by the touch robot during touch operations, including information such as the position, force, and response time of the touch operation.

[0036] The multiple sets of touch response data obtained are input into the trained touch layer bad pixel recognition model. The model analyzes based on the response data to identify whether there are bad pixels (such as dead pixels, ineffective pixels, etc.) on the touch layer. The touch layer bad pixel recognition model is a model based on machine learning or deep learning, dedicated to detecting and identifying bad pixels (such as dead pixels, drift pixels, etc.) on the touch layer of a mobile phone screen. By analyzing touch response data, it determines whether there are defects in the touch layer and identifies the types or positions of these defects.

[0037] The training process of the touch layer dead pixel recognition model is similar to that of the protective layer dead pixel detection model. For the sake of brevity in the specification, the training process will not be repeated. The touch layer dead pixel recognition model processes each set of touch response data and finally generates the dead pixel recognition result for each set of data. Each recognition result indicates whether there is a dead pixel at the touch position. If there is a dead pixel, the coordinates or specific type of the dead pixel are usually returned. The dead pixel recognition results of multiple touch layers are fused, usually using a data fusion algorithm such as the voting method, weighted average method, maximum probability method, etc. The purpose is to improve the reliability of dead pixel detection through the integration of multiple sets of data. By integrating all the recognition results, if a certain area is recognized as a dead pixel in multiple operations, then this area is more likely to actually have a dead pixel. The result of each touch operation is either a dead pixel or normal. After multiple touch operations, using the voting method, the point with the most votes is the dead pixel.

[0038] Based on the fused data, the final dead pixel detection result is generated, including the position, type (such as dead point, drift point, etc.) of each dead pixel and the possible influence range. According to the fused information, a final dead pixel detection report is formed, which may include detailed data such as specific dead pixel coordinates, types, areas, etc., for subsequent processing or screen repair. By fusing the results of multiple touch operations, it is possible to effectively eliminate the misjudgment caused by accidental errors or the deficiencies of a single touch operation. For example, if a certain position is shown as a dead pixel only in one operation and normal in other multiple touch operations, then this position will be recognized as normal.

[0039] Through multiple touch operations and data fusion, the error caused by a single operation can be reduced, and the accuracy of dead pixel detection can be significantly improved. Even if there is an error in a certain touch response data, other data can make up for it, and finally a more reliable dead pixel detection result can be obtained. The setting of the touch layer detection severity enables the detection to be flexibly adjusted to meet the detection requirements of different screens.

[0040] S400: Based on the display layer detection severity, perform dead pixel detection on the display layer according to the third channel of dead pixel detection to obtain the display layer dead pixel detection result.

[0041] Specifically, adjust the parameters of the third channel of dead pixel detection (including the camera device, image intensifier, and K mode display dead pixel recognition models) according to the display layer detection severity to achieve accurate detection of dead pixels on the display layer. According to the display layer detection severity, use the camera device to collect images of the display layer in K predetermined display modes to obtain K mode display acquisition images. The greater the display layer detection severity, the more data for image acquisition of the display layer. The K mode display acquisition images include the acquisition images corresponding to the K predetermined display modes.

[0042] Input the acquired images of K display modes into an image intensifier. Enhance each image through an image enhancement function. As a result, the contrast of the images is improved, the brightness differences in the images are amplified, and bad points (such as dead pixels or bright points) become more obvious. Obtain K enhanced images from the image intensifier, corresponding to different display modes respectively. Input these enhanced images into K bad point recognition models for different display modes. Each model is specifically for one display mode. The model will judge whether there are bad points, as well as the positions and types of the bad points according to the features in the images. By using K different bad point recognition models for different display modes respectively, detection can be carried out under different display modes, and K bad point recognition results for different display modes are obtained, including information such as the positions, types, and degrees of the bad points.

[0043] Fuse the bad point recognition results of K display modes to obtain a unified bad point detection result, including bad point positions, bad point types, the number of bad points, and the final judgment, etc. The bad point positions are the coordinates of the bad points after fusion, usually the coordinate positions of pixels, or the bounding boxes of areas containing multiple bad points; the bad point types such as bright points, dead pixels, color blocks, etc.; the number of bad points is the total number of bad points detected in the entire display layer; the final judgment refers to whether it is considered that there are serious defects on the screen. If there are bad points, output the detailed positions and types of the bad points; if there are no bad points, output normal. By using multiple display modes, different types of bad points are detected, improving the detection accuracy.

[0044] S500: Input the bad point detection results of the protection layer, the bad point detection results of the touch layer, and the bad point detection results of the display layer into the screen quality adaptation inspection channel to obtain the screen quality adaptation inspection result.

[0045] Specifically, the screen quality adaptation inspection channel is an evaluation channel that integrates multiple bad point detection results and is used for the quality adaptation inspection of the mobile phone screen, including a screen quality adaptation evaluator and a screen quality adaptation inspector. The screen quality adaptation evaluator is used to evaluate the quality of each layer of the screen and calculate the adaptation coefficient. The screen quality adaptation inspector judges whether the screen is qualified by setting a quality adaptation evaluation threshold. Input the bad point detection results of the protection layer, the bad point detection results of the touch layer, and the bad point detection results of the display layer into the screen quality adaptation evaluator of the screen quality adaptation inspection channel for screen quality evaluation to obtain the adaptation coefficient of each layer, that is, the protection layer quality adaptation coefficient, the touch layer quality adaptation coefficient, and the display layer quality adaptation coefficient. Perform weighted calculation according to the protection layer quality adaptation coefficient, the touch layer quality adaptation coefficient, the display layer quality adaptation coefficient, and the corresponding weights to obtain the screen quality adaptation evaluation coefficient. Different weights can be assigned to the bad points of each layer according to their influence degrees on the screen quality.

[0046] Input the screen quality adaptation evaluation coefficient into the screen quality adaptation checker for verification to determine whether the screen quality meets the standard. Compare the input screen quality adaptation evaluation coefficient with the set threshold value to generate the final inspection result. The set threshold value is the threshold for judging whether the screen quality is qualified, usually the lower limit of the screen quality adaptation evaluation coefficient. If the screen quality adaptation evaluation coefficient is greater than or equal to the screen quality adaptation evaluation threshold, the screen quality adaptation inspection result is qualified; otherwise, it is unqualified. By comprehensively evaluating the bad point detection results of each layer, screens with relatively serious problems can be efficiently screened out, improving the screening efficiency. By integrating the bad point detection results of multiple levels, the screen quality evaluation becomes more comprehensive and accurate, avoiding potential problems in other levels being missed by single-level bad point detection.

[0047] S600: When the screen quality adaptation inspection result is qualified, generate a screen assembly instruction, and execute the assembly of the target mobile phone screen according to the screen assembly instruction.

[0048] Specifically, when the screen quality adaptation inspection result is qualified, that is, the screen quality adaptation evaluation coefficient is greater than or equal to the screen quality adaptation evaluation threshold, it means that all levels of the screen meet the quality standards and there are no bad points or defects that affect use. According to the screen quality adaptation inspection result, a screen assembly instruction is generated, which can be a digital signal or a physical instruction, instructing the robot or manual operator on the production line to perform the hierarchical assembly of the screen in a certain order. According to the assembly instruction, the equipment on the production line (such as robots, automated robotic arms, etc.) will start the actual mobile phone screen assembly work. At this time, each component of the screen - the protective layer, the touch layer, the display layer, etc. - will be combined according to the predetermined process to complete the screen assembly process. By automatically generating and executing the screen assembly instruction, and passing the screen quality adaptation inspection, only qualified screens will enter the assembly link, avoiding unqualified screens from being wrongly assembled into the final product and reducing the rework and scrap rates.

[0049] Furthermore, S400 of this application includes: The third channel for bad point detection includes a camera device, an image intensifier, and K model display bad point recognition models corresponding to K predetermined display modes, where K is a positive integer greater than 1; based on the detection of the display layer severity and the camera device, image acquisition of the display layer is performed according to the K predetermined display modes to obtain K model display acquisition images; input the K model display acquisition images into the image intensifier to obtain K model display enhanced images; input the K model display enhanced images into the K model display bad point recognition models to output K model display bad point recognition results; perform data fusion according to the K model display bad point recognition results to generate the bad point detection result of the display layer.

[0050] Specifically, the third channel for dead pixel detection is used for dead pixel detection of the display layer, including three main components: a camera device, an image intensifier, and K dead pixel recognition models corresponding to K predetermined display modes. The camera device is a device for image acquisition, usually a high-resolution camera, which is used to capture the image data of the screen, can capture the details on the screen, and can capture the images of the display layer in different display modes. The image intensifier processes the images acquired from the camera device to enhance the clarity and contrast of the images, making the dead pixels more obvious.

[0051] The K predetermined display modes refer to various display modes used for detecting dead pixels of the display layer (such as solid color display mode, grayscale mode, grid mode, etc.). Each display mode helps to identify different types of display problems (such as color difference, dead pixels, bright pixels, etc.) through different color or grayscale distributions. K represents the number of predetermined modes, and K is a positive integer greater than 1. Usually, 5 to 10 different modes are selected. Common display modes usually include but are not limited to: solid color display mode (such as black, white, red, green, blue, a single color tone helps to detect color difference and color block dead pixels), grayscale display mode (displaying the gradual change from black to white, helping to detect bright pixels, dead pixels, and color difference, etc.), grid display mode (displaying a uniform grid on the screen, helping to detect the brightness and distribution of pixel points), color gradient mode (displaying the gradual change from one color to another, which can reveal abnormal color changes on the screen).

[0052] The K dead pixel recognition models for mode display perform dead pixel detection using trained deep learning models (such as convolutional neural networks) for different display modes. Each model is trained for one display mode, can identify the dead pixels in that mode, and output the recognition results.

[0053] According to the severity of display layer detection, the camera device performs multiple image acquisitions on the target display layer, and each acquisition corresponds to a different display mode. The number of image acquisitions depends on the severity of display layer detection. If the severity is high, it indicates a higher demand for detecting dead pixels on the display layer, and the number of images acquired by the camera device will also be more. For example, using a high-resolution camera (such as a 1080p or 4K resolution camera) to capture the details of the screen, especially in different display modes, to capture dead pixels. The camera device performs image acquisition on the display layer through K predefined display modes according to the severity of display layer detection, and obtains K-mode display acquisition images. The K-mode display acquisition images are input into an image intensifier, and the image intensifier processes the images according to a predefined enhancement algorithm (image enhancement function), making the dead pixels on the screen more prominent. Each input image is enhanced through the image enhancement function to obtain K-mode display enhanced images. After being processed by the image enhancement function, the details in the original image are strengthened, especially the changes in brightness and contrast become more obvious, and the enhanced images will be used for subsequent dead pixel detection.

[0054] The K-mode display enhanced images are input into the corresponding K-mode display dead pixel recognition models for dead pixel recognition. The models identify the presence of dead pixels based on the image features (such as color block changes, gray level changes, brightness changes, etc.) in each display mode. The K-mode display dead pixel recognition models refer to the dead pixel detection models used in each mode, usually convolutional neural networks or other deep learning algorithms, which are specifically trained for specific display modes. For example, in the black mode, the convolutional neural network detects multiple black bright spots in a certain area on the screen and marks them as dead pixels; in the grid mode, the model identifies the failure area at the intersection of the grid lines and marks it as a dead pixel.

[0055] The K-mode display dead pixel recognition models acquire images corresponding to each display mode. Each model will learn how to distinguish normal pixels and dead pixels based on the input images. The specific training process is similar to the aforementioned protective layer dead pixel detection model, but there are slight differences. Each model will independently analyze the images and output the recognition results, including the dead pixel position, dead pixel type, dead pixel degree, existing mode, etc. The K-mode display dead pixel recognition results are the output results obtained after being processed by the models, usually a judgment on whether there are dead pixels or the position and type of dead pixels. Each model will output the dead pixel detection results in its corresponding mode based on the input enhanced images. By using K different mode display dead pixel recognition models respectively, detection can be carried out in different display modes, which can cover more types of dead pixel types. In different modes, the display methods and manifestations of dead pixels are different. Using multiple specialized models for analysis can improve the comprehensiveness and accuracy of dead pixel detection.

[0056] Integrate the bad pixel recognition results from different modes to generate a final bad pixel detection result for the display layer. The goal of data fusion is to improve the detection accuracy by synthesizing the detection results of multiple modes, avoid missing potentially existing bad pixels, and address the false positive or false negative issues that may occur in different modes. Take the bad pixel recognition results output by the bad pixel recognition models of K modes as input. Each of these results contains the bad pixel detection situation in that mode, such as the location of the bad pixel, the type of the bad pixel (such as bright pixel, dead pixel, etc.), and the situation where bad pixels may not be detected in some modes. According to specific application requirements, select an appropriate fusion method, such as the weighted voting method (combined by voting, and the location with the most votes is considered the final bad pixel), and merge the bad pixel recognition results of each mode to generate a unified bad pixel detection result.

[0057] If bad pixels are detected at the same location in different modes, then this location is considered to have bad pixels. For example, if there are bad pixels at the same location in the black mode and the red mode, the final detection result will consider that there are bad pixels at this location. If bad pixels are detected in multiple modes, the final result will consider that there are bad pixels, even if some modes fail to detect them individually. After completing data fusion, generate the final bad pixel detection result for the display layer, including the location of the bad pixel, the type of the bad pixel, the number of bad pixels, the final determination, etc. For example, in the black mode, bright pixels are more easily recognized; in the red mode, areas with uneven color or pixel damage are more easily detected. By fusing the detection results of multiple modes, some bad pixels can be avoided from being ignored. Through data fusion, the location of the bad pixel can be accurately located, avoiding false alarms or missed detections caused by the low sensitivity of some modes to bad pixels.

[0058] Furthermore, the present application further includes the following steps: The image intensifier includes an image enhancement function, and the image enhancement function is: where, I enh represents the pixel value of the enhanced image of the I-corresponding mode display, I represents the pixel value of the mode display acquisition image, ALV represents the average luminance value of the mode display acquisition image, SDB represents the standard deviation of the pixel luminance of the mode display acquisition image, and CEF represents the contrast enhancement coefficient.

[0059] Specifically, the image intensifier is a device or algorithm for improving image quality. By adjusting parameters such as the brightness, contrast, and sharpness of the image, key details in the image (such as bad pixels) become more obvious. The image enhancement function is the core algorithm of the image intensifier, which is used to process the original image and generate the enhanced image. The specific form of the image enhancement function is: Among them, I represents the pixel value of the acquisition image in the pattern display; ALV represents the average luminance value of the acquisition image in the pattern display, which is the average value of the luminances of all pixels in the image and is used to reflect the overall luminance level of the image; SDB characterizes the standard deviation of the pixel luminance of the acquisition image in the pattern display. The standard deviation measures the degree of dispersion of the luminance in the image and can reflect the contrast and the richness of details in the image; CEF characterizes the contrast enhancement factor, which is a factor used to enhance the contrast of the image so that the details in the image are more prominent; I enh represents the pixel value of the enhanced image. After being processed by the enhancement function, the pixel value of each pixel in the image will be adjusted to make the details in the image clearer.

[0060] Obtain K acquisition images in the pattern display, which contain the detail information of the screen in different display modes. Calculate the average luminance value (ALV) of the original image, which is the average value of the luminance values of all pixels in the image and is usually calculated by the ratio of the sum of all pixel luminance values to the total number of pixels. Calculate the standard deviation of the pixel luminance (SDB) of the original image. The standard deviation measures the distribution degree of the pixel luminance in the image and is usually obtained by calculating the square of the difference between each pixel luminance value and the average luminance value, adding up all the squared values and then dividing by the total number of pixels to get a value, and then taking the square root of this value. The contrast enhancement factor is a constant value set according to the degree of enhancement required and is usually adjusted through experiments to enhance the image contrast so that the details are more prominent. For example, assume that the pixel values of an image are distributed between 0 and 255, representing the gray levels from black to white, the average luminance value of the image is 100, the standard deviation of the pixel luminance is 20, and the contrast enhancement factor is set to 1.5. If the original luminance value of a certain pixel is 120, then the new luminance value of this pixel after enhancement is 1.5, indicating that the luminance value of this pixel becomes 1.5 after enhancement. After appropriate scaling and adjustment, the details in the image are more prominent.

[0061] By using the contrast enhancement factor, the contrast of the image is significantly improved, making details (such as dead pixels) more prominent; through the processing of the image enhancement function, the luminance difference in the image will be amplified, helping the detection model to more easily identify dead pixels. Especially for those dead pixels with lower luminance or weaker contrast, image enhancement can make these defects more obvious; by improving the clarity and contrast of the image, the model can better identify dead pixels and reduce false positives and false negatives.

[0062] Further, S500 of the present application includes: the screen quality adaptation inspection channel includes a screen quality adaptation evaluator and a screen quality adaptation inspector; input the protective layer dead pixel detection result, the touch layer dead pixel detection result, and the display layer dead pixel detection result into the screen quality adaptation evaluator to obtain a screen quality adaptation evaluation coefficient; input the screen quality adaptation evaluation coefficient into the screen quality adaptation inspector to output the screen quality adaptation inspection result, where the screen quality adaptation inspector includes a screen quality inspection operator, and the screen quality inspection operator includes that if the screen quality adaptation evaluation coefficient is greater than or equal to the screen quality adaptation evaluation threshold, the screen quality adaptation inspection result is qualified, and if the screen quality adaptation evaluation coefficient is less than the screen quality adaptation evaluation threshold, the screen quality adaptation inspection result is unqualified.

[0063] Specifically, the screen quality adaptation inspection channel is a channel for comprehensively evaluating the quality of the mobile phone screen and performing adaptability inspection based on the detection results. It includes a screen quality adaptation evaluator and a screen quality adaptation inspector, which can evaluate and verify the quality of the screen to ensure that the screen meets the quality standards. The screen quality adaptation evaluator is a tool for evaluating the overall quality of the mobile phone screen. According to the dead pixel detection results of different layers (protective layer, touch layer, display layer), a comprehensive screen quality adaptation evaluation coefficient is calculated, including two sub-models, namely the screen quality multi-dimensional evaluation model and the screen quality adaptation calculation model. The screen quality multi-dimensional evaluation model is responsible for evaluating the quality of the three key layers of the mobile phone screen, including the protective layer quality evaluation model, the touch layer quality evaluation model, and the display layer quality evaluation model, which respectively evaluate the quality of the protective layer, the touch layer, and the display layer and output the corresponding quality evaluation coefficients. The screen quality adaptation calculation model calculates the screen quality adaptation evaluation coefficient based on the quality evaluation coefficients output by the screen quality multi-dimensional evaluation model, including three weights, namely the protective layer quality adaptation weight, the touch layer quality adaptation weight, and the display layer quality adaptation weight.

[0064] Input the protective layer dead pixel detection result, the touch layer dead pixel detection result, and the display layer dead pixel detection result into the corresponding models of each layer in the screen quality multi-dimensional evaluation model in the screen quality adaptation evaluator for quality evaluation to obtain the protective layer quality adaptation coefficient, the touch layer quality adaptation coefficient, and the display layer quality adaptation coefficient, and input them into the screen quality adaptation calculation model for weighted calculation, that is, multiply the quality adaptation coefficient of each layer by its corresponding weight and add the three values to obtain the screen quality adaptation evaluation coefficient.

[0065] Input the screen quality adaptation evaluation coefficient into the screen quality adaptation checker for the final quality pass judgment. The screen quality adaptation checker includes a screen quality inspection operator for determining whether the screen quality meets the standard. By comparing the input screen quality adaptation evaluation coefficient with the set threshold, a final inspection result is generated. The screen quality adaptation evaluation threshold is a pre-set threshold used to determine whether the screen quality adaptation evaluation coefficient is qualified and serves as the minimum standard.

[0066] If the screen quality adaptation evaluation coefficient is greater than or equal to the threshold, the screen is judged as qualified; if the screen quality adaptation evaluation coefficient is less than the threshold, the screen is judged as unqualified. Through the combination of the screen quality adaptation evaluator and the screen quality adaptation checker, an objective and standardized evaluation of the screen quality can be achieved, enabling the rapid screening out of screens that do not meet the quality standards and eliminating them, thereby improving the efficiency of the production line and reducing the scrap rate.

[0067] Furthermore, the present application further includes the following steps: The screen quality adaptation evaluator includes a screen quality multi-dimensional evaluation model and a screen quality adaptation calculation model. Among them, the screen quality multi-dimensional evaluation model includes a protective layer quality evaluation model, a touch layer quality evaluation model, and a display layer quality evaluation model. The screen quality adaptation calculation model includes a protective layer quality adaptation weight, a touch layer quality adaptation weight, and a display layer quality adaptation weight; Input the protective layer dead pixel detection result, the touch layer dead pixel detection result, and the display layer dead pixel detection result into the screen quality multi-dimensional evaluation model to obtain a protective layer quality evaluation coefficient, a touch layer quality evaluation coefficient, and a display layer quality evaluation coefficient; According to the protective layer quality evaluation coefficient, the touch layer quality evaluation coefficient, and the display layer quality evaluation coefficient, perform subtraction operations on the protective layer quality expectation coefficient, the touch layer quality expectation coefficient, and the display layer quality expectation coefficient to obtain a protective layer quality adaptation coefficient, a touch layer quality adaptation coefficient, and a display layer quality adaptation coefficient; Input the protective layer quality adaptation coefficient, the touch layer quality adaptation coefficient, and the display layer quality adaptation coefficient into the screen quality adaptation calculation model to obtain the screen quality adaptation evaluation coefficient.

[0068] Specifically, the screen quality adaptation evaluator is used to evaluate the quality of each layer of the screen and calculate the adaptation coefficient, including a multi-dimensional screen quality evaluation model and a screen quality adaptation calculation model. The multi-dimensional screen quality evaluation model is a quality evaluation model that comprehensively considers the quality of each component of the mobile phone screen (such as the protective layer, touch layer, display layer), independently evaluates the quality of each layer, and obtains the quality evaluation coefficient of each layer through multi-dimensional evaluation. The multi-dimensional screen quality evaluation model includes a protective layer quality evaluation model, a touch layer quality evaluation model, and a display layer quality evaluation model, which are respectively used to evaluate the quality of the protective layer, touch layer, and display layer of the mobile phone screen. The screen quality adaptation calculation model is a model used to calculate the overall screen quality adaptability based on the quality evaluation results of each layer, including the importance weights of each layer (protective layer, touch layer, display layer) in the screen quality adaptation calculation. Different layers may have different impacts on the overall screen quality. Therefore, when calculating the adaptation coefficient, different weights need to be given according to the quality evaluation results of each layer.

[0069] Input the bad point detection results of the protective layer, touch layer, and display layer into the multi-dimensional screen quality evaluation model. That is, input the bad point detection result of the protective layer into the protective layer quality evaluation model, input the bad point detection result of the touch layer into the touch layer quality evaluation model, and input the bad point detection result of the display layer into the display layer quality evaluation model. The multi-dimensional screen quality evaluation model will evaluate the quality of each layer according to the bad point detection results of each layer.

[0070] Taking the protective layer as an example, the protective layer quality evaluation model calculates the quality evaluation coefficient of the protective layer based on the input bad point detection results. If the detected number of bad points is large, the quality evaluation coefficient will be low. The protective layer quality evaluation model will preset a threshold, and the number of bad points exceeding the threshold will significantly reduce the quality coefficient; bad points located in the center of the screen have a greater impact than those on the edge; different types of bad points (such as bright points, dead pixels) also have different impacts on the quality evaluation, and corresponding weights are set according to the bad point type. The protective layer quality evaluation model performs weighted calculation based on the above three factors and finally obtains the protective layer quality evaluation coefficient. After the calculation is completed, the protective layer quality evaluation model outputs a quality evaluation coefficient, indicating the overall quality level of the protective layer, usually between 0 (worst quality) and 100 (best quality). Preset the protective layer quality expectation coefficient as the quality value in the ideal state of the screen layer. Subtract the protective layer quality expectation coefficient from the protective layer quality evaluation coefficient to obtain the protective layer quality adaptation coefficient.

[0071] And so on, the above steps are also executed for the touch layer and the display layer to obtain the corresponding touch layer quality evaluation coefficient and display layer quality evaluation coefficient. The touch layer quality expectation coefficient and display layer quality expectation coefficient under ideal conditions are preset in advance, and through subtraction operations, the touch layer quality adaptation coefficient and display layer quality adaptation coefficient are obtained. The quality adaptation coefficients of each layer are input into the screen quality adaptation calculation model according to their corresponding weights for weighted calculation to obtain the final screen quality adaptation evaluation coefficient. That is to say, the screen quality adaptation evaluation coefficient = protection layer quality adaptation coefficient × protection layer quality adaptation weight + touch layer quality adaptation coefficient × touch layer quality adaptation weight + display layer quality adaptation coefficient × display layer quality adaptation weight. By combining the dead pixel detection results of each layer for multi-dimensional quality evaluation, the overall quality of the mobile phone screen can be comprehensively reflected, not only considering the dead pixels of a single layer, but also the impact of different layers on the screen quality. By introducing weighted calculation, reasonable evaluation can be carried out according to the quality impact weight of each layer. The weighted calculation ensures that more important layers (such as the display layer) can account for a larger proportion in the final result, thereby improving the accuracy of the inspection results.

[0072] In summary, a mobile phone screen dead pixel detection method provided by the present application has the following technical effects:

[0073] By evaluating the importance of dead pixel detection for the target mobile phone screen, the protection layer detection severity, touch layer detection severity, and display layer detection severity are obtained. The target mobile phone screen includes a protection layer, a touch layer, and a display layer. Based on the protection layer detection severity, the first dead pixel detection channel is driven to perform dead pixel detection on the protection layer to obtain the protection layer dead pixel detection result. Based on the touch layer detection severity, the second dead pixel detection channel is used to perform dead pixel detection on the touch layer to obtain the touch layer dead pixel detection result. Based on the display layer detection severity, the third dead pixel detection channel is used to perform dead pixel detection on the display layer to obtain the display layer dead pixel detection result. The protection layer dead pixel detection result, the touch layer dead pixel detection result, and the display layer dead pixel detection result are input into the screen quality adaptation inspection channel to obtain the screen quality adaptation inspection result. When the screen quality adaptation inspection result is qualified, a screen assembly instruction is generated, and the assembly of the target mobile phone screen is performed according to the screen assembly instruction. That is to say, by evaluating the importance of detection for the three levels of the target mobile phone screen, determining the detection severity of each level, using dedicated detection channels to detect the dead pixels of the corresponding level according to the detection severity of each level, inputting the dead pixel detection results of the three levels into a comprehensive inspection channel to obtain the inspection result, and generating a screen assembly instruction to assemble the target mobile phone screen when the inspection result is qualified, the accuracy of dead pixel detection is improved, thereby improving the overall quality of the mobile phone screen.

[0074] Embodiment 2. Based on the same inventive concept as the mobile phone screen dead pixel detection method in the foregoing Embodiment 1, the present application also provides a mobile phone screen dead pixel detection system. Please refer to the attached Figure 2 , the mobile phone screen dead pixel detection system includes:

[0075] An importance evaluation module 11, which is used to evaluate the importance of dead pixel detection according to the target mobile phone screen, and obtain the detection severity of the protective layer, the detection severity of the touch layer, and the detection severity of the display layer. The target mobile phone screen includes a protective layer, a touch layer, and a display layer; a first detection module 12, which is used to drive the first dead pixel detection channel to detect dead pixels on the protective layer based on the detection severity of the protective layer, and obtain a protective layer dead pixel detection result; a second detection module 13, which is used to detect dead pixels on the touch layer according to the second dead pixel detection channel based on the detection severity of the touch layer, and obtain a touch layer dead pixel detection result; a third detection module 14, which is used to detect dead pixels on the display layer according to the third dead pixel detection channel based on the detection severity of the display layer, and obtain a display layer dead pixel detection result; an adaptation verification module 15, which is used to input the protective layer dead pixel detection result, the touch layer dead pixel detection result, and the display layer dead pixel detection result into the screen quality adaptation verification channel to obtain a screen quality adaptation verification result; an assembly instruction generation module 16, which is used to generate a screen assembly instruction when the screen quality adaptation verification result is qualified, and execute the assembly of the target mobile phone screen according to the screen assembly instruction.

[0076] Further, the first detection module 12 in the mobile phone screen dead pixel detection system is further used for: the first dead pixel detection channel includes a vision camera, an image filter, and a protective layer dead pixel detection model; based on the detection severity of the protective layer, drive the vision camera to collect an image of the protective layer to obtain a protective layer collected image; input the protective layer collected image into the image filter to obtain a protective layer enhanced image; input the protective layer enhanced image into the protective layer dead pixel detection model to generate the protective layer dead pixel detection result.

[0077] Further, the second detection module 13 in the mobile phone screen dead pixel detection system is further used for: the second dead pixel detection channel includes a touch control robot and a touch layer dead pixel recognition model; based on the detection severity of the touch layer, perform multiple touch control operations on the touch layer according to the touch control robot to obtain multiple groups of touch response data; input the multiple groups of touch response data into the touch layer dead pixel recognition model to obtain multiple groups of touch layer dead pixel recognition results; fuse the multiple groups of touch layer dead pixel recognition results to generate the touch layer dead pixel detection result.

[0078] Furthermore, the third detection module 14 in the mobile phone screen dead pixel detection system is further configured to: The dead pixel detection third channel includes a camera device, an image intensifier, and K model display dead pixel recognition models corresponding to K predetermined display modes, where K is a positive integer greater than 1; Based on the display layer detection severity and the camera device, image acquisition is performed on the display layer according to the K predetermined display modes to obtain K model display acquisition images; Input the K model display acquisition images into the image intensifier to obtain K model display enhanced images; Input the K model display enhanced images into the K model display dead pixel recognition models, and output K model display dead pixel recognition results; Perform data fusion according to the K model display dead pixel recognition results to generate the display layer dead pixel detection result.

[0079] Furthermore, the third detection module 14 in the mobile phone screen dead pixel detection system is further configured to: The image intensifier includes an image enhancement function, and the image enhancement function is: where I enh represents the pixel value of the model display enhanced image corresponding to I, I represents the pixel value of the model display acquisition image, ALV represents the average brightness value of the model display acquisition image, SDB represents the pixel brightness standard deviation of the model display acquisition image, and CEF represents the contrast enhancement coefficient.

[0080] Furthermore, the adaptation verification module 15 in the mobile phone screen dead pixel detection system is further configured to: The screen quality adaptation verification channel includes a screen quality adaptation evaluator and a screen quality adaptation verifier; Input the protection layer dead pixel detection result, the touch layer dead pixel detection result, and the display layer dead pixel detection result into the screen quality adaptation evaluator to obtain a screen quality adaptation evaluation coefficient; Input the screen quality adaptation evaluation coefficient into the screen quality adaptation verifier, and output the screen quality adaptation verification result, where the screen quality adaptation verifier includes a screen quality verification operator, and the screen quality verification operator includes that if the screen quality adaptation evaluation coefficient is greater than or equal to the screen quality adaptation evaluation threshold, the screen quality adaptation verification result is qualified, and if the screen quality adaptation evaluation coefficient is less than the screen quality adaptation evaluation threshold, the screen quality adaptation verification result is unqualified.

[0081] Further, the adaptation verification module 15 in the mobile phone screen dead pixel detection system is further configured to: The screen quality adaptation evaluator includes a screen quality multi-dimensional evaluation model and a screen quality adaptation calculation model. Among them, the screen quality multi-dimensional evaluation model includes a protective layer quality evaluation model, a touch layer quality evaluation model, and a display layer quality evaluation model. The screen quality adaptation calculation model includes a protective layer quality adaptation weight, a touch layer quality adaptation weight, and a display layer quality adaptation weight; Input the protective layer dead pixel detection result, the touch layer dead pixel detection result, and the display layer dead pixel detection result into the screen quality multi-dimensional evaluation model to obtain a protective layer quality evaluation coefficient, a touch layer quality evaluation coefficient, and a display layer quality evaluation coefficient; According to the protective layer quality evaluation coefficient, the touch layer quality evaluation coefficient, and the display layer quality evaluation coefficient, perform subtraction operations on the protective layer quality expected coefficient, the touch layer quality expected coefficient, and the display layer quality expected coefficient to obtain a protective layer quality adaptation coefficient, a touch layer quality adaptation coefficient, and a display layer quality adaptation coefficient; Input the protective layer quality adaptation coefficient, the touch layer quality adaptation coefficient, and the display layer quality adaptation coefficient into the screen quality adaptation calculation model to obtain the screen quality adaptation evaluation coefficient.

[0082] The various embodiments in this specification are described in a progressive manner. The key point of each embodiment is the difference from other embodiments. The Figure 1 A mobile phone screen dead pixel detection method and specific examples in Embodiment 1 are equally applicable to the mobile phone screen dead pixel detection system in this embodiment. Through the detailed description of the mobile phone screen dead pixel detection method above, those skilled in the art can clearly know the mobile phone screen dead pixel detection system in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For related parts, please refer to the description in the method part.

[0083] Embodiment 3, based on the inventive concept of the mobile phone screen dead pixel detection method in Embodiment 1 above, the present application further provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of any one of the mobile phone screen dead pixel detection methods described in Embodiment 1 above.

[0084] Appendix Figure 3 is a schematic structural diagram of an exemplary electronic device of the present application. In Figure 3Among them, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges. Bus 300 connects various circuits of one or more processors represented by processor 302 and a memory represented by memory 304 together. Bus 300 may also connect various other circuits together, such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art. Therefore, they will not be further described herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0085] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather should be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0086] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A method for detecting bad pixels on a mobile phone screen, characterized in that: include: Perform bad pixel detection importance evaluation on a target mobile phone screen to obtain a protection layer detection severity, a touch layer detection severity, and a display layer detection severity, wherein the target mobile phone screen includes a protection layer, a touch layer, and a display layer; Based on the detection severity of the protection layer, driving a first bad pixel detection channel to perform bad pixel detection on the protection layer to obtain a bad pixel detection result of the protection layer; Based on the detection severity of the touch layer, performing bad pixel detection on the touch layer according to a second bad pixel detection channel to obtain a bad pixel detection result of the touch layer; Based on the detection severity of the display layer, performing bad pixel detection on the display layer according to a third bad pixel detection channel to obtain a bad pixel detection result of the display layer; Inputting the protection layer bad pixel detection result, the touch layer bad pixel detection result and the display layer bad pixel detection result into a screen quality adaptation inspection channel to obtain a screen quality adaptation inspection result; When the screen quality adaptation inspection result is qualified, a screen assembly instruction is generated, and the assembly of the target mobile phone screen is performed according to the screen assembly instruction; Inputting the protection layer bad pixel detection result, the touch layer bad pixel detection result and the display layer bad pixel detection result into the screen quality adaptation inspection channel to obtain the screen quality adaptation inspection result, including: The screen quality adaptation inspection channel includes a screen quality adaptation evaluator and a screen quality adaptation inspector; Inputting the protection layer bad pixel detection result, the touch layer bad pixel detection result and the display layer bad pixel detection result into the screen quality adaptation evaluator to obtain a screen quality adaptation evaluation coefficient; Inputting the screen quality adaptation evaluation coefficient into the screen quality adaptation checker, and outputting the screen quality adaptation check result, wherein the screen quality adaptation checker includes a screen quality check operator, and the screen quality check operator includes: if the screen quality adaptation evaluation coefficient is greater than or equal to a screen quality adaptation evaluation threshold, the screen quality adaptation check result is qualified, and if the screen quality adaptation evaluation coefficient is less than the screen quality adaptation evaluation threshold, the screen quality adaptation check result is unqualified; Inputting the protection layer bad pixel detection result, the touch layer bad pixel detection result and the display layer bad pixel detection result into the screen quality adaptation evaluator to obtain a screen quality adaptation evaluation coefficient, including: The screen quality adaptation evaluator includes a screen quality multidimensional evaluation model and a screen quality adaptation calculation model, wherein the screen quality multidimensional evaluation model includes a protection layer quality evaluation model, a touch layer quality evaluation model, and a display layer quality evaluation model, and the screen quality adaptation calculation model includes a protection layer quality adaptation weight, a touch layer quality adaptation weight, and a display layer quality adaptation weight; Inputting the protection layer bad pixel detection result, the touch layer bad pixel detection result and the display layer bad pixel detection result into the screen quality multi-dimensional evaluation model to obtain a protection layer quality evaluation coefficient, a touch layer quality evaluation coefficient and a display layer quality evaluation coefficient; According to the protection layer quality assessment coefficient, the touch layer quality assessment coefficient and the display layer quality assessment coefficient, a subtraction operation is performed on the protection layer quality expectation coefficient, the touch layer quality expectation coefficient and the display layer quality expectation coefficient to obtain a protection layer quality adaptation coefficient, a touch layer quality adaptation coefficient and a display layer quality adaptation coefficient; The protection layer quality adaptation coefficient, the touch layer quality adaptation coefficient and the display layer quality adaptation coefficient are input into the screen quality adaptation calculation model to obtain the screen quality adaptation evaluation coefficient.

2. A method for detecting bad pixels on a mobile phone screen as claimed in claim 1, characterized in that: Based on the detection severity of the protection layer, driving the first bad pixel detection channel to perform bad pixel detection on the protection layer to obtain a bad pixel detection result of the protection layer, including: The first bad pixel detection channel includes a visual camera, an image filter and a protective layer bad pixel detection model; Based on the detection weight of the protective layer, driving the visual camera to collect an image of the protective layer to obtain a protective layer collection image; Inputting the protection layer acquisition image into the image filter to obtain a protection layer enhanced image; The protection layer enhanced image is input into the protection layer bad pixel detection model to generate the protection layer bad pixel detection result.

3. A method for detecting bad pixels on a mobile phone screen as claimed in claim 1, characterized in that: Based on the touch layer detection severity, performing bad pixel detection on the touch layer according to a bad pixel detection second channel to obtain a touch layer bad pixel detection result, including: The second bad pixel detection channel includes a touch robot and a touch layer bad pixel recognition model; Based on the detection weight of the touch layer, the touch robot performs multiple touch operations on the touch layer to obtain multiple sets of touch response data; Inputting the multiple sets of touch response data into the touch layer bad pixel recognition model to obtain multiple sets of touch layer bad pixel recognition results; The multiple groups of touch layer bad pixel recognition results are integrated to generate the touch layer bad pixel detection result.

4. A method for detecting bad pixels on a mobile phone screen as claimed in claim 1, characterized in that: Based on the detection severity of the display layer, performing bad pixel detection on the display layer according to the bad pixel detection third channel to obtain a bad pixel detection result of the display layer, including: The third bad pixel detection channel includes a camera device, an image intensifier, and K pattern display bad pixel recognition models corresponding to K predetermined display modes, wherein K is a positive integer greater than 1; Based on the detection weight of the display layer and the camera device, the display layer is imaged according to the K predetermined display modes to obtain K mode display acquisition images; Inputting the K pattern display acquisition images into the image intensifier to obtain K pattern display enhanced images; Inputting the K pattern display enhanced images into the K pattern display bad pixel recognition models, and outputting K pattern display bad pixel recognition results; Data fusion is performed based on the K pattern display bad pixel identification results to generate the display layer bad pixel detection result.

5. A method for detecting bad pixels on a mobile phone screen as claimed in claim 4, characterized in that: The image enhancer includes an image enhancement function, which is: ； Among them, I enh The mode corresponding to characterization I displays the pixel value of the enhanced image, the I characterization mode displays the pixel value of the acquired image, the ALV characterization mode displays the average brightness value of the acquired image, the SDB characterization mode displays the standard deviation of the pixel brightness of the acquired image, and the CEF characterizes the contrast enhancement factor.

6. A mobile phone screen bad pixel detection system, characterized in that: The method for detecting bad pixels on a mobile phone screen according to any one of claims 1 to 5 is used to implement the steps, wherein the mobile phone screen bad pixel detection system comprises: An importance evaluation module, the importance evaluation module is used to perform bad pixel detection importance evaluation according to a target mobile phone screen, and obtain a protection layer detection severity, a touch layer detection severity, and a display layer detection severity, wherein the target mobile phone screen includes a protection layer, a touch layer, and a display layer; A first detection module, the first detection module is used to drive a first bad pixel detection channel to perform bad pixel detection on the protection layer based on the detection severity of the protection layer, and obtain a bad pixel detection result of the protection layer; A second detection module, the second detection module is used to perform bad pixel detection on the touch layer according to a second bad pixel detection channel based on the touch layer detection severity, and obtain a bad pixel detection result of the touch layer; A third detection module, the third detection module is used to perform bad pixel detection on the display layer according to a third bad pixel detection channel based on the detection severity of the display layer, and obtain a bad pixel detection result of the display layer; An adaptation verification module, the adaptation verification module is used to input the protection layer bad pixel detection result, the touch layer bad pixel detection result and the display layer bad pixel detection result into a screen quality adaptation inspection channel to obtain a screen quality adaptation inspection result; An assembly instruction generation module, wherein the assembly instruction generation module is used to generate a screen assembly instruction when the screen quality adaptation inspection result is qualified, and assemble the target mobile phone screen according to the screen assembly instruction; Further, the adaptation verification module is also used for: the screen quality adaptation verification channel includes a screen quality adaptation evaluator and a screen quality adaptation verifier; the protection layer bad pixel detection result, the touch layer bad pixel detection result and the display layer bad pixel detection result are input into the screen quality adaptation evaluator to obtain a screen quality adaptation evaluation coefficient; the screen quality adaptation evaluation coefficient is input into the screen quality adaptation verifier to output the screen quality adaptation verification result, wherein the screen quality adaptation verifier includes a screen quality verification operator, and the screen quality verification operator includes: if the screen quality adaptation evaluation coefficient is greater than or equal to a screen quality adaptation evaluation threshold, the screen quality adaptation verification result is qualified, and if the screen quality adaptation evaluation coefficient is less than the screen quality adaptation evaluation threshold, the screen quality adaptation verification result is unqualified; Further, the adaptation verification module is also used for: the screen quality adaptation evaluator includes a screen quality multidimensional evaluation model and a screen quality adaptation calculation model, wherein the screen quality multidimensional evaluation model includes a protection layer quality evaluation model, a touch layer quality evaluation model, and a display layer quality evaluation model, and the screen quality adaptation calculation model includes a protection layer quality adaptation weight, a touch layer quality adaptation weight, and a display layer quality adaptation weight; the protection layer bad pixel detection result, the touch layer bad pixel detection result, and the display layer bad pixel detection result are input into the screen quality multidimensional evaluation model to obtain a protection layer quality evaluation coefficient, a touch layer quality evaluation coefficient, and a display layer quality evaluation coefficient; according to the protection layer quality evaluation coefficient, the touch layer quality evaluation coefficient, and the display layer quality evaluation coefficient, a subtraction operation is performed on the protection layer quality expectation coefficient, the touch layer quality expectation coefficient, and the display layer quality expectation coefficient to obtain a protection layer quality adaptation coefficient, a touch layer quality adaptation coefficient, and a display layer quality adaptation coefficient; the protection layer quality adaptation coefficient, the touch layer quality adaptation coefficient, and the display layer quality adaptation coefficient are input into the screen quality adaptation calculation model to obtain the screen quality adaptation evaluation coefficient.

7. An electronic device comprising: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of a method for detecting bad pixels on a mobile phone screen as described in any one of claims 1 to 5.

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