A method and device for detecting and positioning residual images of a curved display screen

Abstract

A method and device for detecting and positioning afterimages of a curved display screen, and relates to the field of image processing technology. The method comprises: obtaining the display brightness value of the curved display screen and the ambient light intensity value of the environment in which it is located; using the ratio between the ambient light intensity value and the display brightness value as the display interference coefficient; matching the first exposure parameter for dark area detection and the second exposure parameter for bright area detection according to the display interference coefficient; receiving an image sequence after image acquisition of the curved display screen based on the first exposure parameter and the second exposure parameter, and synthesizing multiple local images in the image sequence to obtain a detection image; calculating the brightness deviation between the detection image and the standard image at multiple pixel positions; when the brightness deviation at any pixel position is greater than the brightness deviation threshold, determining the pixel position as the afterimage position. The technical solution provided in the present application is implemented to achieve the effect of improving the accuracy of afterimage detection and positioning of the curved display screen.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method, device, electronic device and storage medium for detecting and positioning afterimages on a curved display screen. Background Art

[0002] With the technological innovation of the automotive industry, in-vehicle curved displays are increasingly being integrated into the dashboard, center console and other information display systems of cars because they provide a better visual experience and enhanced user interface interactivity. These high-end display solutions not only enhance the aesthetics and functionality of the vehicle, but also provide drivers and passengers with a more intuitive and convenient operating interface. In order to ensure the display quality of curved displays, regular quality inspections are required, including the detection of residual images on the screen. Residual images refer to the traces left by screen display elements after rapid image changes, which can seriously affect the user's viewing experience. Conventional inspection techniques include the use of standard test patterns and visual inspections under a variety of test environments to evaluate the display performance of the screen.

[0003] At present, the existing afterimage detection and positioning methods for curved display screens usually use fixed parameters in a standard environment to collect images of the curved display screen and conduct an overall evaluation to detect the location of the afterimage. However, in actual applications, due to the extremely variable lighting conditions in the vehicle environment, such as from direct sunlight to low-light environments at night or in tunnels, these different lighting conditions greatly affect the display effect of the curved display screen. Only using fixed parameters for afterimage detection in a standard environment often makes it difficult to consider the lighting changes in actual use, resulting in errors in the afterimage detection results, thereby reducing the accuracy of afterimage detection and positioning of the curved display screen. Summary of the invention

[0004] The present application provides a method and device for detecting and positioning afterimages on a curved display screen, which can improve the accuracy of detecting and positioning afterimages on a curved display screen.

[0005] In a first aspect, the present application provides a method for detecting and positioning an afterimage of a curved display screen, comprising:

[0006] Get the display brightness value of the curved display and the ambient light intensity value of the environment;

[0007] Using the ratio between the ambient light intensity value and the display brightness value as the display interference coefficient of the curved display screen;

[0008] According to the display interference coefficient, matching a first exposure parameter for dark area detection and a second exposure parameter for bright area detection of the curved display screen;

[0009] receiving an image sequence after image acquisition of the curved display screen based on the first exposure parameter and the second exposure parameter, and synthesizing a plurality of local images in the image sequence to obtain a detection image;

[0010] Calculating the brightness deviation between the detection image and the standard image at multiple pixel positions;

[0011] When the brightness deviation of any pixel position is greater than the brightness deviation threshold, the pixel position is determined as the afterimage position.

[0012] In a second aspect of the present application, a residual image detection and positioning device for a curved display screen is provided, the device comprising:

[0013] A data acquisition module, used to acquire a display brightness value of the curved display screen and an ambient light intensity value of the environment in which the curved display screen is located; and a ratio between the ambient light intensity value and the display brightness value is used as a display interference coefficient of the curved display screen;

[0014] An exposure coefficient determination module, used to match a first exposure parameter for dark area detection and a second exposure parameter for bright area detection of the curved display screen according to the display interference coefficient;

[0015] a detection image determination module, configured to receive an image sequence obtained by collecting images of the curved display screen based on the first exposure parameter and the second exposure parameter, and perform synthesis processing on a plurality of local images in the image sequence to obtain a detection image;

[0016] The afterimage position detection module is used to calculate the brightness deviation between the detection image and the standard image at multiple pixel positions; when the brightness deviation of any pixel position is greater than the brightness deviation threshold, the pixel position is determined as the afterimage position.

[0017] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a method for detecting and locating residual images on a curved display screen when loaded and executed by the processor.

[0018] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a method for detecting and positioning an afterimage of a curved display screen.

[0019] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] By adopting the above technical solution, the ratio of the display brightness value and the ambient light intensity value is obtained as the display interference coefficient, which reflects the degree of influence of the ambient light on the display and provides a quantitative basis for subsequent parameter adjustment. Based on this display interference coefficient, the system can intelligently match the first exposure parameter and the second exposure parameter suitable for the current environmental conditions, and realize the differentiated acquisition strategy for dark areas and bright areas. This adaptive parameter adjustment mechanism ensures that clear and information-rich image data can be obtained under different ambient light conditions. By using these optimized exposure parameters for image acquisition and synthesizing multiple local images, the integrity of the image details is guaranteed and the dynamic range of the image is improved, so that the detection image can more accurately reflect the actual display state of the display screen. After obtaining a high-quality detection image, by comparing the brightness at the pixel level with the standard image, the calculated brightness deviation can more accurately reflect the existence and degree of the residual image. When the brightness deviation exceeds the preset threshold, the system can accurately locate the residual image position and avoid misjudgment caused by ambient light interference. This technical solution improves the accuracy of residual image detection and positioning through environmentally adaptive parameter adjustment and image processing strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a method for detecting and positioning residual images on a curved display screen provided in an embodiment of the present application;

[0022] Figure 2 It is a structural schematic diagram of an afterimage detection and positioning device for a curved display screen provided in an embodiment of the present application;

[0023] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0025] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0027] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple devices refer to two or more devices, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0028] The present application embodiment provides a method for detecting and locating residual images on a curved display screen. In one embodiment, please refer to Figure 1 , Figure 1 The present invention is a flowchart of a method for detecting and locating residual images on a curved display screen provided in an embodiment of the present invention. The method can be implemented by a computer program, which can be integrated into an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run on a device for detecting and locating residual images on a curved display screen based on a von Neumann system. Specifically, the method can include the following steps:

[0029] Step 101: Obtain a display brightness value of the curved display screen and an ambient light intensity value of the surrounding environment; and use a ratio between the ambient light intensity value and the display brightness value as a display interference coefficient of the curved display screen.

[0030] The display brightness value refers to the luminous intensity per unit area output by the curved display screen in a normal working state. In the embodiment of the present application, it can be understood as the brightness output level of the curved display screen when displaying a full white image.

[0031] The ambient light intensity value refers to the light intensity in the environment where the curved display screen is located. In the embodiment of the present application, it can be understood as the intensity of external light irradiated on the surface of the curved display screen.

[0032] The display interference coefficient refers to the ratio between the ambient light intensity of the environment in which the curved display is located and the display brightness of the display itself. This coefficient reflects the degree of interference of ambient light on the display effect.

[0033] Specifically, first, a display brightness sensor is used to obtain the current display brightness value of the curved display screen, and the display brightness value reflects the actual brightness output level of the curved display screen in the current display state. At the same time, the ambient light intensity value of the environment in which the curved display screen is located is obtained through the ambient light sensor, and the ambient light intensity value represents the light intensity in the current environment. The ratio of the ambient light intensity value to the display brightness value is defined as the display interference coefficient, which can effectively reflect the degree of influence of the ambient light on the display effect of the curved display screen. For example, in a strong light environment such as direct sunlight, the ambient light intensity value is large, while in a weak light environment such as a tunnel or at night, the ambient light intensity value is small. These different degrees of ambient light will have a significant impact on the display effect of the curved display screen. By calculating the display interference coefficient, the degree of interference of this ambient light on the display effect can be quantitatively characterized, thereby providing a basis for the subsequent adjustment of the residual image detection parameters. Specifically, the display interference coefficient can be obtained in the following way: when the ambient light intensity value is E and the display brightness value is L, the display interference coefficient K=E / L. Taking a certain actual detection as an example, when the detected ambient light intensity value is 1000 lux and the display brightness value is 500 candelas per square meter, the display interference coefficient is 2, indicating that the current ambient light has a high degree of interference with the display effect, and it is necessary to appropriately adjust the detection parameters in the subsequent afterimage detection process. By introducing the display interference coefficient, the afterimage detection process can be adaptively adjusted according to the actual environmental conditions, effectively improving the accuracy and reliability of the detection results. This display interference coefficient calculation method based on the ratio of ambient light intensity value to display brightness value provides a reference for the subsequent afterimage detection parameter matching.

[0034] Step 102: matching a first exposure parameter for dark area detection and a second exposure parameter for bright area detection of the curved display screen according to the display interference coefficient.

[0035] The first exposure parameter refers to a combination of camera exposure setting parameters used for dark area image acquisition when performing afterimage detection on a curved display screen. In the embodiment of the present application, it can be understood as a set of image acquisition parameters including but not limited to exposure time, sensitivity and aperture value, and usually a longer exposure time and a higher sensitivity setting are used.

[0036] The second exposure parameter refers to a combination of camera exposure setting parameters used for bright area image acquisition when performing afterimage detection on a curved display screen. In the embodiment of the present application, it can be understood as a set of image acquisition parameters including but not limited to exposure time, sensitivity and aperture value, and a shorter exposure time and a lower sensitivity setting are usually used.

[0037] Specifically, a database of mapping relationships between display interference coefficients and exposure parameters is first established, and the database contains the optimal first exposure parameters and second exposure parameters under different display interference coefficients. Among them, the first exposure parameter is used for dark area detection, including a longer exposure time and a higher sensitivity setting to ensure that sufficient image information can be captured in the dark area; the second exposure parameter is used for bright area detection, including a shorter exposure time and a lower sensitivity setting to avoid loss of details due to overexposure. For example, when the display interference coefficient is 2, the first exposure parameter for dark area detection (exposure time of 20 milliseconds, sensitivity of 800) and the second exposure parameter for bright area detection (exposure time of 5 milliseconds, sensitivity of 200) can be matched from the database. This dual-parameter matching method based on the display interference coefficient can adaptively adjust the detection parameters according to the changes in the actual lighting environment, so that the faint afterimages in the dark area can be accurately captured in a strong lighting environment, while avoiding overexposure of the bright area in a weak lighting environment, thereby improving the accuracy and reliability of afterimage detection in different display areas. This differentiated parameter matching strategy effectively solves the problem that the traditional single parameter detection method is difficult to take into account the detection needs of different brightness areas, and significantly improves the overall effect of afterimage detection.

[0038] Based on the above embodiment, as an optional embodiment, in step 102: matching the first exposure parameter for dark area detection and the second exposure parameter for bright area detection of the curved display screen according to the display interference coefficient, this step may also include the following steps:

[0039] Step 201: In a preset exposure parameter library, determine the exposure time, aperture value and sensitivity for dark area detection corresponding to the display interference coefficient as the first exposure parameter, and determine the exposure time, aperture value and sensitivity for bright area detection corresponding to the display interference coefficient as the second exposure parameter.

[0040] The preset exposure parameter library refers to a data set storing the optimal exposure parameter combinations corresponding to different display interference coefficients. In the embodiment of the present application, it can be understood as a database containing the mapping relationship between the display interference coefficient and the parameters such as exposure time, aperture value, and sensitivity, wherein two sets of parameter combinations are preset for each display interference coefficient, which are used for dark area detection and bright area detection respectively.

[0041] Specifically, in order to achieve accurate detection of residual image on curved display screens under different lighting environments, it is necessary to establish a preset exposure parameter library containing multiple sets of exposure parameters. This parameter library has pre-stored the optimal exposure parameter combination under different display interference coefficients through the accumulation and optimization of a large amount of experimental data. When performing residual image detection, firstly, according to the acquired display interference coefficient, a search and match is performed in the preset exposure parameter library to determine the exposure parameter combination corresponding to the current display interference coefficient. For dark area detection, the corresponding exposure time (such as 20 milliseconds), aperture value (such as F2.8) and sensitivity (such as 800) are selected to form the first exposure parameter; for bright area detection, the corresponding exposure time (such as 5 milliseconds), aperture value (such as F4.0) and sensitivity (such as 200) are selected to form the second exposure parameter. For example, when the display interference coefficient is 2, the system will search the preset exposure parameter library for the optimal parameter combination corresponding to the display interference coefficient equal to 2, which is used for the detection of dark areas and bright areas respectively. This parameter matching method based on the preset parameter library can quickly and accurately find the optimal detection parameters suitable for the current lighting environment.

[0042] Step 103: receiving an image sequence obtained by capturing images of the curved display screen based on the first exposure parameter and the second exposure parameter, and synthesizing a plurality of local images in the image sequence to obtain a detection image.

[0043] The image sequence refers to a set of ordered images obtained by performing multiple local acquisitions of different areas of the curved display screen using the first exposure parameter and the second exposure parameter. In the embodiment of the present application, it can be understood as a data set containing multiple local images, where some images are acquired by acquiring dark areas using the first exposure parameter, and the other images are acquired by acquiring bright areas using the second exposure parameter.

[0044] A local image refers to a sub-image obtained by performing a single image acquisition of a specific area of ​​the curved display screen using the first exposure parameter or the second exposure parameter. In the embodiment of the present application, it can be understood as a single image unit in an image sequence, and each local image only contains display information of a part of the curved display screen.

[0045] The detection image refers to a complete image obtained by synthesizing multiple local images in an image sequence. In the embodiment of the present application, it can be understood as a high-quality synthetic image covering the entire display area of ​​the curved display screen, which contains both dark area image information collected using the first exposure parameter and bright area image information collected using the second exposure parameter. After feature point matching and image stitching algorithm processing, seamless fusion between different local images is achieved.

[0046] Specifically, after obtaining the first exposure parameter and the second exposure parameter suitable for the current environment, it is necessary to collect images of different areas of the curved display screen. Since the curved display screen has a large curvature and display area, it is difficult to obtain a complete and clear image with a single acquisition, so it is necessary to adopt a method of multiple local acquisitions and image synthesis. In specific implementation, the camera is first used to collect multiple local images of the dark area of ​​the display screen according to the first exposure parameter, and the second exposure parameter is used to collect multiple local images of the bright area of ​​the display screen, so as to obtain a series of local image sequences containing dark area and bright area information. Although these local images are optimized for different brightness areas, they still need to be effectively synthesized into a complete detection image through image processing technology. In the image synthesis process, the overlapping areas of adjacent images in the image sequence are first matched with feature points to determine the position correspondence between the images, and then the image stitching algorithm is used to seamlessly merge these local images, and finally a detection image containing complete display area information is generated. For example, when the display area of ​​the curved display screen is large, it may be necessary to collect 3×3 local images of 9 in total, in which the dark area and the bright area are collected using different exposure parameters, and then these local images are synthesized into a high-quality detection image through the image stitching algorithm. This method based on multi-parameter acquisition and image synthesis not only solves the technical difficulties of large-area acquisition on curved display screens, but also ensures that image details in different brightness areas can be clearly presented, providing image data support for subsequent afterimage detection.

[0047] Based on the above embodiment, as an optional embodiment, in step 103: synthesizing multiple local images in the image sequence to obtain a detection image, this step may also include the following steps:

[0048] Step 301: extracting the difference value between any two adjacent partial images in the image sequence; and dividing any two adjacent partial images whose difference value is less than a difference threshold into the same image group.

[0049] The difference value refers to the degree of difference in pixel brightness between any two adjacent local images in the image sequence in their overlapping areas. In the embodiment of the present application, it can be understood as a value obtained by calculating the average absolute difference in the grayscale values ​​of corresponding pixels in the overlapping area of ​​two adjacent local images, which reflects the difference in display characteristics between the two local images.

[0050] Specifically, the pixel difference value of any two adjacent local images in the image sequence in the overlapping area is calculated, and the difference value reflects the degree of difference in brightness and contrast between the two local images. By setting a reasonable difference threshold (such as the pixel difference value does not exceed 50), the adjacent local images with a difference value less than the threshold can be classified into the same image group, and the local images in these image groups have similar display characteristics. For example, when the image sequence contains 9 local images, 2-3 different image groups may be formed, and the local images in each image group have similar brightness and contrast characteristics. This image grouping method based on differential values ​​provides a better processing basis for subsequent image synthesis, and can ensure that local images with similar characteristics are processed first when images are stitched, thereby reducing stitching traces and improving the quality of the final detection image.

[0051] Based on the above embodiment, as an optional embodiment, in step 301: extracting the difference value between any two adjacent partial images in the image sequence, this step may also include the following steps:

[0052] Step 311: convert each local image in the image sequence into a grayscale image; obtain a first grayscale difference between any two adjacent grayscale images in the horizontal direction of the curved display screen, a second grayscale difference in the vertical direction of the curved display screen, and the curvature of the curved display screen.

[0053] The first grayscale difference refers to the average grayscale value difference of the corresponding row pixels in the overlapping area of ​​any two horizontally adjacent grayscale images in the image sequence. In the embodiment of the present application, it can be understood that for two local images adjacent in the horizontal direction of the curved display screen, in their overlapping area, the absolute value of the grayscale value difference of the pixel points at the corresponding position of each row is calculated, and then the difference values ​​of all rows are averaged to obtain the value.

[0054] The second grayscale difference refers to the average grayscale value difference of the corresponding column pixels in the overlapping area of ​​any two vertically adjacent grayscale images in the image sequence. In the embodiment of the present application, it can be understood that for two vertically adjacent partial images on the curved display screen, in their overlapping area, the absolute value of the grayscale value difference of the pixel points at the corresponding position in each column is calculated, and then the difference values ​​of all columns are averaged to obtain the value.

[0055] Specifically, in order to accurately evaluate the display characteristic differences between adjacent local images, it is necessary to simplify the image information and extract key features. First, the color local images in the image sequence are converted into grayscale images, so that the RGB three-channel information of the image can be converted into a single-channel grayscale value, which simplifies the subsequent difference calculation process. After obtaining the grayscale image, the grayscale difference of adjacent grayscale images in the horizontal direction and the vertical direction is calculated respectively. For the horizontal direction, the average grayscale difference of the corresponding row pixels of the adjacent grayscale images in the overlapping area is calculated as the first grayscale difference; for the vertical direction, the average grayscale difference of the corresponding column pixels of the adjacent grayscale images in the overlapping area is calculated as the second grayscale difference. At the same time, the curvature information of the curved display screen is obtained, because the curvature of the display screen will affect the degree of distortion of the image, and then affect the matching accuracy of adjacent images. For example, when the curvature of the display screen is large, the image in the adjacent area may have obvious geometric deformation, which needs to be compensated accordingly in the subsequent processing. The grayscale difference and curvature information obtained in this way provide an important reference basis for subsequent image registration and splicing, which can more accurately evaluate the display characteristic differences between adjacent images and improve the accuracy of image synthesis.

[0056] Step 321: performing weighted summation on the first grayscale difference and the second grayscale difference between any two adjacent partial images according to the horizontal correction coefficient and the vertical correction coefficient corresponding to the curvature, so as to obtain the difference value between any two adjacent partial images.

[0057] The horizontal correction coefficient refers to a weight coefficient used to compensate for the effect of the horizontal curvature of the curved display screen on image acquisition. In the embodiment of the present application, it can be understood as a coefficient between 0 and 1 calculated according to the curvature value of the horizontal direction of the display screen, and the coefficient decreases as the horizontal curvature increases.

[0058] The vertical correction coefficient refers to a weight coefficient used to compensate for the effect of the vertical curvature of the curved display screen on image acquisition. In the embodiment of the present application, it can be understood as a coefficient between 0 and 1 calculated based on the vertical curvature value of the display screen, and the coefficient decreases as the vertical curvature increases.

[0059] Specifically, firstly, the horizontal correction coefficient and the vertical correction coefficient are determined according to the curvature characteristics of the display screen. The two correction coefficients respectively reflect the influence of the curvature on the image distortion in the horizontal and vertical directions. For example, when the curvature of the display screen in a certain direction is large, the correction coefficient in the corresponding direction should be small to reduce the weight of the grayscale difference in this direction. Then the first grayscale difference is multiplied by the horizontal correction coefficient, and the second grayscale difference is multiplied by the vertical correction coefficient, and the two weighted values ​​are summed to obtain the final differential value. Through this weighted summation method, the grayscale difference of the image in two directions is considered, and the distortion effect caused by the curvature is compensated by the correction coefficient, so that the final differential value can more accurately reflect the actual display characteristic difference between adjacent images. This differential value calculation method based on curvature correction improves the accuracy of image similarity evaluation, provides a criterion for subsequent image grouping and synthesis processing, and effectively improves the quality of the final detection image.

[0060] Step 302: Perform weighted averaging on the pixels of the local images in each image group to obtain the target image of each image group.

[0061] Specifically, after completing the grouping of the local images, it is necessary to perform reasonable fusion processing on the local images in each image group to eliminate the splicing traces in the overlapping area of ​​the images. Since the local images in the same image group have similar display characteristics, a smooth transition can be achieved by a pixel-level weighted average method. In the specific implementation, the corresponding pixel points of the overlapping area of ​​each local image in the image group are first determined, and then the corresponding weight values ​​are assigned according to the positions of the pixels in the overlapping area. For the pixels in the overlapping area, a linear decreasing weight distribution method is usually adopted, that is, the pixels close to the center of the image have a larger weight, and the pixels close to the edge of the image have a smaller weight, so that the naturalness of the image transition can be ensured. For example, when two local images overlap in the horizontal direction, the overlapping area can be divided into several columns, and the weight of the first image is linearly reduced from 1 to 0 from left to right, while the weight of the second image is linearly increased from 0 to 1. Through this weight distribution method, the corresponding pixels in the overlapping area are weighted averaged to obtain the pixel value of the smooth transition. This weighted average processing is performed on the overlapping areas of all local images in the image group, and finally a fused target image is obtained, which not only retains the display details of the original local images, but also achieves seamless transition of the overlapping areas. This image fusion method based on weighted average can effectively eliminate the boundary effect of image splicing, improve the visual continuity of the synthesized image, and provide better quality image data for subsequent residual image detection.

[0062] Step 303: stitching the target images of each image group into a detection image.

[0063] Specifically, after obtaining the target images of each image group, it is necessary to perform a final splicing process on these target images to obtain a complete detection image. Since the target images of different image groups may be fused by local images collected with different exposure parameters, there may be differences in display characteristics between them, so a special splicing strategy is needed to ensure the quality of the final detection image. First, the spatial position relationship of each target image in the final detection image is determined, which can be determined by the position information of the original local image. Then, a transition area is established at the junction of the target images. The width of the area can be set according to actual needs, and a transition band width of 20-30 pixels is usually selected. In the transition area, an image fusion algorithm based on the gradient domain is adopted. The algorithm achieves a smooth transition of brightness while maintaining image details by solving the Poisson equation. For example, when there is a brightness difference at the junction of two target images, the algorithm automatically adjusts the pixel value in the transition area according to the gradient information of the image, so that the brightness change is more natural. In this way, the final detection image not only retains the display details of different areas, but also ensures the coherence of image transition. This gradient domain-based image stitching method not only solves the technical difficulties of image stitching under different exposure parameters, but also improves the overall quality of the detection image, providing a reliable image data basis for subsequent residual image detection. Through this stitching process, the final generated detection image can accurately reflect the overall display status of the curved display screen, ensuring both the detail clarity of the dark area and the display effect of the bright area, thus improving the accuracy of residual image detection.

[0064] Step 104: Calculate the brightness deviation between the detection image and the standard image at multiple pixel positions; when the brightness deviation at any pixel position is greater than a brightness deviation threshold, determine the pixel position as an afterimage position.

[0065] The pixel position refers to the spatial positioning information of each pixel in the image in a two-dimensional coordinate system. In the embodiment of the present application, it can be understood as the position determined by the row coordinates and column coordinates of a pixel in the detection image or standard image, usually represented by a (x, y) coordinate pair, where x represents the position index of the pixel in the horizontal direction, and y represents the position index of the pixel in the vertical direction.

[0066] The brightness deviation refers to the brightness difference between the test image and the standard image at the same pixel position. In the embodiment of the present application, it can be understood as the absolute value of the difference between the grayscale value of a certain pixel position in the test image and the grayscale value of the pixel at the corresponding position in the standard image, which reflects the brightness difference between the two images at that position.

[0067] The afterimage position refers to the pixel area position with obvious brightness abnormality in the detected image. In the embodiment of the present application, it can be understood as the pixel position coordinates in the detected image whose brightness deviation exceeds the preset threshold, and the brightness abnormality of these positions has exceeded the normal brightness fluctuation range of the display screen.

[0068] Specifically, the detection image is first aligned with the standard image at the pixel level, and then the brightness deviation is calculated for each pixel position. The deviation can be obtained by calculating the absolute value of the difference between the grayscale values ​​of the corresponding pixels of the two images. In order to accurately determine the existence of afterimages, it is necessary to set a reasonable brightness deviation threshold, and the setting of the threshold needs to take into account the normal brightness fluctuation range of the display screen. When the brightness deviation of a certain pixel position exceeds the preset threshold, it indicates that there may be an afterimage phenomenon at that position. For example, if the preset brightness deviation threshold is 20 grayscale values, then when the absolute value of the difference between the grayscale value of a pixel in the detection image and the grayscale value of the corresponding position of the standard image exceeds 20, the pixel position is marked as the afterimage position. Through this afterimage detection method based on brightness deviation, the afterimage area on the display screen can be effectively identified, providing accurate position information for subsequent afterimage evaluation and processing.

[0069] Based on the above embodiment, as an optional embodiment, in step 104: calculating the brightness deviation between the detection image and the standard image at multiple pixel positions, this step may also include the following steps:

[0070] Step 401: determine the alignment position of the detection image and the standard image; extract a preset number of feature points in the detection image, and obtain a first brightness value corresponding to each feature point.

[0071] The alignment position refers to the positional relationship where the detected image completely overlaps with the standard image at the pixel level after spatial transformation. In the embodiment of the present application, it can be understood as a set of transformation parameters calculated by feature matching and spatial transformation, which enable the content in the detected image to accurately correspond to the corresponding content in the standard image in spatial position.

[0072] Feature points refer to pixel locations with significant local features in an image, which usually have unique brightness distribution patterns or gradient change characteristics. In the embodiments of the present application, it can be understood as a preset number of representative points extracted from the detection image by a feature detection algorithm, which are often located at the edges, corners or texture-rich areas of the image.

[0073] The first brightness value refers to the pixel brightness information at each feature point position in the detection image. In the embodiment of the present application, it can be understood as the grayscale value of the pixel position where the feature point is located extracted from the detection image, which reflects the actual display brightness of the display screen at the corresponding position.

[0074] Specifically, in order to accurately evaluate the residual image in the detection image, it is necessary to first ensure that the detection image and the standard image are accurately aligned in spatial position, so as to ensure the accuracy of subsequent brightness comparison. When performing image alignment, the corresponding feature points in the two images are found by the feature matching algorithm, and then the spatial transformation matrix is ​​calculated to transform the detection image to the same position as the standard image. After the alignment is completed, representative feature points need to be extracted from the detection image. The number of these feature points is determined by a preset value. Usually, the positions in the image where the brightness changes significantly and are easy to identify are selected as feature points. The extraction of feature points can use algorithms such as Harris corner detection or SIFT, which can find local features with strong distinguishing properties in the image. For each extracted feature point, the corresponding first brightness value is obtained, that is, the pixel grayscale value at the feature point position. The first brightness value reflects the brightness information of the detection image at the feature point position, and this information will be used for subsequent brightness comparison with the corresponding position of the standard image. Through this feature point-based processing method, not only the accuracy of image alignment is ensured, but also reliable brightness sampling points are provided, providing a data basis for residual image detection.

[0075] Based on the above embodiment, as an optional embodiment, in step 401: determining the alignment position of the detection image and the standard image, this step may also include the following steps:

[0076] Step 411: extract corner point features from the detection image and the standard image; and establish a mapping relationship between the corner point features of the detection image and the corner point features of the standard image.

[0077] Among them, the corner feature refers to the pixel position where the gradient value in the image changes significantly in multiple directions at the same time. In the embodiment of the present application, it can be understood as a set of feature points with significant gradient changes in the local area extracted from the test image and the standard image. These points are usually located at the intersection of the edges of objects in the image, at sharp corners, or in areas with drastic texture changes.

[0078] Specifically, in order to achieve accurate alignment of the detection image and the standard image, it is necessary to first find the feature points with corresponding relationships in the two images. Corner point features are positions where the gradient changes significantly in the image. They have good stability and repeatability and are suitable as reference points for image alignment. In actual processing, algorithms such as Harris corner detection or SIFT can be used to extract corner point features from the two images. Specifically, the image is first preprocessed to reduce the influence of noise, and then the gradient information of the image is calculated, and the corner point position is detected based on the gradient change characteristics. In order to improve the reliability of feature extraction, it is usually necessary to perform non-maximum suppression on the corner points initially detected to retain the corner points with the strongest response. After obtaining the corner point features of the two images, it is necessary to establish a corresponding relationship between them, which can be achieved by matching feature descriptors. For each corner point feature, the feature descriptor of its local area is calculated, and then the nearest neighbor matching algorithm is used to find the corner point pairs with the most similar descriptors in the two images. These matching pairs constitute the mapping relationship between the two images. In order to eliminate possible false matches, algorithms such as RANSAC can be used for optimization to retain only matching pairs that meet the consistency of spatial transformation. The corner feature mapping relationship established in this way provides a reliable basis for subsequent image alignment and can effectively ensure the accuracy of spatial correspondence in the afterimage detection process.

[0079] Step 421: determine the horizontal offset and vertical offset of the detection image according to the mapping relationship; perform position correction on the detection image according to the horizontal offset and the vertical offset to obtain a corrected detection image; determine the overlapping position of the corrected detection image and the standard image as the alignment position.

[0080] Specifically, in order to achieve accurate alignment between the detection image and the standard image, it is necessary to calculate the spatial position deviation between the two images based on the established corner feature mapping relationship. By analyzing the relative position relationship between the matched corner feature pairs, the overall offset of the detection image relative to the standard image in the horizontal and vertical directions can be calculated. For all matched corner feature pairs, the coordinate difference of each pair of feature points in the horizontal and vertical directions is calculated, and then the overall horizontal offset and vertical offset are obtained by statistical methods (such as median or weighted average). After obtaining the offset, the detection image needs to be positionally corrected. The detection image can be moved by the corresponding offset in the horizontal and vertical directions through translation transformation to align it with the standard image in spatial position. In order to ensure the accuracy of the correction, an interpolation algorithm (such as bilinear interpolation or cubic spline interpolation) can be used to process the pixel resampling to avoid introducing additional image distortion during the correction process. After the correction is completed, the detection image and the standard image should reach the best coincidence state in spatial position, and this coincidence position is determined as the alignment position of the two images. This offset-based correction method is simple and effective, and can ensure accurate pixel-level comparison during subsequent image detection. Through this precise image alignment, it can effectively avoid misjudgments caused by position deviation and improve the accuracy of image detection.

[0081] Step 402: According to the alignment position, determine the reference points corresponding to the positions of the feature points in the standard image, and obtain the second brightness values ​​corresponding to the reference points.

[0082] The reference point refers to the pixel position in the standard image that corresponds to the position of the feature point in the detection image. In the embodiment of the present application, it can be understood as the corresponding coordinate position obtained by mapping the feature point coordinates in the detection image to the standard image space through the spatial transformation relationship, and these positions represent the same physical position on the display screen as the feature point.

[0083] The second brightness value refers to the pixel brightness information at each reference point position in the standard image. In the embodiment of the present application, it can be understood as the grayscale value of the pixel position of the reference point corresponding to the position of the detected image feature point in the standard image, which represents the standard brightness that the corresponding position of the display screen should have under normal display state.

[0084] Specifically, after the alignment position of the detection image and the standard image has been determined, it is necessary to find the position in the standard image that accurately corresponds to the feature point of the detection image in order to perform an accurate brightness comparison. Since the spatial transformation relationship between the two images has been established, the coordinates of the feature points in the detection image can be mapped to the standard image space through the transformation matrix, thereby determining the corresponding reference point position in the standard image. This corresponding point determination method based on spatial transformation can ensure that the reference point does correspond to the same physical position on the display screen. After determining the reference point, it is necessary to obtain the second brightness value of these reference point positions, that is, the grayscale value of the corresponding pixel position in the standard image. The second brightness value is obtained in a similar way to the first brightness value. The grayscale value of the reference point pixel position can be directly read, or the weighted average value of the local area around the reference point can be used to take into account possible minor positioning errors. The second brightness value obtained in this way represents the brightness information that the display screen should have under normal display conditions, and provides a reference standard for subsequent residual image detection.

[0085] Step 403: taking the difference between the first brightness value of each feature point and the second brightness value of the corresponding reference point as the brightness deviation of multiple pixel positions.

[0086] Specifically, in order to quantitatively evaluate the degree of afterimage at each position of the display screen, it is necessary to calculate the brightness difference between the detection image and the standard image. By comparing the first brightness value of the feature point with the second brightness value of its corresponding reference point, the brightness deviation reflecting the degree of display abnormality can be obtained. For each pair of corresponding feature points and reference points, the first brightness value of the feature point position is subtracted from the second brightness value of the reference point position, and the absolute value of the difference is taken as the brightness deviation of the position. For example, if the first brightness value of a feature point is 150 and the second brightness value of the corresponding reference point is 130, the brightness deviation of the position is |150-130|=20. This calculation method based on brightness difference can intuitively reflect the degree of brightness abnormality at each position of the display screen. The larger the difference, the more serious the display deviation of the position is from the normal state. By performing such difference calculation on a preset number of feature point-reference point pairs, the brightness deviation of multiple representative pixel positions can be obtained, and these data provide a quantitative basis for the subsequent judgment of the afterimage position.

[0087] Based on the above embodiment, as an optional embodiment, in step 104: calculating the brightness deviation between the detection image and the standard image at multiple pixel positions, after this step, the following steps may also be included:

[0088] Step 404: when the brightness deviation at any pixel position is less than or equal to the brightness deviation threshold, the temperature value and vibration frequency of the curved display screen within a preset time period are obtained.

[0089] Specifically, during the detection process, when it is found that the brightness deviation of any pixel position does not exceed the preset brightness deviation threshold, it is necessary to further collect the environmental parameter data of the display screen to analyze other factors that may affect the display quality. Here, we mainly focus on two important environmental parameters, temperature and vibration, because temperature changes will affect the physical properties of the display panel, and vibration may cause mechanical stress changes in the display device, which may be potential causes of display abnormalities. Using a temperature sensor attached to the surface or inside of the curved display screen, the temperature value of the display screen is recorded at a fixed sampling interval (such as once per minute) within a preset time period (such as continuous monitoring for 30 minutes); at the same time, the vibration frequency data of the display screen in the same time period is recorded through a vibration sensor installed on the display screen bracket or around it. The collection of these environmental parameter data needs to be synchronized with the working state of the display screen so as to accurately reflect the actual environmental conditions during the display process. By collecting these data, the correlation between display quality and environmental parameters can be established, providing reference information for quality control and fault prevention of the display screen.

[0090] Step 405: Determine in the database the temperature compensation coefficient corresponding to the temperature value and the vibration compensation coefficient corresponding to the vibration frequency; and perform compensation processing on the brightness deviation of the pixel position by combining the temperature compensation coefficient and the vibration compensation coefficient to obtain the target brightness deviation.

[0091] Specifically, since the display performance of the curved display screen will be affected by the ambient temperature and vibration, it is necessary to compensate and correct the measured brightness deviation accordingly to obtain a more accurate afterimage evaluation result. First, based on the pre-established database, the corresponding temperature compensation coefficient is searched according to the currently measured temperature value, which reflects the degree of influence of temperature change on the display brightness; at the same time, the corresponding vibration compensation coefficient is searched according to the measured vibration frequency, which characterizes the degree of influence of mechanical vibration on display stability. The search process can use a table lookup or interpolation method to ensure that the compensation coefficient that best matches the current environmental parameters is obtained. After obtaining these two compensation coefficients, they are comprehensively calculated with the original brightness deviation. Specifically, a weighted compensation method can be used, that is, the brightness deviation is multiplied by the temperature compensation coefficient and the vibration compensation coefficient respectively, and then the results are weighted averaged to obtain the target brightness deviation that takes into account the influence of environmental factors. This compensation processing method can effectively eliminate the measurement errors caused by environmental factors and make the afterimage detection results more accurate and reliable. For example, when the ambient temperature is high, the brightness of the display may naturally decrease. Temperature compensation can avoid misjudging this normal brightness change as afterimage. Similarly, when there is strong vibration, vibration compensation can eliminate the impact of temporary display fluctuations caused by vibration.

[0092] Step 406: If the target brightness deviation is greater than the brightness deviation threshold, the pixel position is determined as the afterimage position.

[0093] Specifically, after completing the environmental factor compensation, it is necessary to determine whether the display screen has afterimages by comparing the target brightness deviation with the preset brightness deviation threshold. When the target brightness deviation of a certain pixel position exceeds this threshold, it indicates that there is a significant difference between the actual display brightness of the position and the brightness under the standard state, and this difference has exceeded the normal brightness fluctuation range of the display screen. Therefore, it can be determined that there is afterimage phenomenon at this position. The specific judgment process is: compare the target brightness deviation obtained after temperature and vibration compensation with the preset brightness deviation threshold. If the target brightness deviation is greater than the threshold, the pixel position is marked as the afterimage position. This threshold-based judgment method is simple, intuitive and reliable. By setting a suitable threshold, the sensitivity and reliability of the detection can be effectively balanced. For example, if the target brightness deviation of a certain pixel position is 25, and the preset brightness deviation threshold is 20, it can be determined that there is afterimage at this position. This judgment method can not only accurately locate the afterimage position, but also reflect the severity of the afterimage by the size of the target brightness deviation, providing a specific reference for subsequent quality improvement.

[0094] Reference Figure 2 , is a device for detecting and positioning an afterimage of a curved display screen provided in an embodiment of the present application, the device comprising: a data acquisition module, an exposure coefficient determination module, a detection image determination module, and an afterimage position detection module, wherein:

[0095] A data acquisition module is used to acquire the display brightness value of the curved display screen and the ambient light intensity value of the environment in which it is located; and the ratio between the ambient light intensity value and the display brightness value is used as the display interference coefficient of the curved display screen;

[0096] An exposure coefficient determination module, used to match a first exposure parameter for dark area detection and a second exposure parameter for bright area detection of the curved display screen according to the display interference coefficient;

[0097] A detection image determination module is used to receive an image sequence after image acquisition of the curved display screen based on the first exposure parameter and the second exposure parameter, and synthesize multiple local images in the image sequence to obtain a detection image;

[0098] The afterimage position detection module is used to calculate the brightness deviation between the detection image and the standard image at multiple pixel positions; when the brightness deviation at any pixel position is greater than the brightness deviation threshold, the pixel position is determined as the afterimage position.

[0099] Based on the above embodiment, the exposure coefficient determination module is also used to determine the exposure time, aperture value and sensitivity for dark area detection corresponding to the display interference coefficient as the first exposure parameter in a preset exposure parameter library, and to determine the exposure time, aperture value and sensitivity for bright area detection corresponding to the display interference coefficient as the second exposure parameter.

[0100] On the basis of the above-mentioned embodiment, the detection image determination module is also used to extract the difference value between any two adjacent local images in the image sequence; divide any two adjacent local images whose difference value is less than the difference threshold into the same image group; perform weighted averaging on the pixels of the local images in each image group to obtain the target image of each image group; and splice the target images of each image group into a detection image.

[0101] On the basis of the above embodiment, the detection image determination module is further used to convert each local image in the image sequence into a grayscale image; obtain a first grayscale difference value in the horizontal direction of the curved display screen, a second grayscale difference value in the vertical direction of the curved display screen, and the curvature of the curved display screen between any two adjacent grayscale images; and perform weighted summation of the first grayscale difference value and the second grayscale difference value of any two adjacent local images according to the horizontal direction correction coefficient and the vertical direction correction coefficient corresponding to the curvature to obtain a differential value between any two adjacent local images.

[0102] Based on the above embodiments, the afterimage position detection module is also used to determine the alignment position of the detection image and the standard image; extract a preset number of feature points in the detection image, and obtain the first brightness value corresponding to each feature point; according to the alignment position, determine the reference point corresponding to the position of each feature point in the standard image, and obtain the second brightness value corresponding to each reference point; use the difference between the first brightness value of each feature point and the second brightness value of the corresponding reference point as the brightness deviation of multiple pixel positions.

[0103] On the basis of the above-mentioned embodiment, the afterimage position detection module is also used to extract corner point features in the detection image and the standard image; establish a mapping relationship between the corner point features of the detection image and the corner point features of the standard image; determine the horizontal offset and vertical offset of the detection image according to the mapping relationship; perform position correction on the detection image according to the horizontal offset and the vertical offset to obtain a corrected detection image; and determine the overlapping position of the corrected detection image and the standard image as the alignment position.

[0104] On the basis of the above embodiment, the afterimage position detection module is also used to obtain the temperature value and vibration frequency of the curved display screen within a preset time period when the brightness deviation of any pixel position is less than or equal to the brightness deviation threshold; determine the temperature compensation coefficient corresponding to the temperature value and the vibration compensation coefficient corresponding to the vibration frequency in the database; compensate for the brightness deviation of the pixel position by combining the temperature compensation coefficient and the vibration compensation coefficient to obtain the target brightness deviation; if the target brightness deviation is greater than the brightness deviation threshold, the pixel position is determined as the afterimage position.

[0105] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0106] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0107] The communication bus 302 is used to realize the connection and communication between these components.

[0108] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0109] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0110] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes operating devices, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0111] The memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating device, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 305 may optionally be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 The memory 305 as a computer storage medium may include an operating device, a network communication module, a user interface module, and an application program for a method for detecting and positioning residual images on a curved display screen.

[0112] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program storing a method for detecting and positioning an afterimage of a curved display screen in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0113] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0117] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0118] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0119] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A method for detecting and positioning residual images of a curved display screen, characterized in that: include: Get the display brightness value of the curved display and the ambient light intensity value of the environment; Using the ratio between the ambient light intensity value and the display brightness value as the display interference coefficient of the curved display screen; According to the display interference coefficient, matching a first exposure parameter for dark area detection and a second exposure parameter for bright area detection of the curved display screen; Receiving an image sequence after image acquisition of the curved display screen based on the first exposure parameter and the second exposure parameter, and synthesizing a plurality of local images in the image sequence to obtain a detection image; Calculating the brightness deviation between the detection image and the standard image at multiple pixel positions; When the brightness deviation of any pixel position is greater than the brightness deviation threshold, the pixel position is determined as the residual image position; The synthesizing process of the multiple local images in the image sequence to obtain the detection image includes: Extracting a difference value between any two adjacent local images in the image sequence; Dividing any two adjacent local images whose difference values ​​are smaller than the difference threshold into the same image group; Performing weighted averaging on pixels of the local images in each of the image groups to obtain a target image of each of the image groups; splicing the target images of each of the image groups into a detection image; The step of extracting a difference value between any two adjacent local images in the image sequence comprises: Convert each local image in the image sequence into a grayscale image; Acquire a first grayscale difference value between any two adjacent grayscale images in the horizontal direction of the curved display screen, a second grayscale difference value in the vertical direction of the curved display screen, and a curvature of the curved display screen; According to the horizontal correction coefficient and the vertical correction coefficient corresponding to the curvature, a weighted sum is performed on the first grayscale difference value and the second grayscale difference value of the any two adjacent partial images to obtain a difference value between the any two adjacent partial images.

2. The afterimage detection and positioning method for a curved display screen according to claim 1, characterized in that: The step of matching a first exposure parameter for dark area detection and a second exposure parameter for bright area detection of the curved display screen according to the display interference coefficient includes: In a preset exposure parameter library, the exposure time, aperture value and sensitivity for dark area detection corresponding to the display interference coefficient are determined as the first exposure parameter, and the exposure time, aperture value and sensitivity for bright area detection corresponding to the display interference coefficient are determined as the second exposure parameter.

3. The afterimage detection and positioning method for a curved display screen according to claim 1, characterized in that: The step of calculating the brightness deviation between the detection image and the standard image at a plurality of pixel positions includes: Determining an alignment position between the detection image and the standard image; Extracting a preset number of feature points from the detection image, and obtaining a first brightness value corresponding to each of the feature points; According to the alignment position, determining a reference point corresponding to the position of each of the feature points in the standard image, and acquiring a second brightness value corresponding to each of the reference points; The difference between the first brightness value of each feature point and the second brightness value of the corresponding reference point is used as the brightness deviation of multiple pixel positions.

4. The afterimage detection and positioning method for a curved display screen according to claim 3, characterized in that: Determining the alignment position between the detection image and the standard image includes: Extracting corner features from the detection image and the standard image; Establishing a mapping relationship between the corner point features of the detection image and the corner point features of the standard image; Determine the horizontal offset and the vertical offset of the detection image according to the mapping relationship; Performing position correction on the detection image according to the horizontal offset and the vertical offset to obtain a corrected detection image; The overlapping position of the corrected detection image and the standard image is determined as the alignment position.

5. The afterimage detection and positioning method for a curved display screen according to claim 1, characterized in that: After calculating the brightness deviation between the detection image and the standard image at a plurality of pixel positions, the method further includes: When the brightness deviation at any pixel position is less than or equal to the brightness deviation threshold, obtaining the temperature value and vibration frequency of the curved display screen within a preset time period; Determining in a database a temperature compensation coefficient corresponding to the temperature value and a vibration compensation coefficient corresponding to the vibration frequency; Combining the temperature compensation coefficient and the vibration compensation coefficient, compensating the brightness deviation of the pixel position to obtain a target brightness deviation; If the target brightness deviation is greater than the brightness deviation threshold, the pixel position is determined as the afterimage position.

6. A device for detecting and positioning residual images of a curved display screen, characterized in that: The device comprises: A data acquisition module, used to acquire a display brightness value of the curved display screen and an ambient light intensity value of the environment in which the curved display screen is located; and a ratio between the ambient light intensity value and the display brightness value is used as a display interference coefficient of the curved display screen; An exposure coefficient determination module, used to match a first exposure parameter for dark area detection and a second exposure parameter for bright area detection of the curved display screen according to the display interference coefficient; a detection image determination module, configured to receive an image sequence obtained by collecting images of the curved display screen based on the first exposure parameter and the second exposure parameter, and perform synthesis processing on a plurality of local images in the image sequence to obtain a detection image; The residual image position detection module is used to calculate the brightness deviation between the detection image and the standard image at multiple pixel positions; when the brightness deviation of any pixel position is greater than the brightness deviation threshold, the pixel position is determined as the residual image position; The synthesizing process of the multiple local images in the image sequence to obtain the detection image includes: Extracting a difference value between any two adjacent local images in the image sequence; Dividing any two adjacent local images whose difference values ​​are smaller than the difference threshold into the same image group; Performing weighted averaging on pixels of the local images in each of the image groups to obtain a target image of each of the image groups; splicing the target images of each of the image groups into a detection image; The step of extracting a difference value between any two adjacent local images in the image sequence comprises: Convert each local image in the image sequence into a grayscale image; Acquire a first grayscale difference value between any two adjacent grayscale images in the horizontal direction of the curved display screen, a second grayscale difference value in the vertical direction of the curved display screen, and a curvature of the curved display screen; According to the horizontal correction coefficient and the vertical correction coefficient corresponding to the curvature, a weighted sum is performed on the first grayscale difference value and the second grayscale difference value of the any two adjacent partial images to obtain a difference value between the any two adjacent partial images.

7. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the afterimage detection and positioning method for the curved display screen as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the afterimage detection and positioning method for a curved display screen as described in any one of claims 1 to 5 is executed.

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