Image defective pixel correction method and apparatus and battery test method and apparatus

Abstract

The present application belongs to the technical field of batteries. Provided are an image defective pixel correction method and apparatus and a battery test method and apparatus. The image defective pixel correction method comprises: acquiring a plurality of sequence original image frames, wherein the plurality of sequence original image frames are a plurality of image frames consecutively acquired within a preset period of time; performing noise reduction processing on the plurality of sequence original image frames to obtain a single original image frame; performing defective pixel identification on the single original image frame to determine a defective pixel; and correcting a grayscale value of the defective pixel on the basis of grayscale values of several pixel points adjacent to the defective pixel, so as to obtain a test image.

Description

Image bad pixel correction method and device, battery detection method and device

[0001] Cross-references

[0002] This application refers to Chinese Patent Application No. 202310973645.2, filed on August 3, 2023, entitled “Image bad pixel correction method and device, battery detection method and device”, which is incorporated into this application in its entirety by reference. Technical Field

[0003] The present application relates to the field of battery technology, and in particular to a method and device for correcting image bad pixels, and a method and device for detecting batteries. Background Art

[0004] With the development of imaging technology, imaging modules are widely used in security, military, medical, and other fields. However, due to factors such as manufacturing processes and usage environments, imaging modules often contain bad pixels, resulting in images that appear too bright, too dark, or flickering. Therefore, it is necessary to detect and correct bad pixels in the generated images to eliminate their impact on imaging quality.

[0005] Summary of the Invention

[0006] The present application aims to solve at least one of the technical problems in the background art. To this end, one purpose of the present application is to provide a method and device for correcting bad pixels in an image, and a method and device for detecting batteries, so as to correct bad pixels in an image and improve image quality.

[0007] An embodiment of the first aspect of the present application provides an image bad pixel correction method, which includes: obtaining a multi-frame sequence original image; wherein the multi-frame sequence original image is a multi-frame image continuously obtained within a preset time; performing noise reduction processing on the multi-frame sequence original image to obtain a single-frame original image; identifying bad pixels in the single-frame original image to determine the bad pixels; and correcting the grayscale value of the bad pixel based on the grayscale values ​​of several pixel points adjacent to the bad pixel to obtain a detection image.

[0008] In the technical solution of the embodiments of the present application, a single original image is obtained by performing noise reduction processing on a multi-frame sequence. This single original image no longer contains electronic noise, which can reduce interference with bad pixel identification and make the identification of bad pixels more accurate. Furthermore, the grayscale value of the bad pixel is corrected based on the grayscale values ​​of several pixels adjacent to the bad pixel. This allows for correction of bad pixels without the use of a correction template, making it applicable to any type and number of bad pixels, thereby expanding the scope of application of the image bad pixel correction method.

[0009] In some embodiments, performing noise reduction processing on a multi-frame sequence of original images to obtain a single-frame original image includes: determining whether electronic noise exists at a pixel point based on multiple grayscale values ​​corresponding to any pixel point at the same position in the multi-frame sequence of original images; and determining the grayscale value of the corresponding pixel point in the single-frame original image based on the determination result. Determining whether electronic noise exists at a pixel point based on multiple grayscale values ​​corresponding to any pixel point at the same position in the multi-frame sequence of original images, and determining the grayscale value of the corresponding pixel point in the single-frame original image based on the determination result, can improve the accuracy of the grayscale values ​​of each pixel point in the single-frame original image.

[0010] In some embodiments, determining whether electronic noise exists at a pixel based on multiple grayscale values ​​corresponding to any pixel at the same position in a multi-frame sequence of original images includes: calculating a standard deviation of the pixel based on the multiple grayscale values ​​corresponding to any pixel at the same position in the multi-frame sequence of original images; and determining the presence of electronic noise at the pixel in response to the standard deviation of the pixel being greater than a preset threshold. Determining the presence of electronic noise at a pixel by comparing the standard deviation of the grayscale values ​​of the pixel calculated from the multiple grayscale values ​​corresponding to any pixel at the same position in the multi-frame sequence of original images with a preset threshold can simplify the process of determining whether electronic noise exists at a pixel, thereby improving the efficiency of image bad pixel correction.

[0011] In some embodiments, determining the grayscale value of a pixel corresponding to a single-frame original image based on the judgment result includes: in response to the presence of electronic noise in the pixel, performing a clustering operation on multiple grayscale values ​​corresponding to the pixel in a multi-frame sequence of original images, and marking the multiple grayscale values ​​as normal or abnormal based on the result of the clustering operation; calculating the average of the grayscale values ​​marked as normal among the multiple grayscale values ​​and using this average as the grayscale value of the corresponding pixel in the single-frame original image. The clustering operation can effectively separate normal grayscale values ​​from abnormal grayscale values, and calculating the average of the normal grayscale values ​​among the multiple grayscale values ​​as the grayscale value of the corresponding pixel in the single-frame original image can make the grayscale value of the corresponding pixel in the single-frame original image more accurate, thereby improving the effect of image bad pixel correction.

[0012] In some embodiments, determining the grayscale value of a pixel corresponding to the single-frame original image based on the determination result further includes: in response to the absence of electronic noise at the pixel, calculating an average of multiple grayscale values ​​corresponding to the pixel in the multiple-frame sequence of original images and using the average as the grayscale value of the corresponding pixel in the single-frame original image. By calculating the average of multiple grayscale values ​​corresponding to the pixel in the multiple-frame sequence of original images and using the average as the grayscale value of the corresponding pixel in the single-frame original image, the grayscale value of the corresponding pixel in the single-frame original image can be made more accurate.

[0013] In some embodiments, bad pixel identification is performed on a single-frame original image, and determining bad pixels includes: determining a normal grayscale value range for the single-frame image; and determining bad pixels in the single-frame original image based on the grayscale values ​​of the pixels in the single-frame image and the normal grayscale value range. Determining bad pixels in the single-frame original image based on the grayscale values ​​of the pixels in the single-frame image and the normal grayscale value range simplifies the bad pixel determination process, thereby improving the efficiency of image bad pixel correction.

[0014] In some embodiments, determining a normal grayscale range for a single-frame image includes: selecting a region of interest from the single-frame image; wherein the region of interest includes a plurality of pixels; calculating a histogram distribution of the grayscale values ​​of the pixels in the region of interest; and determining the normal grayscale range for the single-frame image based on the histogram distribution. By selecting a region of interest from the single-frame image and determining the normal grayscale range for the single-frame image based on the calculated histogram distribution of the grayscale values ​​of the pixels in the region of interest, the determined normal grayscale range can be more accurately determined.

[0015] In some embodiments, determining a bad pixel in a single-frame original image based on the grayscale value of the pixel in the single-frame image and the normal grayscale value range includes: determining the pixel as a bad pixel in response to the grayscale value of the pixel exceeding the normal grayscale value range. When the grayscale value of the pixel exceeds the normal grayscale value range, the pixel can be determined as a bad pixel. That is, the bad pixel can be determined by comparing the grayscale value, further simplifying the bad pixel determination process.

[0016] In some embodiments, correcting the grayscale value of a bad pixel based on the grayscale values ​​of several pixels adjacent to the bad pixel to obtain a detection image includes: traversing the bad pixels, establishing a local window for any bad pixel, the local window including at least one pixel whose grayscale value is within a normal grayscale value range; using the median of the grayscale values ​​of at least one pixel whose grayscale value is within the normal grayscale value range as the grayscale value of the bad pixel; and performing histogram equalization on the grayscale values ​​of the pixels within the local window to obtain a detection image. By establishing a local window for the bad pixel and using the median of the grayscale values ​​of at least one pixel whose grayscale value is within the normal grayscale value range included in the local window as the grayscale value of the bad pixel, the difficulty of correcting the bad pixel in the image can be reduced and the accuracy of the correction can be improved; and by simultaneously performing histogram equalization on the grayscale values ​​of the pixels within the local window to obtain a detection image, the contrast of the detection image can be enhanced.

[0017] An embodiment of the second aspect of the present application provides a battery detection method, comprising: acquiring an image of a battery, wherein the image comprises a multi-frame sequence of original images; processing the multi-frame sequence of original images using the image bad pixel correction method of the above embodiment to obtain a detection image; and determining a battery detection result based on the detection image. By processing the battery image using the image bad pixel correction method of the above embodiment to obtain a detection image, and determining the battery detection result based on the detection image, the problem of low battery detection result accuracy caused by the presence of bad pixels in the image can be solved, thereby improving the accuracy of the battery detection result.

[0018] In some embodiments, the battery testing method further includes, in response to the battery test result indicating that the battery is unqualified, rejecting the unqualified battery. In response to the battery test result indicating that the battery is unqualified, the unqualified battery can be transferred to a waste outlet. This battery testing method is suitable for large-scale batch production testing and can, to a certain extent, prevent unqualified batteries from entering the market.

[0019] The third aspect of the present application provides an image bad pixel correction device, comprising an acquisition module, a noise reduction module, a bad pixel identification module, and a correction module. The acquisition module is configured to acquire a multi-frame sequence original image; wherein the multi-frame sequence original image is a multi-frame image acquired continuously within a preset time; the noise reduction module is configured to perform noise reduction processing on the multi-frame sequence original image to obtain a single-frame original image; the bad pixel identification module is configured to identify bad pixels in the single-frame original image and determine the bad pixels; and the correction module is configured to correct the grayscale value of the bad pixel based on the grayscale values ​​of several pixels adjacent to the bad pixel to obtain a detection image.

[0020] An embodiment of the fourth aspect of the present application provides a battery detection device, including an image acquisition module, an image processing module and a defect detection module. The image acquisition module is configured to obtain a detection image of the battery, wherein the detection image includes a multi-frame sequence of original images; the image processing module is configured to use the image bad pixel correction method in the above embodiment to process the multi-frame sequence of original images to obtain a battery detection image; the defect detection module is configured to determine the battery detection result based on the detection image.

[0021] An embodiment of the fifth aspect of the present application provides an electronic device, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein 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 execute the image bad pixel correction method in the above-mentioned embodiment or the battery detection method in the above-mentioned embodiment.

[0022] An embodiment of the sixth aspect of the present application provides a battery production device, including the electronic device in the above embodiment.

[0023] An embodiment of the seventh aspect of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the image bad pixel correction method in the above embodiment or the battery detection method in the above embodiment is implemented.

[0024] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present application and should not be regarded as limiting the scope of the present application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on the drawings without paying creative work.

[0026] FIG1 is a flow chart of a method for correcting bad pixels in an image provided by some embodiments of the present application;

[0027] FIG2 is a flow chart of a battery detection method provided by some embodiments of the present application;

[0028] FIG3 is an image bad pixel correction device provided by some embodiments of the present application;

[0029] FIG4 is a battery detection device provided in some embodiments of the present application;

[0030] FIG5 is a flowchart of another method for correcting bad pixels in an image provided by some embodiments of the present application. DETAILED DESCRIPTION

[0031] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0033] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0034] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0035] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0036] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0037] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0038] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0039] Currently, indirect conversion flat-panel detectors (FPDs) using cesium iodide as a scintillator account for approximately 90% of the flat-panel detector market, thanks to their high detection efficiency, cost-effectiveness, excellent imaging quality, and long service life. This share is expected to increase further in the automation industry. Indirect conversion FPDs operate by converting high-energy X-rays absorbed by a scintillator into low-energy visible light, which is then converted through photoelectric signal conversion circuitry to produce an X-ray image.

[0040] With the use of indirect conversion flat panel detectors, bad pixels are more likely to appear on them. There are two main types of bad pixels: those that appear in fixed locations due to manufacturing process problems, and those that appear relatively fixed over a period of time during use. The presence of bad pixels seriously affects subsequent image processing and the final inspection results based on the images, especially when detecting tiny particle defects.

[0041] In order to correct bad pixels detected by flat panel, a bad pixel template based on dark field images can be used for correction. The disadvantage of this solution is that it can correct bad pixels caused by process problems, but when new bad pixels appear, the bad pixel template must be remade. The production of bad pixel templates is time-consuming and labor-intensive, and seriously affects the production capacity and product safety in the field of industrial quality inspection.

[0042] Based on the above considerations, in order to solve the above-mentioned problem of time-consuming and labor-intensive production caused by the production of bad pixel templates, which seriously affects the production capacity and product safety in the industrial quality inspection field, a bad pixel correction method is designed. The grayscale histogram of the average dark field image, the average bright field image, and the gain coefficient matrix is ​​used to calculate the judgment range of three types of bad pixels, namely, dark current exceeding the limit bad pixels, unstable bad pixels, and response exceeding the limit pixels. The bad pixels are marked and divided into the first and second categories according to the number of bad pixels. The first category of bad pixels is first corrected using bilinear interpolation and tri-spline interpolation, and then the average value of normal pixels around the second category of bad pixels is used for correction. However, in such a bad pixel correction method, dark field, bright field, and gain coefficient images are required to correct bad pixels in fixed positions, and new bad pixels appear after a period of use, making this bad pixel correction method unfeasible.

[0043] Based on this, it is necessary to provide an image bad pixel correction method and device, a battery detection method and device to address the above technical problems. By noise reduction processing, bad pixel identification and correction of the grayscale values ​​of bad pixels based on the grayscale values ​​of several pixels adjacent to the bad pixels, bad pixels can be corrected without using a correction template. Therefore, it can be applicable to any type and any number of bad pixels, thereby expanding the scope of application of the image bad pixel correction method.

[0044] The image bad pixel correction method disclosed in the embodiment of the present application can be applied to battery image detection, and can be applied to, but not limited to, the detection process in the production process of batteries. The image bad pixel correction method of the present application corrects the grayscale value of the bad pixel by the grayscale values ​​of several pixel points adjacent to the bad pixel, and can correct bad pixels in different positions, which can improve the rate of bad pixel correction.

[0045] FIG1 is a flow chart of a method for correcting bad pixels in an image provided by some embodiments of the present application. As shown in FIG1 , the method for correcting bad pixels in an image includes:

[0046] Step S110 , obtaining a multi-frame sequence of original images; wherein the multi-frame sequence of original images is a multi-frame image obtained continuously within a preset time.

[0047] Step S120 , performing noise reduction processing on the multi-frame sequence original image to obtain a single-frame original image.

[0048] Step S130 , performing bad pixel identification on the single frame original image to determine the bad pixels.

[0049] Step S140 , correcting the grayscale value of the bad pixel based on the grayscale values ​​of several pixels adjacent to the bad pixel to obtain a detection image.

[0050] In an embodiment of the present application, a multi-frame sequence of original images can be acquired using a radiation source and a flat-panel detector. The radiation emitted by the radiation source passes through a detection object and is projected onto the flat-panel detector, which then acquires an image based on the received radiation. Images can be acquired continuously at a preset frequency, and multiple frames of images can be acquired continuously within a preset time. These continuously acquired multiple frames of images can serve as the multi-frame sequence original image. In an embodiment of the present application, the preset time can be determined based on the number of multi-frame sequence original images to be acquired and the frequency of image acquisition.

[0051] In an embodiment of the present application, the shape, size, and number of pixels contained in each frame image in the multi-frame sequence original image are all the same. In the process of acquiring the multi-frame sequence original image, electronic noise may exist in the multi-frame sequence original image due to the influence of equipment or environmental factors. The grayscale values ​​of the pixels in the multi-frame sequence original image where electronic noise exists will be abnormal, and the grayscale values ​​of the pixels will be too large or too small. In order not to affect the correction of bad pixels in the image, the multi-frame sequence original image can be subjected to noise reduction processing. The method of performing noise reduction processing on the multi-frame sequence original image can be to filter each frame image in the multi-frame sequence original image, or to perform noise reduction processing on the multi-frame sequence original image at the same time based on the grayscale values ​​of the pixels in each frame image in the multi-frame sequence original image.

[0052] In the embodiment of the present application, the shape, size, and number of pixels of the single-frame original image are identical to the shape, size, and number of pixels of each frame in the multi-frame sequence original image. The grayscale values ​​of the corresponding pixels of the single-frame original image can be determined based on the multiple grayscale values ​​of the multi-frame sequence original image after noise reduction processing, thereby obtaining the single-frame original image. The single-frame original image is free of electronic noise.

[0053] In the embodiment of the present application, for example, when the devices for acquiring the original multi-frame sequence are a radiation source and a flat-panel detector, in the flat-panel detector, when a certain pixel does not respond to the radiation or responds abnormally, such a detector pixel is referred to as a bad pixel. Specifically, a bad pixel can refer to a pixel that does not change with the light sensitivity and always presents a single color (for example, white, black, or color), thereby destroying the clarity and integrity of the image. The presence of bad pixels in the image is one of the reasons for the degradation of image quality. Moreover, due to the increase in bad pixels, the image in a low-noise environment will be even worse, which is likely to affect the subsequent use of the image.

[0054] In the embodiments of the present application, bad pixels can be identified based on the normal grayscale value range of pixels in the image. When the grayscale value of a pixel exceeds the normal grayscale value range, the pixel can be determined to be a bad pixel. Alternatively, a pre-trained bad pixel identification model can be used to identify bad pixels in a single frame of the original image. After bad pixels are identified, they can be marked.

[0055] In an embodiment of the present application, at least one pixel having a grayscale value within a normal range is included among several pixel points adjacent to the bad pixel. The grayscale value of the bad pixel can be corrected based on the grayscale value of the pixel points having a grayscale value within a normal range included among several pixel points adjacent to the bad pixel, so that the pixel value of the bad pixel is also within the normal range, thereby obtaining a detection image without bad pixels.

[0056] In the embodiments of the present application, a single original image is obtained by performing noise reduction on a multi-frame sequence of original images. This single original image no longer contains electronic noise, which reduces interference with bad pixel identification and makes the identification of bad pixels more accurate. Furthermore, the grayscale value of the bad pixel is corrected based on the grayscale values ​​of several pixels adjacent to the bad pixel. This allows for correction of bad pixels without the use of a correction template, making it applicable to any type and number of bad pixels, thereby expanding the scope of application of the image bad pixel correction method.

[0057] According to some embodiments of the present application, denoising is performed on a multi-frame sequence original image to obtain a single-frame original image, including: judging whether electronic noise exists at a pixel point based on multiple grayscale values ​​corresponding to any pixel point at the same position in the multi-frame sequence original image; and determining the grayscale value of the pixel point corresponding to the single-frame original image based on the judgment result.

[0058] In an embodiment of the present application, each frame in a multi-frame sequence image has the same shape, size, and number of pixels. Since a multi-frame sequence image is a collection of images acquired continuously within a preset timeframe, theoretically, the grayscale values ​​corresponding to pixels at the same position in each frame should be the same or similar. This means that the multiple grayscale values ​​corresponding to pixels at the same position in a multi-frame sequence image should be the same or similar. However, when interference such as electronic noise is present at a pixel, the grayscale value corresponding to that pixel may suddenly change. Therefore, the presence of electronic noise can be determined based on the multiple grayscale values ​​corresponding to any pixel at the same position in the multi-frame sequence image. Specifically, the grayscale value corresponding to any pixel at any position in any image can be compared with the grayscale values ​​at the same position in other images. If the grayscale value corresponding to any pixel at any position in an image differs from the grayscale values ​​at the same position in other images, electronic noise may be present at that pixel in that image. The presence of electronic noise can also be determined by calculating the standard deviation of the pixel based on the multiple grayscale values ​​corresponding to any pixel at the same position in the multi-frame sequence image.

[0059] In an embodiment of the present application, the judgment results include whether electronic noise exists in a pixel and whether electronic noise does not exist in the pixel. When electronic noise exists in a pixel, the grayscale value of the corresponding pixel in the single-frame original image can be determined based on the normal grayscale value among the multiple grayscale values ​​corresponding to the pixel at the same position in the multi-frame sequence original image. When electronic noise does not exist in a pixel, the grayscale value of the corresponding pixel in the single-frame original image can be determined based on the multiple grayscale values ​​corresponding to the pixel at the same position in the multi-frame sequence original image.

[0060] In an embodiment of the present application, by using multiple grayscale values ​​corresponding to any pixel at the same position in a multi-frame sequence original image, it is determined whether there is electronic noise in the pixel point, and the grayscale value of the pixel point corresponding to the single-frame original image is determined based on the judgment result. This can eliminate the adverse effects of electronic noise on the image to a certain extent and improve the image quality.

[0061] According to some embodiments of the present application, judging whether electronic noise exists at a pixel point based on multiple grayscale values ​​corresponding to any pixel point at the same position in a multi-frame sequence original image includes: calculating the standard deviation of the pixel point based on multiple grayscale values ​​corresponding to any pixel point at the same position in the multi-frame sequence original image; and judging that electronic noise exists at the pixel point in response to the standard deviation of the pixel point being greater than a preset threshold.

[0062] In an embodiment of the present application, the standard deviation of the grayscale values ​​of a pixel can reflect the degree of discreteness of the multiple grayscale values ​​corresponding to any pixel at the same position in the multi-frame sequence original image. The larger the standard deviation of the grayscale values ​​of a pixel, the more discrete the multiple grayscale values ​​corresponding to any pixel at the same position in the multi-frame sequence original image. The more discrete the multiple grayscale values ​​corresponding to any pixel at the same position in the multi-frame sequence original image, the more interference such as electronic noise is present at the pixel. Therefore, the standard deviation of the grayscale values ​​of a pixel can be calculated based on the multiple grayscale values ​​corresponding to any pixel at the same position in the multi-frame sequence original image to determine whether electronic noise exists at the pixel. In response to the standard deviation of the pixel being greater than a preset threshold, it can be determined that electronic noise exists at the pixel.

[0063] In an embodiment of the present application, the preset threshold can be set according to the actual usage scenario of the image bad pixel correction method, and the embodiment of the present disclosure does not limit it. For example, when a 16-bit image is selected, the preset threshold can be 10.

[0064] In an embodiment of the present application, the standard deviation of the grayscale value of a pixel point calculated from multiple grayscale values ​​corresponding to any pixel point at the same position in the original multi-frame sequence image is compared with a preset threshold to determine whether electronic noise exists in the pixel point. This can make the process of determining whether electronic noise exists in the pixel point simpler, thereby improving the efficiency of image bad pixel correction.

[0065] According to some embodiments of the present application, determining the grayscale value of a pixel point corresponding to a single-frame original image based on a judgment result includes: in response to the presence of electronic noise in the pixel point, performing a clustering operation on multiple grayscale values ​​corresponding to the pixel point of a multi-frame sequence original image, and marking the multiple grayscale values ​​as normal or abnormal based on the result of the clustering operation; calculating the average value of the grayscale values ​​marked as normal among the multiple grayscale values ​​and using it as the grayscale value of the corresponding pixel point in the single-frame original image.

[0066] In the embodiment of the present application, a clustering operation is performed on the pixels corresponding to the multiple grayscale values ​​of the multi-frame sequence original image to obtain the clustering operation results. The clustering operation results are then separated, and the grayscale values ​​of the multi-frame sequence original image that are less than or equal to the reference threshold are marked as normal, and the others are marked as abnormal. The abnormal grayscale values ​​are electronic noise, and normal grayscale values ​​are distinguished from electronic noise.

[0067] In an embodiment of the present application, due to the influence of environmental factors, etc., the grayscale values ​​marked as normal are not necessarily completely equal. In order to make the grayscale values ​​in a single-frame image more accurate, the average value of the grayscale values ​​marked as normal among multiple grayscale values ​​can be calculated and used as the grayscale value of the corresponding pixel point in the single-frame original image.

[0068] In the embodiment of the present application, normal grayscale values ​​and abnormal grayscale values ​​can be effectively separated through clustering operations, and the average value of the normal grayscale values ​​among multiple grayscale values ​​is calculated as the grayscale value of the corresponding pixel point in the single-frame original image. The grayscale value of the corresponding pixel point in the single-frame original image can eliminate image anomalies caused by abnormal grayscale values ​​generated by electronic noise to a certain extent, thereby improving the image quality.

[0069] According to some embodiments of the present application, determining the grayscale value of a pixel point corresponding to a single-frame original image based on a judgment result also includes: in response to the absence of electronic noise at the pixel point, calculating the average value of multiple grayscale values ​​corresponding to the pixel point in a multi-frame sequence of original images and using it as the grayscale value of the corresponding pixel point in the single-frame original image.

[0070] In the embodiment of the present application, when electronic noise is absent from the pixels, the multiple grayscale values ​​of the multi-frame sequence original image are all normal values. However, due to environmental factors, the multiple grayscale values ​​of the multi-frame sequence original image may not be completely equal. To make the grayscale value of the corresponding pixel in a single-frame original image more accurate, the average of the multiple grayscale values ​​corresponding to the pixel in the multi-frame sequence original image can be calculated and used as the grayscale value of the corresponding pixel in the single-frame original image.

[0071] In an embodiment of the present application, by calculating the average value of multiple grayscale values ​​corresponding to the pixel points of the multi-frame sequence original image and using it as the grayscale value of the corresponding pixel point in the single-frame original image, the image abnormality caused by the abnormal grayscale value of a single pixel point can be reduced to a certain extent, making the image display more uniform and improving the image quality.

[0072] According to some embodiments of the present application, bad pixels are identified for a single-frame original image, and determining the bad pixels includes: determining a normal grayscale value range of the single-frame image; and determining the bad pixels in the single-frame original image based on the grayscale values ​​of the pixels in the single-frame image and the normal grayscale value range.

[0073] In an embodiment of the present application, the normal grayscale value range of a single-frame image includes the maximum normal grayscale value of the single-frame image and the minimum normal grayscale value of the single-frame image. The grayscale value range of a single-frame image can be obtained by statistically analyzing the grayscale values ​​of the single-frame image. However, in this case, the grayscale values ​​of the single-frame image may include the grayscale values ​​of bad pixels in the single-frame original image. The pixels with the largest grayscale value and the pixels with the smallest grayscale value are most likely to be bad pixels. Therefore, the grayscale value range of the single-frame image can be determined as the normal grayscale value range of the single-frame image by removing the maximum grayscale value and the minimum grayscale value. The normal grayscale value range of the single-frame image can also be determined by multiplying the grayscale value range of the single-frame image by a preset ratio.

[0074] In an embodiment of the present application, when the grayscale value of a pixel exceeds the normal grayscale value range of a single-frame image, it can be determined that the pixel is a bad pixel in the single-frame original image.

[0075] In an embodiment of the present application, bad pixels in a single-frame original image are determined based on the grayscale values ​​of the pixels in the single-frame image and the normal grayscale value range, which can make the process of determining bad pixels simpler, thereby improving the efficiency of image bad pixel correction.

[0076] According to some embodiments of the present application, determining the normal grayscale value range of a single-frame image includes: selecting a region of interest of the single-frame image; wherein the region of interest includes multiple pixels; calculating the histogram distribution of the grayscale values ​​of the pixels in the region of interest; and determining the normal grayscale range of the single-frame image based on the histogram distribution.

[0077] In the embodiments of the present application, for example, the probability of bad pixels appearing in the center region of a single-frame image is generally low, and thus the center region of the single-frame image can be selected as the region of interest for the single-frame image. Since the region of interest for a single-frame image is selected to determine the normal grayscale range of the single-frame image, the region of interest needs to include multiple pixels, and the specific number of pixels is not specifically limited in the embodiments of the present application.

[0078] In the embodiments of the present application, the pixel grayscale value histogram calculates the probability of occurrence of the grayscale values ​​of all pixels in the image, based on their grayscale values. A pixel grayscale value histogram is a function of grayscale level and can represent the number of pixels with a certain grayscale value in the image, reflecting the probability of a certain grayscale value occurring in the image. Based on the histogram distribution of the pixel grayscale values ​​in the region of interest, the normal grayscale range of a single frame image can be determined.

[0079] In an embodiment of the present application, by selecting a region of interest of a single-frame image and determining the normal grayscale range of the single-frame image based on the calculated histogram distribution of the grayscale values ​​of the pixels in the region of interest, the determined normal grayscale range can be made more accurate.

[0080] According to some embodiments of the present application, determining bad pixels in a single-frame original image based on the grayscale value of the pixel point in the single-frame image and the normal grayscale value range includes: determining the pixel point as a bad pixel in response to the grayscale value of the pixel point exceeding the normal grayscale value range.

[0081] In an embodiment of the present application, the grayscale value of the pixel exceeds the normal grayscale range of a single-frame image, including the grayscale value of the pixel being greater than the maximum normal grayscale value of a single-frame image or less than the minimum normal grayscale value of a single-frame image.

[0082] In the embodiment of the present application, when the grayscale value of a pixel exceeds the normal grayscale value range, the pixel can be determined as a bad pixel, that is, the bad pixel can be determined by comparing the grayscale values, which further simplifies the process of determining the bad pixel.

[0083] According to some embodiments of the present application, correcting the grayscale value of the bad point based on the grayscale values ​​of several pixel points adjacent to the bad point to obtain a detection image includes: traversing the bad points, establishing a local window for any bad point, the local window including at least one pixel point whose grayscale value is within the normal grayscale value range; taking the median of the grayscale value of at least one pixel point whose grayscale value is within the normal grayscale value range as the grayscale value of the bad point; and performing histogram equalization on the grayscale values ​​of the pixel points within the local window to obtain a detection image.

[0084] In an embodiment of the present application, bad pixels may include independent bad pixels and bad pixel clusters consisting of multiple bad pixels. The size of the local window established for the bad pixel can be determined based on the size of the bad pixel. For example, when the bad pixel is an independent bad pixel, the established local window may include a 2×2 local window, the independent bad pixel is a pixel in the local window, and the local window includes three pixels whose grayscale values ​​are within the normal grayscale value range. The grayscale values ​​of the three pixels whose grayscale values ​​are within the normal grayscale value range can be sorted, and the median of the grayscale values ​​of the three pixels whose grayscale values ​​are within the normal grayscale value range is used as the grayscale value of the bad pixel.

[0085] In the embodiment of the present application, histogram equalization can change the grayscale values ​​of pixels in a local window from a relatively concentrated grayscale interval to a uniform distribution within the entire grayscale range, thereby effectively enhancing the contrast of the image.

[0086] In the embodiment of the present application, after assigning grayscale values ​​to bad pixels, histogram equalization is performed on the grayscale values ​​of the pixels within the local window. By traversing all bad pixels and performing the above operation on all bad pixels, an image with the bad pixels corrected can be obtained, that is, a detection image.

[0087] In an embodiment of the present application, by establishing a local window for the bad pixel and taking the median of the grayscale values ​​of at least one pixel within the local window whose grayscale value is within the normal grayscale value range as the grayscale value of the bad pixel, the difficulty of correcting the bad pixel in the image can be reduced and the accuracy of the correction can be improved; at the same time, histogram equalization is performed on the grayscale values ​​of the pixel points within the local window to obtain a detection image, which can enhance the contrast of the detection image.

[0088] FIG2 is a flow chart of a battery detection method provided in some embodiments of the present application. As shown in FIG2 , the battery detection method includes:

[0089] Step S210: Acquire an image of the battery, wherein the image includes a plurality of original frames of a sequence of images.

[0090] Step S220: using the image bad pixel correction method of the above embodiment to process the multi-frame sequence original image to obtain a detection image.

[0091] Step S230: determining the battery detection result based on the detection image.

[0092] In an embodiment of the present application, an image of a battery can be obtained using a flat-panel detector under irradiation with a radiation source. The image of the battery can include a multi-frame sequence of original images obtained continuously within a preset time. By processing the multi-frame sequence of original images using the image defect pixel correction method of the above embodiment, a defect-free detection image can be obtained.

[0093] In an embodiment of the present application, the battery detection operation can be performed using a detection model or a recognition algorithm, and the selection can be made based on the actual application and needs. For example, in order to implement battery detection, a detection model can be pre-trained to obtain a pre-trained detection model. After obtaining a detection image, the detection image is input into the pre-trained detection model. After being processed by the detection model, the battery detection result can be obtained. The battery detection result can include whether the battery is qualified or unqualified.

[0094] In an embodiment of the present application, by adopting the image bad pixel correction method of the aforementioned embodiment to process the image of the battery to obtain a detection image, and determining the battery detection result based on the detection image, the problem of low accuracy of the battery detection result due to the presence of bad pixels in the image can be solved, thereby improving the accuracy of the battery detection result.

[0095] According to some embodiments of the present application, the battery detection method further includes: in response to the battery detection result indicating that the battery is unqualified, rejecting the unqualified battery.

[0096] In the embodiment of the present application, in response to the battery test result indicating that the battery is unqualified, the unqualified battery can be transferred to a waste outlet. This battery testing method is suitable for large-scale batch production testing and can, to a certain extent, prevent unqualified batteries from entering the market.

[0097] FIG3 is an image bad pixel correction device provided by some embodiments of the present application. As shown in FIG3 , the image bad pixel correction device 300 includes an acquisition module 301, a noise reduction module 302, a bad pixel identification module 303, and a correction module 304. The acquisition module 301 is configured to acquire a multi-frame sequence original image; wherein the multi-frame sequence original image is a multi-frame image acquired continuously within a preset time; the noise reduction module 302 is configured to perform noise reduction processing on the multi-frame sequence original image to obtain a single-frame original image; the bad pixel identification module 303 is configured to identify bad pixels in the single-frame original image and determine the bad pixels; and the correction module 304 is configured to correct the grayscale value of the bad pixel based on the grayscale values ​​of several pixels adjacent to the bad pixel to obtain a detection image.

[0098] In some embodiments, the noise reduction module 302 includes: a judgment module, configured to judge whether there is electronic noise at a pixel point based on multiple grayscale values ​​corresponding to any pixel point at the same position in a multi-frame sequence original image; and a determination module, configured to determine the grayscale value of the pixel point corresponding to the single-frame original image based on the judgment result.

[0099] In some embodiments, the judgment module includes: a standard deviation calculation module, configured to calculate the standard deviation of the grayscale value of a pixel point based on multiple grayscale values ​​corresponding to any pixel point at the same position in the original multi-frame sequence image; a first judgment submodule, configured to determine that electronic noise exists in the pixel point in response to the standard deviation of the pixel point being greater than a preset threshold.

[0100] In some embodiments, the determination module includes: an operation module, configured to perform clustering operations on multiple grayscale values ​​corresponding to the pixel points of a multi-frame sequence original image in response to the presence of electronic noise in the pixel points, and mark the multiple grayscale values ​​as normal or abnormal based on the results of the clustering operation; a first grayscale value calculation module, configured to calculate the average value of the grayscale values ​​marked as normal among the multiple grayscale values ​​and use it as the grayscale value of the corresponding pixel point in the single-frame original image.

[0101] In some embodiments, the judgment module also includes: a second grayscale value calculation module, which is configured to calculate the average value of multiple grayscale values ​​corresponding to the pixel points of the multi-frame sequence original image in response to the absence of electronic noise in the pixel points and use it as the grayscale value of the corresponding pixel point in the single-frame original image.

[0102] In some embodiments, the bad pixel identification module 303 includes: a grayscale value range determination module, configured to determine the normal grayscale value range of a single frame image; and a bad pixel determination module, configured to determine the bad pixels in the single frame original image based on the grayscale value of the pixel points in the single frame image and the normal grayscale value range.

[0103] In some embodiments, the grayscale value range determination module includes: a selection module, configured to select a region of interest of a single frame image; wherein the region of interest includes multiple pixels; a histogram distribution calculation module, configured to calculate the histogram distribution of the grayscale values ​​of the pixels in the region of interest; and a normal grayscale range determination module, configured to determine the normal grayscale range of the single frame image based on the histogram distribution.

[0104] In some embodiments, the bad pixel determination module includes: a bad pixel determination submodule configured to determine a pixel as a bad pixel in response to a grayscale value of the pixel exceeding a normal grayscale value range.

[0105] In some embodiments, the correction module 304 includes: a local window establishment module, configured to traverse the bad pixels and establish a local window for any bad pixel, the local window including at least one pixel whose grayscale value is within the normal grayscale value range; an assignment module, configured to use the median of the grayscale values ​​of at least one pixel whose grayscale value is within the normal grayscale value range as the grayscale value of the bad pixel; and a histogram equalization module, configured to perform histogram equalization on the grayscale values ​​of the pixel points within the local window to obtain a detection image.

[0106] FIG4 illustrates a battery testing device according to some embodiments of the present application. As shown in FIG4 , the battery testing device 400 includes an image acquisition module 401, an image processing module 402, and a defect detection module 403. The image acquisition module 401 is configured to acquire an image of the battery, wherein the image comprises a multi-frame sequence of original images. The image processing module 402 is configured to process the multi-frame sequence of original images using the image defect pixel correction method described in the above embodiment to obtain a battery test image. The defect detection module 403 is configured to determine the battery test result based on the test image.

[0107] An embodiment of the present application provides an electronic device, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein 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 execute the image bad pixel correction method in the above-mentioned embodiment or the battery detection method in the above-mentioned embodiment.

[0108] Various embodiments of the systems and techniques described above in this application can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (ASSPs), system on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0109] An embodiment of the present application provides a battery production device, including the electronic device in the above embodiment.

[0110] Some embodiments of the present application further provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the image bad pixel correction method in the above-mentioned embodiment or the battery detection method in the above-mentioned embodiment.

[0111] Computer readable media can be tangible media that can contain or store programs for use by or in conjunction with an instruction execution system, device or equipment. Machine-readable media can be machine-readable signal media or machine-readable storage media. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disc read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0112] According to some embodiments of the present application, a computer program product is provided, including a computer program, which, when executed by a processor, implements the battery image processing method in the above embodiment or the image bad pixel correction method in the above embodiment.

[0113] The technical solution of the present application is further described below through a specific embodiment. FIG5 is a flow chart of another image bad pixel correction method provided by some embodiments of the present application. As shown in FIG5 , the image bad pixel correction method includes:

[0114] Step S501: Frame sequence original image: obtain a multi-frame sequence original image; wherein the multi-frame sequence original image is a multi-frame image continuously obtained within a preset time.

[0115] Step S502: discard the first few frames of original images.

[0116] Step S503: Calculating the standard deviation of the grayscale values ​​of the same pixel position in the original images of different frame sequences: Calculating the standard deviation of the grayscale values ​​of the pixel point based on multiple grayscale values ​​corresponding to any pixel point at the same position in the original images of multiple frame sequences.

[0117] Step S504: Whether the standard deviation is greater than a threshold: Whether the standard deviation of the pixel is greater than a preset threshold, it is determined whether there is electronic noise in the pixel.

[0118] Step S505: Clustering operation marks the noise points and calculates the average value to obtain a single-frame original image: In response to the presence of electronic noise in the pixel points, clustering operation is performed on the multiple grayscale values ​​corresponding to the pixel points of the multi-frame sequence original image, and the multiple grayscale values ​​are marked as normal or abnormal according to the results of the clustering operation; the average value of the grayscale values ​​marked as normal among the multiple grayscale values ​​is calculated and used as the grayscale value of the corresponding pixel point in the single-frame original image.

[0119] Step S506: directly calculating the mean to obtain a single-frame original image: in response to the absence of electronic noise at the pixel point, calculating the average of multiple grayscale values ​​corresponding to the pixel point in the multi-frame sequence original image and using it as the grayscale value of the corresponding pixel point in the single-frame original image.

[0120] Step S507: Acquire the central area of ​​the battery in the single-frame image: select a region of interest in the single-frame image; wherein the region of interest includes a plurality of pixels.

[0121] Step S508: Calculate the normal grayscale range of the single-frame image based on the grayscale value histogram distribution of the region of interest: calculate the histogram distribution of the grayscale values ​​of the pixels in the region of interest; and determine the normal grayscale range of the single-frame image based on the histogram distribution.

[0122] Step S509: Mark the position in the original image where the grayscale value exceeds the normal grayscale range as a bad pixel: In response to the grayscale value of the pixel exceeding the normal grayscale value range, the pixel is determined to be a bad pixel.

[0123] Step S510: traverse all bad pixels and calculate the size and concentration of the bad pixels.

[0124] Step S511: Select a corresponding window based on the size of the bad pixel, and obtain a corresponding thumbnail with the bad pixel as the center: traverse the bad pixels and establish a local window for any bad pixel. The local window includes at least one pixel whose grayscale value is within the normal grayscale value range.

[0125] Step S512: filter out the small images in the window around the bad pixel and arrange the grayscale values ​​of the window: take the median of the grayscale values ​​of at least one pixel whose grayscale value is within the normal grayscale value range as the grayscale value of the bad pixel.

[0126] Step S513: Select the median value in the normal response grayscale value sequence as the grayscale value of the center point of the small image and then perform grayscale histogram equalization assignment: perform histogram equalization on the grayscale values ​​of the pixels in the local window.

[0127] Step S514: Original image without bad pixels: obtaining a battery detection image.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A method for correcting bad pixels of an image, comprising: Acquire a multi-frame sequence of original images; wherein the multi-frame sequence of original images is a multi-frame image acquired continuously within a preset time; Performing noise reduction processing on the multi-frame sequence original image to obtain a single-frame original image; Perform bad pixel identification on the single frame original image to determine the bad pixels; The grayscale value of the bad point is corrected based on the grayscale values ​​of a plurality of pixel points adjacent to the bad point to obtain a detection image.

2. The image bad pixel correction method according to claim 1, wherein: The performing noise reduction processing on the multi-frame sequence original image to obtain a single-frame original image includes: According to a plurality of grayscale values ​​corresponding to any pixel at the same position in the original image of the multi-frame sequence, it is determined whether the pixel has electronic noise; According to the judgment result, the grayscale value of the pixel corresponding to the single frame original image is determined.

3. The image bad pixel correction method according to claim 2, wherein: The determining, based on the multiple grayscale values ​​corresponding to any pixel at the same position in the multi-frame sequence original image, whether the pixel has electronic noise comprises: Calculate the standard deviation of the grayscale value of the pixel point according to multiple grayscale values ​​corresponding to any pixel point at the same position in the multi-frame sequence original image; In response to the standard deviation of the pixel being greater than a preset threshold, it is determined that electronic noise exists at the pixel.

4. The image bad pixel correction method according to claim 2 or 3, wherein: Determining the grayscale value of the pixel corresponding to the single frame original image according to the judgment result includes: In response to the existence of electronic noise at the pixel point, clustering operations are performed on the plurality of grayscale values ​​of the pixel point corresponding to the plurality of frame sequence original images, and marking the plurality of grayscale values ​​as normal or abnormal according to the result of the clustering operation; An average value of the grayscale values ​​marked as normal among the multiple grayscale values ​​is calculated and used as the grayscale value of the corresponding pixel point in the single frame original image.

5. The image bad pixel correction method according to any one of claims 2 to 4, wherein: Determining the grayscale value of the pixel corresponding to the single frame original image according to the judgment result also includes: In response to the absence of electronic noise at the pixel point, an average value of a plurality of grayscale values ​​of the pixel point corresponding to the multi-frame sequence original images is calculated and used as the grayscale value of the corresponding pixel point in the single frame original image.

6. The image bad pixel correction method according to any one of claims 1 to 5, wherein: The step of identifying bad pixels on the single frame original image and determining the bad pixels comprises: Determining a normal grayscale value range of the single-frame image; Determine the bad pixels in the single-frame original image according to the grayscale values ​​of the pixels in the single-frame image and the normal grayscale value range.

7. The image bad pixel correction method according to claim 6, wherein: Determining the normal grayscale value range of the single frame image includes: Selecting a region of interest of the single-frame image; wherein the region of interest includes a plurality of pixel points; Calculating the histogram distribution of the grayscale values ​​of the pixels in the region of interest; A normal grayscale range of the single-frame image is determined according to the histogram distribution.

8. The image bad pixel correction method according to claim 6 or 7, wherein: Determining the bad pixels in the single-frame original image according to the grayscale values ​​of the pixels in the single-frame image and the normal grayscale value range includes: In response to the grayscale value of the pixel point exceeding the normal grayscale value range, the pixel point is determined as a bad pixel.

9. The image bad pixel correction method according to any one of claims 1 to 8, wherein: The step of correcting the grayscale value of the bad point based on the grayscale values ​​of a plurality of pixels adjacent to the bad point to obtain a detection image comprises: Traversing the bad pixels, establishing a local window for any of the bad pixels, wherein the local window includes at least one pixel whose grayscale value is within the normal grayscale value range; Taking the median of the grayscale values ​​of the pixel points whose grayscale value is within the normal grayscale value range as the grayscale value of the bad pixel; Histogram equalization is performed on the grayscale values ​​of the pixels in the local window to obtain a detection image.

10. A battery detection method, comprising: Acquire an image of the battery, wherein the image includes a plurality of original frames of a sequence of images; The method for correcting bad pixels in an image according to any one of claims 1 to 9 is used to process the original multi-frame sequence images to obtain a detection image; A detection result of the battery is determined based on the detection image.

11. The battery detection method according to claim 10, wherein: The method further comprises: In response to the test result of the battery indicating that the battery is unqualified, the unqualified battery is rejected.

12. An image bad pixel correction device, comprising: An acquisition module is configured to acquire a multi-frame sequence of original images; wherein the multi-frame sequence of original images is a multi-frame image acquired continuously within a preset time; A noise reduction module is configured to perform noise reduction processing on the multi-frame sequence original image to obtain a single-frame original image; A bad pixel identification module is configured to identify bad pixels on the single frame original image and determine the bad pixels; The correction module is configured to correct the grayscale value of the bad point based on the grayscale values ​​of a plurality of pixel points adjacent to the bad point to obtain a detection image.

13. A battery detection device, comprising: An image acquisition module is configured to acquire a detection image of the battery, wherein the detection image includes a plurality of frame sequence original images; An image processing module, configured to process the multi-frame sequence original image using the image bad pixel correction method according to any one of claims 1 to 9 to obtain a battery detection image; The defect detection module is configured to determine a detection result of the battery based on the detection image.

14. An electronic device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein, 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 execute the image bad pixel correction method described in any one of claims 1 to 9 or the battery detection method described in claim 10 or 11.

15. A battery production device comprising the electronic device according to claim 14.

16. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the image bad pixel correction method according to any one of claims 1 to 9 or the battery detection method according to claim 10 or 11 is implemented.

Images