Battery diaphragm dispensing detection system and detection method

Abstract

The invention belongs to the technical field of image data processing, and particularly relates to a battery diaphragm dispensing detection system and method, and the system comprises a control module and a multi-station visual detection device. The control module collects an original gray level image after the battery diaphragm is dispensed through the multi-station visual inspection device; the control module divides the original grayscale image into a plurality of grayscale adjustment areas, and performs corresponding grayscale adjustment on each grayscale adjustment area to form a grayscale image to be identified; the control module carries out image recognition on the to-be-recognized gray level image to judge whether a point coating defect exists or not; according to the invention, the original gray level image is divided into a plurality of gray level adjustment areas, and each gray level adjustment area is provided with a proper gray level value, so that the coating point and the background area can be obviously displayed, and meanwhile, the problem that the point coating defect cannot be accurately identified due to the fact that the local area in the original gray level image is too bright or too dark can be avoided; the recognition precision and the recognition efficiency are improved.

Description

Technical Field

[0001] This invention belongs to the field of image data processing technology, specifically relating to image analysis systems, and more particularly to a battery separator spot coating detection system and detection method. Background Technology

[0002] When industrial cameras identify painted spots in images, factors such as lighting angle, camera acquisition angle, and the position of the painted spot can cause differences in grayscale at different locations in the image during visual recognition. If the image recognition uses the same grayscale to identify all painted spots, some painted spots will be misjudged.

[0003] Therefore, there is an urgent need to develop a new battery separator coating detection system and method to solve the technical problem of misjudgment caused by grayscale deviation during the collection and identification process of coating spots.

[0004] It should be noted that the information disclosed in this background section is only for understanding the background technology of the present application concept, and therefore, the above description is not considered to constitute prior art information. Summary of the Invention

[0005] This disclosure provides at least one battery separator spot coating detection system and detection method.

[0006] In a first aspect, embodiments of this disclosure provide a battery separator spot coating inspection system, comprising: a control module and a multi-station visual inspection device; wherein the multi-station visual inspection device is electrically connected to the control module; the control module is configured to acquire an original grayscale image of the battery separator after spot coating through the multi-station visual inspection device; the control module is further configured to divide the original grayscale image into several grayscale adjustment regions, and to perform corresponding grayscale adjustments on each grayscale adjustment region to form a grayscale image to be identified; and the control module is further configured to perform image recognition on the grayscale image to be identified to determine whether spot coating defects exist in the grayscale image to be identified.

[0007] In one optional embodiment, the multi-station visual inspection device includes: several cameras and several line-scan LED light sources; the line-scan LED light sources emit light to the battery separator, so that the light is reflected or transmitted through the battery separator and then enters the corresponding camera, thereby the control module acquires the corresponding original grayscale image.

[0008] In one optional embodiment, the battery separator is conveyed by a set of conveying rollers, and several cameras and corresponding line scan LED light sources are located on the same side of the battery separator. The cameras are arranged in sequence parallel to the line scan LED light sources. The line scan LED light sources are adapted to illuminate the battery separator and reflect the light back to each camera after the battery separator is coated, thereby acquiring the original grayscale image of the battery separator after the coating is applied.

[0009] In one optional embodiment, the battery separator is conveyed by a set of conveying rollers, and several cameras and corresponding line scan LED light sources are located on both sides of the battery separator. The cameras are arranged in sequence parallel to the line scan LED light sources. The line scan LED light sources are adapted to illuminate the battery separator and are transmitted through the battery separator to each camera, that is, to collect the original grayscale image of the battery separator after dot coating.

[0010] In one optional implementation, the control module performs image recognition on the grayscale image to be identified to determine whether there are dot painting defects in the grayscale image to be identified. This includes: dividing the grayscale image to be identified into several blocks; calculating the grayscale threshold of each block using the Niblack algorithm to distinguish between painted dots and background areas; and detecting painted dots and background areas in the blocks using image recognition to determine whether there are dot painting defects.

[0011] In one alternative implementation, the defects in dot coating include: sticky spots, leaks, abnormal dot diameter, abnormal lateral spacing of dots, abnormal longitudinal spacing of dots, and abnormal dot coverage.

[0012] In one optional implementation, the control module divides the original grayscale image into several grayscale adjustment regions and performs corresponding grayscale adjustments on each grayscale adjustment region to form a grayscale image to be recognized. This includes: obtaining the average grayscale value of the image background; obtaining global grayscale feature parameters of the original grayscale image; inputting the average grayscale value of the image background and the global grayscale feature parameters into the Sauvola algorithm to calculate grayscale boundary parameters; dividing the original grayscale image into several grayscale adjustment regions according to the grayscale boundary parameters; and performing corresponding grayscale adjustments on each grayscale adjustment region to form a grayscale image to be recognized.

[0013] In one optional implementation, the average grayscale value of the image background and global grayscale feature parameters are input into the Sauvola algorithm to calculate grayscale boundary parameters, which include: ; ;in, To adaptively adjust parameters, This represents the lower limit boundary of the grayscale adjustment area. This represents the upper limit boundary of the grayscale adjustment area.

[0014] In one optional implementation, the original grayscale image is divided into several grayscale adjustment regions according to grayscale boundary parameters, which includes: dividing the original grayscale image into several blocks according to block size parameters, and calculating the corresponding number of rows and columns, i.e., obtaining the coordinates of each block; calculating the average grayscale of each block, and obtaining the comparison result with the grayscale boundary parameters; and constructing the corresponding grayscale adjustment region according to the coordinates of each block and the comparison result.

[0015] Secondly, this disclosure also provides a detection method using the battery separator spot coating detection system described above, comprising: a control module acquiring an original grayscale image of the battery separator after spot coating through a multi-station visual inspection device; the control module dividing the original grayscale image into several grayscale adjustment areas, and performing corresponding grayscale adjustments on each grayscale adjustment area to form a grayscale image to be identified; and the control module performing image recognition on the grayscale image to be identified to determine whether there are spot coating defects in the grayscale image to be identified.

[0016] The beneficial effects of this invention are that by dividing the original grayscale image into several grayscale adjustment areas and setting appropriate grayscale values ​​for each grayscale adjustment area, the invention can clearly display the painted dots and background areas, while avoiding the problem of inaccurate identification of painted dots due to local areas being too bright or too dark in the original grayscale image, thereby improving recognition accuracy and efficiency.

[0017] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a structural diagram of a battery separator spot coating inspection system provided in an embodiment of the present disclosure; Figure 2 A schematic block diagram of a battery separator spot coating detection system provided in this embodiment of the present disclosure; Figure 3 A flowchart for forming a grayscale image to be identified is provided in this embodiment of the disclosure; Figure 4 A flowchart for establishing a grayscale adjustment area is provided as an embodiment of this disclosure; Figure 5 A flowchart for judging spot coating defects provided in this embodiment of the present disclosure; Figure 6This is a schematic diagram illustrating a dot-coating defect as a sticky spot, provided as an embodiment of the present disclosure. Figure 7 This is a schematic diagram illustrating an abnormal horizontal spacing or an abnormal vertical spacing of dots in a dot coating, as provided in an embodiment of this disclosure. Figure 8 This is a schematic diagram illustrating a spot coating defect as a leak point, provided as an embodiment of the present disclosure.

[0021] In the picture: 1. Multi-station visual inspection device; 11. Camera; 12. Line scan LED light source; 2. Battery separator; 3. Conveyor roller assembly. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The terminology used herein is for the purpose of describing specific exemplary configurations only and is not intended to be limiting. As used herein, the singular articles “a,” “an,” and “the” may also be intended to include plural forms unless otherwise clearly stated herein. The terms “comprising,” “including,” and “having” are inclusive and thus specify the presence of features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein should not be construed as requiring them to be performed in the specific order discussed or shown, unless specifically identified as such. Additional or alternative steps may be employed.

[0024] As used herein, the phrases “in one embodiment,” “according to one embodiment,” “in some embodiments,” etc., generally refer to the fact that a particular feature, structure, or characteristic following the phrase can be included in at least one embodiment of this disclosure. Therefore, a particular feature, structure, or characteristic can be included in more than one embodiment of this disclosure, such that these phrases do not necessarily refer to the same embodiment. As used herein, the terms “example,” “exemplary,” etc., are used to “serve as an example, instance, or illustration.” Any implementation, aspect, or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or superior to other implementations, aspects, or designs. Rather, the use of the terms “example,” “exemplary,” etc., is intended to present concepts in a specific manner.

[0025] Research has found that when industrial cameras identify painted spots in images, factors such as lighting angle, camera acquisition angle, and the location of the painted spots can cause differences in grayscale at different locations in the image during visual recognition. If the image recognition uses the same grayscale to identify all painted spots, some painted spots will be misidentified.

[0026] Based on the above research, this disclosure provides a battery separator spot coating detection system and method. By dividing the original grayscale image into several grayscale adjustment areas and setting appropriate grayscale values ​​for each grayscale adjustment area, the coating spots and background areas can be clearly displayed. At the same time, it can avoid the problem of inaccurate identification of spot coating defects caused by local areas being too bright or too dark in the original grayscale image, thereby improving the recognition accuracy and recognition efficiency.

[0027] The shortcomings of the above solutions are the result of the inventor's practical experience and careful research. Therefore, the discovery process of the above problems and the solutions proposed in this disclosure below should be considered as the inventor's contribution to this disclosure.

[0028] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0029] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0030] like Figures 1 to 8 As shown, at least one embodiment provides a battery separator spot coating inspection system, which includes: a control module and a multi-station visual inspection device 1; wherein the multi-station visual inspection device 1 is electrically connected to the control module; the control module is configured to acquire an original grayscale image of the battery separator 2 after spot coating through the multi-station visual inspection device 1; the control module is further configured to divide a plurality of grayscale adjustment regions in the original grayscale image, and to perform corresponding grayscale adjustments on each grayscale adjustment region to form a grayscale image to be identified; and the control module is further configured to perform image recognition on the grayscale image to be identified to determine whether there are spot coating defects in the grayscale image to be identified.

[0031] In at least one embodiment, by dividing the original grayscale image into several grayscale adjustment regions and setting an appropriate grayscale value for each grayscale adjustment region, the painted dots and background areas can be clearly displayed. At the same time, the problem of inaccurate identification of painted dots due to local areas being too bright or too dark in the original grayscale image can be avoided, thereby improving the recognition accuracy and recognition efficiency.

[0032] In at least one embodiment, please refer to Figure 1 The multi-station visual inspection device 1 includes: a plurality of cameras 11 and a plurality of line-scan LED light sources 12; the line-scan LED light sources 12 emit light to the battery separator 2 so that the light is reflected or transmitted through the battery separator 2 and then enters the corresponding camera 11, thereby the control module acquires the corresponding original grayscale image.

[0033] Specifically, camera 11 is a high-definition industrial camera 11 (resolution not less than 8K).

[0034] In at least one embodiment, please refer to Figure 1 The battery separator 2 is conveyed by the conveying roller group 3. Several cameras 11 and corresponding line scan LED light sources 12 are located on the same side of the battery separator 2. Each of the cameras 11 is arranged in sequence parallel to the line scan LED light sources 12. The line scan LED light sources 12 are adapted to illuminate the battery separator 2 and reflect the light back to each of the cameras 11 after the battery separator 2 is coated, that is, to collect the original grayscale image of the battery separator 2 after the coating is applied.

[0035] Specifically, several cameras 11 and corresponding line scan LED light sources 12 are located on the same side of the battery separator 2 and are all facing the front of the battery separator 2, which can capture the original grayscale image of the front of the battery separator 2 after dot coating.

[0036] Specifically, several cameras 11 and corresponding line scan LED light sources 12 are located on the same side of the battery separator 2 and are all facing the back of the battery separator 2, which can capture the original grayscale image after the back of the battery separator 2 is dotted.

[0037] In at least one embodiment, please refer to Figure 1 The battery separator 2 is conveyed by the conveying roller group 3. Several cameras 11 and corresponding line scan LED light sources 12 are located on both sides of the battery separator 2. The cameras 11 are arranged in sequence parallel to the line scan LED light sources 12. The line scan LED light sources 12 are adapted to illuminate the battery separator 2 and are transmitted through the battery separator 2 to each camera 11, that is, to collect the original grayscale image of the battery separator 2 after dot coating.

[0038] In at least one embodiment, please refer to Figure 5 The control module performs image recognition on the grayscale image to be identified in order to determine whether there are dot painting defects in the grayscale image to be identified. This includes: dividing the grayscale image to be identified into several blocks; calculating the grayscale threshold of each block using the Niblack algorithm to distinguish between painted dots and background areas; and detecting painted dots and background areas in the blocks through image recognition to determine whether there are dot painting defects.

[0039] Specifically, the size of the block is 100×100, and the size of the grayscale image to be recognized is 5000×8000.

[0040] In at least one embodiment, please refer to Figures 6 to 8 Spot coating defects include: sticky spots, leaks, abnormal spot diameter, abnormal horizontal spacing of spots, abnormal vertical spacing of spots, and abnormal spot coverage.

[0041] Specifically, the dot-coated particle threshold is used to detect particles. This threshold is adaptive based on product film detection. When the image contrast is low, it is set to 5-10, and when the contrast is high, it is set to 15-20. When viewing the defect thumbnail, the mouse is used to move the black dot to obtain a grayscale value and the uncoated background also obtains a grayscale value. The difference between the two is the contrast. If the input of this value is too high, it will cause false detection, and if it is too low, it will cause missed detection.

[0042] Specifically, the background mask width and background mask height are used to estimate the height and width of the background mask. Input the center distance between two adjacent paint points in pixels. When viewing the defect thumbnail, the horizontal and vertical center distance in pixels is obtained by subtracting the pixel value of the center of the previous paint point from the pixel value of the center of the next paint point. Input this value into the background mask height and background mask width. The background mask width and background mask height can be set to 8, but are not limited to this setting.

[0043] Specifically, the particle spacing size is determined by how many intervals there are no other particles around each particle, which is considered a leak. The input is the difference between the center distance pixel and the diameter pixel. When viewing the defect miniature image, the diameter pixel is obtained by subtracting the pixel values ​​on both sides of the particle. The value obtained by subtracting the diameter pixel from the center distance pixel is the input value for the horizontal and vertical spacing of the leak. A small value makes it easy to miss the leak, while a large value makes it easy to misjudge.

[0044] Specifically, the particle spacing size for sticky spots is the range within which each particle's size is considered a sticky spot. The input is (pixel diameter / 2 + 2 to 3 pixels). To view the defect thumbnail, the input value is (pixel diameter / 2 + 2 to 3 pixels). Note: a smaller value makes it easier to miss a spot, while a larger value makes it easier to misjudge.

[0045] Specifically, when there are false positives for leaks and adhesions, the factors are: 1. Image contrast and threshold mismatch; 2. Leak interval too small; 3. Adhesion interval too small. The method to determine the source is to first increase both sets of intervals to 30-40 to eliminate factor 1, then increase one set of leak intervals and adhesion intervals while keeping the other set unchanged to determine whether the source is a leak or adhesion, and increase the corresponding interval accordingly.

[0046] Specifically, during the detection of the coating dot diameter, the diameter of the coating dot is output and a curve trend graph is plotted. When the diameter exceeds the limit, it is considered a defect and a defect graph is output.

[0047] Specifically, when detecting the lateral or longitudinal spacing of the coating dots, the center distance between the coating dots is output and a trend curve is generated. If the distance exceeds the standard, it is considered a defect.

[0048] Specifically, coverage detection refers to the proportion of the area covered by a single camera 11 to the total detection area of ​​the camera 11.

[0049] In at least one embodiment, please refer to Figure 3 The control module divides the original grayscale image into several grayscale adjustment regions and performs corresponding grayscale adjustments on each grayscale adjustment region to form a grayscale image to be recognized. This includes: obtaining the average grayscale value of the image background; obtaining the global grayscale feature parameters of the original grayscale image; inputting the average grayscale value of the image background and the global grayscale feature parameters into the Sauvola algorithm to calculate grayscale boundary parameters; dividing the original grayscale image into several grayscale adjustment regions according to the grayscale boundary parameters; and performing corresponding grayscale adjustments on each grayscale adjustment region to form a grayscale image to be recognized.

[0050] Specifically, obtaining the average grayscale value of the image background includes: ;in, The average grayscale value of the image background. Set the initial grayscale value for the image background.

[0051] Specifically, the initial grayscale of the image background refers to the grayscale obtained after the light source illuminates the film under test.

[0052] Specifically, The value range is mapped to 0-1 to unify the dimensions of all subsequent grayscale-related calculations and avoid calculation deviations caused by differences in the original grayscale value range (such as 0-255 and 0-1).

[0053] Specifically, the global grayscale feature parameters of the original grayscale image are obtained, including: ; ; ; ; ;in, Flatten the grayscale matrix of a two-dimensional image into a one-dimensional array. The median gray level of the image. The maximum grayscale value of the image. The minimum grayscale value of the image. The global grayscale range. Interquartile range, It is the 25th percentile. It is at the 75th percentile.

[0054] Specifically, It can ensure that the grayscale values ​​of all pixels are included in the statistical range.

[0055] Specifically, It can effectively suppress interference from extreme grayscale values.

[0056] Specifically, It reflects the dynamic distribution range of image grayscale.

[0057] Specifically, Used to quantify the dispersion of grayscale distribution, and to help determine the uniformity of grayscale in an image.

[0058] In at least one embodiment, please refer to Figure 3 The image background grayscale average value and global grayscale feature parameters are input into the Sauvola algorithm to calculate grayscale boundary parameters, which include: ; ;in, To adaptively adjust parameters, This represents the lower limit boundary of the grayscale adjustment area. This represents the upper limit boundary of the grayscale adjustment area.

[0059] Specifically, boundary verification: , .

[0060] Specifically, the original algorithm dynamically adjusts the local threshold by using T=mk×s (T is the local threshold, m is the local mean, and s is the local standard deviation), mapping the regulatory effect of the k value to the construction of the matching region boundary.

[0061] Specifically, the parameter k enables dynamic adaptation of partition sensitivity. For images with uniform gray-level distribution and low noise, a smaller k value can improve partition accuracy and precisely locate gray-level anomaly regions. For images with discrete gray-level distribution and high noise, a larger k value can reduce the partition misclassification rate and prevent normal regions from being misclassified as abnormal due to gray-level fluctuations. k is the core adaptive adjustment parameter of the Sauvola algorithm, with a default value of 0.2. The k value controls partition sensitivity by influencing the offset of the matching region boundary. The larger the k value, the greater the offset of the matching region boundary to both sides, and the higher the tolerance for gray-level fluctuations. The smaller the k value, the smaller the offset of the matching region boundary, and the lower the tolerance for gray-level fluctuations.

[0062] In at least one embodiment, please refer to Figure 4 The original grayscale image is divided into several grayscale adjustment regions according to the grayscale boundary parameters. This includes: dividing the original grayscale image into several blocks according to the block size parameters, and calculating the corresponding number of rows and columns, i.e., obtaining the coordinates of each block; calculating the average grayscale of each block, and obtaining the comparison result with the grayscale boundary parameters; and constructing the corresponding grayscale adjustment region according to the coordinates of each block and the comparison result.

[0063] Specifically, the original grayscale image is divided into several blocks according to the block size parameters, and the corresponding number of rows and columns is calculated, including: ; ;in, These are the block size parameters. The image height in pixels. The image width in pixels. This represents the number of blocks in the vertical direction of the image. The number of horizontal blocks in the image is denoted by ceil, which is a rounding function that ensures that edge regions of the image (regions smaller than a standard block size) can be completely divided, avoiding the omission of edge pixels and ensuring the integrity of block coverage.

[0064] Specifically, the coordinates of each block are obtained, including: ; ; ; Where i is the row index of the block, j is the column index of the block, y1 is the pixel row coordinate of the top left corner of the block, x1 is the pixel column coordinate of the top left corner of the block, y2 is the pixel row coordinate of the bottom right corner of the block, and x2 is the pixel column coordinate of the bottom right corner of the block.

[0065] Specifically, the average gray level of each block is calculated, which includes: Where b is the block index, This is the grayscale matrix corresponding to the b-th block. Let be a flattened one-dimensional array of the segmented grayscale matrix, and mean be the arithmetic mean of the one-dimensional array. This is a one-dimensional array that stores the average grayscale values ​​of all blocks.

[0066] Specifically, the comparison results with the grayscale boundary parameters are obtained, including: when The block is determined to belong to the first type of grayscale adjustment area; when The block is determined to belong to the second type of grayscale adjustment area; when The block was determined to belong to the third type of grayscale adjustment area.

[0067] Based on the same technical concept, at least one embodiment also provides a detection method using the battery separator 2 dot coating detection system as described above, which includes: a control module acquiring the original grayscale image of the battery separator 2 after dot coating through a multi-station visual inspection device 1; the control module dividing the original grayscale image into several grayscale adjustment areas, and performing corresponding grayscale adjustments on each grayscale adjustment area to form a grayscale image to be identified; and the control module performing image recognition on the grayscale image to be identified to determine whether there is a dot coating defect in the grayscale image to be identified.

[0068] In summary, by dividing the original grayscale image into several grayscale adjustment areas and setting appropriate grayscale values ​​for each grayscale adjustment area, the present invention can clearly display the painted dots and background areas, while avoiding the problem of inaccurate identification of painted dots due to local areas being too bright or too dark in the original grayscale image, thereby improving recognition accuracy and efficiency.

[0069] The disclosures and other solutions, examples, embodiments, modules, and functional operations described in this document can be implemented in digital electronic circuits, or computer software, firmware, or hardware, including the structures disclosed in this document and their structural equivalents, or combinations thereof. The disclosures and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible and non-volatile computer-readable medium for execution by a data processing apparatus or for controlling the operation of the data processing apparatus. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a storage device, a material composition that influences machine-readable propagated signals, or one or more of these. The terms "data processing unit" or "data processing apparatus" include all means, devices, and machines for processing data, including, for example, programmable processors, computers, or multiprocessors or computer groups. In addition to hardware, the apparatus may also include code that creates an execution environment for a computer program, such as code constituting processor firmware, a protocol stack, a database management system, an operating system, or combinations thereof. The propagated signals are artificially generated signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information for transmission to a suitable receiver device.

[0070] Computer programs (also known as programs, software, software applications, scripts, or code) can be written in any programming language (including compiled or interpreted languages) and can be deployed in any form, including as standalone programs or as modules, components, subroutines, or other units suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to that program, or in multiple coordinating files (e.g., a file storing one or more modules, subroutines, or portions of code). Computer programs can be deployed and executed on one or more computers located at a single site or distributed across multiple sites interconnected by a communication network.

[0071] The processing and logic flows described in this document can be executed by one or more programmable processors that execute one or more computer programs to perform functions by manipulating input data and generating outputs. The processing and logic flows can also be executed by special-purpose logic circuitry, and the devices can be implemented as special-purpose logic circuitry, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits).

[0072] For example, processors suitable for executing computer programs include general-purpose and special-purpose microprocessors, as well as any one or more of any type of digital computer. Typically, the processor receives instructions and data from read-only memory or random access memory, or both. The basic components of a computer are a processor that executes instructions and one or more storage devices that store the instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or operatively coupled to receive data from or transfer data to mass storage devices, or both. However, a computer does not necessarily have such devices. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and optical disc read-only memory (CD ROM) and digital versatile optical disc read-only memory (DVD-ROM). The processor and memory may be supplemented by dedicated logic circuitry or incorporated into dedicated logic circuitry.

[0073] While this patent document contains numerous details, it should not be construed as limiting the scope of any invention or claim, but rather as a description of features of specific embodiments of a particular invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various functions described in the context of a single embodiment may also be implemented individually in multiple embodiments, or in any suitable sub-combination. Furthermore, although the foregoing features may be described as functioning in certain combinations, or even initially claimed to be so, in certain circumstances, one or more features from a combination of claims may be removed from the combination, and a combination of claims may refer to a sub-combination or a variation of a sub-combination.

[0074] Similarly, although the operations are described in a specific order in the accompanying drawings, this should not be construed as requiring the specific order or sequence shown to perform such operations, or all the described operations, in order to obtain the desired result. Furthermore, the separation of various system components in the embodiments of this patent document should not be construed as requiring such separation in all embodiments.

[0075] Only some implementations and examples are described; other implementations, enhancements, and variations can be made based on the content described and illustrated in this patent document.

[0076] When no intermediate component exists other than a line, trace, or other medium between the first and second components, the first component is directly coupled to the second component. When an intermediate component other than a line, trace, or other medium exists between the first and second components, the first component is indirectly coupled to the second component. The term "coupling" and its variations include direct coupling and indirect coupling. Unless otherwise stated, the term "about" is used to mean a range including upper and lower 10% of the value.

[0077] While several embodiments are provided in this disclosure, it should be understood that the disclosed systems and methods may be embodied in many other specific forms without departing from the spirit or scope of this disclosure. The present examples are intended to be illustrative rather than restrictive and are not limited to the details given. For example, various elements or components may be combined or integrated into another system, or certain features may be omitted or not implemented.

[0078] In the several embodiments provided herein, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0079] Furthermore, without departing from the scope of this disclosure, the discrete or individual technologies, systems, subsystems, and methods described and illustrated in the various embodiments may be combined or integrated with other systems, modules, technologies, or methods. Other items shown or discussed as coupled may be directly connected or indirectly coupled or communicated via some interface, device, or intermediate component in an electrical, mechanical, or other manner. Those skilled in the art can identify other examples of changes, substitutions, and modifications without departing from the spirit and scope of this disclosure.

Claims

1. A battery separator spot coating detection system, characterized by, include: Control module and multi-station vision inspection device; in The multi-station visual inspection device is electrically connected to the control module; The control module is configured to acquire the original grayscale image of the battery separator after spot coating using a multi-station visual inspection device. The control module is also configured to divide the original grayscale image into several grayscale adjustment regions, and to perform corresponding grayscale adjustments on each grayscale adjustment region to form a grayscale image to be recognized. as well as The control module is also configured to perform image recognition on the grayscale image to be recognized in order to determine whether there are dotted defects in the grayscale image.

2. The battery separator spot coating inspection system as described in claim 1, characterized in that, The multi-station visual inspection device includes: several cameras and several line scan LED light sources; The line-scan LED light source emits light to the battery separator, so that the light is reflected or transmitted through the battery separator and then enters the corresponding camera, thereby the control module acquires the corresponding original grayscale image.

3. The battery separator spot coating inspection system as described in claim 2, characterized in that, The battery separator is conveyed by a set of conveying rollers. Several cameras and corresponding line scan LED light sources are located on the same side of the battery separator. The cameras are arranged in sequence parallel to the line scan LED light sources. The line-scanning LED light source is adapted to illuminate the battery separator, and the light is reflected by the battery separator to each of the cameras, thereby acquiring the original grayscale image of the battery separator after spot coating.

4. The battery separator spot coating inspection system as described in claim 2, characterized in that, The battery separator is conveyed by a set of conveying rollers. Several cameras and corresponding line scan LED light sources are located on both sides of the battery separator. The cameras are arranged in sequence parallel to the line scan LED light sources. The line-scan LED light source is suitable for illuminating the battery separator, and the light is transmitted through the battery separator to each of the cameras, thereby acquiring the original grayscale image of the battery separator after it has been dotted.

5. The battery separator spot coating inspection system as described in claim 1, characterized in that, The control module performs image recognition on the grayscale image to be identified in order to determine whether there are dotted defects in the grayscale image, which includes: The grayscale image to be identified is divided into several blocks; The grayscale threshold of each block is calculated using the Niblack algorithm to distinguish between painted areas and background areas; Image recognition is used to detect the painted dots and background areas in the blocks to determine whether there are any painting defects.

6. The battery separator spot coating inspection system as described in claim 5, characterized in that, Defects in spot coating include: sticky spots, leaks, abnormal spot diameter, abnormal horizontal spacing of spots, abnormal vertical spacing of spots, and abnormal spot coverage.

7. The battery separator spot coating inspection system as described in claim 1, characterized in that, The control module divides the original grayscale image into several grayscale adjustment regions, and performs corresponding grayscale adjustments on each grayscale adjustment region to form a grayscale image to be recognized, which includes: Obtain the average grayscale value of the image background; Obtain the global grayscale feature parameters of the original grayscale image; The image background grayscale average value and global grayscale feature parameters are input into the Sauvola algorithm to calculate the grayscale boundary parameters; The original grayscale image is divided into several grayscale adjustment regions according to the grayscale boundary parameters. Each grayscale adjustment area is adjusted separately to form a grayscale image to be recognized.

8. The battery separator spot coating inspection system as described in claim 7, characterized in that, The image background grayscale average value and global grayscale feature parameters are input into the Sauvola algorithm to calculate the grayscale boundary parameters, which include: ； ； wherein is an adaptive adjustment parameter, is a lower limit boundary of the gray scale adjustment region, is an upper limit boundary of the gray scale adjustment region.

9. The battery separator spot coating inspection system as described in claim 8, characterized in that, The original grayscale image is divided into several grayscale adjustment regions according to the grayscale boundary parameters, including: The original grayscale image is divided into several blocks according to the block size parameters, and the corresponding number of rows and columns is calculated to obtain the coordinates of each block. Calculate the average gray level of each block and obtain the comparison results with the gray level boundary parameters; The corresponding grayscale adjustment area is constructed based on the coordinates of each block and the comparison results.

10. A method of detection using the battery separator spot coating detection system of any one of claims 1-9, characterized in that, include: The control module acquires the original grayscale image of the battery separator after spot coating through a multi-station visual inspection device; The control module divides the original grayscale image into several grayscale adjustment areas and performs corresponding grayscale adjustments on each grayscale adjustment area to form a grayscale image to be recognized. The control module performs image recognition on the grayscale image to be identified in order to determine whether there are dotted defects in the grayscale image.

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