CN115060731A - Method for detecting scratch exposed metal of negative membrane particles by using variance algorithm - Google Patents
Method for detecting scratch exposed metal of negative membrane particles by using variance algorithm Download PDFInfo
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Abstract
The invention provides a method for detecting scratch and metal exposure of negative membrane particles by using a variance algorithm, which comprises the following steps of: s1: irradiating the negative electrode diaphragm by using a light source, and shooting the surface of the negative electrode diaphragm; s2: graying the image captured in step S1 to obtain a target image; s3: marking a region of interest in the target image; s4: detecting the fluctuation condition of the gray level of the whole pixels in the region of interest; s5: judging whether the region of interest is a particle scratch according to the fluctuation condition of the gray scale detected in the step S4, and the invention discloses a method for detecting the metal exposure of the negative membrane particle scratch by using a variance algorithm. The invention belongs to the result of establishing a model by a core algorithm, and is reliable and effective when a battery pole piece pursues the CTS (clear to send) safety quality. The method is simple and easy to operate, consumes less time and has high economic value.
Description
Technical Field
The invention relates to the field of lithium battery production, in particular to a method for detecting scratch and metal exposure of negative membrane particles by using a variance algorithm.
Background
A current traditional visual flaw detection tool binarization threshold segmentation algorithm; when no image pre-processing is used or the spatial domain is converted to the frequency domain for analysis. It is difficult to effectively analyze and judge the irregular features (neighborhood gray level fluctuation, trend, etc.). At present, dark marks and particle marks of a negative electrode membrane in the lithium battery industry are one of appearance characteristics which are difficult to distinguish. Among them, dark marks: the method generally refers to slight concave-convex deformation caused by cold pressing, die cutting and other processes in a coating thinning area, and strip-shaped dark stripes appear in a CCD photographing field, and the coating thickness does not exceed the specification. No lithium is separated out through the in-situ formation test. Particle scratching: the method refers to that slurry particles are blocked at the lip of an extrusion head of a coating machine to cause coating leakage in a membrane area, but carbon powder is accumulated in a scratch area due to flowing of slurry on the surface of a membrane in an oven, and metal copper foil is not exposed. The overall feature appears as a dark streak under CCD vision. But the thickness of the film coating is obviously reduced, and the film coating has potential quality safety hazards. The 'dark mark' and 'particle scratch' of the negative electrode diaphragm can be effectively distinguished, and the excellent rate of the material preparation process is ensured on the premise of solving the problem of quality safety.
The 8K/16K linear array camera is used under the condition that a shooting film is in high-speed tape walking (more than or equal to 60 m/min); analysis using image pre-processing schemes or converting the image space domain to the frequency domain (fourier transform) takes too long. Therefore, the asynchronous image acquisition timeout error is inevitably caused.
And the defect of the product to be detected is rough or concave, the characteristic becomes dark after the light source is reflected to the lens through diffusion, the difference between the defect characteristic and the coating characteristic is not obvious, and the copper foil is an isolated discontinuous point and cannot be exposed under the condition that the double-layer coating is carried out on the base coat. The CCD is not killed or killed by the detection of the dotted metal or the bright trace.
In the prior art, after an original image is simulated off line by using a binarization threshold segmentation algorithm, the characteristics of 'dark marks' and 'particle scratches' cannot be effectively distinguished. The mechanism is that the binarization algorithm is a relatively simple threshold segmentation algorithm for distinguishing contrast. Only so-called bright and dark contrast conditions can be distinguished. When a gradient derivative algorithm is used, the problem that partial 'particle scratches' are missed to kill can be solved. But in the horizontal projection direction, the 'particle scratch' is a random light and dark field characteristic; the main characteristic is that the polarities are respectively (dark → bright → dark, bright → bright, and bright and dark fields exist at the same time) in the horizontal projection direction. And the dark mark horizontal projection direction polarity is (light → dark → light); the gradient derivation can only reflect the gray level variation trend in the image area in the mathematical algorithm. All fail to achieve a full effective detection mechanistically.
Disclosure of Invention
The invention aims to provide a method for detecting scratch and dew metal of negative membrane particles by using a variance algorithm, so that the scratch of the negative membrane particles can be accurately and effectively detected.
In order to achieve the above purpose, the invention provides the following technical scheme: a method for detecting scratch metal exposure of negative membrane particles by using a variance algorithm comprises the following steps: s1: irradiating the negative electrode diaphragm by using a light source, and shooting the surface of the negative electrode diaphragm; s2: graying the image captured in step S1 to obtain a target image; s3: marking a region of interest in the target image; s4: detecting the fluctuation condition of the gray level of the whole pixels in the region of interest; s5: and judging whether the region of interest is a particle scratch or not according to the fluctuation condition of the gray level detected in the step S4.
Further, step S3 includes: s31: calculating an average value of gray levels of pixels of each column in the target image in a width direction of the target image
(ii) a S32: calculating an average of gray levels of all pixels of the target image
(ii) a S33: calculating an average of gray levels of pixels of each column
With the average of the grey levels of all pixels
A difference of (a); s34: obtainingA difference set value, namely judging whether the difference A of each row is greater than the difference set value, and marking all the rows of the difference A greater than the difference set value in the target image as suspected interesting areas; s35: calculating the gray level X of each pixel in the suspected region of interest 1 Judging the gray level X of each of the suspected regions of interest 1 With the average of the grey levels of all pixels
Obtaining the number N of pixels of which the difference B is greater than the difference set value; s36: and acquiring a set value of the number of pixels, and when the number N is greater than the set value of the number of pixels, marking the suspected region of interest as the region of interest.
Further, step S4 includes: s41: acquiring the gray level Xi of each pixel point in the region of interest; s42: obtaining the average value of the gray levels of all the pixel points in the region of interest
(ii) a S43: calculating the average value of the gray scale Xi and the gray scale
The variance S of (A); s44: and acquiring a variance set value, and comparing the variance S with the variance set value so as to acquire the fluctuation condition of the gray scale of the whole pixels in the region of interest.
Further, the apparatus for photographing the surface of the negative diaphragm in step S1 is a 16K line camera.
Further, the pixel precision of the 16K line camera is 0.030mm/pix-0.038 mm/pix.
Further, the pixel number setting value is 900-.
Further, the difference set value is 12-16.
The method can effectively detect the scratches of the negative diaphragm particles under the condition of not introducing an advanced learning AI module. The invention belongs to the result of establishing a model by a core algorithm, and is reliable and effective when a battery pole piece pursues CTS (clear to send) safety quality. The method is simple and easy to operate, consumes less time and has high economic value.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. Wherein:
FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. The various examples are provided by way of explanation of the invention, and not limitation of the invention. In fact, it will be apparent to those skilled in the art that modifications and variations can be made in the present invention without departing from the scope or spirit thereof. For instance, features illustrated or described as part of one embodiment, can be used with another embodiment to yield a still further embodiment. It is therefore intended that the present invention encompass such modifications and variations as fall within the scope of the appended claims and equivalents thereof.
In the description of the present invention, the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are for convenience of description of the present invention only and do not require that the present invention must be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. The terms "connected," "connected," and "disposed" as used herein are intended to be broadly construed, and may include, for example, fixed and removable connections; can be directly connected or indirectly connected through intermediate components; the connection may be a wired electrical connection, a wireless electrical connection, or a wireless communication signal connection, and a person skilled in the art can understand the specific meaning of the above terms according to specific situations.
One or more examples of the invention are illustrated in the accompanying drawings. The detailed description uses numerical and letter designations to refer to features in the drawings. Like or similar designations in the drawings and description have been used to refer to like or similar parts of the invention. As used herein, the terms "first," "second," "third," and "fourth," etc. may be used interchangeably to distinguish one component from another and are not intended to denote position or importance of the individual components.
As shown in fig. 1, according to an embodiment of the present invention, there is provided a method for detecting metal exposure of a scratch on a negative electrode membrane particle by using a variance algorithm, including the following steps:
s1: irradiating the negative electrode diaphragm by using a light source, and shooting the surface of the negative electrode diaphragm;
in practical application, the negative diaphragm is in motion, and the motion speed can be more than or equal to 60m/min, for example: 60m/min, 65m/min, 70m/min and 75 m/min. The light source and the photographing apparatus are arranged back and forth along the moving direction of the negative diaphragm.
S2: graying the image captured in step S1 to obtain a target image;
the graying process is a process of converting a color image into a grayscale image. In a grayscale image, each pixel has a corresponding grayscale value, which ranges from 0 to 255. Through this step, the gray scale value (or gray scale level) of each pixel in the gray scale image can be obtained.
S3: marking a region of interest in the target image;
in order to improve the detection efficiency and reduce the time required by detection, a part of the region in the target image is marked as a region of interest, and in the subsequent steps, only the region of interest in the target image is processed.
Specifically, step S3 includes:
s31: calculating an average value of gray levels of pixels of each column in the target image in a width direction of the target image
；
The width direction of the target image is a direction perpendicular to the moving direction of the negative electrode diaphragm, i.e., the width direction of the negative electrode diaphragm. A column in the target image is taken as an example for explanation. Obtaining the gray level of each pixel in the row, performing summation operation on the gray level of each pixel, and performing division operation on the summation result and the total number of the pixels in the row to obtain the average value
。
S32: calculating an average of gray levels of all pixels of a target image
；
Can be as follows: average value of gray levels of pixels for each column
Summing, dividing the sum by the number of columns in the target image to obtain the average value
。
The method can also be as follows: obtaining the gray scale of each pixel in the target image, performing summation operation on the gray scales of all the pixels, and performing division operation on the summation result and the total number of all the pixels in the target image to obtain the average value
. This embodiment does not obtain the average value
The method of (a).
S33: calculating an average of gray levels of pixels of each column
With the average of the grey levels of all pixels
A difference of (a);
s34: obtaining a difference set value, judging whether the difference A of each row is greater than the difference set value, and marking all the rows of the difference A greater than the difference set value in the target image as suspected interesting regions;
s35: calculating the gray level X of each pixel in the suspected interested region 1 Judging the gray level X of each pixel in the suspected region of interest 1 With the average of the grey levels of all pixels
Obtaining the number N of pixels of which the difference B is greater than the difference set value;
s36: acquiring a set value of the number of pixels, marking the suspected region of interest as a region of interest when the number N is greater than the set value of the number of pixels,
the judgment of the region of interest can pre-judge the area of the suspected particle scratches, so that the particle scratches can be accurately judged in the subsequent steps;
s4: detecting the fluctuation condition of the gray level of the whole pixels in the region of interest;
step S4 includes: s41: acquiring the gray level Xi of each pixel point in the region of interest; s42: obtaining the average value of the gray scale of all pixel points in the interested area
(ii) a S43: calculating the average value of the gray scale Xi and the gray scale
The variance S of (A); s44: obtaining a variance set value, and comparing the variance S with the variance set value to obtain a comparison result, thereby obtaining the gray scale of the whole pixel in the region of interest according to the comparison resultThe larger the variance is, the more obvious the light and shade alternation of the gray level of the pixel is;
s5: judging whether the region of interest is a particle scratch or not according to the fluctuation of the gray scale detected in the step S4,
and when the variance S is larger than the variance set value, judging the region of interest as a particle scratch, wherein the variance S is used for measuring the fluctuation of the gray scale (0-255). Reflecting the high frequency part of the image. If the variance is large, the data jitter is severe and the fluctuation is large, and if the variance is small, the data jitter is slow and the fluctuation is small.
When the detection result is judged, if the gray level of the edge of the region of interest is higher than the gray levels of other regions of the target image and the gray level of the center of the region of interest is lower than the gray levels of other regions of the target image, the region of interest can be judged as a particle scratch, and if the gray level of the edge of the region of interest and the gray level of the center of the region of interest are both higher than the gray levels of other regions of the target image, the region of interest can be judged as a dark mark (a dark mark is generated during production of the negative electrode film but not a defect of the negative electrode film).
Preferably, the apparatus for photographing the surface of the negative diaphragm in step S1 is a 16K line camera.
Preferably, the pixel precision of the 16K line camera is 0.030mm/pix-0.038mm/pix, and the pixel precision can be selected from 0.030mm/pix, 0.032mm/pix, 0.034mm/pix, 0.036mm/pix and 0.038mm/pix, and is preferably 0.034 mm/pix.
Preferably, the set value of the pixel number is 900-.
Preferably, the difference value is set to 12-16, the setting value can be selected to be 12, 13, 14, 15, 16, and is preferably 16, and the smaller the set value, the more sensitive the extracted suspected region of interest is.
Compared with the prior art, the method for detecting the scratch and the metal exposure of the negative membrane particles by the variance algorithm can effectively detect the scratch of the negative membrane particles under the condition that an advanced learning AI module is not introduced. The invention belongs to the result of establishing a model by a core algorithm, and is reliable and effective when a battery pole piece pursues the CTS (clear to send) safety quality. The method is simple and easy to operate, consumes less time and has high economic value.
The embodiment of the invention also provides a device for detecting the scratch metal exposure of the negative membrane particles by using the variance algorithm, which is used for executing the method for detecting the scratch metal exposure of the negative membrane particles by using the variance algorithm provided by the embodiment, and comprises the following steps: the device comprises an acquisition module, an obtaining module, a marking module, a detection module and a judgment module.
The acquisition module is used for surface images of the negative electrode diaphragm, and the negative electrode diaphragm is irradiated by a light source. The obtaining module is used for carrying out graying processing on the shot image to obtain a target image. The marking module is used for marking a region of interest in the target image. The detection module is used for detecting the fluctuation condition of the gray level of the whole pixels in the region of interest. The judging module is used for judging whether the region of interest is a particle scratch according to the detected fluctuation condition of the gray level.
It should be noted that: the device for detecting scratch and metal exposure of negative electrode membrane particles by using the variance algorithm provided by the above embodiment is only illustrated by dividing the above functional modules when detecting scratch and metal exposure of negative electrode membrane particles, and in practical application, the above function distribution can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the above described functions. In addition, the apparatus for detecting the scratch and the metal exposure of the negative electrode diaphragm particles by using the variance algorithm and the method embodiment for detecting the scratch and the metal exposure of the negative electrode diaphragm particles by using the variance algorithm provided by the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiment and are not repeated here.
An embodiment of the present invention provides an electronic device, including: a memory and a processor. The processor is connected with the memory and configured to execute the above-mentioned variance algorithm to detect the metal exposure of the anode membrane particle scratch based on the instructions stored in the memory. The number of processors may be one or more, and the processors may be single core or multi-core. The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory may be an example of the computer-readable medium described below.
An embodiment of the present invention provides a computer-readable storage medium having at least one instruction, at least one program, a set of codes, or a set of instructions stored thereon, which is loaded and executed by a processor to implement the above-mentioned variance algorithm for detecting metal exposure from scratch of anode membrane particles. The computer-readable storage medium includes: permanent and non-permanent, removable and non-removable media may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of the storage medium of the computer include, but are not limited to: phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which may be used to store information that may be accessed by a computing device.
In combination with the content of the foregoing embodiment, an embodiment of the present invention further provides a system for detecting metal exposure by scratching of anode membrane particles by using a variance algorithm, including: shooting equipment and detection device.
The photographing apparatus may be a line camera, e.g. an 8K line camera, a 16K line camera. The image sensor of the line camera is mainly a CCD (Charge Coupled Device).
The detection device is used for detecting the scratch and metal exposure of the negative membrane particles by the variance algorithm.
The specific use steps of the invention are as follows: arranging a light source, irradiating light emitted by the light source on the negative diaphragm, keeping the position of the light source unchanged during irradiation, and shooting one side, irradiated by the light source, of the negative diaphragm by using a 16K linear array camera, wherein the 16K linear array camera is generally opposite to the position of the light source, namely the 16K linear array camera is not positioned on the same side as the light source, and analyzing a shot image after the 16K linear array camera finishes shooting;
in the analysis process, firstly, carrying out gray level processing on an image, setting program parameters, predetermining a variance set value according to the structural performance and related parameters of a currently processed negative electrode diaphragm, finding out a part with a larger gray level variance in the image, marking the part as an interested area, judging the negative electrode diaphragm as a qualified product if the interested area is not found, dividing the interested area from the whole image, extracting gray level data of all pixel points in the image by a computer, collecting and calculating gray level average values of all pixel points, solving the variance of the gray level of each pixel point and the gray level average values of all pixel points in the interested area, comparing the measured variance with the variance set value, and judging whether the negative electrode diaphragm is scratched by particles according to the comparison result.
Compared with the prior art, the method for detecting the scratch and the metal exposure of the negative membrane particles by the variance algorithm can effectively detect the scratch of the negative membrane particles under the condition that an advanced learning AI module is not introduced. The invention belongs to the result of establishing a model by a core algorithm, and is reliable and effective when a battery pole piece pursues the CTS (clear to send) safety quality. The method is simple and easy to operate, consumes less time and has high economic value.
The above is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A method for detecting scratch metal exposure of negative membrane particles by using a variance algorithm is characterized by comprising the following steps:
s1: irradiating the negative electrode diaphragm by using a light source, and shooting the surface of the negative electrode diaphragm;
s2: graying the image captured in step S1 to obtain a target image;
s3: marking a region of interest in the target image;
s4: detecting the fluctuation condition of the gray level of the whole pixels in the region of interest;
s5: and judging whether the region of interest is a particle scratch or not according to the fluctuation condition of the gray level detected in the step S4.
2. The method for detecting the scratch metal exposure of the anode membrane particles according to the claim 1, wherein the step S3 includes:
s31: calculating an average value of gray levels of pixels of each column in the target image in a width direction of the target image
；
S32: calculating an average of gray levels of all pixels of the target image
；
S33: calculating an average of gray levels of pixels of each column
With the average of the grey levels of all pixels
A difference of (a);
s34: obtaining a difference set value, judging whether the difference A of each row is larger than the difference set value, and marking all the rows of the difference A larger than the difference set value in the target image as suspected interesting regions;
s35: calculating the gray level X of each pixel in the suspected region of interest 1 Judging the gray level X of each of the suspected regions of interest 1 With the average of the grey levels of all pixels
Obtaining the number N of pixels of which the difference B is greater than the difference set value;
s36: and acquiring a set value of the number of pixels, and when the number N is greater than the set value of the number of pixels, marking the suspected region of interest as the region of interest.
3. The method for detecting the scratch metal exposure of the anode membrane particles according to the claim 1, wherein the step S4 includes:
s41: acquiring the gray level Xi of each pixel point in the region of interest;
s42: obtaining the average value of the gray levels of all the pixel points in the region of interest
；
S43: calculating the average value of the gray scale Xi and the gray scale
The variance S of (A);
s44: and acquiring a variance set value, and comparing the variance S with the variance set value so as to acquire the fluctuation condition of the gray scale of the whole pixels in the region of interest.
4. The method for detecting the scratch metal exposure of the anode membrane particles through the variance algorithm according to claim 1, wherein the device for photographing the surface of the anode membrane in the step S1 is a 16K line camera.
5. The method for detecting the metal exposure of the anode membrane sheet particle scratches through the variance algorithm as claimed in claim 4, wherein the pixel precision of the 16K line camera is 0.030mm/pix-0.038 mm/pix.
6. The method as claimed in claim 2, wherein the pixel number setting value is 900-1100.
7. The method for detecting the scratching exposure of the anode membrane particles by the variance algorithm as claimed in claim 2, wherein the set value of the difference value is 12-16.
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