CN114937015A - Intelligent visual identification method and system in lithium battery pole piece manufacturing - Google Patents
Intelligent visual identification method and system in lithium battery pole piece manufacturing Download PDFInfo
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- CN114937015A CN114937015A CN202210569283.6A CN202210569283A CN114937015A CN 114937015 A CN114937015 A CN 114937015A CN 202210569283 A CN202210569283 A CN 202210569283A CN 114937015 A CN114937015 A CN 114937015A
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- 238000000034 method Methods 0.000 title claims abstract description 27
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 25
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 25
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 19
- 230000000007 visual effect Effects 0.000 title claims abstract description 13
- 239000011248 coating agent Substances 0.000 claims abstract description 39
- 238000000576 coating method Methods 0.000 claims abstract description 39
- 230000007547 defect Effects 0.000 claims description 15
- 230000002950 deficient Effects 0.000 claims description 15
- 238000001914 filtration Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 3
- 238000002372 labelling Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 230000001629 suppression Effects 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G06T5/00—Image enhancement or restoration
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/168—Segmentation; Edge detection involving transform domain methods
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
- G06T2207/20032—Median filtering
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- G—PHYSICS
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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Abstract
The invention relates to a machine vision technology, and discloses an intelligent visual identification method and system in lithium battery pole piece manufacturing; the invention intelligently analyzes the image of the lithium battery pole piece product, intelligently identifies the flaw distribution conditions of the front and back surfaces of the product and the category of each flaw at high speed, and judges whether the coating quality of the lithium battery pole piece product is qualified. The size of product and coating area quantity and position can intelligent identification to different products, reduce the step of artifical setting product specification.
Description
Technical Field
The invention relates to a machine vision technology, in particular to an intelligent vision identification method and system in lithium battery pole piece manufacturing.
Background
At present, the lithium battery is more and more widely applied in the fields of automobile power batteries and the like, and pole piece coating is an important process in the manufacturing process of the lithium battery. The requirements for each application are extremely strict and if not uniform enough or mixed with impurities, the product quality is seriously affected. Because the products are conveyed at a high speed, the traditional detection mode is manual sampling detection, the efficiency is low, the productivity of a production line is influenced, and the abnormal production can not be found in time. Therefore, the coating condition of the pole piece is detected on line in real time, and the method is very important for improving the product quality.
For example, in the prior art, as disclosed in patent No. CN201110108839.3, the detection technique is complicated and has low detection efficiency.
Disclosure of Invention
The invention provides an intelligent visual identification method and system in lithium battery pole piece manufacturing, aiming at the problems of complex operation and low detection efficiency of a detection technology in the prior art.
In order to solve the technical problem, the invention is solved by the following technical scheme:
an intelligent visual identification method in the manufacture of a lithium battery pole piece comprises 2 groups of industrial cameras and light sources corresponding to the industrial cameras; the method comprises the following steps:
s1, acquiring a product image, namely acquiring a front image by one group of industrial cameras and acquiring a back image by the other group of industrial cameras;
s2, adjusting the average gray scale of the product image, and adjusting the gray scale of the product image by adjusting the light source corresponding to the industrial camera; when the gray scale of the product image reaches the gray scale threshold, continuing to execute S3;
s3, calculating the position of the product image area and the product image coating area, and calculating the position of the product image area and the product image coating area of the product image adjusted in the step 2;
s4, calculating the position and the size of a coating area of an original product image, calculating an edge contour line of the original image through a canny operator, carrying out hough transformation on the original image so as to determine a linear equation of the edge of the coating area, and determining the position and the size of the coating area according to the linear equation of the edge of the coating area;
s5, labeling defective pixels of the product image, sequentially performing median filtering processing on the product image in the coating area and the product image in the non-coating area, comparing the image pixels subjected to the median filtering processing with the average gray scale in the S2 to obtain a difference value, and determining the defective pixels when the difference value is greater than a set defect threshold value;
s6, calculating the position, the size and the area of the connected region of the flaw image, and obtaining the position, the size and the area of the connected region of the flaw image through a BLOB analysis operator for the flaw image in S5;
s7, sequentially inputting the defective product images of S7 into a trained ResNet deep learning network model, and automatically calculating the confidence degree of the defect type of the defective product images so as to determine the defect type of the defective product images;
and S8, marking the unqualified products according to the defect type and the defect number.
Preferably, at S2, the method of calculating the product image area position and the product image coating area includes:
reducing the product image by K times;
calculating an edge contour line after the image is reduced, and calculating the edge contour line after the image is reduced through a sobel operator;
calculating the position of the product image, namely fitting an edge straight line equation of the product image by a least square method from left to right according to the gradient direction of the reduced product image so as to determine a left edge straight line and a right edge straight line, and determining the position of the product image according to the left edge straight line and the right edge straight line;
and determining the product image area, and calculating the number of the coating areas and the image of the non-coating area of the product image by a watershed segmentation algorithm.
Preferably, the method for calculating the edge contour of the original image by the canny operator comprises,
step 1, smoothing an image through a Gaussian filter;
step 2, calculating gradient amplitude and direction of the filtered image by using first-order partial derivative finite difference;
step 3, carrying out non-maximum suppression on the gradient amplitude;
and 4, detecting and connecting edges by using a double-threshold algorithm.
In order to solve the technical problem, the invention also provides an intelligent visual identification system in the manufacturing of the lithium battery pole piece, which comprises an upper computer, 2 groups of industrial cameras and light sources corresponding to the industrial cameras; the system is characterized in that a group of industrial cameras are used for acquiring front images of products after receiving image acquisition trigger signals; the other group of industrial cameras are used for acquiring the reverse side image of the product after receiving the image acquisition trigger signal;
the encoder is in contact with the product and provides an image acquisition trigger signal for the industrial camera after the product is conveyed for a certain distance;
the light source is used for illuminating a lithium battery pole piece placed under the industrial camera;
the upper computer comprises a processor and a memory, and the memory comprises the intelligent visual identification method in the manufacturing process of the lithium battery pole piece.
Due to the adoption of the technical scheme, the invention has the remarkable technical effects that:
the invention intelligently analyzes the image of the lithium battery pole piece product, intelligently identifies the flaw distribution conditions of the front and back surfaces of the product and the category of each flaw at high speed, and judges whether the coating quality of the lithium battery pole piece product is qualified. The size, the number and the positions of coating areas of different products can be intelligently identified, the step of manually setting the specification of the product is reduced, the lithium battery pole piece manufacturing process is identified through the method, the identification efficiency is high, and the flaw identification precision is high.
Drawings
FIG. 1 is a block diagram of the present invention;
fig. 2 is a flow chart of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Example 1
An intelligent visual identification method in the manufacture of a lithium battery pole piece comprises 2 groups of industrial cameras and light sources corresponding to the industrial cameras; the method comprises the following steps:
s1, acquiring a product image, namely acquiring a front image by one group of industrial cameras and acquiring a back image by the other group of industrial cameras;
s2, adjusting the average gray scale of the product image, and adjusting the gray scale of the product image by adjusting the light source corresponding to the industrial camera; when the gray scale of the product image reaches the gray scale threshold, continuing to execute S3;
s3, calculating the position of the product image area and the product image coating area, and calculating the position of the product image area and the product image coating area of the product image adjusted in the step 2;
s4, calculating the position and the size of a coating area of an original product image, calculating an edge contour line of the original image through a canny operator, carrying out hough transformation on the original image so as to determine a linear equation of the edge of the coating area, and determining the position and the size of the coating area according to the linear equation of the edge of the coating area;
s5, labeling defective pixels of the product image, sequentially carrying out median filtering processing on the product image of the coating area and the product image of the non-coating area, comparing the image pixels subjected to the median filtering processing with the average gray scale in S2 to obtain a difference value, and determining the defective pixels when the difference value is greater than a set defect threshold value;
s6, calculating the position, the size and the area of the connected region of the flaw image, and obtaining the position, the size and the area of the connected region of the flaw image through a BLOB analysis operator for the flaw image in S5;
s7, sequentially inputting the defective product images of S7 into a trained ResNet deep learning network model, and automatically calculating the confidence degree of the defect type of the defective product images so as to determine the defect type of the defective product images;
and S8, marking the unqualified products according to the defect type and the defect number.
S2, the method of calculating the product image area position and the product image coating area includes:
reducing the product image by K times;
calculating an edge contour line after the image is reduced, and calculating the edge contour line after the image is reduced through a sobel operator;
calculating the position of the product image, fitting an edge straight line equation of the product image by a least square method from left to right according to the gradient direction of the reduced product image so as to determine a left edge straight line and a right edge straight line, and determining the position of the product image according to the left edge straight line and the right edge straight line;
and determining the product image area, and calculating the number of the coating areas and the image of the non-coating area of the product image by a watershed segmentation algorithm.
The method for calculating the edge contour of the original image by the canny operator comprises the following steps,
step 1, smoothing an image through a Gaussian filter;
step 2, calculating gradient amplitude and direction of the filtered image by using first-order partial derivative finite difference;
step 3, carrying out non-maximum suppression on the gradient amplitude;
and 4, detecting and connecting edges by using a double-threshold algorithm.
Example 2
On the basis of embodiment 1, the intelligent visual recognition system in the manufacture of the lithium battery pole piece, which is realized by the embodiment, comprises an upper computer, 2 groups of industrial cameras and a light source corresponding to the industrial cameras; the system is characterized in that a group of industrial cameras are used for acquiring front images of products after receiving image acquisition trigger signals; the other group of industrial cameras are used for acquiring reverse images of the products after receiving the image acquisition trigger signals;
the encoder is in contact with the product and provides an image acquisition trigger signal for the industrial camera after the product is conveyed for a certain distance;
the light source is used for illuminating a lithium battery pole piece placed under the industrial camera;
the upper computer comprises a processor and a memory, and the memory comprises the intelligent visual identification method in the manufacturing process of the lithium battery pole piece.
Claims (4)
1. An intelligent visual identification method in the manufacture of a lithium battery pole piece comprises 2 groups of industrial cameras and light sources corresponding to the industrial cameras; the method comprises the following steps:
s1, acquiring a product image, namely acquiring a front image by one group of industrial cameras and acquiring a back image by the other group of industrial cameras;
s2, adjusting the average gray scale of the product image, and adjusting the gray scale of the product image by adjusting the light source corresponding to the industrial camera; when the gray scale of the product image reaches the gray scale threshold, continuing to execute S3;
s3, calculating the position of the product image area and the coating area of the product image, and calculating the position of the product image area and the coating area of the product image adjusted in the step 2;
s4, calculating the position and the size of a coating area of an original product image, calculating an edge contour line of the original image through a canny operator, carrying out hough transformation on the original image so as to determine a linear equation of the edge of the coating area, and determining the position and the size of the coating area according to the linear equation of the edge of the coating area;
s5, labeling defective pixels of the product image, sequentially performing median filtering processing on the product image in the coating area and the product image in the non-coating area, comparing the image pixels subjected to the median filtering processing with the average gray scale in the S2 to obtain a difference value, and determining the defective pixels when the difference value is greater than a set defect threshold value;
s6, calculating the position, the size and the area of the connected region of the flaw image, and obtaining the position, the size and the area of the connected region of the flaw image through a BLOB analysis operator for the flaw image in S5;
s7, sequentially inputting the defective product images of S7 into a trained ResNet deep learning network model, and automatically calculating the confidence degree of the defect type of the defective product images so as to determine the defect type of the defective product images;
and S8, marking the unqualified products according to the defect type and the defect number.
2. The method for intelligently visually recognizing a lithium battery pole piece in manufacturing according to claim 1, wherein the step of calculating the position of the product image area and the product image coating area at S2 comprises the following steps:
reducing the product image by K times;
calculating an edge contour line after the image is reduced, and calculating the edge contour line after the image is reduced through a sobel operator;
calculating the position of the product image, namely fitting an edge straight line equation of the product image by a least square method from left to right according to the gradient direction of the reduced product image so as to determine a left edge straight line and a right edge straight line, and determining the position of the product image according to the left edge straight line and the right edge straight line;
and determining the product image area, and calculating the number of the coating areas and the image of the non-coating area of the product image by a watershed segmentation algorithm.
3. The method of claim 1, wherein the edge contour of the original image is calculated by a canny operator, the method comprises,
step 1, smoothing an image through a Gaussian filter;
step 2, calculating gradient amplitude and direction of the filtered image by using first-order partial derivative finite difference;
step 3, carrying out non-maximum suppression on the gradient amplitude;
and 4, detecting and connecting edges by using a double-threshold algorithm.
4. An intelligent visual identification system in the manufacturing of a lithium battery pole piece comprises an upper computer, 2 groups of industrial cameras and light sources corresponding to the industrial cameras; the system is characterized in that a group of industrial cameras are used for acquiring front images of products after receiving image acquisition trigger signals; the other group of industrial cameras are used for acquiring reverse images of the products after receiving the image acquisition trigger signals;
the encoder is in contact with the product and provides an image acquisition trigger signal for the industrial camera after the product is conveyed for a certain distance;
the light source is used for illuminating a lithium battery pole piece placed under the industrial camera;
the upper computer comprises a processor and a memory, and the memory comprises the intelligent visual identification method in the manufacturing of the lithium battery pole piece as claimed in any one of the claims 1 to 3.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115593884A (en) * | 2022-11-28 | 2023-01-13 | 江苏时代新能源科技有限公司（Cn） | Pole piece stockline position grabbing method, system, device, equipment and storage medium |
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