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Self-adaptive Methods with Flexible Detection Criteria of the Battery Cell Dent Defect

Published: 19 June 2023 Publication History

Abstract

For the battery cell production lines using automatic defect detection equipment, it becomes necessary to control the fluctuation of the yield rate of the production line, and adapt to the difference among the cell replacements and the incoming material processes of each batch. So the defect detection algorithm needs to adjust the detection criteria according to the target preferential rate range, which is a new challenge for artificial intelligent manufacturing in the battery industry. Taking the dent defect on the side of the battery cell as an example, based on both the traditional algorithm and the deep learning algorithm, two kinds of self-adaptive adjustment method of the defect detection criteria are proposed. The traditional algorithm employs the linear interpolation method to classify good and bad products based on depth and area information, and calculates the optimal critical values of depth and area that meet the target yield rate. The deep learning algorithm combines the convolutional neural network and the support vector machine classifiers, with the histogram of oriented gradient feature as the classifier input, so as to classify different degrees of defective products. The test results show that the detection criteria can be adjusted flexibly and automatically for the side dent defects, which could realize the self-adaption of algorithm to the target yield rate, save the manual operation time, and improve the production efficiency.

References

[1]
Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1063-6919.
[2]
https://scikit-learn.org/stable/index.html.
[3]
https://tensorflow.google.cn/.

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      CVIPPR '23: Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition
      April 2023
      93 pages
      ISBN:9798400700033
      DOI:10.1145/3596286
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

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      Publication History

      Published: 19 June 2023

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      Author Tags

      1. defect detection of battery cell
      2. histogram of oriented gradient
      3. linear interpolation
      4. support vector machine

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      CVIPPR 2023

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      CVIPPR '23 Paper Acceptance Rate 14 of 38 submissions, 37%;
      Overall Acceptance Rate 14 of 38 submissions, 37%

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