| Issue |
E3S Web Conf.
Volume 261, 2021
2021 7th International Conference on Energy Materials and Environment Engineering (ICEMEE 2021)
|
|
|---|---|---|
| Article Number | 01021 | |
| Number of page(s) | 5 | |
| Section | Energy Development and Energy Storage Technology Research and Development | |
| DOI | https://doi.org/10.1051/e3sconf/202126101021 | |
| Published online | 21 May 2021 | |
Research on Defect Recognition of Lithium Battery Pole Piece Based on Deep Learning
1
Shanghai DianJi University, Shanghai, 201306, China
2
Shanghai DianJi University, Shanghai, 201306, China
3
Shanghai DianJi University, Shanghai, 201306, China
* Corresponding author: 1545928587@qq.com
In the field of defect recognition, deep learning technology has the advantages of strong generalization and high accuracy compared with mainstream machine learning technology. This paper proposes a deep learning network model, which first processes the self-made 3, 600 data sets, and then sends them to the built convolutional neural network model for training. The final result can effectively identify the three defects of lithium battery pole pieces. The accuracy rate is 92%. Compared with the structure of the AlexNet model, the model proposed in this paper has higher accuracy.
© The Authors, published by EDP Sciences, 2021
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