Author:
Li Jiwei,Li Linsheng,Xu Changlu
Abstract
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.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Semi-synthetic Data Set Based Lithium-Ion Battery Pole Piece Defect Inspection;2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL);2024-04-19