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.
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