Triplet siamese network model for lithium-ion battery defects classification using few-shot learning approach

Abstract

In this paper, we propose a triplet siamese model for lithium-ion battery defects classification. It is a difficult task to detect the surface defects of lithium-ion batteries with stainless steel surface. The lack of three-dimensional information and the lack of marker datasets due to reflections prevent two-dimensional computer vision detection methods from meeting classification needs. In this work, the multiple exposure structured light method is utilized to obtain the three-dimensional shape of a lithium-ion battery with a stainless steel surface. The defect point cloud with three-dimensional information is obtained by this method, and then the 3D information of the defect point cloud is converted into grayscale information, and the grayscale image is used as the target domain data of the triplet siamese network. The public dataset MiniImageNet is utilized as the training data of the triplet siamese network model. The accuracies of the experimental results are 88.9%, 95.6%, and 97.8% for 1-shot, 5-shot, and 10-shot respectively. This result proves that our method can be used for lithium-ion battery defect detection.