Cross-Domain Few-Shot Learning Approach for Lithium-Ion Battery Surface Defects Classification Using an Improved Siamese Network

It is difficult to detect the surface defects of a lithium battery with an aluminum/steel shell. The reflectivity, lack of 3D information on the battery surface, and the shortage of many datasets make the 2D detection method hard to apply in this field. In this paper, a cross-domain few-shot learning (FSL) approach for lithium-ion battery defect classification using an improved siamese network (BSR-SNet) is proposed. To obtain the critical 3D surface of the lithium-ion battery, a multiexposure-based structured light method is utilized. Then, the heights of the 3D cloud points are transferred to grayscale information and are saved as 8-bit 2D images. For the FSL task, the DAGM 2007 datasets are used as the source domain to pre-train the improved siamese model. To avoid negative mitigation in the target domain, batch spectral regularization (BSR) is added as a penalizer in the loss function. The accuracies of the experimental results are 93.3% for 10-shot batteries and 91.0% for 5-shot batteries, which means that our method can be used to classify the surface defects of lithium batteries well.