Few-shot learning approach for 3D defect detection in lithium battery

Abstract

Detecting the surface defects in a lithium battery with an aluminium/steel shell is a difficult task. The effect of reflectivity, the limitation of acquiring the 3D information, and the shortage of massive amounts of labelled training data make the 2D detection method hard to classify surface defects. In this work, a few-shot learning approach for 3D defect detection in lithium batteries is proposed. The multi-exposure-based structured light method is introduced to reconstruct the 3D shape of the lithium battery. Then, the anomaly part of the 3D point cloud is transferred into 2D images by the height-gray transformation. The MiniImageNet datasets are used as the source domain to pretrain the Cross-Domain Few-Shot Learning (CD-FSL) model. The accuracy in our experiment result is 97.17%, which means that our method can be used to classify the surface defects of the lithium battery.