Sim-YOLOv5s: A method for detecting defects on the end face of lithium battery steel shells

Abstract

The detection of lithium battery shell defects is an important aspect of lithium battery production. The presence of pits, R-angle injuries, hard printing, and other defects on the end face of lithium battery shells severely affects the production safety and usage safety of lithium battery products. In this study, we propose an effective defect-detection model, called Sim-YOLOv5s, for lithium battery steel shells. In this model, we propose a fast spatial pooling pyramid structure, SimSPPF, to speed up the model and embed the attention mechanism convolutional block attention module in the backbone. Contextual information can be aggregated over a large perceptual field by using the new upsampling operator, which has a larger field of perception. A cross-layer connection operation is performed to fuse shallow feature information with deep feature information. The experimental results show that the proposed Sim-YOLOv5s model has a better overall performance with a mean average precision of 88.3%, which is 6.9% better than that of YOLOv5s. Therefore, the proposed Sim-YOLOv5s can lay the foundation for the industrial implementation of real-time inspection of lithium battery products.