A Lightweight Deep-Learning Algorithm for Welding Defect Detection in New Energy Vehicle Battery Current Collectors

The future direction of global automotive development is electrification, and the battery current collector (BCC) is an essential component of new energy vehicle batteries. However, the welding defects in the BCC during the welding process are characterized by a disorganized distribution, extensive size variations, multiple types, and ambiguous features, posing challenges for detecting welding defects in the current collector. This article proposes a lightweight deep-learning algorithm called MGNet for detecting welding defects in the current collectors. We introduce a lightweight MDM module based on multiscale channels, which utilizes deep dynamic convolutions as its basic structure to extract compelling features while reducing computational complexity. We also propose a lightweight feature fusion network called GS_GFPN, which fully leverages the semantic information of the backbone network feature maps while reducing parameter redundancy and maintaining detection accuracy. Experimental evaluations on both the BCC surface defect database and the publicly available Northeastern University (NEU) surface defect database demonstrate that MGNet outperforms existing methods with significant improvements in detection accuracy. The mean average precision (mAP) at IoU threshold 0.5 is 93.9%–78.0%, respectively, with frames per second (FPS) of 212.8 and 238.1 and a model weight of only 3.1 M. Moreover, the algorithm is successfully deployed on the NVIDIA Jetson Nano embedded device, enabling real-time defect detection for practical industrial applications.