Battery Pouch Cell Defect Detection System Using YOLOv11 Lightweight Model `Pouch Cell Defect Detection using Lightweight YOLOv11`

This study introduces a real-time vision-based defect detection system for lithium-ion battery pouch cells, leveraging a lightweight YOLOv11n architecture.A dataset comprising 15,312 high-resolution images (10241024 pixels) was constructed from a commercial production line and categorized into five surface defect classes: pass, leakage, pinhole, swelling, and scratch.To mitigate the dataset's inherent class imbalance-with leakage representing only 8.8% of samples-comprehensive data augmentation strategies were employed, including mosaic augmentation (0.8 probability), MixUp (0.2), and random erasing (0.35) to enhance class distribution equilibrium and strengthen model generalization capabilities.The proposed Lightweight YOLOv11n model was developed through systematic optimizations: global channel-width reduction (0.75 ratio), C2K depthwise-separable residual blocks, SPPF-Lite spatial pyramid pooling, C2PSA attention modules, unified 64-channel detection heads, removal of stride-32 detection branch, magnitude-based weight pruning (threshold: 110), and mixed-precision optimization (FP32 to FP16).Comparative experiments with YOLOv11n and YOLOv12n were conducted on an NVIDIA A100 GPU.Lightweight2