YOLOv8-UCB: Visual Detection of Pouch Battery Using Improved YOLOv8

The aluminum laminate pouch of pouch batteries is highly prone to deformation, which can cause various surface defects, thereby affecting their service life and potentially posing safety hazards. To address this problem, we propose an algorithm named YOLOv8-UCB for detecting surface defects in pouch batteries, which is based on the YOLOv8 model. First, while retaining the original network structure, we replaced the backbone of YOLOv8 with the UniRepLKNet feature extraction network to achieve a larger receptive field. Second, we constructed a distributed focal detection head CLLAHead, to better capture the features at different scales. Additionally, after each Concat layer in the head, we incorporated BiFormer attention mechanisms to enhance content-aware perception. Finally, we replaced the loss function with Slide Loss to address the issue of sample classification imbalance. The experimental results indicate that the improved algorithm achieved significant increases in precision, recall, mAP@0.5 and mAP@0.5:0.95, with improvements of 11.7%, 8.2%, 8.9% and 6.5% respectively, over the baseline model. In comparative studies against contemporaneous methodologies, the refined algorithm exhibited pronounced superiority in detection capabilities.