A lithium batteries bottom surface defect detection model based on dual-path downsampling

Abstract With the wide application of lithium batteries in new energy vehicles, consumer electronics, energy storage systems and other fields, their quality and safety not only directly affect the performance and life of the product, but also relate to the safety of users’ lives and properties. During manufacturing and transportation, various defects will be produced on lithium batteries, such as scratches, stains, and pits. Thus, defect detection is crucial for ensuring battery quality. However, the complex background texture on the bottom surface of lithium batteries, coupled with the small size of some defects and the low contrast between defects and background, poses a great challenge for traditional detection methods. To address these issues, an improved YOLO11-based lithium battery bottom surface defect detection model (called DFCL-YOLO11) is proposed in this paper. In the proposed model, a new downsampling module is presented to retain more spatial information and enhance the feature representation capability. Then, the C3k2 module of the traditional YOLO11 model is improved by combining FasterBlock and convolutional gated linear unit, achieving higher detection accuracy with reduced computational complexity. Finally, the large separable kernel attention module is introduced into the proposed defect detection, to reduce information loss caused by the spatial pyramid pooling operations. In addition, a lithium battery defect dataset is constructed by collecting the images of the bottom surface defects of Tesla 21 700 lithium batteries and labeling the defects in this study. Experimental results demonstrate that the proposed DFCL-YOLO11 model outperforms baseline YOLO11 and other mainstream defect detection models.