In response to the challenges of complex lithium battery surface defect morphology, difficulties in detecting small targets, and the trade-off between accuracy and speed in existing detection methods, this paper proposes an improved object detection network based on YOLOv7-tiny. The method focuses on enhancing the model’s adaptability to complex defects at the feature extraction level, primarily achieved through two key designs: First, we introduce a simple parameter-free attention module (SimAM) to enhance the network’s ability to characterize small-scale defect features with minimal computational overhead. Second, a lightweight large-kernel attention module is incorporated into the neck network, which builds long-range spatial dependencies to improve the model’s generalization and understanding of irregularly shaped defects. To validate the effectiveness of the model, comprehensive experiments were conducted on a self-built lithium battery surface defect dataset. The results show that the proposed method achieves an mAP@0.5 of 93.14%, representing an improvement of 3.08 percentage points over the baseline YOLOv7-tiny (You Only Look Once v7, lightweight variant) model. At the same time, the detection speed reaches 94 Frames Per Second (FPS), which is 17 FPS faster than the original YOLOv7. The experiments demonstrate that the network outperforms existing comparative methods in both detection accuracy and inference speed, providing an effective technical solution for high-precision, real-time online defect detection in lithium battery production processes.
Wang et al. (Mon,) studied this question.