DGNet: An Adaptive Lightweight Defect Detection Model for New Energy Vehicle Battery Current Collector

As an essential component of the new energy vehicle battery, current collectors affect the performance of battery and are crucial to the safety of passengers. The significant differences in shape and scale among defect types make it challenging for the model detection of current collector defects. In order to reduce application costs, and conduct real-time detection with limited computing resources, we propose an end-to-end adaptive and lightweight defect detection model for the battery current collector, DGNet. Firstly, we designed an adaptive lightweight backbone network (DOS module) to adaptively extract useful features adaptively along all four dimensions of kernel space while maintaining low computational complexity. Secondly, we designed a lightweight feature fusion network (GS_FPN), which reduces parameter redundancy and fully utilizes the semantic information of the feature maps of backbone network while ensuring detection accuracy. Experimental results show that DGNet achieves a mAP(50) (mean Average Precision at IoU threshold 0.5) of 91.8% on the self-made BCC surface defect database, with a model size of 4.0M, only 3.7 GFLOPs (Giga Floating-point Operations per Second), and FPS (Frames Per Second) of 181.8. To further demonstrate the capabilities of DGNet, we test it on the publicly NEU surface defect database, and the results showed that the DGNet exhibited good generalization. Compared to current advanced lightweight network models, it achieves higher detection accuracy and lower computational overhead, reaching the state-of-the-art level. Finally, we deployed DGNet on the embedded platform NVIDIA Jetson Nano for real-time detection, achieving a detection time of 0.074s per image, meeting the accuracy and real-time detection requirements for battery current collector defect detection tasks in practical industrial applications.