As an important component of the new energy vehicle battery, the quality of the collector tray is related to the performance of the battery and has an important impact on the life safety of the vehicle occupants. In practical industrial applications, real-time detection of battery collector tray defects with limited computational resources is a challenging task. In order to reduce the model size and computational effort, and to reduce the application cost, this paper proposes a lightweight new energy vehicle battery collector tray defect detection model (SGCNet). First, ShuffleNet V2 is used as the backbone feature extraction network, and group convolution and channel shuffle techniques are adopted to reduce the computational complexity and the number of parameters while extracting effective features. Secondly, a lightweight feature fusion network GC-FPN is designed with lightweight GhostNet and CARAFE upsampling operators to fully retain the semantic information of the feature map while reducing parameter redundancy and ensuring detection accuracy, thus reducing the computational cost. The experimental results show that the SGCNet model achieves 90. 6% detection accuracy, the model size is 3. 2 M, the GFLOPs are only 3. 6, and the FPS reaches 178. 6. Compared with the current advanced lightweight network models, it has higher detection accuracy and lower computational effort. Finally, the SGCNet model is deployed on the embedded platform NVIDIA Jetson Nano for real-time detection, with a detection time of 0. 07 s per image, meeting the requirements for accuracy and real-time performance for battery collector defect detection tasks in real industry.