SGCNet:一种轻量化的新能源汽车电池集流盘缺陷检测模型

集流盘作为新能源汽车电池的重要组成部件，其质量好坏关系到电池的性能，对车内人员的生命安全有着重要影响。实际工业应用中，在有限的计算资源下对电池集流盘缺陷进行实时检测是一项具有挑战性的任务。为了减小模型大小和计算量，降低应用成本，本文提出一种轻量化的新能源汽车电池集流盘缺陷检测模型(SGCNet)。首先，采用ShuffleNet V2作为主干特征提取网络，采用分组卷积和通道重排技术，在提取有效特征的同时降低计算复杂度，降低参数量。其次，设计了轻量化的特征融合网络GC-FPN,采用轻量级GhostNet和CARAFE上采样算子，在减少参数冗余和保证检测精度的情况下充分保留特征图的语义信息，从而降低了计算成本。实验结果表明，SGCNet模型检测准确率达到了90.6%,模型大小为3.2 M,GFLOPs仅为3.6,帧率达到了178.6 fps。与目前先进的轻量化网络模型相比，具有较高检测精度和较低的计算量。最后，在嵌入式平台NVIDIA Jetson Nano上部署SGCNet模型，进行实时检测，每张图片的检测时间为0.07 s,满足实际工业中电池集流盘缺陷检测任务对精度和实时性的要求。

SGCNet: A lightweight defect detection model for new energy vehicle battery collector tray

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.