School of Electrical and Information Engineering, Hubei Key Laboratory of Energy Storage and Power Battery, Hubei University of Automotive Technology, Shiyan, China.
PLoS One. 2024 Oct 29;19(10):e0311269. doi: 10.1371/journal.pone.0311269. eCollection 2024.
The future trend in global automobile development is electrification, and the current collector is an essential component of the battery in new energy vehicles. Aiming at the misjudgment and omission caused by the confusing distribution, a wide range of sizes and types, and ambiguity of target defects in current collectors, an improved target detection model DCS-YOLO (DC-SoftCBAM YOLO) based on YOLOv5 is proposed. Firstly, the detection rate of defects with different scales is improved by adding detection layers; Secondly, we use the designed DC module as the backbone network to help the model capture the global information and semantic dependencies of the target, and effectively improve the generalization ability and detection performance of the model. Finally, in the neck part, we integrate our designed Convolutional Block Attention Module (SoftPool Convolutional Block Attention Module, SoftCBAM), which can adaptively learn the importance of channels, enhance feature representation, and enable the model to better deal with target details. Experimental results show that the mAP50 of the proposed DCS-YOLO model is 92.2%, which is 5.1% higher than the baseline model. The FPS reaches 147.1, and the detection accuracy of various defect categories is improved, especially Severely bad and No cover, and the detection recall rate reaches 100%. This method has high target detection model efficiency and meets the requirements of real-time detection of battery collector defects.
全球汽车发展的未来趋势是电动化，集流器是新能源汽车电池的重要组成部分。针对集流器目标缺陷分布混乱、尺寸和类型广泛、目标缺陷不明确导致的误判和遗漏问题，提出了一种基于 YOLOv5 的改进目标检测模型 DCS-YOLO（DC-SoftCBAM YOLO）。首先，通过添加检测层提高了不同尺度缺陷的检测率；其次，使用设计的 DC 模块作为骨干网络，帮助模型捕获目标的全局信息和语义依赖关系，有效提高模型的泛化能力和检测性能。最后，在颈部部分，我们集成了我们设计的卷积块注意力模块（SoftPool 卷积块注意力模块，SoftCBAM），可以自适应地学习通道的重要性，增强特征表示，使模型能够更好地处理目标细节。实验结果表明，所提出的 DCS-YOLO 模型的 mAP50 为 92.2%，比基线模型高 5.1%。FPS 达到 147.1，提高了各种缺陷类别的检测精度，特别是严重不良和无覆盖，检测召回率达到 100%。该方法具有较高的目标检测模型效率，满足电池集流器缺陷实时检测的要求。
