基于改进YOLOv5s的接触线缺陷检测算法

Contact Line Defect Detection Algorithm Based on Improved YOLOv5s

摘要: 本文以轨道交通供电系统中的接触线为研究对象，针对接触线缺陷识别效率提升的核心问题，以YOLOv5s_6.0算法网络结构为基础框架，对其进行改进优化：将Resunit残差模块中的普通卷积替换为Ghost模块，并在GCSP模块后嵌入CA (Coordinate Attention)注意力模块。基于自建接触线缺陷数据集开展对比实验，结果表明，改进后的YOLOv5s_6.0算法相较于原始算法，各项性能指标均得到优化：精确率提升1.63个百分点，召回率提升1.89个百分点，平均精度均值提升0.67个百分点，每秒帧率(FPS)提升13.58，模型体积缩减56MB。进一步计算可知，在精确率、召回率及平均精度均值基本保持稳定的前提下，FPS较原始算法提升18.44%，模型体积较原始算法缩减31.46%。研究结果证实，改进后的YOLOv5s_6.0算法具有更轻量化的模型体量与更高的识别精度。

Abstract: This paper focuses on the contact wire in the power supply system of rail transit and addresses the core issue of improving the efficiency of contact wire defect identification. Based on the framework of the YOLOv5s_6.0 algorithm network structure, it is improved and optimized by replacing the ordinary convolution in the Resunit residual module with the Ghost module and embedding the CA (Coordinate Attention) attention module after the GCSP module. Comparative experiments were conducted based on the self-built contact wire defect dataset. The results show that the improved YOLOv5s_6.0 algorithm has optimized various performance indicators compared to the original algorithm: the precision rate has increased by 1.63 percentage points, the recall rate has increased by 1.89 percentage points, the average precision mean has increased by 0.67 percentage points, the frame rate (FPS) has increased by 13.58, and the model size has been reduced by 56MB. Further calculation reveals that, under the premise of basically maintaining the same precision rate, recall rate, and average precision mean, the FPS has increased by 18.44% compared to the original algorithm, and the model size has been reduced by 31.46% compared to the original algorithm. The research results confirm that the improved YOLOv5s_6.0 algorithm has a more lightweight model size and higher recognition accuracy.