基于YOLO算法的高速铁路客运车站钢结构雨棚螺栓缺失检测系统

当前，我国高速铁路客运车站钢结构雨棚螺栓缺失检测过于依赖人工目测，其危险系数大、成本高、效率低且误检率高。为解决该问题，提出一种基于YOLO(You Only Look Once)算法的螺栓缺失检测系统。该系统采用YOLOv4卷积神经网络，对现场采集的钢结构雨棚和接触网螺栓进行标注，通过K-means聚类算法，确定锚框数目和尺寸；利用CutMix和Mosaic等数据增强操作，增加训练数据的多样性，避免出现训练过拟合。试验结果表明，该系统类别识别准确率可达85%以上，识别效果较好，满足检测实时性要求。

Missing bolt detection system for steel structure canopy of high-speed railway passenger station based on YOLO algorithm

At present, the missing detection of steel structure canopy bolts in high-speed railway passenger stations in China relies too much on manual visual inspection, which has great risk coefficient, high cost, low efficiency and high false detection rate. In order to solve this problem, this paper proposed a bolt missing detection system based on YOLO algorithm. The system used YOLO v4 convolution neural network to mark the steel structure canopy and catenary bolts collected on site, determined the number and size of anchor frames by K-means clustering algorithm, and used cutmix, mosaic and other data to enhance the operation, increase the diversity of training data, and avoid training over fitting. The trial results show that the category recognition accuracy of the system can reach more than 85%, and the recognition effect is good, which meets the requirements of real-time detection.