Temperature detection system for key components of metro running gear equipment based on deep learning
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摘要: 地铁列车走行部的良好运行状态是列车安全运行的保障。针对其关键部件发热故障的检测问题，研发了基于深度学习的地铁列车走行部关键部件温度检测系统。该系统采用红外热像仪获取走行部热成像图，引入注意力机制模块和CIoU损失函数，改进YOLOv5目标检测模型，识别、定位出关键部件；对关键部件图像进行灰度化处理和自适应阈值分割等操作，提取温度。基于实验室的Pytorch深度学习平台，在南京地铁运营公司马群车辆段对所研发的系统进行实验。实验结果表明，该系统可以获取走行部热成像图，准确定位关键部件并提取其温度信息。Abstract: The good operation status of the running gear of metro trains is a guarantee of safe train operation. In order to detect the heating faults of key components, this paper developed a temperature detection system for key components of metro running gear equipment based on deep learning. The system used an infrared thermal imager to obtain the thermal image of the running gear, introduced the attention mechanism module and CIoU Loss function, improved the YOLOv5 target detection model, and identifies and locates the key components, performed grayscale processing and adaptive threshold segmentation on key component images to extract temperature. Based on the laboratory's Pytorch deep learning platform, the system developed was tested in Maqun Depot of Nanjing Metro Operation Company. The experimental results show that the system can obtain thermal imaging images of the running gear, accurately locate key components, and extract their temperature information.
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Keywords:
- metro train /
- running gear /
- infrared thermogram /
- target recognition /
- temperature extraction.
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表 1 改进前模型检测精度
精度（Precision） 招回率（Recall） 平均精度（AP） 轴箱 0.951 0.962 0.952 牵引电机 0.949 0.950 0.953 齿轮箱 0.933 0.943 0.951 
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表 2 改进后模型检测精度
精度（Precision） 招回率（Recall） 平均精度（AP） 轴箱 0.963 0.960 0.971 牵引电机 0.958 0.961 0.970 齿轮箱 0.969 0.954 0.954
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表 3 模型改进前、后的训练结果对比
模型 帧速率/FPS 平均精度均值（mAP） mAP@0.5:0.95 YOLOv5s 61.0 0.952 0.840 Improved YOLOv5s 64.5 0.965 0.857
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