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.