This chapter proposes a collision warning method for high-speed maglev trains based on an improved YOLOv7. The method involves creating a detection dataset to train the improved YOLOv7 neural network model, which is used for real-time detection and localization of high-speed maglev trains and track tractors. Improvement measures include the introduction of a lightweight parameter-free attention module, SimAM, reducing the number of detection heads to accelerate the detection process, and incorporating the CBAM module to enhance the model's perception capabilities, thereby improving performance without increasing network complexity. Furthermore, this chapter provides a detailed description of the processes involved in constructing the dataset, training the model, and performing real-time detection. By comparing the original YOLOv7 network with the improved YOLOv7 network, it was found that the learning speed and detection speed of the improved network have been significantly enhanced. Finally, the trained network is applied to real-time images obtained from four surveillance cameras on the tracks, serving as the basis for vehicle detection.