针对井下电机车行车过程中的防碰撞预警问题，提出了一种基于深度学习的井下轨道障碍物测距方法。结合Yolov5目标检测和UFLD轨道检测2种算法，借用轨道实际宽度不变的特性进行单目测距：首先使用Yolov5检测框底侧y坐标对目标障碍物和同一水平线上的轨道进行定位，其次对图像窗口像素坐标系中的轨道宽度进行计算，最后通过小孔成像原理和多种坐标系的转换，测得目标障碍物的距离。实验结果表明：在相机可视范围内测距系统的误差率<5%，视频帧平均运行时间为35 ms，满足了井下轨道电机车智能控制的实时性要求。

Aiming at the problem of anti-collision warning during underground electric locomotive running, a deep learning-based obstacle location method for underground track is proposed. Combining Yolov5 target detection and UFLD track detection algorithms, the monocular distance measurement is carried out by using the property of constant actual track width. First, the y coordinate at the bottom of the Yolov5 detection frame is used to locate the target obstacle and the track on the same horizontal line. Secondly, the track width in the pixel coordinate system of the image window is calculated. Finally, the distance of the target obstacle is measured through the principle of small-hole imaging and the conversion of multiple coordinate systems. The experimental results show that the error rate of the system is less than 5% in the visual range, and the average running time of the video frame is 35 ms, which meets the real-time requirement of the intelligent control of underground track electric locomotive.