社会车辆运行状态、交通灯管控状态及轨道部件的精准识别成为制约轨道巡检机器人推广应用的技术瓶颈。针对轨道健康状态巡检需求，首先给出了轨道巡检机器人的视觉检测系统及基于“北斗+5G”的导航系统的技术方案；其次，基于YOLOv8算法搭建了视觉检测系统模型，并创新性地采用网络爬虫技术从网络开源视频资源中抓取获得有关交通信号灯、汽车尾灯等样本数据来训练视觉检测模型；随后，对训练后的模型，进一步采用迁移学习法和早停法优化模型检测精度。研究结果表明：采用YOLOv8算法并使迁移学习法和早停法针对模型进行优化后，巡检机器人能有效巡检轨道部件及道岔路口的车辆以及交通信号灯，同时有效提高巡检效率和准确性。

In the intersection scenario, the running state of social vehicles, the control state of traffic lights and the accurate identification of track components have become the technical bottlenecks restricting the promotion and application of track inspection robots. Aiming at the requirements of track health inspection, firstly, the vision inspection system of the track inspection robot and the technical scheme of the navigation system was presented based on "Beidou + 5G". Secondly, the vision detection system model was built based on YOLOv8 algorithm, and the web crawler technology was innovatively used to capture sample data about traffic lights and car taillights from open source video resources to train the vision detection model. Then, transfer learning method and early stop method were used to optimize the detection accuracy of the trained model. The research results show that after adopting YOLOv8 algorithm and optimizing the model with transfer learning method and early stop method, the inspection robot can effectively detect the track components, vehicles and traffic lights at the switch junction, and effectively improve the inspection efficiency and accuracy.