Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram - XIONG Qunfang; LIN Jun; YUAN Xiwen; XU Yanghan; YUE Wei; LI Yuanzhengyu (机车电传动)
The autonomous rail rapid transit (ART) system is an intelligent express transport solution developed independently by CRRC Zhuzhou institute co., ltd. The detection and recognition of traffic lights are critical for improving the safety of the ART automatic operation system. However, existing means for traffic light detection and recognition often fall short of meeting the detection requirements in the automatic operation environments characterized by customized traffic lights, with only a few regular signals. This paper presents study efforts in this field through the application of a deep learning algorithm. Firstly, regions of interest (RoIs) for traffic lights were determined using high-precision map information to narrow the detection range and improve the detection speed. Secondly, an improved YOLOV5s network was employed to extract features from the RoIs, facilitating the recognition of ART traffic lights. Finally, the extracted traffic signal images were classified using a MobileNetV2 lightweight network to identify specific signal categories. In order to further enhance the model's generalization performance, an image diagnosis algorithm was introduced before signal recognition, which generates warnings for complex conditions, such as overexposure and backlighting. These abnormal image data were saved and utilized for further training and optimizing the model. Experimental results reveals that the proposed approach effectively detected and recognized ART traffic lights, achieving an average detection precision of 84.76% on designated roads during the daytime while exhibiting good real-time performance.