Traffic lights recognition in foggy weather based on YOLOv5

Abstract

Autonomous driving technology has gradually matured. The main application is unmanned vehicles, which have begun to test on the road. However, there is a critical technical problem with mitigating interfering factors in efficient traffic light detection and recognition. Due to the size of traffic lights and the complex road conditions, the detection accuracy is not up to satisfactory. This paper proposes a Traffic Light Recognition system based on YOLOv5, which has high speed and accuracy. The system also tests foggy data which gain from image processing. The experimental results show that the method achieved 0.9943 in normal conditions and 0.9937 in foggy conditions. Overall, the efficiency of YOLOv5 meets the practical application requirements.