Real-time Detection of Vehicle and Traffic Light for Intelligent and Connected Vehicles Based on YOLOv3 Network

Abstract

Real-time detection of vehicle and traffic light is essential for intelligent and connected vehicles especially in urban environment. In this paper, a new vehicle and traffic light dataset is established and a real-time detection model of vehicle and traffic light based on You Look Only Once (YOLO) network is presented. A joint training method for target classification and detection is proposed by YOLOv3, aiming to balance the detection accuracy and speed. The YOLOv3 network has lower requirements on hardware devices than other target detection algorithms like Faster R-CNN. Through the experimental analysis of the measured images in urban environment, it is shown that the designed model can not only satisfy the real-time requirements, but also improve the accuracy of the detection of vehicles and traffic lights.