Recognition new energy vehicles based on improved YOLOv5

Abstract

In the field of Intelligent Traffic Systems (ITS), vehicle recognition is a hot research topic. Although different types of vehicles can already be recognized, further identification and statistics of new energy and fuel vehicles in unknown and complex environments remain a challenging task. In this paper, we propose a New Energy Vehicle Recognition and Traffic Flow Statistics (NEVTS) approach. Specifically, we first utilized the You Only Look Once v5 (YOLOv5) algorithm to detect vehicles in the target area, in which we applied Task-Specific Context Decoupling (TSCODE) to decouple the prediction and classification tasks of YOLOv5. This approach significantly enhanced the performance of vehicle detection. Then, track them upon detection. Finally, we use the YOLOv5 algorithm to locate and classify the color of license plates. Green license plates indicate new energy vehicles, while non-green license plates indicate fuel vehicles, which can accurately and efficiently calculate the number of new energy vehicles. The effectiveness of the proposed NEVTS in recognizing new energy vehicles and traffic flow statistics is demonstrated by experimental results. Not only can NEVTS be applied to the recognition of new energy vehicles and traffic flow statistics, but it can also be further employed for traffic timing pattern extraction and traffic situation monitoring and management.