Recognition new energy vehicles based on improved YOLOv5

Frontiers in Neurorobotics

Neuroscience

Mingming Kong¹  Yannan Hu¹  Mingsheng Zhou¹  Zhanbo Sun² 

[1] School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China;School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China;

关键词: Intelligent Traffic Systems;    new energy vehicle;    traffic flow statistics;    detect vehicles;    license plate;   

DOI  :  10.3389/fnbot.2023.1226125

 received in 2023-05-20, accepted in 2023-07-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Hu, Kong, Zhou and Sun.