Recognition new energy vehicles based on improved YOLOv5 - Hu, Yannan; Kong, Mingming; Zhou, Mingsheng; Sun, Zhanbo (Frontiers in Neurorobotics, 2023)
In the field of Intelligent Traffic Systems (ITS), recognized vehicles is a research hot-pot. However, it may be difficult to recognize vehicles with different types in intricate and unknown surroundings. In this paper, we propose an New Energy Vehicle Recognition and Traffic Flow Statistics approach (NEVTS). Specifically, we first utilize 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 locate and classify the colors of the license plate, which accurately and efficiently calculates the number of new energy vehicles. The effectiveness of the proposed NEVTS in recognizing new energy vehicles is demonstrated by the experimental results.