Journal of Guangxi Normal University(Natural Science Edition) ›› 2026, Vol. 44 ›› Issue (1): 23-32.doi: 10.16088/j.issn.1001-6600.2025010101

• Intelligent Transportation • Previous Articles     Next Articles

YOLOv8-based Helmet Detection Method for Electric Vehicle Riders Combining Intelligent Communication and UAV-Assistance

LIU Zhihao1,2, LI Zili1,2*, SU Min1,2   

  1. 1. Key Laboratory of Nonlinear Circuits and Optical Communications (Guangxi Normal University), Guilin Guangxi 541004, China;
    2. School of Electronic and Information Engineering/School of Integrated Circuits, Guangxi Normal University, Guilin Guangxi 541004, China
  • Received:2025-01-01 Revised:2025-03-03 Online:2026-01-05 Published:2026-01-26

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Abstract: Nowadays, the safety of electric vehicle (EV) riders has now become a focal issue in society, and wearing safety helmets was proven to be an effective way to reduce injury in accidents. In order to enhance road traffic safety and improve regulatory efficiency, an UAV-assisted helmet intelligent detection algorithm based on intelligent communication and deep learning is proposed. By combining intelligent communication technology, UAVs can transmit video data in real time and analyze it quickly by intelligent algorithms. First, an improved Outlook-C2f architecture was proposed to enhance the algorithm’s focus on the small targets; Second, CARAFE is proposed to used in the Feature Pyramid Network (FPN) to dynamically generate weights for precise feature reconstruction and improved spatial resolution; Finally, WIoU (Wise Intersection over Union) was integrated to improve the accuracy of positional information. The experimental results show that, based on the road real-time dataset, the improved YOLOv8 algorithm achieves 96.7% mAP and 26.91 FPS, which are significantly better than the traditional method, demonstrating its potential for application in complex traffic scenarios.

Key words: helmet detection, intelligent communication, YOLO, attention mechanism, UAV aerial photography

CLC Number:  U492.8
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