广西师范大学学报（自然科学版）2026，Vol.44Issue(1)：23-32,10.DOI:10.16088/j.issn.1001-6600.2025010101
智能通信与无人机结合的YOLOv8电动车骑行者头盔佩戴检测方法
YOLOv8-based Helmet Detection Method for Electric Vehicle Riders Combining Intelligent Communication and UAV-Assistance
摘要
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.关键词
头盔检测/智能通信/YOLO/注意力机制/无人机航拍Key words
helmet detection/intelligent communication/YOLO/attention mechanism/UAV aerial photography分类
交通工程引用本文复制引用
刘志豪,李自立,苏珉..智能通信与无人机结合的YOLOv8电动车骑行者头盔佩戴检测方法[J].广西师范大学学报（自然科学版）,2026,44(1):23-32,10.基金项目
国家自然科学基金(62361006) （62361006）
