非机动电动车骑行人头盔佩戴检测研究  

Helmet Wearing Detection of Non-motorized Electric Vehicle Riders

作　　者：王贵成 杨号问 杨雨泽 黄建斌 程于飞 尹丰丰 WANG Guicheng;Yang Haowen;YANG Yuze;HUANG Jianbin;CHENG Yufei;YIN Fengfeng(School of Electrical and Electronic Engineering,Shanghai Institute Technology,Shanghai 201418,China;State Key Laboratory of Intelligent Optimized Manufacturing in Mining&Metallurgy Process and Beijing Key Laboratory of Process Automation in Mining&Metallurgy,Beijing 102628,China;BGRIMM Technology Group Co.,Ltd.,Beijing 100160,China)

机构地区：[1]上海应用技术大学电气与电子工程学院,上海201418 [2]矿冶过程智能优化制造全国重点实验室/矿冶过程自动控制技术北京市重点实验室,北京102628 [3]矿冶科技集团有限公司,北京100160

出　　处：《控制工程》2024年第8期1522-1528,共7页Control Engineering of China

基　　金：矿冶过程智能优化制造全国/矿冶过程自动控制技术北京市重点实验室基金资助项目(BGRIMM-KZSKL-2023-6)。

摘　　要：随着我国城市交通的不断发展,非机动电动车的数量不断上升,绿色出行的同时,交通事故也日益增多。为了保证非机动电动车骑行人在道路上的安全,国家逐步制定法规,要求出行需佩戴头盔,各地交通部门执行检查与处罚,对骑行人佩戴头盔的自动检测也提上了日程。提出用改进YOLOv5算法视频识别头盔佩戴情况,通过自组织非机动电动车骑行人头盔佩戴情况的数据集,选取聚类算法修改初始锚定框参数。然后,改进算法来适应样本集合,利用聚合输出网络提高判别的准确率。其中,采用迁移学习来减少训练资源的消耗。经各种场景实验测试的结果表明,在每秒30帧视频流下,检测的均值平均精度(mean average precision,mAP)达到了94.53%,满足对头盔佩戴检测精度和速度的要求。With the continuous development of urban transportation in my country,the number of non-motorized electric vehicles is rising,and the number of traffic accidents caused by green travel is also increasing.In order to ensure the safety of non-motorized electric vehicle cyclists on the road,the state has gradually formulated regulations requiring helmets to be worn when traveling,and local traffic departments have implemented inspections and penalties,then automatic detection of cyclists wearing helmets has been put on the agenda.It is proposed to use the improved YOLOv5 algorithm for video recognition of helmet wearing.Through the data set of self-organized non-motorized electric vehicle cyclists’helmet wearing situation,a clustering algorithm is selected to modify the initial anchor frame parameters.Then,the algorithm is improved to adapt to the sample set,and the discriminant accuracy is improved by using the aggregated output network.Among them,transfer learning is adopted to reduce the consumption of training resources.The results of experimental tests in various scenarios show that under the video stream of 30 fps,the mean average precision(mAP)of detection reaches 94.53%,which meets the accuracy of helmet wearing detection and speed requirements.

关 键 词：目标识别 YOLO算法 头盔佩戴 图像处理 聚合输出网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]