南京信息工程大学学报2024，Vol.16Issue(1)：1-10,10.DOI:10.13878/j.cnki.jnuist.20221027003
基于改进YOLOv5m的电动车骑行者头盔与车牌检测方法
Helmet and license plate detection for electric bike rider based on improved YOLOv5m
摘要
Abstract
It has become a mandatory requirement for electric bike riders to wears helmet on riding.To automatically check if the electric bike rider wears helmet,a helmet and license plate detection approach based on improved YOLOv5m model is herein proposed,which can locate and recognize the license plate of the unhelmeted rider,so as to track down the violators.The model is trained with self-built dataset,uses DIOU loss function instead of GIOU loss function,and uses DIOU_NMS to replace weighted NMS so as to enhance the recognition ability for dense cycling scenes.Meanwhile,the ECA attention mechanism is added to the Backone and the Neck parts to im-prove the recognition accuracy for small-and medium-sized targets.Then,the K-means algorithm is used to re-cluster the anchor frame size.Finally,the Mosaic data enhancement method is improved.The experimental results show that the mAP of the proposed approach is 92.7%,which is 2.15,5.7,and 6.9 percentage points higher than the original YOLOv5m,YOLOv4 tiny,and Faster RCNN,respectively.It can be concluded that the improved YOLOv5m model can accurately recognize rider's helmet and electric bike's license plate.关键词
头盔检测/车牌检测/YOLOv5m/注意力机制/DIOU/K-means算法/改进Mo-saic数据增强Key words
helmet detection/license plate detection/YOLOv5m/attention mechanism/DIOU/K-means algorithm/improved Mosaic data enhancement分类
信息技术与安全科学引用本文复制引用
庄建军,叶振兴..基于改进YOLOv5m的电动车骑行者头盔与车牌检测方法[J].南京信息工程大学学报,2024,16(1):1-10,10.基金项目
国家重点研发计划(2021YFE0105500) （2021YFE0105500）
国家自然科学基金(62171228) （62171228）
