重庆理工大学学报2024，Vol.38Issue(13)：101-109,9.DOI:10.3969/j.issn.1674-8425(z).2024.07.013
基于改进YOLOv5的行人检测方法研究
Research on pedestrian detection method based on improved YOLOv5
摘要
Abstract
To address target occlusion and missed detections of small-scale pedestrians in pedestrian detection,a modified pedestrian detection model called DROE-YOLO is proposed based on YOLOv5.Specifically,the residual structure of Res2Net is introduced into the C3 module of YOLOv5 to enhance the network's representation capability for pedestrian targets.Additionally,Dynamic Head is employed as the detection head for YOLOv5 to improve detection accuracy and robustness.The Simplified OTA method is adopted for label assignment strategy,which enables more accurate matching between ground truth boxes and predicted boxes.Finally,the soft-NMS+EIOU method is used to further improve the detection accuracy of pedestrian targets.Our experimental results on the CrowdHuman dataset demonstrate that DROE-YOLO achieves excellent performances in pedestrian detection tasks.Compared to the baseline model,with a slight increase in parameters,DROE-YOLO model improves the precision by 3.3%and the recall by 6.5%,making it more suitable for practical pedestrian detection tasks.关键词
行人检测/Res2Net/Dynamic-Head/Simplified-OTA/Soft-NMSKey words
pedestrian-detection/Res2Net/Dynamic-Head/Simplified-OTA/Soft-NMS分类
信息技术与安全科学引用本文复制引用
薛继伟,薛鹏杰,胡馨元..基于改进YOLOv5的行人检测方法研究[J].重庆理工大学学报,2024,38(13):101-109,9.基金项目
黑龙江省省属本科高校基本科研业务费项目(2022TSTD-03) （2022TSTD-03）
