Research on pedestrian detection method based on improved YOLOv5
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.
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