Pedestrian Target Detection Method Based on Developed YOLOv5
With the aim of detecting small targets and occlusions in pedestrian detection,a pedestrian detection method was proposed based on improved YOLOv5.In combination with GhostNet,CSP module in YOLOv5 was improved to CSPGhost module,so complex folding operation was simplified for similar functions on linear operation.Channel attention module was used behind each CSPGhost module to ensure model recognition speed and high detection accuracy.The pooling level of spatial pyramid was optimized to reduce time cost of the algorithm without changing original effect.The frame regression loss function GIoU was optimized for EIoU,which took into account the length loss and width loss.Their regression rate was faster and the regression results were better.Experimental results showed that the pedestrian target detection method based on the improved YOLOv5 on the basis of CSPGhost had a mAP value of 55.8％in COCO dataset,and detection speed reached 374 FPS.It had stronger detection capability for small targets,lower error detection rate for targets under occlusion,and faster detection speed,which could meet practical application requirements of pedestrian detection.
万方数据
维普
