In the current study,pedestrian detection accuracy in dense scenes is low.In order to improve the detection ac- curacy,an improved method based on YOLOv5 network,V-YOLO,is proposed in this paper.The bi directional feature pyramid network (BiFPN)isused to improve the path aggregation network (PANet)in the original network to strength- en the multi-scale feature fusion capability.Improve the ability of pedestrian target detection.For retain more feature in- formation and improve the feature extraction capability of the backbone network,a residual structure VBlock is added. Select kernel networks(SKNet)were introduced to integrate the feature maps of different receptive fields dynamically to improve the utilization rate of different pedestrian features.In this paper,CrowdHumandata set is used for training and testing.The experimental results show that compared with the original network,the accuracy,recall rate and average accuracy of the proposed algorithm are increased by 1.8%,2.3%and 2.6%,respectively,which verifies that the pro posed algorithm can effectively improve the accuracy of pedestrian target detection in dense scenes