Dense pedestrian detection algorithm based on improved Yolov5-DCN

Abstract

Paper investigates an enhanced approach on YOLOv5 method that solves the challenges posed by complex environmental backgrounds, intensive repeated target detection, and varying pedestrian sizes. In the backbone network, feature extraction mainly depends on the texture information and shape of the target. We use deformable convolutional network (DCN) to replace the traditional convolution.;In Non-Maximum Suppression(NMS),The Generalized Intersection over Union(GIOU) used by original network is in place of through Distance Intersection over Union Loss(DIOU) solve problem of the densely populated repeated test.We use crowdhuman training data set to evaluate the effectiveness of the algorithm.We observed that the detection accuracy of the improved Yolov5-DCN model was 83.8%, which was 1.6% higher than that of the basic model. Moreover, it can effectively improve the accuracy of pedestrian target detection in dense scenes, especially for the detection of dense occluded targets, and the effect is significantly improved.