High-density pedestrian detection algorithm based on deep information fusion

In order to improve the accuracy of high-density population detection, a high density pedestrian detection algorithm (YOLOv4-HDPD) is proposed based on deep information fusion. By increasing the connection points of cross-layer fusion, high-level semantic information is further integrated with feature information. The improved Iterative Self-Organizing Data Analysis algorithm (ISODATA) makes the anchor value more suitable for the network model without increasing the number of parameters. Moreover, the network anti-interference ability is increased by replacing the CIOU algorithm target detection object. Compared with the original network, the YOLOv4-HDPD network has improved in mAP and avgIOU. Under the premise that the detection speed of the network is basically not affected, mAP is increased by 5.28% and avgIOU is increased by 5.73%. In terms of the current results, the network algorithm has been improved the detection effect of high-density pedestrians. At the same time, the network provides a new idea for solving the clustering and detection of dense targets in real scenes.