Pedestrian detection based on multi-layer feature fusion

Abstract

This paper proposes a goal detection network of end-to-end multi-scale feature fusion because of the tiny pedestrian target and blocking in pedestrian detection. This algorithm is based on the YOLOv3 network, fully integrates multi-scale features, enhances the expression ability of small target features, improves the robustness of pedestrian detection in complex environments, and improves pedestrian detection accuracy based on guaranteeing real-time detection. In the experiment, the current mainstream pedestrian detection algorithm is compared. This algorithm effectively improves the detection accuracy in INRIA and KITTI data sets, and the average accuracy of Yolov3 in two different data sets is improved by 6% and 24.7%, respectively.