Pedestrian detection algorithms based on Anchor-free architecture
Zhang Qingwu，Guan Shengxiao
(School of Microelectronics,University of Science and Technology of China,Hefei 230026,China)
Abstract： This paper designed a pedestrian detection algorithm based on the Anchor-free detection framework.The deep residual network (ResNet) was used as a feature extraction network,combined with the feature pyramid structure (FPN),and finally multiscale prediction was used for prediction.This paper also regarded the target center point and size as an advanced semantic feature,and combined the shallow feature map with more detailed information and the deep feature map with more semantic information.The experiments were verified on the Citypersons dataset.Compared with the existing pedestrian detection algorithms, the detection results were respectively improved by 1.11%~3.01%, 0.15%~6.55% and 0.59%~6.39% in the case of slight occlusion, general occlusion and severe occlusion, and the detection effect is better.
Key words : Anchor-free;pedestrian detection;feature fusion;multi-scale detection
