基于Anchor-free架构的行人检测方法

Pedestrian detection algorithms based on Anchor-free architecture

使用无预选框(Anchor-free)的检测框架,设计了一种行人检测算法。将深度残差网络(ResNet)作为特征提取网络,与特征金字塔网络(FPN)结构相结合,使用了多尺度预测的方式进行预测。把目标中心点和尺寸作为一种高级的语义特征,将含有更多细节信息的浅层特征图和含有更多语义信息的深层特征图进行融合。在Citypersons数据集上进行了实验验证,相较现有行人检测算法,提出的算法在轻微遮挡、一般遮挡和严重遮挡情况下漏检率分别提升了1.11%~3.01%,0.15%~6.55%和0.59%~6.39%,检测效果更好。

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 multi-scale 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.