A lightweight pedestrian detection algorithm based on feature fusion is proposed to solve the problem of low detection accuracy caused by dense pedestrian targets,small target scales,and varying background illumination around the target.Firstly,build a new backbone feature extraction network(CSPDarknet53-S),and add a new feature extraction module(REM)to the original backbone network to enhance the network's ability to extract pedestrian features.Secondly,improve the feature fusion structure.After extracting high-low feature maps from the backbone network,add a feature fusion module(RM block)between the backbone network and the feature fusion network to increase the receptive field.And then introduce shallow feature information to retain more small target features to form a new feature fusion network(IFFM).Finally,the fused feature map is processed through YOLO Head to obtain the output results.The above steps are based on the basic framework of TinyYOLOv4.Experimental results show that the proposed algorithm achieves higher detection accuracy and better detection results on pedestrian data sets(PASCAL VOC2007 and VOC2012 person data).
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