A MFFBSNet crowd counting algorithm based on multi-scale feature fusion and background suppression
Aiming at the problems of scale variation,uneven distribution,and background occlusion of dense crowds in complex scenes,a crowd counting algorithm MFFBSNet based on multi-scale feature fusion and background suppression is proposed.The first 13 layers of the visual geometry group network VGG-16 are utilized as the front-end of the network.An atrous spatial pyramid pooling(ASPP)and a pyramid split attention(PSA)mechanism based on a lightweight design are introduced to construct a multi-scale feature fusion module,which addresses the problem of scale variation in dense crowds;In the middle of this network,spatial and channel attention mechanisms are incorporated to refine the fea-ture maps,highlighting the head regions in the image;The backend of this network employs atrous con-volution,which enlarges the receptive field without losing image resolution,to generate a background segmentation attention map.This suppresses background noise in the image and enhances the quality of the crowd density map.Experimental results on three public datasets,namely ShanghaiTech,UCF_CC_50,and NWPU-Crowd,demonstrate that the proposed crowd counting algorithm based on the MFFBSNet achieves higher counting accuracy compared to methods such as MCNN,SwitchCNN,and CSRNet.
万方数据
维普
