比例融合与多层规模感知的人群计数方法OA北大核心CSTPCD

To deal with the problems of insufficient multiscale feature acquisition,poor fusion,and insufficient utiliza-tion of global features as a result of the changing view angles or distances of crowd images in dense scenes,we propose a crowd counting network based on proportion fusion and multilayer scale-aware.First,the backbone network VGG16 is employed to extract the initial characteristics of the population density.Subsequently,a multilayer scale-aware mod-ule is developed to acquire a rich expression of multiscale information from the crowd.Afterward,a proportional fusion strategy is designed to reconstruct the multiscale information based on the feature weights captured by the convolution layer and extract the significant crowd features.Lastly,convolution regression is utilized to regress the density map.Concurrently,a local consistency loss function is proposed,which improves the similarity between the generated dens-ity map and the real density map by regionalizing the density map and enhances the counting performance.The results of the experiments on multiple population datasets exhibit that the model proposed here surpasses the existing state-of-the-art methods of population density counting and has good generalization in vehicle counting.