A small target detection method for dense crowd based on YOLOv4
Aiming at the problem of poor small target detection accuracy due to visual obstruction and target occlusion in dense crowds,this paper incorporates the convolutional-pixel block attention module(CBAM-PIX)into the backbone network CSPDarknet53 based on the YOLOv4 model,and improves the feature fusion network by making use of cascading ideas.The attention mechanism method and feature fusion method not only enhance the data richness,but also improve the ability to extract information from spatial channel pixels and the accuracy of target detection.In addition,lowering the computational amount and parameters by reducing the number of network layers,so as to improve adaptability of the network model under limited computational resources and equipment requirements.Experimental results show that the improved model algorithm has improved accuracy by 1.96%and increased robustness when it is used for small target detection in a dense crowd.The algorithm provides an effective solution to solve the small target detection of dense crowds in complex backgrounds,having application value.
国家科技期刊平台
NSTL
万方数据
维普
