DSNet: A vehicle density estimation network based on multi-scale sensing of vehicle density in video images

It is one of the hot topics in the field of computer vision to estimate the density of vehicles on the road by using UAV equipped camera. Because the vehicle scale in video images is variable, the scene is complex and the change of vehicle density has strong randomness, the estimation results of vehicle density obtained by the existing methods have large errors. To solve these problems, this paper proposes a vehicle density estimation network based on multi-scale sensing of vehicle density (DSNet). The network adaptively senses the vehicle density in video images through an innovatively designed density sensing module and generates feature maps with different densities. Combined with the context information extracted from the network, the appropriate weight for each density-specific feature map is generated. By fusing the feature maps with different weights to enhance the feature of vehicle. Finally, the enhanced feature map is further refined to obtain a high-resolution density map. In addition, to solve the problem of the global counting loss (LC) that the global estimation results are good but the local estimation errors exist, we design and add the new region-aware loss (LG) to enhance the local feature extraction ability of the network and improve the robustness of the network. Experimental results show that the GAME of DSNet on the VisDrone 2019 dataset and DETRAC dataset is reduced to 5.45 and 6.07, respectively. Compared with the current state-of-the-art vehicle density estimation methods, DSNet achieves higher accuracy in the vehicle density estimation tasks of different scenes.