# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 1 Introduction  
In recent years, with the rapid development of living standards and transportation, there has been a dramatic improvement in crowd counting. It plays a critical role in the maintenance of public security. For instance, in the congested scene, the monitoring device can real-time monitor the change of crowd to prevent overcrowding and abnormal conditions. Whereas the task suffers from occlusions, perspective distortions, scale variations, and background interference. It immensely remains a challenge to reach remarkable accuracy on the prediction of crowd counting in a single image. Extensive studies and a mountain of efforts have been attempted. There has been considerable progress in crowd counting. Early works detected each individual pedestrian in a crowd scene [1], or used multiple handcrafted features to regress the number of persons [2]. But they got trouble in detecting pedestrians in congested scenes caused by severe occlusions. Nowadays, mainstream works aim to generate crowd density maps instead of counting persons directly to alleviate the occlusions. Furthermore, density maps also contain spatial information, which can be better applied in public security. GAN- based [3; 4] and CNN-based [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] methods have achieved excellent improvements. Due to the different distances from the surveillance camera and perspective problems, there are persons with unequal sizes of head in a single image. Hence, the biggest conundrum is the scale diversity, which restrains the counting accuracy. Some works [5; 6; 7; 8; 9] use multi-scale architecture with different convolutional kernels to address the scale variation, while some works [10; 11] suggest to replace diverse convolutional kernels with piles of kernels with the same size. Furthermore, due to background interference, the density maps estimated may have a large deviation and extra information is added to the training process to remedy the problem [14; 15]. Unfortunately, these methods still retain disadvantages that cannot solve scale variation well. [PERSON] et al. [11] demonstrated that each column indifferent branches learned nearly identical features, having little contribution to scale variation. When the network becomes tanglesome, calculation and computational complexity sharply increase. It also leads to the tardiness of the training speed and gradient explosion. Based on this, to extract various scale features and eliminate the background noise influence, we consider using the network with a single-column and single-size convolutional kernel, fusing the feature maps from the frontend and the backend. It is proved that different levels of layers in the network contain different scale feature information, and multiple convolution kernels of the same size have the same feature learning ability as large convolution kernels. For example, two succeeding \(3\times 3\) convolution kernels are equivalent to one \(5\times 5\) convolution kernel and three succeeding \(3\times 3\) convolution kernels are equivalent to a \(7\times 7\) convolution kernel. Therefore, the low-level and high-level feature maps contain features of different scales. In addition, different levels of layers also contain different levels of semantic information. A low-level convolution can extract detailed edge patterns and effectively regress the congested area to obtain density maps, and a high-level can selectively obtain useful semantic information to distinguish between human and background noise. This obtains different scale information while not increasing the amount of computation and network structure complexity. Nevertheless, various features of the fusion of different sizes of human head regions are difficult to be selectively enhanced well through straightforward connections. And the channel of convolution is always ignored which may lose crucial information especially the global information.