The challenging problem that the authors solved in this study is to precisely estimate the number of objects in an image. Combining the spatial attention mechanism and pyramid structure, a novel atrous pyramid attention module is introduced to extract precise dense multi-scale features for object counting. Also, a global attention feature module is designed to enhance the ability of the network to learn feature representation based on channel attention mechanism. Combining the proposed atrous pyramid attention module and global attention feature module, a novel object counting method based on a dual attention network is established in this study. The experiments on public vehicle counting dataset including TRANCOS and crowd counting dataset including Mall and Shanghitech_A datasets demonstrate the proposed method achieves competitive performance, and the ablation study verifies the structure rationality of the designed modules.

中文翻译：

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基于双重关注网络的物体计数方法

作者在这项研究中解决的挑战性问题是精确估计图像中的对象数量。结合空间注意机制和金字塔结构，引入了一种新颖的多孔金字塔注意模块，以提取精确的密集多尺度特征以进行物体计数。而且，设计了全局关注特征模块以增强网络基于信道关注机制学习特征表示的能力。结合提出的多孔金字塔注意力模块和全局注意力特征模块，建立了一种基于双重注意力网络的物体计数新方法。对包括TRANCOS的公共车辆计数数据集和包括Mall和Shanghitech_A数据集的人群计数数据集进行的实验表明，该方法可实现竞争性能，