Accurately counting the number of dense objects, such as crowds or vehicles, in an image is a challenging and meaningful task widely used in public safety management and traffic flow prediction. The existing CNN-based density map estimation methods are ineffective for extracting the counting features of long-distance queuing vehicles in traffic jams; In addition, these methods do not focus on counting in complex scenes, such as vehicle counting in the human-vehicle mixed scenes. To tackle this issue, we propose MSCNet, a novel multi-scale dilated convolution channel-aware deep network for vehicle counting. The proposed network solves the problem of scale variation for long-distance queuing vehicles and improves the ability to extract vehicle features in human-vehicle mixed scenes. The MSCNet consists of a front-end module and three functional modules: the front-end module is used to extract the initial features of the counting image; the direction-based perspective coding module (DPCM) encodes the perspective information of the image from four directions to extract continuous long-distance features; the multi-scale dilated residual module (MDRM) can densely extract the large-scale variation features; the channel-aware attention module (CAM) effectively enhances the channel features that are important for vehicle counting in mixed human-vehicle scenes. The MSCNet has conducted extensive comparative experiments on the TRANCOS dataset, the VisDrone2021 Vehicle&Crowd dataset, and the ShanghaiTech dataset. The experimental results show that the MSCNet outperforms the state-of-the-art counting networks for dense vehicle counting, especially in mixed human-vehicle scenes.

准确计算图像中密集物体（例如人群或车辆）的数量是一项具有挑战性且有意义的任务，广泛应用于公共安全管理和交通流量预测。现有基于CNN的密度图估计方法对于提取交通拥堵中长途排队车辆的计数特征效果不佳；此外，这些方法并不关注复杂场景中的计数，例如人车混合场景中的车辆计数。为了解决这个问题，我们提出了 MSCNet，一种用于车辆计数的新型多尺度扩张卷积通道感知深度网络。该网络解决了长距离排队车辆的尺度变化问题，提高了人车混合场景中提取车辆特征的能力。MSCNet由前端模块和三个功能模块组成：前端模块用于提取计数图像的初始特征；基于方向的透视编码模块（DPCM）从四个方向对图像的透视信息进行编码，以提取连续的长距离特征；多尺度扩张残差模块（MDRM）可以密集地提取大尺度变化特征；通道感知注意力模块（CAM）有效增强了通道特征，这对于人车混合场景中的车辆计数非常重要。MSCNet 对 TRANCOS 数据集、VisDrone2021 Vehicle&Crowd 数据集和 ShanghaiTech 数据集进行了广泛的对比实验。