In the field of crowd counting research, many recent deep learning based methods have demonstrated robust capabilities for accurately estimating crowd sizes. However, the enhancement in their performance often arises from an increase in the complexity of the model structure. This paper discusses how to construct high-performance crowd counting models using only simple structures. We proposes the Fuss-Free Network (FFNet) that is characterized by its simple and efficieny structure, consisting of only a backbone network and a multi-scale feature fusion structure. The multi-scale feature fusion structure is a simple structure consisting of three branches, each only equipped with a focus transition module, and combines the features from these branches through the concatenation operation. Our proposed crowd counting model is trained and evaluated on four widely used public datasets, and it achieves accuracy that is comparable to that of existing complex models. Furthermore, we conduct a comprehensive evaluation by replacing the existing backbones of various models such as FFNet and CCTrans with different networks, including MobileNet-v3, ConvNeXt-Tiny, and Swin-Transformer-Small. The experimental results further indicate that excellent crowd counting performance can be achieved with the simplied structure proposed by us.

翻译：在人群计数研究领域，许多基于深度学习的最新方法已展现出准确估计人群规模的鲁棒能力。然而，其性能的提升往往源于模型结构复杂度的增加。本文探讨了如何仅使用简单结构构建高性能人群计数模型。我们提出了结构简洁高效的Fuss-Free Network（FFNet），该网络仅由骨干网络和多尺度特征融合结构组成。多尺度特征融合结构是一种仅包含三个分支的简单结构，每个分支仅配备一个焦点转换模块，并通过拼接操作融合这些分支的特征。我们在四个广泛使用的公共数据集上对所提出的人群计数模型进行训练和评估，其达到了与现有复杂模型相当的精度。此外，我们通过将FFNet和CCTrans等各种模型的现有骨干网络替换为不同网络（包括MobileNet-v3、ConvNeXt-Tiny和Swin-Transformer-Small）进行了综合评估。实验结果进一步表明，采用我们提出的简化结构可以实现优异的人群计数性能。