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 introduces the Fuss-Free Network (FFNet), a crowd counting deep learning model that is characterized by its simplicity and efficiency in terms of its structure. The model comprises only a backbone of a neural network and a multi-scale feature fusion structure.The multi-scale feature fusion structure is a simple architecture 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.The experimental results further indicate that excellent performance in crowd counting tasks can also be achieved by utilizing a simple, low-parameter, and computationally efficient neural network structure.

使用简单、高效的神经网络结构，该论文介绍了一种名为Fuss-Free Network (FFNet)的人群计数深度学习模型，通过多尺度特征融合结构实现了与复杂模型相媲美的准确性。

无烦恼网络：一种简化和高效的用于人群计数的神经网络