In this paper, we propose a novel encoder-decoder model called Semantic-refined Spatial Pyramid Network (SSPNet) for generating high-quality density maps, which aims to build a scale-aware counting network to estimate the number of crowds accurately. The SSPNet consists of the front-end based on VGG-16, spatial pyramid multi-scale module (SPMM), and semantic enhancement module (SEM). First, a series of convolutional neural layers are utilized as the front-end to get deeper features without the extra computational cost. Moreover, the SPMM, which has a spatial pyramid structure with multiple receptive fields, is employed to capture multi-scale features. Furthermore, the SEM is designed to refine the features captured by SPMM, which uses deep semantic information to better integrate multi-scale features. Finally, the shallow texture information is adopted to compensate for the detail of the feature map to enhance the quality of the density map. Extensive experiments and comparisons on three challenge datasets, including ShanghaiTech Part_A & Part_B, UCF_CC_50, and UCF-QNRF, illustrate the superiority of our method.

在本文中，我们提出了一种新的编码器-解码器模型，称为语义优化空间金字塔网络（SSPNet），用于生成高质量的密度图，旨在构建一个尺度感知的计数网络来准确估计人群的数量。SSPNet由基于VGG-16的前端、空间金字塔多尺度模块（SPMM）和语义增强模块（SEM）组成。首先，一系列卷积神经层被用作前端来获得更深层次的特征，而无需额外的计算成本。此外，SPMM 具有具有多个感受野的空间金字塔结构，用于捕获多尺度特征。此外，SEM 旨在细化 SPMM 捕获的特征，它使用深度语义信息来更好地集成多尺度特征。最后，采用浅纹理信息来补偿特征图的细节，以提高密度图的质量。在 ShanghaiTech Part_A & Part_B、UCF_CC_50 和 UCF-QNRF 三个挑战数据集上进行的大量实验和比较，说明了我们方法的优越性。