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Haotian Bai

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Proceedings Article | 2 May 2023 Paper

Crowd counting network based on multi-scale convolution and attention mechanism

Fang-jun Luan, Hao-tian Bai, Zhi-li Chen

Proceedings Volume 12642, 126421U (2023) https://doi.org/10.1117/12.2674852

KEYWORDS: Convolution, Feature extraction, Amplifiers, Education and training, Feature fusion, Head, Data modeling, Semantics, Neural networks, Image processing

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In order to solve the problem of multi-scale in a single image gathering crowd counting, a new crowd counting network based on the fusion of dilating convolution pyramid and context attention mechanism (DCPCANet) is proposed. With the first ten convolutional layers of VGG16 as the front-end network, an dilated convolutional pyramid fusion attention mechanism module (AMP) is proposed, which is introduced into the three-level upsampling feature fusion module to extract fused multi-scale features, and the AMP module stack is used as the back-end network to capture and fuse multiscale features, The context attention module (CAM) is used to generate the feature map with weight, and high-quality crowd density map is output at the same time. Three mainstream public data sets are adopted, ShanghaiTech PartA,ShanghaiTech PartB,UCF_CC_50. Compared with the previous algorithm, the MAE of the UCF_CC_50 dataset is reduced by 11%, which preliminarily verifies the accuracy and robustness of the model.

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