Crowd Counting Estimation Algorithm of Railway Stations Based on Improved P2PNet

doi:10.3969/j.issn.1006-2475.2025.11.007

Abstract

Abstract: Abstract: An improved P2PNet based algorithm for estimating the number of people in railway stations is proposed. The algorithm has made significant modifications and optimizations to the traditional P2PNet algorithm. Firstly， the algorithm model adopts BiFPN path aggregation to enhance the network’s ability to fuse feature maps of different scales， solving the problem of large differences in personnel size and scale in images. Secondly， the network introduces the A-SPP structure into the low-level feature maps to increase the receptive field range and enhance its ability to extract multi-scale features. Thirdly， the CSAM attention mechanism is adopted before the OutLayer in the network to dynamically adjust the importance of each channel and spatial position in the feature map， which more effectively regresses the position and classification of people in the image. Finally， Focal Loss replaces traditional cross entropy in loss function to solve the problems of imbalanced positive and negative samples and imbalanced difficult and easy samples. The results of comparative experiments on publicly datasets and proprietary datasets show that this algorithm outperforms current advanced algorithms in terms of mean absolute error. In actual station video scenarios， this algorithm can accurately estimate the number of people.

Key words: Key words: , crowd counting estimation； P2PNet； multi-scale features； attention mechanism； feature fusion

CLC Number:

中图分类号：TP391

WAN Chengkai1, AN Gaoyun2, CUI Lan3. Crowd Counting Estimation Algorithm of Railway Stations Based on Improved P2PNet[J]. Computer and Modernization, 2025, 0(11): 58-64.

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