Dense crowd detection method based on improved YOLOv7

Yuxiang Wang; Lihong Yue

doi:10.1117/12.3035293

19 July 2024 Dense crowd detection method based on improved YOLOv7

Yuxiang Wang, Lihong Yue

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132O (2024) https://doi.org/10.1117/12.3035293
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Real-time monitoring of dense crowds can provide data support for early risk warning, evacuation guidance, and emergency resource scheduling. The YOLOv7 algorithm for dense crowd detection has been improved to effectively obtain crowd information. The YOLOv7 framework has been pruned to create a lightweight network, and CARAFE has replaced the upsampling module for adaptive content awareness to improve small target attention. NWD is used instead of C-IoU to enhance model generalization and optimize loss function convergence. Compared with the original YOLOv7 algorithm, parameter reduction is 50%, AP@0.5 increased by 7.9%, and FPS increased by 9.2%. Results demonstrate that this improved algorithm can efficiently improve accuracy in detecting dynamic videos of dense crowds; results from pedestrian street detection show feasibility and effectiveness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuxiang Wang and Lihong Yue "Dense crowd detection method based on improved YOLOv7", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132O (19 July 2024); https://doi.org/10.1117/12.3035293

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KEYWORDS

Object detection

Detection and tracking algorithms

Mathematical optimization

Target detection

Small targets

Video surveillance

Video

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