@article{ZHENG2026, 
author = {Diwen ZHENG and Yangyu SHI and Chengjie XIE and Shuhua LU},
title = {RGB-T crowd counting method with multi-scale perception and infrared feature enhancement},
year = {2026},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {52},
number = {6},
pages = {2208-2218},
keywords = {crowd counting, feature enhancement, cross-modal feature fusion, multi-scale perception, infrared and visible image},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2024.0250},
doi = {10.13700/j.bh.1001-5965.2024.0250},
abstract = {In order to overcome the difficulty of crowd counting in low light, RGB-T crowd counting attempts to create maps of crowd density utilizing complimentary information from visual and thermal imagery. However, existing RGB-T crowd counting methods face issues such as scale variation and background interference during cross-modality information fusion. To tackle these challenges, we propose an RGB-T crowd counting method based on multi-scale perception and infrared feature enhancement (MSENet). Our approach presents an RGB-T feature fusion mechanism (RTFM) that creates an infrared enhancement structure to completely capture crowd information in thermal images and uses a multi-branch structure for multi-scale feature extraction. Additionally, we utilize dense connections and information divergence mechanisms to transfer complementary features to each modality, achieving a reusable expression of complementary features and enhanced modality features. We evaluate our proposed method on the RGBT-CC dataset and the ShanghaiTechRGBD dataset through comparative experiments. The results demonstrate that our method outperforms existing state-of-the-art approaches on the RGBT-CC dataset, exhibiting good accuracy, robustness and good generalization.}
}