{"id":11677,"date":"2026-09-11T17:12:22","date_gmt":"2026-09-11T09:12:22","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11677"},"modified":"2026-09-11T19:45:05","modified_gmt":"2026-09-11T11:45:05","slug":"jase-202612-35-025","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-025","title":{"rendered":"Lightweight Pyramid Network-Based Multi-Modal Feature Fusion for Crowd Counting"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Han Shi, Rongkai Wang<\/i><\/a>, Deqing Li<\/i><\/a><\/p>\n\n\n\n

College of Information and Electronic Technology, Jiamusi University, Jiamusi, 154007, China<\/p>\n<\/div>\n\n\n\n

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Received: May 31, 2026
Accepted: August 17, 2026
Publication Date: September 11, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Proposed crowd counting structure <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.025<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Accurate crowd counting under complex scenes remains challenging due to scale variation, occlusion, and background noise. This paper introduces a lightweight pyramid network that fuses multi-modal features for robust crowd density estimation. The architecture integrates VGG-16, a multi-scale feature fusion module (MFFM) and a convolutional block attention module (CBAM) to suppress noise and enhance head-region focus. A dilated convolution module (DCM) further refines features into background attention and density maps, which are element-wise multiplied to produce the final prediction. A joint loss function combining Euclidean and adaptive Bayesian losses improves density map accuracy. Evaluated on ShanghaiTech, UCF-CC-50, and NWPU-Crowd datasets, the proposed method achieves state-of-the-art MAE and RMSE across sparse and dense scenes. Ablation studies confirm the effectiveness of MFFM, CBAM, and DCM modules. The model demonstrates strong generalization and robustness, even under low-light, occlusion, and background clutter.<\/p>\n\n\n\n

Keywords: Crowd counting, Lightweight pyramid network, Multi-modal feature fusion, Dilated convolution module.<\/em><\/p>\n\n\n\n

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