Accurate Crowd Counting Using an Enhanced LCDANet with Multi-Scale Attention Modules
Abeuov N. Absatov D. Mutaliyev Y. Serek A.
September 2025Universitas Ahmad Dahlan
Buletin Ilmiah Sarjana Teknik Elektro
2025#7Issue 3657 - 667 pp.
Accurate crowd counting remains a challenging task due to occlusion, scale variation, and complex scene layouts. This study proposes ME-LCDANet, an enhanced deep learning framework built upon the LCDANet backbone, integrating multi-scale feature extraction via Micro Atrous Spatial Pyramid Pooling (MicroASPP) and attention refinement using CBAMLite modules. A preprocessing pipeline with Gaussian-based density maps, synchronized augmentations, and a dual-objective loss function combining density and count supervision supports effective training and generalization. Experimental evaluation on the ShanghaiTech Part B dataset demonstrates a Mean Absolute Error (MAE) of 11.50 (95% CI: 10.20–12.91) and a Root Mean Squared Error (RMSE) of 11.54 (95% CI: 10.26–12.99). Training dynamics indicate steadily declining loss and reduced validation MAE, while gradient norm analysis suggests reliable convergence. Comparative results show that, although CSRNet and SaNet achieve slightly lower MAE, ME-LCDANet attains a notably reduced RMSE, reflecting robustness against large prediction deviations. While the study focuses on a single benchmark dataset, the proposed architecture offers a promising approach for robust crowd counting in diverse scenarios.
Attention Mechanismss , Crowd Counting , Density Estimation , Inference of Crowd , MicroASPP
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School of Information Technologies and Engineering, Kazakh-British Technical University, Almaty, Kazakhstan
Institute of Information and Computational Technologies, Satbayev University, Almaty, Kazakhstan
School of Digital Technologies, Narxoz University, Almaty, Kazakhstan
Department of Computer Science, SDU University, Kaskelen, Kazakhstan
School of Information Technologies and Engineering
Institute of Information and Computational Technologies
School of Digital Technologies
Department of Computer Science
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