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Ling, Miaogen
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Engineering

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LSKD: Lightweight Self-Knowledge Distillation Framework for Fast and Robust Crowd Counting Raza, Muhammad; Ling, Miaogen; Ur Rahman, Atta; Pallewatta, Pandula; Hersi, Aboubakar Abdinur; Beruwalage, Shehan Maxwell; Kannangara, Deshan Sachintha
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.436

Abstract

Crowd counting plays an important role in the surveillance of the safety of the people, traffic, and intelligent surveillance systems. However, the exact density estimations remain hard to achieve in highly congested scenes due to the tough occlusion, large-scale variance, and complicated background. Although the recent deep-learning methods have high performance, several of them do not need computationally efficient underlying backbone networks, and rather, they employ an external teacher-student distillation architecture, which can limit their use in resource-constrained applications. To avoid this problem, we introduce LSKD, a lightweight self-knowledge distillation network that is density map regression-specific. Unlike other conventional teacher-dependent processes, LSKD can also independently carry out internal multi-level feature alignment within a single small network that is not in need of an external teacher model. The structure integrates a Feature Matching Block (FMB) and a Context Fusion (CoFuse) block to enhance the hierarchical match of features and global awareness of context. The large experiments demonstrate that LSKD obtain competitive performance using the number of parameters as 2.65 million and GFLOPs as 10.23. Particularly, it has 63.17 MAE on ShanghaiTech Part A, 8.94 on ShanghaiTech Part B, 143.7 on UCF-QNRF, and 223.88 on UCF-CC-50, which is a good ratio between the accuracy and the efficiency of the calculations. Such results indicate that LSKD has an implementable and efficient solution to the real-time counting of crowds at the edge devices.
NRCC-LC: Noise-Robust Crowd Counting with Dynamic Label Correction under Noisy Supervision Hersi, Abubakar Abdinur; Ling, Miaogen; Raza, Muhammad; Hassan, Abdirahman Mohamed; Hussien, Idris Aweis
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.494

Abstract

Crowd counting remains a challenge within computer vision due to many factors that affect the performance of available methods such as occlusion, scale variability, and perspective distortion. Additionally, many labels associated with crowd counting systems have high levels of noise caused by various real-world conditions. Although crowd counting methodologies have improved accuracy over recent years, the majority of crowd counting models still rely on clean real-time supervision and lack systems that can correct for dynamically corrupted labels, resulting in low robustness for crowd counting models when deployed in real-world applications. In this work we present a Noise-Robust Crowd Counting with Label Correction (NRCC-LC) framework to obtain reliable density estimates from noisy supervision. To accomplish this, our approach uses a combined CNN-Transformer architecture to capture both locally- and globally-relevant visual information (i.e., image content and context), along with a Noise-Robust Module (NRM) and a Dynamic Label Correction (DLC) mechanism. Our principle experimental results evaluated across four benchmark datasets: ShanghaiTech Part A, ShanghaiTech Part B, NWPU-Crowd, and JHU-Crowd++, indicate that the NRCC-LC exhibits competitive performance with respect to existing state-of-the-art crowd-counting methods; most notably, producing per-image MAEs of 97.8 and 392.3 on NWPU-Crowd. These experimental results additionally have real-world implications for improving public safety and urban planning; thus, through our novel method of noise-aware feature learning combined with iterative label correction, we can establish the potential of automated monitoring systems in complex, real-world environments to be significantly more reliable.
Co-Authors Beruwalage, Shehan Maxwell Hassan, Abdirahman Mohamed Hersi, Aboubakar Abdinur Hersi, Abubakar Abdinur Hussien, Idris Aweis Kannangara, Deshan Sachintha Pallewatta, Pandula Raza, Muhammad Ur Rahman, Atta