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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Suriana Ismail
Universiti Kuala Lumpur
Computer Science & IT Engineering

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YOLOv11 optimization for tiny object in crowded scenes Husna Sarirah Husin; Howard Chong Yun Hao; Pan Yuan Fei; Mohsen Marjani; Suriana Ismail
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3452-3463

Abstract

Small object detection in crowded urban and aerial scenes remains a critical challenge due to limited pixel information and information loss in deep neural networks. This study introduces a novel optimization framework for YOLOv11, specifically engineered for tiny-scale targets by integrating convolutional block attention modules (CBAM), k-means anchor clustering, and an enhanced feature pyramid network (FPN). Evaluated on the TinyPerson and COCO-mini datasets, the YOLOv11-optimized model achieves significant performance breakthroughs, delivering a +7.3% gain in mean average precision (mAP) and a +10.5% increase in recall over the baseline. Notably, the model achieved a recall of 0.072 on the TinyPerson dataset, with double sensitivity of standard YOLOv11. With a high-speed inference rate of 27.3 FPS, this research demonstrates that strategic architectural refinements can drastically improve small object detection reliability without compromising real-time viability on edge devices.
Co-Authors Howard Chong Yun Hao Husna Sarirah Husin Mohsen Marjani Pan Yuan Fei