Optimizing Data Augmentation for Real-Time Small UAV Detection: A Lightweight Context-Aware Approach
Visual detection of Unmanned Aerial Vehicles (UAVs) is a critical task in surveillance systems due to their small physical size and environmental challenges. Although deep learning models have achieved significant progress, deploying them on edge devices necessitates the use of lightweight models, such as YOLOv11 Nano, which possess limited learning capacity. In this research, an efficient and context-aware data augmentation pipeline, combining Mosaic strategies and HSV color-space adaptation, is proposed to enhance the performance of these models. Experimental results on four standard datasets demonstrate that the proposed approach, compared to heavy and instance-level methods like Copy-...
| Authors | Amir Zamani, Zeinab Abedini |
|---|---|
| Primary category | cs.CV |
| Published | 21 April 2026 |
| Last updated at source | 9 October 2026 |
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