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This paper introduces a context-aware data augmentation pipeline tailored for lightweight YOLOv11 Nano models to improve small UAV detection. The pipeline combines Mosaic augmentation with HSV color-space adaptation to enhance model performance and generalization. Experiments on four datasets demonstrate that this approach outperforms heavier augmentation techniques like Copy-Paste and MixUp, particularly in challenging conditions like fog, while avoiding synthetic artifacts and overfitting.
Lightweight UAV detectors get a surprisingly large boost in accuracy and robustness from a carefully tuned Mosaic and HSV augmentation pipeline, outperforming more complex methods.
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-Paste, not only prevents the generation of synthetic artifacts and overfitting but also significantly improves mean Average Precision (mAP) across all scenarios. Furthermore, the evaluation of generalization capability under foggy conditions revealed that the proposed method offers the optimal balance between Precision and stability for real-time systems, whereas alternative methods, such as MixUp, are effective only in specific applications.
