Optimizing Data Augmentation for Real-Time Small UAV Detection: A Lightweight Context-Aware Approach
This work addresses the challenge of deploying effective UAV detection in surveillance systems using resource-constrained models, though it is incremental as it builds on existing data augmentation and lightweight model techniques.
The paper tackles the problem of improving real-time small UAV detection on edge devices by proposing a lightweight context-aware data augmentation pipeline, which significantly enhances mean Average Precision (mAP) across four standard datasets and offers optimal balance between Precision and stability in foggy conditions.
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.