Optimizing Data Augmentation for Real-Time Small UAV Detection: A Lightweight Context-Aware Approach

TL;DR

This paper introduces a lightweight, context-aware data augmentation pipeline that improves UAV detection accuracy and robustness on edge devices, balancing precision and stability in real-time scenarios.

Contribution

It proposes a novel augmentation method combining Mosaic and HSV adaptation, outperforming heavy methods like Copy-Paste for small UAV detection on limited-capacity models.

Findings

Significant mAP improvement across four datasets.

Prevents synthetic artifacts and overfitting.

Balances precision and stability under foggy conditions.

Abstract

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.