轻量级无人机检测新数据增强方法，提升实时性与稳定性
Optimizing Data Augmentation for Real-Time Small UAV Detection: A Lightweight Context-Aware Approach
- 结合马赛克与色彩空间调整，构建上下文感知的增广流程
- 在四个数据集上显著提升mAP，且避免合成伪影与过拟合
- 适合边缘设备部署，尤其在雾霾条件下表现最优
由于小型无人机的物理尺寸小及环境干扰大，其视觉检测在监控系统中至关重要。尽管深度学习模型已取得显著进展，但在边缘设备部署时需使用轻量级模型（如YOLOv11 Nano），其学习能力有限。本文提出一种高效且上下文感知的数据增强管道，融合马赛克策略与HSV色彩空间调整，以提升此类模型性能。在四个标准数据集上的实验表明，相较于重载且实例级别的复制粘贴方法，该方法不仅有效避免了合成伪影和过拟合，还在所有场景下显著提升了平均精度均值（mAP）。此外，在雾天条件下的泛化能力评估显示，该方法在精度与稳定性之间实现了最佳平衡，而其他方法（如MixUp）仅在特定应用中有效。
原文摘要 · Abstract (English)
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.
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