首个多光谱无人机检测数据集，提升小尺寸无人机识别准确率。
Towards UAV Detection in the Real World: A New Multispectral Dataset UAVNet-MS and a New Method
- 构建双流网络融合可见光与多光谱信息，解决成像偏移问题。
- 在低对比度小目标场景下，检测精度比纯可见光方法高6.2% AP50。
- 适合从事无人机监控、多光谱图像分析的研究者参考。
无人机数量激增催生了精准监测的迫切需求。现有基于可见光（RGB）的系统依赖空间特征，在小尺度目标、类型相似、背景干扰和低对比度条件下性能下降。多光谱成像（MSI）可捕捉材料特异性光谱特征，但因缺乏专用数据集，细粒度小无人机检测研究仍不充分。本文提出UAVNet-MS，首个用于细粒度小无人机检测的多光谱数据集，包含15,618个时间同步的RGB-MSI数据立方体（1440x1080），带边界框标注。数据集涵盖挑战性小目标（93.7% ≤ 32²像素，平均18²像素，约占图像面积0.02%）且处于低对比度环境。我们提出MFDNet，一种双流基线模型，解决阵列引起的视差问题并实现时空谱融合。在仅用RGB、仅用MSI及融合协议下，对20种检测器进行评估，MFDNet相比最佳可见光方法在AP50上提升6.2%，证明光谱线索提供了超越空间线索的材料信息。本工作为多光谱无人机监测研究提供了基础数据集、强基线与评测基准。
原文摘要 · Abstract (English)
The proliferation of unmanned aerial vehicles (UAVs) has created urgent demand for precise UAV monitoring. Existing RGB-based systems rely on spatial cues that degrade at small scales, particularly with high inter-type similarity, target-clutter ambiguity, and low contrast. Multispectral imaging (MSI) encodes material-aware spectral signatures, yet MSI-based fine-grained small-UAV detection remains underexplored due to lack of dedicated datasets. We introduce UAVNet-MS, the first multispectral dataset for fine-grained small-UAV detection, comprising 15,618 temporally synchronized RGB-MSI data cubes (1440x1080) with bounding box annotations. The dataset features challenging small objects (93.7% <= 32^2 pixels, average 18^2 pixels, ~0.02% image area) under low contrast. We propose MFDNet, a dual-stream baseline addressing array-induced parallax and spatial-spectral fusion. Extensive evaluation under RGB-only, MSI-only, and RGB+MSI protocols against 20 detectors shows MFDNet achieves +6.2% AP50 improvement over best RGB-only methods, demonstrating spectral cues provide complementary material evidence beyond spatial cues. This work provides foundational dataset, strong baseline, and benchmark for multispectral UAV monitoring research.
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