Towards UAV Detection in the Real World: A New Multispectral Dataset UAVNet-MS and a New Method

• URL: http://arxiv.org/abs/2605.20963v1
• Date: Wed, 20 May 2026 09:49:04 GMT
• Title: Towards UAV Detection in the Real World: A New Multispectral Dataset UAVNet-MS and a New Method
• Abstract summary: UAVNet-MS is the first multispectral dataset for fine-grained small-UAV detection.<n>We propose MFDNet, a dual-stream baseline addressing array-induced parallax and spatial-spectral fusion.<n>This work provides foundational dataset, strong baseline, and benchmark for multispectral UAV monitoring research.
• Score: 80.50361258830928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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