UAV-CB: A Complex-Background RGB-T Dataset and Local Frequency Bridge Network for UAV Detection

Shenghui Huang; Menghao Hu; Longkun Zou; Hongyu Chi; Zekai Li; Feng Gao; Fan Yang; Qingyao Wu

cs.CVMar 18, 2026

UAV-CB: A Complex-Background RGB-T Dataset and Local Frequency Bridge Network for UAV Detection

Authors: Shenghui Huang, Menghao Hu, Longkun Zou, Hongyu Chi, Zekai Li, Feng Gao, Fan Yang, Qingyao Wu, +1 more

Organizations: Pengcheng Laboratory, China · South China University of Technology, China · Xinjiang University, China · Harbin Institute of Technology, China · Peking University, China

Abstract

Detecting Unmanned Aerial Vehicles (UAVs) in low-altitude environments is essential for perception and defense systems but remains highly challenging due to complex backgrounds, camouflage, and multimodal interference. In real-world scenarios, UAVs are frequently visually blended with surrounding structures such as buildings, vegetation, and power lines, resulting in low contrast, weak boundaries, and strong confusion with cluttered background textures. Existing UAV detection datasets, though diverse, are not specifically designed to capture these camouflage and complex-background challenges, which limits progress toward robust real-world perception. To fill this gap, we construct UAV-CB, a new RGB-T UAV detection dataset deliberately curated to emphasize complex low-altitude backgrounds and camouflage characteristics. Furthermore, we propose the Local Frequency Bridge Network (LFBNet), which models features in localized frequency space to bridge both the frequency-spatial fusion gap and the cross-modality discrepancy gap in RGB-T fusion. Extensive experiments on UAV-CB and public benchmarks demonstrate that LFBNet achieves state-of-the-art detection performance and strong robustness under camouflaged and cluttered conditions, offering a frequency-aware perspective on multimodal UAV perception in real-world applications.

Figures & tables

Figure 1: Representative RGB–T examples of UAV detection under complex low-altitude backgrounds.

Modality	Dataset	Data Scale	Description
Visible	Real-World [ 21 ]	56,821 images	Real-world UAV detection under diverse backgrounds.
	RCSD-UAV [ 6 ]	2,844 images	UAV detection in complex real-world scenes.
	DUT-Anti-UAV [ 45 ]	10,000 images	UAV detection and tracking in urban scenarios.
Thermal	Anti-UAV410 [ 10 ]	410 sequences	Thermal UAV tracking benchmark for tiny targets.
	CST Anti-UAV [ 32 ]	220 sequences	Thermal UAV tracking in complex environments.
Multi-modal	Anti-UAV [ 13 ]	318 sequences	RGB–T UAV detection and tracking benchmark.

Table 1: Overview of some representative UAV datasets.

Figure 2: Illustration of the data acquisition platform and the UAV targets used in constructing the UAV-CB dataset.

Figure 3: Representative RGB–T image pairs from the UAV-CB dataset covering five complex background categories: buildings, vegetation, power lines, near-ground clutter, and clouds. Each pair shows the visible (left) and thermal (right) modalities with annotated UAV targets (orange boxes).

Panel of Figure 4 — Figure 4: Statistical analysis of the proposed UAV-CB dataset.

Figure 5: Overview of the proposed LFBNet. The LFCA module aligns RGB and thermal modalities in localized frequency space, while the Local Frequency Guidance Map (LFGM) guides the FGSA module for deformable, frequency-aware spatial fusion. The fused four-scale features ( $N=4$ ) are then fed into an FPN-based detection head (YOLOv5s) for UAV detection.

Method	Modality	AP 50 (%)	AP 75 (%)	AP (0.5:0.95) (%)	Param(M)	FLOPs(G)
Faster R-CNN (NeurIPS’2015) [ 24 ]	Visible	50.7	29.6	28.5	41.3	75.5
Cascade R-CNN (CVPR’2018) [ 1 ]		63.8	33.5	34.7	69.2	103.0
YOLOv3 (ArXiv’2018) [ 23 ]		63.7	35.3	36.1	61.5	62.0
YOLOv5s (Ultralytics’2020) [ 27 ]		65.4	37.1	37.6	7.2	13.5
Cascade RPN (NeurIPS’2019) [ 28 ]		69.3	39.4	38.7	41.9	64.2
YOLOX-S (ArXiv’2021) [ 5 ]		66.5	34.7	37.2	9.0	10.7

Table 2: Comparison of UAV detection performance on the UAV-CB dataset.

Method	mAP 50
UA-CMDet (IEEE TCSVT’2022) [ 26 ]	64.0
TSFADet (ECCV’2022) [ 35 ]	70.4
C 2 Former (IEEE TITS’2024) [ 36 ]	72.8
OAFA (CVPR’2024) [ 2 ]	79.4
LFBNet (ours)	80.1

Table 3: Performance comparison on the DroneVehicle RGB–T detection benchmark.

Variant	LFCA	FGSA	AP (0.5:0.95) (%)
YOLOv5s+Add	✗	✗	38.5
+ LFCA only	✓	✗	47.9
+ FGSA only	✗	✓	49.3
Full LFBNet	✓	✓	54.4

Table 4: Ablation study of key components in LFBNet on the UAV-CB validation set.

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Multispectral Object Detection Small Object Detection Aerial Object Detection Fine-Grained Visual Perception Multimodal Sensor Fusion

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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.

Abstract

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