Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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, Ke Chen

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.

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

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.
Paper Structure (14 sections, 17 equations, 2 figures, 4 tables)

This paper contains 14 sections, 17 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. UAV Detection and Datasets
  4. RGB--T Fusion
  5. Camouflage-Aware UAV Detection
  6. Method
  7. Overview
  8. Local Frequency Cross-Modal Alignment
  9. Frequency-Guided Spatial Alignment
  10. Experiments
  11. Experimental Settings
  12. Results Comparisons
  13. Ablation Study on Key Components
  14. Conclusion

Figures (2)

  • Figure 1: Representative RGB–T examples of UAV detection under complex low-altitude backgrounds.
  • Figure 2: 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.