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

TL;DR

This paper introduces UAV-CB, a new RGB-T dataset emphasizing complex backgrounds and camouflage for UAV detection, and proposes LFBNet, a frequency-based neural network that improves detection robustness in challenging real-world scenarios.

Contribution

The paper presents a novel UAV detection dataset focused on complex backgrounds and camouflage, and introduces LFBNet, a frequency-aware network that enhances multimodal UAV detection performance.

Findings

LFBNet achieves state-of-the-art results on UAV-CB and public benchmarks.

LFBNet demonstrates strong robustness under camouflage and cluttered conditions.

The dataset UAV-CB effectively captures complex background challenges 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.