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

2026-03-18

AI Summary

This study addresses the challenge of effectively detecting drones in low-altitude, complex environments where factors such as camouflage, low contrast, and multimodal interference severely hinder performance. To tackle this issue, the authors introduce UAV-CB, the first RGB-thermal drone detection dataset specifically curated for low-altitude camouflaged scenarios, and propose the Local Frequency-domain Bridging Network (LFBNet). LFBNet enables efficient fusion of RGB and thermal modalities through localized frequency-domain feature modeling. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on UAV-CB as well as multiple public benchmarks, significantly enhancing detection robustness under challenging conditions involving camouflage and cluttered backgrounds.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• UAV detection
• complex background
• camouflage
• RGB-T dataset
• low-altitude environment

Innovation

• RGB-T fusion
• frequency-space modeling
• camouflage detection
• UAV detection
• multimodal perception