UAV-CB：面向无人机检测的复杂背景 RGB-T 数据集与局部频率桥接网络
UAV-CB: A Complex-Background RGB-T Dataset and Local Frequency Bridge Network for UAV Detection
LFBNet 在局部频率空间建模特征，以弥合频域-空域融合鸿沟与跨模态差异，这一思路可直接迁移到其他 RGB-T 或可见光-红外检测器的融合模块设计中；UAV-CB 则提供了面向伪装与杂乱背景的评测基准。
原文依据 we propose the Local Frequency Bridge Network (LFBNet), which models features in localized frequency space
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在低空环境中检测无人机（UAV）对感知与防御系统至关重要，但由于复杂背景、伪装以及多模态干扰，该任务仍极具挑战性。在真实场景中，无人机常常在视觉上与建筑、植被、电力线等周围结构混为一体，导致对比度低、边界微弱，并与杂乱的背景纹理产生强烈混淆。现有无人机检测数据集虽然多样，但并非专门为刻画这些伪装与复杂背景挑战而设计，这限制了面向真实世界鲁棒感知的进展。为填补这一空白，我们构建了 UAV-CB，这是一个新的 RGB-T 无人机检测数据集，专门精心构建以突出复杂的低空背景与伪装特性。此外，我们提出了局部频率桥接网络（LFBNet），它在局部频率空间中建模特征，以弥合 RGB-T 融合中的频域-空域融合鸿沟与跨模态差异鸿沟。在 UAV-CB 与公开基准上的大量实验表明，LFBNet 取得了最优的检测性能，并在伪装与杂乱条件下表现出强鲁棒性，为真实应用中的多模态无人机感知提供了频率感知的视角。UAV-CB 数据集将公开于：https://github.com/hye999/UAV-CB。
英文原文摘要 1426 字符
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. The UAV-CB dataset will be publicly available at: https://github.com/hye999/UAV-CB.