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

Source: [UAV-CB: A Complex-Background RGB-T Dataset and Local Frequency Bridge Network for UAV Detection](<https://www.sota2.com/research/paper/69bb83220636e5bf4afc1b69>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-20T05:00:04.947Z

Authors: Shenghui Huang, Menghao Hu, Longkun Zou, Hongyu Chi, Zekai Li, Feng Gao, Fan Yang, Qingyao Wu, Ke Chen

Published: 2026-03-18T08:55:35.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [UAV Detection on UAV-CB RGB+T](<https://www.sota2.com/research/sota/uav-detection-on-uav-cb-rgb-plus-t.md>): UAV Detection; UAV-CB RGB+T; AP50: 84.6.

- [Object Detection on DroneVehicle RGB-T](<https://www.sota2.com/research/sota/object-detection-on-dronevehicle-rgb-t.md>): Object Detection; DroneVehicle RGB-T; mAP50: 80.1.

- [UAV Detection on UAV-CB Visible](<https://www.sota2.com/research/sota/uav-detection-on-uav-cb-visible.md>): UAV Detection; UAV-CB Visible; AP50: not recorded.

- [UAV Detection on UAV-CB Thermal](<https://www.sota2.com/research/sota/uav-detection-on-uav-cb-thermal.md>): UAV Detection; UAV-CB Thermal; AP50: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.17492>)

[PDF](<https://arxiv.org/pdf/2603.17492.pdf>)
