DroneRFc-MM: Anti-UAV Multi-modal Detection Measured Dataset

YU Taosong; YANG Qianqian; HU Zhuo; LI Mingkai; WU Jiajun; SU Yifan; PAN Junyu; SHI Zhiguo; CHEN Jiming

doi:10.11999/JEIT260889

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YU Taosong, YANG Qianqian, HU Zhuo, LI Mingkai, WU Jiajun, SU Yifan, PAN Junyu, SHI Zhiguo, CHEN Jiming. DroneRFc-MM: Anti-UAV Multi-modal Detection Measured Dataset[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260889

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YU Taosong, YANG Qianqian, HU Zhuo, LI Mingkai, WU Jiajun, SU Yifan, PAN Junyu, SHI Zhiguo, CHEN Jiming. DroneRFc-MM: Anti-UAV Multi-modal Detection Measured Dataset[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260889

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DroneRFc-MM: Anti-UAV Multi-modal Detection Measured Dataset

doi: 10.11999/JEIT260889 cstr: 32379.14.JEIT260889

1.
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
2.
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
3.
International Research Center for Information Science and Electronic Engineering, Zhejiang University, Jiaxing 314400, China

Funds: National Key Research and Development Program of China(2025YFF0514600)

Received Date: 2026-06-30
Accepted Date: 2026-07-29
Rev Recd Date: 2026-07-11

Available Online: 2026-08-08

Abstract

Abstract

Multimodal data fusion can effectively improve the generalization, robustness, and scene adaptability of counter-unmanned aerial vehicle (UAV) detection systems. To address the limitations of existing counter-UAV datasets in sensing modalities, UAV models, and annotation granularity, the paper releases DroneRFc-MM, a multimodal counter-UAV detection dataset. DroneRFc-MM synchronously collects data from six types of sensors, including a pan-tilt-zoom camera, a wide-angle (fisheye) camera, radio-frequency antennas, LiDAR, millimeter-wave radar, and a microphone array. The dataset covers six consumer UAV models and provides fine-grained annotations such as model category, position, attitude, and velocity. It supports multiple tasks, including target detection, UAV model recognition, and motion-direction reasoning, and is accompanied by user-friendly sample extraction tools. Finally, as a demonstration of dataset usage, the paper evaluates the performance of recent Qwen-series models on UAV flight-direction reasoning.Objective: The work aims to build a more comprehensive benchmark for multimodal counter-UAV detection. Existing datasets often cover limited sensing modalities and UAV models, making it difficult to represent realistic low-altitude scenarios and diverse target characteristics. Their annotations are also typically coarse, such as category labels or bounding boxes, and thus cannot fully support downstream tasks requiring precise spatial, motion, and cross-modal information. DroneRFc-MM addresses these gaps by providing synchronized multimodal data, richer UAV coverage, and fine-grained annotations for detection, model recognition, trajectory analysis, motion reasoning, and multimodal fusion evaluation.Methods: The DroneRFc-MM dataset was synchronously captured via six heterogeneous sensors—including a pan-tilt-zoom(PTZ) camera, fisheye camera, radio frequency(RF) antenna, LiDAR, millimeter-wave radar, and microphone array—on an open rooftop of a university in Zhejiang Province. Featuring a representative urban low-altitude scenario, this dataset contains data recordings of six consumer-grade DJI drones. All devices were time-synchronized via network timestamp, and drones flew in rectangular and vertical reciprocating trajectories within 20–60 meters. Fine-grained annotations including drone type, position, attitude and velocity were provided. For flight direction reasoning task, 5-second multimodal clips were generated: videos for cameras and RF spectrograms, audio for microphones, text coordinates for radar point clouds. Zero-shot inference was conducted on Qwen 3.6-Plus and Qwen 3.5-Omni-Plus models with unified prompts, and accuracy and inference time were evaluated by comparing predicted directions with ground truth calculated from drone positioning data.Conclusions: Experiments on Qwen-series models show that general-purpose multimodal large models can capture weak motion-related features from drone-related videos, audio, RF spectrograms and point clouds, but only achieve limited flight-direction reasoning accuracy ranging from 20% to 30%. Meanwhile, the long inference time and unstable latency make them unable to satisfy the real-time and stability demands of practical low-altitude surveillance systems. These results demonstrate that domain-specific pre-training, supervised fine-tuning, knowledge enhancement and lightweight inference optimization are essential for deploying multi-modal LLMs in real anti-UAV detection scenarios. Future work will focus on expanding the dataset scale and enriching application scenarios to support the development of intelligent and efficient low-altitude airspace management systems.
- Anti-UAV detection,
- multimodal dataset,
- low-altitude economy

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References(22)

References

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