DroneRFc-MM: Anti-UAV Multimodal Detection Measured Dataset

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

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DroneRFc-MM: Anti-UAV Multimodal 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: The 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

Objective: A comprehensive multimodal benchmark is developed for Anti-Unmanned Aerial Vehicle (UAV) detection in low-altitude urban environments. Existing datasets generally provide limited sensing modalities and UAV models, with relatively coarse annotations that constrain tasks requiring spatial, motion, and cross-modal information. DroneRFc-MM addresses these limitations by providing synchronized multimodal data, broader coverage of consumer-grade DJI UAV models, and fine-grained annotations for target detection, UAV model recognition, trajectory analysis, flight-direction reasoning, and multimodal fusion evaluation. Methods: DroneRFc-MM is synchronously collected using six heterogeneous sensor types: a Pan-Tilt-Zoom (PTZ) camera, a fisheye camera, a Radio Frequency (RF) antenna, LiDAR, millimeter-wave radar, and a microphone array. Data are acquired on an open rooftop at a university in Zhejiang Province, representing a typical urban low-altitude environment. The dataset contains recordings of six consumer-grade DJI UAV models. All devices are synchronized using a common network time reference, with inter-device timestamp discrepancies of approximately 0.3 s. The UAVs fly in “H”-shaped and vertical reciprocating trajectories at distances of 20–60 m from the sensor array. Fine-grained annotations, including UAV model, position, attitude, and velocity, are derived from flight logs. For the flight-direction reasoning task, approximately 5-s multimodal clips are generated, including camera videos, RF spectrogram videos, microphone audio, and coordinate-based text representations of radar point-cloud data. Zero-shot inference is conducted using Qwen 3.6-Plus and Qwen 3.5-Omni-Plus with unified prompts. Prediction accuracy and inference time are evaluated by comparing predicted directions with ground-truth directions calculated from UAV positioning data. Results and Discussions: The DroneRFc-MM dataset provides multimodal data from six sensor types and six consumer-grade DJI UAV models, together with fine-grained annotations and sample extraction tools. In the flight-direction reasoning task, the Qwen-series multimodal large language models (MLLMs) achieve accuracies ranging from 20% to 30% across the different input modalities. The inference time is also relatively long, with the mean response time exceeding 40 s for most sensor inputs. These results indicate that current general-purpose MLLMs can capture weak motion-related information from UAV videos, audio, RF spectrograms, and point-cloud data, but their accuracy and response speed remain insufficient for practical real-time Anti-UAV detection. Conclusions: DroneRFc-MM provides a multimodal benchmark for Anti-UAV detection, UAV model recognition, flight-direction reasoning, and multimodal model evaluation. The dataset integrates six sensor types, six consumer-grade DJI UAV models, and fine-grained annotations within a common measurement framework. The experimental results show that current general-purpose MLLMs remain limited in flight-direction reasoning and real-time inference in Anti-UAV scenarios. Domain-specific pre-training, supervised fine-tuning, knowledge augmentation, and lightweight inference are therefore needed to improve their practical utility. Future work will expand the dataset scale and application scenarios to support 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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