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2026 , Vol. 31 >Issue 1: 209 - 220

DOI: https://doi.org/10.1007/s12204-026-2903-3

Intelligent Robots

Synthetic Data-Driven Multi-Task Framework for UAV Detection and Classification

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  • School of Automation, Southeast University, Nanjing 210096, China

Received date: 2025-09-15

  Revised date: 2025-10-10

  Accepted date: 2025-10-15

  Online published: 2026-02-03

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Abstract

With the rapid development of unmanned aerial vehicle (UAV) technology, there is an increasingly urgent demand for intelligent detection, identification, and performance parameter inference techniques. However, existing UAV datasets face severe challenges including high acquisition costs, labor-intensive annotation, and data scarcity, and lack methods for directly predicting functional performance from single images. This paper proposes a UAV analysis framework based on synthetic data generation and multi-task deep learning. We construct an adaptive dataset generation system based on Unreal Engine 5, incorporating UAV size classification, adaptive distance adjustment algorithms, and enhanced 3D-to-2D coordinate transformation techniques for automated high-quality synthetic data generation. We design a multi-task collaborative learning network integrating visual information, distance information, and uncertainty quantification modules, supporting UAV detection, parameter prediction, functional classification, and size prediction. Experimental results demonstrate high similarity between synthetic and real data (Fr´echet inception distance is 21.05), with parameter prediction achieving mean absolute percentage errors of 29.1% and 27.0% for maximum speed and altitude respectively, and military-civilian classification accuracy reaching 62.5%. This method provides a low-cost, efficient solution for intelligent UAV analysis with considerable practical value.

Cite this article

Zhang Jingkai, Li Xinde, Wei Wangzichao, Wang Ziyao, Ma Ke . Synthetic Data-Driven Multi-Task Framework for UAV Detection and Classification[J]. Journal of Shanghai Jiaotong University(Science), 2026 , 31(1) : 209 -220 . DOI: 10.1007/s12204-026-2903-3

References

[1] Bikov T, Mihaylov G, Iliev T, et al. Drone surveillance in the modern agriculture [C]//2022 8th International Conference on Energy Efficiency and Agricultural Engineering. Ruse: IEEE, 2022: 1-4.
[2] Qubaa A R, Thannoun R G, Mohammed R M. UAVs/drones for photogrammetry and remote sensing: Nineveh archaeological region as a case study [J]. World Journal of Advanced Research and Reviews, 2022, 14(3): 358-368.
[3] Zhang H H, Tian T, Feng O G, et al. Research on public air route network planning of urban low-altitude logistics unmanned aerial vehicles [J]. Sustainability, 2023, 15(15): 12021.
[4] Wang W, Cheng N, Liu Y L, et al. Content delivery analysis in cellular networks with aerial caching and mmWAVE backhaul [J]. IEEE Transactions on Vehicular Technology, 2021, 70(5): 4809-4822.
[5] Yu N, Mao S, Zhou C, et al. DroneRFa: A large-scale dataset of drone radio frequency signals for detecting low-altitude drones [J]. Journal of Electronics & Information Technology, 2024, 46(4): 1147-1156 (in Chinese).
[6] Perrusquía A, Guo W S, Fraser B, et al. Uncovering drone intentions using control physics informed machine learning [J]. Communications Engineering, 2024, 3: 36.
[7] Khan M A, Menouar H, Eldeeb A, et al. On the detection of unauthorized drones: Techniques and future perspectives: A review [J]. IEEE Sensors Journal, 2022, 22(12): 11439-11455.
[8] Shi X F, Yang C Q, Xie W G, et al. Anti-drone system with multiple surveillance technologies: Architecture, implementation, and challenges [J]. IEEE Communications Magazine, 2018, 56(4): 68-74.
[9] Rahman M H, Sejan M A S, Aziz M A, et al. A comprehensive survey of unmanned aerial vehicles detection and classification using machine learning approach: Challenges, solutions, and future directions [J]. Remote Sensing, 2024, 16(5): 879.
[10] Phung K P, Lu T H, Nguyen T T, et al. Multi-model deep learning drone detection and tracking in complex background conditions [C]//2021 International Conference on Advanced Technologies for Communications. Ho Chi Minh City: IEEE, 2021: 189-194.
[11] Wang B S, Li Q, Mao Q C, et al. A survey on vision-based anti unmanned aerial vehicles methods [J]. Drones, 2024, 8(9): 518.
[12] Unlu E, Zenou E, Riviere N, et al. Deep learning-based strategies for the detection and tracking of drones using several cameras [J]. IPSJ Transactions on Computer Vision and Applications, 2019, 11(1): 7.
[13] Zheng Y, Chen Z, Lv D L, et al. Air-to-air visual detection of micro-UAVs: An experimental evaluation of deep learning [J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1020-1027.
[14] Wisniewski M, Rana Z A, Petrunin I, et al. Drone detection using deep neural networks trained on pure synthetic data [PP/OL]. arXiv (2024-11-13). https://arXiv.org/abs/2411.09077.
[15] Rizzoli G, Barbato F, Caligiuri M, et al. SynDrone–multi-modal UAV dataset for urban scenarios [C]//2023 IEEE/CVF International Conference on Computer Vision Workshops. Paris: IEEE, 2023: 2202-2212.
[16] Lenhard T R, Weinmann A, Franke K, et al. SynDroneVision: A synthetic dataset for image-based drone detection [C]//2025 IEEE/CVF Winter Conference on Applications of Computer Vision. Tucson: IEEE, 2025: 7637-7647.
[17] Dieter T R, Weinmann A, Jäger S, et al. Quantifying the simulation–reality gap for deep learning-based drone detection [J]. Electronics, 2023, 12(10): 2197.
[18] Seidaliyeva U, Ilipbayeva L, Taissariyeva K, et al. Advances and challenges in drone detection and classification techniques: A state-of-the-art review [J]. Sensors, 2024, 24(1): 125.
[19] Liu Z Y, An P, Yang Y, et al. Vision-based drone detection in complex environments: A survey [J]. Drones, 2024, 8(11): 643.
[20] Mrabet M, Sliti M, Ben Ammar L. Machine learning algorithms applied for drone detection and classification: Benefits and challenges [J]. Frontiers in Communications and Networks, 2024, 5: 1440727.
[21] Wisniewski M, Rana Z A, Petrunin I. Drone model classification using convolutional neural network trained on synthetic data [J]. Journal of Imaging, 2022, 8(8): 218.
[22] El-Latif E I A, El-Dosuky M. Predicting power consumption of drones using explainable optimized mathematical and machine learning models [J]. The Journal of Supercomputing, 2025, 81(5): 646.
[23] Bińkowski M, Sutherland D J, Arbel M, et al. Demystifying MMD GANs [PP/OL]. V5. arXiv (2021-01-14) [2025-09-13]. https://arXiv.org/abs/1801.01401.

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