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<p>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 (Fréchet 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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年2月3日

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  • Jingkai Zhang,
  • Xinde Li,
  • Zichao Wei-Wang,
  • Ziyao Wang,
  • Ke Ma

摘要

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 (Fréchet 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.