发表机构
Institute for the Protection of Terrestrial Infrastructures, German Aerospace Center (DLR); ACIDA Lab, Technical University of Applied Sciences Würzburg-Schweinfurt（德国航空航天中心陆地基础设施保护研究所; 维尔茨堡-施韦因富特应用技术大学ACIDA实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对真实无人机检测的恶劣天气与季节数据获取难题，推出合成数据集SynDroneVision-Weather，可提升YOLO检测器在恶劣条件下的可靠性，减少漏检误报，将公开发布。
AI 中文摘要
在真实部署条件下进行可靠的无人机检测，需要覆盖完整操作设计域的训练数据，包括恶劣天气和季节外观变化。然而，大规模获取并标注此类数据资源密集度极高，因为恶劣天气条件本质上难以控制、复制和系统采样。因此现有数据集通常仅有限覆盖此类条件。相反，合成数据提供了可扩展的替代方案：环境变化变得可控，基于现代游戏引擎的流水线能提供逼真渲染和自动标注。利用这一潜力，我们推出SynDroneVision-Weather（SDV-W），这是SynDroneVision（SDV）的系统性扩展，针对城市无人机检测中的恶劣天气和季节域偏移。SDV-W包含来自三个城市环境的55187张标注高分辨率图像，渲染时覆盖三种季节配置及多样天气条件，包括不同 severity 等级的雨、雪和雾。通过保留SDV的场景和轨迹配置，SDV-W实现了干净-恶劣条件的匹配对比，并能量化特定条件下检测器的性能下降。在代表性YOLO模型和真实世界数据集上，我们表明SDV-W提升了检测器在恶劣外观偏移下的可靠性，减少了漏检和误报，作为通用合成无人机检测数据的补充时效果最佳。SDV-W将在论文被接受后公开发布。
英文摘要
Reliable drone detection under real-world deployment conditions requires training data that spans the full operational design domain, including adverse weather and seasonal appearance variation. However, acquiring and annotating such data at scale remains highly resource-intensive, as adverse-weather conditions are inherently difficult to control, reproduce, and sample systematically. Existing datasets therefore typically provide only limited coverage of such conditions. Conversely, synthetic data offers a scalable alternative: environmental variation becomes controllable, while modern game-engine-based pipelines provide realistic rendering and automatic annotations. Leveraging this potential, we introduce SynDroneVision-Weather (SDV-W), an systematic extension of SynDroneVision (SDV) targeting adverse-weather and seasonal domain shifts in urban drone detection. SDV-W comprises 55,187 annotated high-resolution images from three urban environments, rendered across three seasonal configurations and diverse weather conditions, including rain, snow, and fog at multiple severity levels. By preserving SDV's scene and trajectory configuration, SDV-W enables matched clean-adverse comparisons and quantification of condition-specific detector degradation. Across representative YOLO models and real-world datasets, we show that SDV-W improves detector reliability under adverse appearance shifts, reduces missed detections and false alarms, and is most effective as a complement to general-purpose synthetic drone-detection data. SDV-W will be publicly released upon paper acceptance.
