发表机构
ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机检测数据集存在的问题，引入开源数据集SkyEV，其含高度同步的未压缩RGB和基于事件的数据，能捕捉复杂现实条件。提供统一数据加载器，用多模态架构建立实验基线，证明该数据集对检测小尺度目标有效。
AI 中文摘要
由于无人机的广泛可用性和易于使用，在空域中检测无人机变得越来越重要。然而，由于其尺寸小，通常难以在足够远的距离进行检测。为了训练优化的检测算法，已发布了数据集，涵盖从红外到常规RGB再到基于事件传感器的光学传感方法。但这些数据集往往无法反映现实的反无人机场景，缺乏相机自身运动、极小目标尺度和多样镜头配置等关键因素，且在帧图像上引入压缩伪像。为填补这一空白，我们引入了SkyEV，一个具有高度同步未压缩RGB和基于事件数据的开源数据集。SkyEV通过捕捉复杂的现实世界条件脱颖而出，包括显著的相机运动和多样的光学设置，这对于测试视野和检测范围之间的基本权衡至关重要。此外，我们提供了统一的数据加载器，并使用多模态架构建立了实验基线，证明了该数据集在检测具有挑战性的小尺度目标方面的有效性。
英文摘要
Detecting UAVs in air spaces has become increasingly important due to UAVs widespread availability and easy usage. However, due to their small size, they are typically difficult to detect at a sufficient range. For the training of optimized detection algorithms, datasets have been published, covering optical sensing methods ranging from infrared to regular RGB to event-sensor-based. However, these datasets often fail to reflect realistic counter-UAV scenarios, lacking critical factors such as camera ego-motion, extremely small target scales, and diverse lens configurations, and introduce compression artefacts on the frame images. To address this gap, we introduce SkyEV, an open-source dataset featuring highly synchronized uncompressed RGB and event-based data. SkyEV distinguishes itself by capturing complex real-world conditions, including significant camera motion and varied optical setups, which are essential for testing the fundamental trade-off between Field of View and detection range. Furthermore, we provide a unified data loader and establish an experimental baseline using a multi-modal architecture, demonstrating the dataset's efficacy in detecting challenging, small-scale targets.
