构建首个高同步无压缩的RGB-事件无人机检测数据集，支持真实复杂场景下的小目标追踪。
SkyEV: RGB-Event UAV detection and tracking dataset and baseline
- 同步采集未压缩的RGB与事件数据，还原真实飞行场景
- 涵盖大幅相机运动与多样镜头配置，挑战探测范围与视野权衡
- 提供多模态基线模型，助力小目标检测算法研发
由于无人机的普及和易用性，空中无人机检测日益重要。但因其尺寸小，远距离探测困难。现有数据集多聚焦于红外、可见光或事件传感器等单一感知方式，却难以反映真实反无人机场景，普遍缺乏相机自运动、极小目标尺度、多样镜头配置等关键因素，且图像存在压缩伪影。为弥补这一空白，我们提出SkyEV——一个开源的高同步、无压缩RGB与事件数据集。该数据集捕捉了复杂真实条件，包括显著相机运动和多种光学设置，有助于测试视场与探测距离之间的根本权衡。此外，我们提供统一数据加载器，并基于多模态架构建立实验基线，验证了该数据集在检测极具挑战性的微小目标方面的有效性。
原文摘要 · Abstract (English)
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.
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