Spatial and Temporally aligned Visible and Infrared UAV images (labelled) and videos (not labelled)

This dataset was created for sensing of UAVs in the context of the Counter-UAS problem. To implement data fusion methodologies for imaging sensors, namely at pixel level, the data from the visible and infrared sensors must be spatial and temporally aligned. To this end, flight tests were conducted at the University of Victoria's Center for Aerospace Research (UVIC-CfAR) using a TASE 200 gimbal (Visible sensor: SONY FCB-EX1020 PAL, Infrared sensor: FLIR TAU 640 PAL). Additional data collected at the Universidade de Lisboa - Instituto Superior Técnico (IST) using a TeAx ThermalCapture Fusion Zoom was provided. This resulted in two separate sub-datasets: one of labelled UAV images, and one of UAV videos not labelled. All data from Visible and Infrared sensors are spatial and temporally aligned.

The labelled dataset includes real frames of a DJI Mavic 2, the VTOL Mini-E (developed at UVIC-CfAR), the hybrid multirotor MIMIQ (developed at UVIC-CfAR), a DJI Mini 3 Pro, and a Zeta FX-61 Phantom Wing and artificial frames of quadcopters, a hexacopter, and a fixed-wing. It includes variety in operational conditions and characteristics, namely range, lighting, blurry and partially cut UAV, presence of birds, and background texture. Images are labelled in the YOLO format. Folders were organized in the YOLO format with 80-10-10 partition for training, test and validation sets. Images were randomly selected for each folder.

The dataset of videos that are not labelled includes videos of a DJI Mavic 2, the VTOL Mini-E (developed at UVIC-CfAR), and a DJI Inspire 1. Some videos are in their original unprocessed version. Others are separated into videos of interest, which include the segments with better spatial and temporal alignment and isolation of operational conditions and characteristics.

本数据集专为反无人机系统（Counter-UAS）场景下的无人机感知任务构建。为实现成像传感器尤其是像素级的数据融合方法，需将可见光与红外传感器采集的数据完成空间与时间配准。为此，研究团队于维多利亚大学航空研究中心（UVIC-CfAR）开展飞行试验，搭载TASE 200云台（可见光传感器：SONY FCB-EX1020 PAL，红外传感器：FLIR TAU 640 PAL）完成数据采集。此外，研究团队还获取了里斯本大学高等理工学院（IST）使用TeAx ThermalCapture Fusion Zoom采集的补充数据。最终形成两个独立子数据集：分别为带标注的无人机图像数据集与无标注的无人机视频数据集，且所有可见光与红外传感器采集的数据均已完成空间与时间配准。

带标注的数据集涵盖多款无人机的实拍帧与人工合成帧：实拍机型包括DJI Mavic 2、UVIC-CfAR自研的VTOL Mini-E、UVIC-CfAR自研的混合多旋翼无人机MIMIQ、DJI Mini 3 Pro以及Zeta FX-61 Phantom Wing；人工合成帧则涵盖四旋翼无人机、六旋翼无人机与固定翼无人机。该数据集覆盖多样的作业条件与参数特征，包括拍摄距离、光照环境、存在模糊或局部裁切的无人机、鸟类干扰以及背景纹理差异。图像采用YOLO格式进行标注，数据集文件夹亦按YOLO格式组织，并以80-10-10的比例划分为训练集、测试集与验证集，各子集内的图像均为随机选取。

无标注的视频数据集包含DJI Mavic 2、UVIC-CfAR自研的VTOL Mini-E以及DJI Inspire 1的相关视频。部分视频保留原始未处理版本，其余则被划分为目标视频片段：这类片段具备更优的时空配准效果，且能更好地隔离特定作业条件与参数特征。