MOBDrone：一个用于OverBoard救援的无人机视频数据集
MOBDrone: a Drone Video Dataset for Man OverBoard Rescue
摘要：
配备摄像头的现代无人机（UAV）可以在加速识别和救援落水人员方面发挥重要作用，即，人员落水（MOB）。为此，可以利用人工智能技术来自动理解从无人机获取的视觉数据。然而，在航空图像中检测海上人员是具有挑战性的，主要是由于缺乏专门的注释数据集来训练和测试用于此任务的检测器。为了填补这一空白，我们推出并公开发布了MOBDrone基准测试，这是一个在海洋环境中在不同海拔高度、相机拍摄角度和照明等多种条件下收集的超过125K的无人机视图图像。我们手动注释了超过180K个对象，其中约有113K人落水，使用边界框精确定位它们。此外，我们对MOBDrone数据上的几种最先进的物体探测器进行了彻底的性能分析，作为进一步研究的基线。
英文摘要：
Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting people at sea in aerial imagery is challenging primarily due to the lack of specialized annotated datasets for training and testing detectors for this task. To fill this gap, we introduce and publicly release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination. We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes. Moreover, we conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
