With the increasing reliance on small Unmanned Aerial Systems (sUAS) for Emergency Response Scenarios, such as Search and Rescue, the integration of computer vision capabilities has become a key factor in mission success. Nevertheless, computer vision performance for detecting humans severely degrades when shifting from ground to aerial views. Several aerial datasets have been created to mitigate this problem, however, none of them has specifically addressed the issue of occlusion, a critical component in Emergency Response Scenarios. Natural Occluded Multi-scale Aerial Dataset (NOMAD) presents a benchmark for human detection under occluded aerial views, with five different aerial distances and rich imagery variance. NOMAD is composed of 100 different Actors, all performing sequences of walking, laying and hiding. It includes 42,825 frames, extracted from 5.4k resolution videos, and manually annotated with a bounding box and a label describing 10 different visibility levels, categorized according to the percentage of the human body visible inside the bounding box. This allows computer vision models to be evaluated on their detection performance across different ranges of occlusion. NOMAD is designed to improve the effectiveness of aerial search and rescue and to enhance collaboration between sUAS and humans, by providing a new benchmark dataset for human detection under occluded aerial views.

翻译：随着小型无人机系统（sUAS）在搜索救援等应急响应场景中的日益依赖，计算机视觉能力的集成已成为任务成功的关键因素。然而，从地面视角切换至航空视角时，用于检测行人的计算机视觉性能会显著下降。为缓解此问题，虽已有多个航空数据集被构建，但尚无数据集专门针对遮挡问题——应急响应场景中的关键要素。自然遮挡多尺度航空数据集（NOMAD）为遮挡航空视角下的人体检测提供了基准，涵盖五种不同航空距离及丰富的图像差异性。NOMAD包含100名不同角色，均执行行走、躺卧和隐藏等连贯动作序列，由5.4K分辨率视频中提取的42,825帧图像组成，并通过边界框及描述10种不同可见度级别的标签进行人工标注，可见度根据边界框内人体可见百分比分类。这使得计算机视觉模型可评估其在不同遮挡范围内的检测性能。NOMAD旨在通过提供遮挡航空视角下人体检测的新型基准数据集，提升航空搜索救援的有效性，并增强小型无人机系统与人类之间的协作。