机载光学剖面搜索与救援
Search and Rescue with Airborne Optical Sectioning
摘要：
我们表明，在遮挡条件下的自动人检测可以显着提高分类前结合多视角图像。在这里，我们采用了机载光学切片（AOS）的图像集成-一种使用无人机相机捕获非结构化热光场的合成孔径成像技术-以实现96/93%的精确度/召回率。在茂密的森林中寻找失踪或受伤的人通常是不可行的热记录，但成为实用的AOS积分图像的使用。我们的发现为未来有效的搜索和救援技术奠定了基础，这些技术可以与自主或有人驾驶飞机结合使用。他们也可以是有益的其他领域，目前遭受不准确的分类部分闭塞的人，动物，或对象。
英文摘要：
We show that automated person detection under occlusion conditions can be significantly improved by combining multi-perspective images before classification. Here, we employed image integration by Airborne Optical Sectioning (AOS)---a synthetic aperture imaging technique that uses camera drones to capture unstructured thermal light fields---to achieve this with a precision/recall of 96/93%. Finding lost or injured people in dense forests is not generally feasible with thermal recordings, but becomes practical with use of AOS integral images. Our findings lay the foundation for effective future search and rescue technologies that can be applied in combination with autonomous or manned aircraft. They can also be beneficial for other fields that currently suffer from inaccurate classification of partially occluded people, animals, or objects.
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。
