In the future, rescuing lost, ill or injured persons will increasingly be carried out by autonomous drones. However, discovering humans in densely forested terrain is challenging because of occlusion, and robust detection mechanisms are required. We show that automated person detection under occlusion conditions can be notably improved by combining multi-perspective images before classification. Here, we employ 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 and recall of 96% and 93%, respectively. Finding lost or injured people in dense forests is not generally feasible with thermal recordings, but becomes practical with the 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.

将来，越来越多的自动无人机将拯救丢失，生病或受伤的人。然而，由于遮挡，在茂密的森林地带发现人类是一项挑战，因此需要强大的检测机制。我们显示，通过在分类之前组合多视角图像，可以显着改善遮挡条件下的自动人员检测。在这里，我们采用机载光学切片（AOS）进行图像集成（一种使用照相机无人机捕获非结构化热光场的合成孔径成像技术），以实现这一目标的精度和召回率分别为96％和93％。使用热记录通常无法在茂密的森林中找到失散或受伤的人，但是使用AOS积分图像变得切实可行。我们的发现为将来可与自动或有人驾驶飞机结合使用的有效搜索和救援技术奠定了基础。它们对于当前遭受部分遮挡的人，动物或物体分类不准确的其他领域也可能是有益的。