Institute of Computer Graphics, Johannes Kepler University, 4040, Linz, Austria.
Sci Rep. 2022 Mar 9;12(1):3804. doi: 10.1038/s41598-022-07733-z.
Fully autonomous drones have been demonstrated to find lost or injured persons under strongly occluding forest canopy. Airborne optical sectioning (AOS), a novel synthetic aperture imaging technique, together with deep-learning-based classification enables high detection rates under realistic search-and-rescue conditions. We demonstrate that false detections can be significantly suppressed and true detections boosted by combining classifications from multiple AOS-rather than single-integral images. This improves classification rates especially in the presence of occlusion. To make this possible, we modified the AOS imaging process to support large overlaps between subsequent integrals, enabling real-time and on-board scanning and processing of groundspeeds up to 10 m/s.
已经有研究证明，全自主无人机可以在强遮挡的森林树冠下寻找失踪或受伤人员。航空光学截面成像（AOS）是一种新颖的合成孔径成像技术，结合基于深度学习的分类，可以在现实的搜索和救援条件下实现高检测率。我们证明，通过结合来自多个 AOS 而不是单个积分图像的分类，可以显著抑制假检测并提高真检测的概率。这尤其可以提高在存在遮挡情况下的分类率。为了实现这一点，我们修改了 AOS 成像过程，以支持后续积分之间的大重叠，从而能够实时和板载处理高达 10 m/s 的地面速度。
