  Small unmanned aircraft systems (sUAS) are becoming prominent components of
many humanitarian assistance and disaster response (HADR) operations. Pairing
sUAS with onboard artificial intelligence (AI) substantially extends their
utility in covering larger areas with fewer support personnel. A variety of
missions, such as search and rescue, assessing structural damage, and
monitoring forest fires, floods, and chemical spills, can be supported simply
by deploying the appropriate AI models. However, adoption by
resource-constrained groups, such as local municipalities, regulatory agencies,
and researchers, has been hampered by the lack of a cost-effective,
readily-accessible baseline platform that can be adapted to their unique
missions. To fill this gap, we have developed the free and open-source ADAPT
multi-mission payload for deploying real-time AI and computer vision onboard a
sUAS during local and beyond-line-of-site missions. We have emphasized a
modular design with low-cost, readily-available components, open-source
software, and thorough documentation (https://kitware.github.io/adapt/). The
system integrates an inertial navigation system, high-resolution color camera,
computer, and wireless downlink to process imagery and broadcast georegistered
analytics back to a ground station. Our goal is to make it easy for the HADR
community to build their own copies of the ADAPT payload and leverage the
thousands of hours of engineering we have devoted to developing and testing. In
this paper, we detail the development and testing of the ADAPT payload. We
demonstrate the example mission of real-time, in-flight ice segmentation to
monitor river ice state and provide timely predictions of catastrophic flooding
events. We deploy a novel active learning workflow to annotate river ice
imagery, train a real-time deep neural network for ice segmentation, and
demonstrate operation in the field.
