  Sensor-equipped unoccupied aerial vehicles (UAVs) have the potential to help
reduce search times and alleviate safety risks for first responders carrying
out Wilderness Search and Rescue (WiSAR) operations, the process of finding and
rescuing person(s) lost in wilderness areas. Unfortunately, visual sensors
alone do not address the need for robustness across all the possible terrains,
weather, and lighting conditions that WiSAR operations can be conducted in. The
use of multi-modal sensors, specifically visual-thermal cameras, is critical in
enabling WiSAR UAVs to perform in diverse operating conditions. However, due to
the unique challenges posed by the wilderness context, existing dataset
benchmarks are inadequate for developing vision-based algorithms for autonomous
WiSAR UAVs. To this end, we present WiSARD, a dataset with roughly 56,000
labeled visual and thermal images collected from UAV flights in various
terrains, seasons, weather, and lighting conditions. To the best of our
knowledge, WiSARD is the first large-scale dataset collected with multi-modal
sensors for autonomous WiSAR operations. We envision that our dataset will
provide researchers with a diverse and challenging benchmark that can test the
robustness of their algorithms when applied to real-world (life-saving)
applications.

Computer Vision and Pattern Recognition