WiSARD: A Labeled Visual and Thermal Image Dataset for Wilderness Search and Rescue

TL;DR

WiSARD is a large-scale, multi-modal dataset of visual and thermal images collected from UAVs in diverse wilderness conditions, designed to advance autonomous search and rescue algorithms.

Contribution

This paper introduces WiSARD, the first extensive multi-modal dataset for WiSAR UAVs, addressing the need for robust vision-based algorithms in challenging wilderness environments.

Findings

Provides 56,000 labeled images across various terrains and conditions

Enables testing of robustness of vision algorithms in real-world WiSAR scenarios

Facilitates development of autonomous UAV search and rescue systems

Abstract

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.