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.

翻译：摘要：搭载传感器的无人驾驶飞行器（UAV）有望缩短搜救时间，并降低野外搜救（WiSAR）行动中一线救援人员的安全风险。然而，视觉传感器本身无法满足WiSAR行动中各种地形、天气和光照条件下的鲁棒性需求。多模态传感器（尤其是可见光-热成像相机）的应用是使WiSAR无人机能在多样化操作环境中运行的关键。但受限于野外环境带来的独特挑战，现有基准数据集难以支撑自主WiSAR无人机基于视觉的算法开发。为此，我们提出WiSARD数据集——包含约56,000张标注可见光与热成像图像，这些图像采集自不同地形、季节、天气及光照条件下的无人机飞行实验。据我们所知，WiSARD是首个面向自主WiSAR任务、使用多模态传感器采集的大规模数据集。我们期望该数据集能为研究人员提供多样化且具挑战性的基准测试，用以验证其算法在真实（拯救生命的）应用场景中的鲁棒性。