Preliminary Evaluation of TinyYOLO on a New Dataset for Search-and-Rescue with Drones

Unmanned aerial vehicles (UAVs), more commonly known as drones, are increasingly used as a technological support tool for search-and-rescue (SAR) operations and also for post-disaster area explorations. UAVs can be equipped with high-resolution cameras and embed GPUs powerful enough to provide effective and efficient aid to emergency rescue operations. Victims can be unconscious or injured, and any means of locating them as quickly as possible is critical to increasing their chances of survival. In particular, using drones that can automatically detect people in scenes can increase detection rate, while reducing rescue time. In this work, we present a new dataset specifically designed for SAR operations from drones using computer vision. Being small in size, the dataset is currently intended for testing and evaluation purposes only. We also provide baseline results on this dataset, obtained with the state-of-the-art TinyYOLOv3 object detector. The purpose of this preliminary work is to stimulate interest and encourage contributions to this research topic.