Investigating YOLOv5 for Search and Rescue Operations Involving UAVs: Investigating YOLO5

Abstract

Mountain recreation has become more popular, with mountaineering, rock climbing, skiing, mountain biking, hiking, and mushroom picking among the most popular sports including desert safari. Despite this tendency, there is currently limited research available explaining the rise in search and rescue as well as the injuries and illnesses that entail aid in tourist-friendly areas. Deep learning has been termed as potentially effective tool for SAR applications. Even if the individual is partially veiled, a trained deep learning system can recognize them from a variety of perspectives. Existing state-of-the-art detectors such as Faster R-CNN, YOLOv4, RetinaNet, and Cascade R-CNN have been investigated in literature on various datasets to simulate rescue scenes with acceptable results. In this research, the YOLOv5L detector is investigated for further investigation on Search and rescue dataset because of its great speed and accuracy, as well as claimed small number of false detections. The results illustrate the highest mean average accuracy and is compared with other detectors.