TY - GEN
T1 - Investigating YOLOv5 for Search and Rescue Operations Involving UAVs
AU - Bachir, Namat
AU - Memon, Qurban
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/8/19
Y1 - 2022/8/19
N2 - 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.
AB - 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.
KW - Drone
KW - Search and Rescue
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85142652894&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142652894&partnerID=8YFLogxK
U2 - 10.1145/3561613.3561644
DO - 10.1145/3561613.3561644
M3 - Conference contribution
AN - SCOPUS:85142652894
T3 - ACM International Conference Proceeding Series
SP - 200
EP - 204
BT - ICCCV 2022 - Proceedings of the 5th International Conference on Control and Computer Vision
PB - Association for Computing Machinery
T2 - 5th International Conference on Control and Computer Vision, ICCCV 2022
Y2 - 19 August 2022 through 21 August 2022
ER -