Investigating YOLOv5 for Search and Rescue Operations Involving UAVs

Namat Bachir, Qurban Memon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationICCCV 2022 - Proceedings of the 5th International Conference on Control and Computer Vision
PublisherAssociation for Computing Machinery
Pages200-204
Number of pages5
ISBN (Electronic)9781450397315
DOIs
Publication statusPublished - Aug 19 2022
Event5th International Conference on Control and Computer Vision, ICCCV 2022 - Virtual, Online, China
Duration: Aug 19 2022Aug 21 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Control and Computer Vision, ICCCV 2022
Country/TerritoryChina
CityVirtual, Online
Period8/19/228/21/22

Keywords

  • Drone
  • Search and Rescue
  • UAV

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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