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Search and rescue operation using UAVs: a case study

  • Ignacio Alpiste
  • , Gelayol Golcarenarenji
  • , Qi Wang
  • , Jose M. Alcaraz Calero

    Research output: Contribution to journalArticlepeer-review

    133 Downloads (Pure)

    Abstract

    Many people go missing in the wild every year. In this paper, the Search and Rescue (SAR) mission is conducted using a novel system comprising an Unmanned Aerial Vehicle (UAV) coupled with real-time machine-learning-based object detection system embedded on a smartphone. Human detection from UAV in the wilderness is a challenging task, because of many constraints involved such as lack of computing and communication infrastructures. We proposed a novel combination of a robust architecture deployed on a smartphone and a novel Convolutional Neural Network (CNN) model to fulfil the goals of the project. Our approach achieved 94.73% of accuracy and 6.8 FPS on a smartphone. Our approach is highly portable, cost-effective, fast with high accuracy. This novel system is expected to contribute significantly to maximise chances of saving lives in the wild. This developed system has been recently launched by Police Scotland to facilitate the SAR teams to locate missing persons in Scotland wilderness.
    Original languageEnglish
    Article number114937
    Number of pages9
    JournalExpert Systems with Applications
    Volume178
    Early online date5 Apr 2021
    DOIs
    Publication statusPublished - 15 Sept 2021

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being
    2. SDG 10 - Reduced Inequalities
      SDG 10 Reduced Inequalities

    Keywords

    • unmanned aerial vehicle
    • search and rescue
    • machine learning
    • object detection
    • human detection
    • YOLOv3

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