Altitude-adaptive and cost-effective object recognition in an integrated smartphone and UAV system

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Abstract

Human Search and Rescue (SAR) tasks are mission-critical and take place in the wild, and thus solutions require timely and accurate human detection on a highly portable platform. This paper proposes a novel lightweight and practical SAR system that meets those demanding requirements by running optimised machine learning in a smartphone, interoperable with Unmanned Aerial Vehicles (UAV) that provides live video feed. In particular, the proposed approach significantly extends a standard machine learning algorithm to achieve adaptive object recognition in response to changing altitudes to accelerate the speed of finding missing people and eliminate redundant computing. Our approach achieved 91.02% of accuracy and real-time speed on a smartphone that hosts the machine learning platform and the new algorithm. This proposed system is highly portable, cost-effective, fast with high accuracy suitable for UAV applications.
Original languageEnglish
Title of host publication2020 European Conference on Networks and Communications (EuCNC)
PublisherIEEE
Pages316-320
Number of pages5
ISBN (Electronic)9781728143552, 9781728143569
DOIs
Publication statusPublished - 21 Sep 2020

Publication series

NameIEEE Conference Proceedings
PublisherIEEE
ISSN (Print)2475-6490
ISSN (Electronic)2475-4912

Keywords

  • UAV
  • machine learning
  • deep learning
  • SAR missions
  • YOLOv3

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