Real-time low-pixel infrared human detection from unmanned aerial vehicles

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)
36 Downloads (Pure)

Abstract

To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and deployed on an embedded system. The proposed approach has been evaluated under real-world conditions on a Jetson AGX Xavier platform and the results have shown a well-balanced system in terms of accuracy, speed and portability. Moreover, the system demonstrates its resilience to perform low-pixel human detection on infrared images received from an Unmanned Aerial Vehicle (UAV) at low-light conditions, different altitudes and postures such as sitting, walking and running. The proposed approach has achieved in a constrained environment a total of 89.26% of accuracy and 24.6 FPS, surpassing the barrier of real-time object recognition.

Original languageEnglish
Title of host publicationDIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications
PublisherAssociation for Computing Machinery
Pages9-15
Number of pages7
ISBN (Electronic)9781450381215
DOIs
Publication statusPublished - 16 Nov 2020
Event10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2020 - Virtual, Online, Spain
Duration: 16 Nov 202020 Nov 2020

Publication series

NameDIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

Conference

Conference10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2020
Country/TerritorySpain
CityVirtual, Online
Period16/11/2020/11/20

Keywords

  • Jetson AGX Xavier
  • machine learning
  • thermal imagery
  • UAV
  • YOLO

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