Long-Range Visual UAV Detection and Tracking System with Threat Level Assessment

Abdel Gafoor Haddad, Muhammad Ahmed Humais, Naoufel Werghi, Abdulhadi Shoufan

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

9 Scopus citations

Abstract

Unmanned aerial vehicles (UAVs) can pose a serious threat to critical infrastructure which has motivated researchers to develop solutions for early detection. Nevertheless, the problem remains unsolved due to the limitations of the current detection techniques. In this paper, a vision-based approach using deep learning and a pan-tilt-zoom camera is proposed. In addition to detecting and tracking UAVs at long distances, the approach also assesses the threat level of the intruder UAVs based on their orientation. The proposed system offers long-range coverage while being cheap and practically feasible.

Original languageBritish English
Title of host publicationProceedings - IECON 2020
Subtitle of host publication46th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
Pages638-643
Number of pages6
ISBN (Electronic)9781728154145
DOIs
StatePublished - 18 Oct 2020
Event46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 - Virtual, Singapore, Singapore
Duration: 19 Oct 202021 Oct 2020

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2020-October

Conference

Conference46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
Country/TerritorySingapore
CityVirtual, Singapore
Period19/10/2021/10/20

Keywords

  • airport security
  • counter-UAV technologies
  • drone detection
  • UAV detection and tracking
  • UAV threat

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