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Power line detection via background noise removal

  • Beihang University
  • Beijing Laboratory for General Aviation Technology
  • University of Florida

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

Abstract

Tiny target detections, especially power line detection, have received great attention due to its critical role in ensuring the flight safety of low-flying unmanned aerial vehicles (UAVs). In this paper, an accurate and robust power line detection method is proposed, wherein background noise is mitigated by an embedded convolution neural network (CNN) classifier before conducting the final power line extractions. Our proposed method operates in three steps: 1) extract edge features of power lines from a testing image, 2) employ a CNN classifier to remove the background noise, 3) use a Hough-Transform (HT) based fine-selection module to locate power lines. Comprehensive experiments demonstrate the superiority of the proposed method, compared to the state-of-the-art methods.

Original languageEnglish
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages871-875
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - 19 Apr 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: 7 Dec 20169 Dec 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Conference

Conference2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Country/TerritoryUnited States
CityWashington
Period7/12/169/12/16

Keywords

  • Classification
  • Edge feature
  • Image processing
  • Machine learning
  • Power line detection

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