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Machine Vision and Applications

, Volume 21, Issue 5, pp 677–686 | Cite as

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

  • Zhengrong Li
  • Yuee Liu
  • Rodney Walker
  • Ross Hayward
  • Jinglan Zhang
Special Issue Paper

Abstract

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Keywords

Machine vision Power line inspection system Unmanned aerial vehicles (UAVs) Hough transform Pulse coupled neural filter Knowledge-based system 

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Zhengrong Li
    • 1
  • Yuee Liu
    • 1
  • Rodney Walker
    • 2
  • Ross Hayward
    • 1
  • Jinglan Zhang
    • 1
  1. 1.School of Information TechnologyQueensland University of TechnologyBrisbaneAustralia
  2. 2.Australian Research Center for Aerospace AutomationQueensland University of TechnologyBrisbaneAustralia

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