Knowledge-based power line detection for UAV surveillance and inspection systems
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Description
Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in the automatic surveillance of electrical power infrastructure. For an automatic vision based power line inspection system, detecting power lines from cluttered background an important and challenging task. In this paper, we propose a knowledge-based power line detection method for a vision based UAV surveillance and inspection system. A PCNN filter is developed to remove background noise from the images prior to the Hough transform being employed to detect straight lines. Finally knowledge based line clustering is applied to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective.
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ID Code: | 16731 | ||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||
ORCID iD: |
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Measurements or Duration: | 6 pages | ||
Keywords: | Hough transform, K-means clustering, PCNN, Power line detection, UAV | ||
DOI: | 10.1109/IVCNZ.2008.4762118 | ||
ISBN: | 978-1-4244-2582-2 | ||
Pure ID: | 33559953 | ||
Divisions: | ?? 16 ?? Past > QUT Faculties & Divisions > Faculty of Science and Technology Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Research Centres > Australian Research Centre for Aerospace Automation |
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Copyright Owner: | Copyright 2008 IEEE | ||
Copyright Statement: | Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | ||
Deposited On: | 09 Dec 2008 22:35 | ||
Last Modified: | 17 Jun 2024 00:10 |
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