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

Authors:
Zhengrong Li;Yuee Liu;Rodney Walker;Ross Hayward;Jinglan Zhang
Affiliations:
Queensland University of Technology, School of Information Technology, Brisbane, Australia;Queensland University of Technology, School of Information Technology, Brisbane, Australia;Queensland University of Technology, Australian Research Center for Aerospace Automation, Brisbane, Australia;Queensland University of Technology, School of Information Technology, Brisbane, Australia;Queensland University of Technology, School of Information Technology, Brisbane, Australia
Venue:
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Year:
2010

Citing 4
Cited 4

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Applications of Pulse-Coupled Neural Networks
Line detection in images through regularized hough transform

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Integrated imaging and vision techniques for industrial inspection: a special issue on machine vision and applications

Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Review: Pulse coupled neural networks and its applications

Expert Systems with Applications: An International Journal
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Power line detection from optical images

Neurocomputing

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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.