Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform |
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Authors: | Zhengrong Li Yuee Liu Rodney Walker Ross Hayward Jinglan Zhang |
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Affiliation: | (1) Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada, T2N 1N4;(2) Division of Ophthalmology, Department of Surgery, Alberta Children’s Hospital, 2888 Shaganappi Trail NW, Calgary, Alberta, Canada, T3B 6A8; |
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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. |
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