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Remote Sens. 2015, 7(11), 15605-15629; doi:10.3390/rs71115605

Automatic Object Extraction from Electrical Substation Point Clouds

Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
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Author to whom correspondence should be addressed.
Academic Editors: Juha Hyyppä, Devrim Akca, Randolph H. Wynne and Prasad S. Thenkabail
Received: 17 September 2015 / Revised: 28 October 2015 / Accepted: 13 November 2015 / Published: 19 November 2015
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)

Abstract

The reliability of power delivery can be profoundly improved by preventing wildlife-related power outages. This can be achieved by insulating electrical substation components with non-conductive covers. The manufacture of custom-built covers requires as-built models of the salient components. This study presents new, automated methodology to recognize key components of electrical substations from 3D LiDAR data acquired using terrestrial laser scanning. The proposed methodology includes six novel algorithms to recognize key components (fence, cables, circuit breakers, bushings and bus pipes) of electrical substations. Three datasets with different resolutions and configurations are used in this study. A Leica HDS 6100 laser scanner was used to acquire the first dataset and a Faro Focus3D laser scanner was employed to collect the second and third datasets. The obtained results indicate that 178 and 171 out of 181 electrical substation elements were successfully recognized in the first and second dataset, respectively, and 183 out of 191 components were identified in the third dataset. The results also demonstrate that an average 97.8% accuracy and average 98.8% precision at the point cloud level can be achieved. View Full-Text
Keywords: LiDAR; laser scanning; object recognition; segmentation; electrical substation LiDAR; laser scanning; object recognition; segmentation; electrical substation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Arastounia, M.; Lichti, D.D. Automatic Object Extraction from Electrical Substation Point Clouds. Remote Sens. 2015, 7, 15605-15629.

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