Paper
2 March 2016 Extraction of power lines from mobile laser scanning data
Qing Xiang, Jonathan Li, Chenglu Wen, Pengdi Huang
Author Affiliations +
Proceedings Volume 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015); 990105 (2016) https://doi.org/10.1117/12.2234848
Event: 2015 ISPRS International Conference on Computer Vision in Remote Sensing, 2015, Xiamen, China
Abstract
Modern urban life is becoming increasingly more dependent on reliable electric power supply. Since power outages cause substantial financial losses to producers, distributors and consumers of electric power, it is in the common interest to minimize failures of power lines. In order to detect defects as early as possible and to plan efficiently the maintenance activities, distribution networks are regularly inspected. Carrying out foot patrols or climbing the structures to visually inspect transmission lines and aerial surveys (e.g., digital imaging or most recent airborne laser scanning (ALS) are the two most commonly used methods of power line inspection. Although much faster in comparison to the foot patrol inspection, aerial inspection is more expensive and usually less accurate, in complex urban areas particularly. This paper presents a scientific work that is done in the use of mobile laser scanning (MLS) point clouds for automated extraction of power lines. In the proposed method, 2D power lines are extracted using Hough transform in the projected XOY plane and the 3D power line points are visualized after the point searching. Filtering based on an elevation threshold is applied, which is combined with the vehicle’s trajectory in the horizontal section.
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Qing Xiang, Jonathan Li, Chenglu Wen, and Pengdi Huang "Extraction of power lines from mobile laser scanning data", Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 990105 (2 March 2016); https://doi.org/10.1117/12.2234848
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KEYWORDS
3D modeling

Laser scanners

Inspection

Clouds

Data modeling

Hough transforms

Image segmentation

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