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  • An Automatic Technique for Power Line Pylon Detection from Point Cloud Data

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    AwrangjebPUB2714.pdf (4.255Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Awrangjeb, Mohammad
    Jonas, David
    Zhou, Jun
    Griffith University Author(s)
    Zhou, Jun
    Awrangjeb, Mohammad
    Year published
    2017
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    Abstract
    This paper proposes a new pylon detection technique from point cloud data. Two masks are created from the non-ground points that mainly represent trees and power line components. The first mask is the power line mask Mₘ, which contains the power line components and trees and where successive pylons are found connected with wires. The second mask is the pylon mask Mₚ, where successive pylons are found disconnected, and thus is exploited to obtain candidate pylons using a connected component analysis. By contrasting the area, shape and symmetry properties between trees and pylons majority of the false candidates (trees) are ...
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    This paper proposes a new pylon detection technique from point cloud data. Two masks are created from the non-ground points that mainly represent trees and power line components. The first mask is the power line mask Mₘ, which contains the power line components and trees and where successive pylons are found connected with wires. The second mask is the pylon mask Mₚ, where successive pylons are found disconnected, and thus is exploited to obtain candidate pylons using a connected component analysis. By contrasting the area, shape and symmetry properties between trees and pylons majority of the false candidates (trees) are removed from Mₚ. Finally, long straight lines that represent wires between successive pylons are extracted from Mₘ and used to remove the remaining trees from Mₚ. Experimental results show that the proposed technique provides a high pylon detection rate in terms of both completeness (100%) and correctness (100%).
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    Conference Title
    2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA)
    Volume
    2017-December
    DOI
    https://doi.org/10.1109/DICTA.2017.8227407
    Copyright Statement
    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Photogrammetry and remote sensing
    Publication URI
    http://hdl.handle.net/10072/368376
    Collection
    • Conference outputs

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