In this paper, a new automatic system in almost real time for overhead power line inspection by Unmanned Aerial Vehicle (UAV) is presented. This method is focused on the prevention and maintenance of aerial power lines. The main contribution and novelty is the design and development of a new fully automatic aerial inspection system, without requiring manual intervention on the ground station or control center, to detect possible anomalies. The main goal of the method is to process the consecutive images together with the telemetry data sent from the autopilot in order to identify online areas of vegetation, trees and buildings close to power lines and to calculate the distance between power lines and the vegetation, trees and buildings. Simultaneously, the system processes the images captured by the infrared camera in order to detect bad conductivity and hotspots in the power lines, transformers and electrical substations. The system response times does have a constant of one second for calculating distance measurement and down to 0.3 seconds for detecting hotspots. In addition, the system automatically generates reports offline in the ground station once the flight is finished. These reports require more execution time and more CPU resources. The results show 3D information obtained through processing of consecutive frames of the HD images together with the telemetry data sent by autopilot. This paper is structured as follows: the first section briefly introduces the current methods of inspection of power lines; the second section presents our proposed system “RELIFO” the third section describes the algorithms designed and developed for this system; the fourth section presents and discusses the experimental results; the next section offers the conclusions drawn. Finally, we cite the references and publications used in this paper.


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    Titel :

    Automatic system for overhead power line inspection using an Unmanned Aerial Vehicle — RELIFO project


    Beteiligte:


    Erscheinungsdatum :

    2013-05-01


    Format / Umfang :

    2309384 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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