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Authors: Anicetus Odo 1 ; Stephen McKenna 1 ; David Flynn 2 and Jan Vorstius 1

Affiliations: 1 School of Science & Engineering, University of Dundee, Dundee, DD1 4HN, Scotland, U.K. ; 2 School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, U.K.

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): Visual Inspection, Electricity Pylons, Transfer Learning, Unmanned Aerial Vehicles.

Abstract: Visual inspection of electricity transmission and distribution networks relies on flying a helicopter around energized high voltage towers for image collection. The sensed data is taken offline and screened by skilled personnel for faults. This poses high risk to the pilot and crew and is highly expensive and inefficient. This paper reviews work targeted at detecting components of electricity transmission and distribution lines with attention to unmanned aerial vehicle (UAV) platforms. The potential of deep learning as the backbone of image data analysis was explored. For this, we used a new dataset of high resolution aerial images of medium-to-low voltage electricity towers. We demonstrated that reliable classification of towers is feasible using deep learning methods with very good results.

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Paper citation in several formats:
Odo, A.; McKenna, S.; Flynn, D. and Vorstius, J. (2020). Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2, ISSN 2184-4321, pages 566-573. DOI: 10.5220/0009345005660573

@conference{visapp20,
author={Anicetus Odo. and Stephen McKenna. and David Flynn. and Jan Vorstius.},
title={Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={566-573},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009345005660573},
isbn={978-989-758-402-2},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images
SN - 978-989-758-402-2
AU - Odo, A.
AU - McKenna, S.
AU - Flynn, D.
AU - Vorstius, J.
PY - 2020
SP - 566
EP - 573
DO - 10.5220/0009345005660573

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