Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images

Anicetus Odo, Stephen McKenna, David Flynn, Jan Vorstius

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 Harvard Style

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, pages 566-573. DOI: 10.5220/0009345005660573


in Bibtex Style

@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},
}


in EndNote Style

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