Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images

Anicetus Odo, Stephen McKenna, David Flynn, Jan Vorstius

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
246 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz
Number of pages8
ISBN (Electronic)9789897584022
Publication statusPublished - 27 Feb 2020
EventVISAPP 2020: 15th International Conference on Computer Vision Theory and Applications - Valletta, Malta
Duration: 27 Feb 202029 Feb 2020


ConferenceVISAPP 2020
Internet address


  • Electricity Pylons
  • Transfer Learning
  • Unmanned Aerial Vehicles
  • Visual Inspection

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Computer Vision and Pattern Recognition


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