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.
Original language | English |
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Title of host publication | Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
Editors | Giovanni Maria Farinella, Petia Radeva, Jose Braz |
Publisher | VISAPP |
Pages | 566-573 |
Number of pages | 8 |
Volume | 5 |
ISBN (Electronic) | 9789897584022 |
DOIs | |
Publication status | Published - 27 Feb 2020 |
Event | VISAPP 2020: 15th International Conference on Computer Vision Theory and Applications - Valletta, Malta Duration: 27 Feb 2020 → 29 Feb 2020 http://www.visapp.visigrapp.org/?y=2020 |
Conference
Conference | VISAPP 2020 |
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Country/Territory | Malta |
City | Valletta |
Period | 27/02/20 → 29/02/20 |
Internet address |
Keywords
- 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
Fingerprint
Dive into the research topics of 'Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images'. Together they form a unique fingerprint.Student theses
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Image Analysis using Deep Learning for Electrical Overhead Line Tower Management
Odo, A. (Author), Vorstius, J. (Supervisor) & McKenna, S. (Supervisor), 2022Student thesis: Doctoral Thesis › Doctor of Philosophy
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Profiles
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Vorstius, Jan Bernd
- Mechanical and Industrial Engineering - Senior Lecturer (Teaching and Research)
Person: Academic