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 data set 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 |
Publisher | SciTePress |
Pages | 566-573 |
Number of pages | 8 |
Volume | 5 |
ISBN (Electronic) | 978-989-758-402-2 |
DOIs | |
Publication status | Published - 27 Feb 2020 |
Event | 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2020 - Valletta, Malta Duration: 27 Feb 2020 → 29 Feb 2020 http://www.visapp.visigrapp.org/?y=2020 |
Conference
Conference | 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2020 |
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Abbreviated title | VISIGRAPP 2020 |
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