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.
|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|
|Number of pages||8|
|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
|Period||27/02/20 → 29/02/20|
- Electricity Pylons
- Transfer Learning
- Unmanned Aerial Vehicles
- Visual Inspection
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Student thesis: Doctoral Thesis › Doctor of PhilosophyFile