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DC Field | Value | Language |
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dc.contributor.author | Bellou, E | - |
dc.contributor.author | Pisica, I | - |
dc.contributor.author | Banitsas, K | - |
dc.date.accessioned | 2023-11-06T20:15:23Z | - |
dc.date.available | 2023-11-06T20:15:23Z | - |
dc.date.issued | 2023-08-30 | - |
dc.identifier | ORCID iD: Ioana Pisica https://orcid.org/0000-0002-9426-3404 | - |
dc.identifier | ORCID iD: Konstantinos Banitsas https://orcid.org/0000-0003-2658-3032 | - |
dc.identifier.citation | Bellou, E., Pisica, I. and Banitsas, K. (2023) 'Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)', 2023 58th International Universities Power Engineering Conference (UPEC), Dublin, Ireland, 30 August-1 September, pp. 1 - 6. doi: 10.1109/upec57427.2023.10294447. | en_US |
dc.identifier.isbn | 979-8-3503-1683-4 (ebk) | - |
dc.identifier.issn | 979-8-3503-1684-1 (PoD) | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/27557 | - |
dc.description.abstract | Unmanned Aerial Vehicles (UAVs) are gaining significant scientific interest in critical infrastructure inspection due to their flexibility, cost-effectiveness and advanced computer vision capabilities. This research focuses on high-voltage powerline surveillance, where automatic inspection is a priority for grid companies to prevent power failures. To address the need for real-time detection with limited computational power, we evaluate the recently developed object detection algorithm, YOLOvS. We propose a fine-tuned model trained on a custom dataset to detect key components, i. e. towers, insulators and conductors. The proposed method achieves an overall accuracy rate of 82.3% (mAp@O.S) and enables real-time detection, demonstrating its suitability for inspection tasks and visual-based navigation. Our model was also tested on a custom-built quadcopter with an Nvidia Jetson Nano (4GB) on board, achieving a frame rate of 33fps on live video under real environmental conditions. | en_US |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. See: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.source | 2023 58th International Universities Power Engineering Conference (UPEC) | - |
dc.source | 2023 58th International Universities Power Engineering Conference (UPEC) | - |
dc.subject | unmanned aerial vehicles (UAVs) | en_US |
dc.subject | high-voltage powerlines | en_US |
dc.subject | computer vision | en_US |
dc.subject | object detection | en_US |
dc.subject | custom dataset | en_US |
dc.title | Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV) | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.doi | https://doi.org/10.1109/upec57427.2023.10294447 | - |
dc.relation.isPartOf | 2023 58th International Universities Power Engineering Conference (UPEC) | - |
pubs.finish-date | 2023-09-01 | - |
pubs.finish-date | 2023-09-01 | - |
pubs.publication-status | Published | - |
pubs.start-date | 2023-08-30 | - |
pubs.start-date | 2023-08-30 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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