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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 | 2024-05-27T09:04:49Z | - |
dc.date.available | 2024-05-27T09:04:49Z | - |
dc.date.issued | 2024-05-24 | - |
dc.identifier | ORCiD: Elisavet Bellou https://orcid.org/0000-0002-7088-5700 | - |
dc.identifier | ORCiD: Ioana Pisica https://orcid.org/0000-0002-9426-3404 | - |
dc.identifier | ORCiD: Konstantinos Banitsas https://orcid.org/0000-0003-2658-3032 | - |
dc.identifier | 2535 | - |
dc.identifier.citation | Bellou, E., Pisica, I. and Banitsas, K. (2024) 'Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector', Energies, 17 (11), 2535, pp. 1 - 17. doi: 10.3390/en17112535. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29064 | - |
dc.description | Data Availability Statement: The models included in this research as well as the full dataset with annotation files are openly available at the following Github repository: https://github.com/Elizbellou/Tower-Insulator-Conductors-TIC-Dataset-and-Object-Detection-Models.git (accessed on 15 January 2024). | en_US |
dc.description | Supplementary Materials: Inference on drone footage (VideoS1.mp4) is openly available at: https://drive.google.com/drive/folders/1ZLePzH2bEddZNVCc389al3SokTV9EC7G?usp=sharing (accessed on 15 January 2024). | - |
dc.description.abstract | The aerial inspection of electricity infrastructure is gaining high interest due to the rapid advancements in unmanned aerial vehicle (UAV) technology, which has proven to be a cost- and time-effective solution for deploying computer vision techniques. Our objectives are focused on enabling the real-time detection of key power line components and identifying missing caps on insulators. To address the need for real-time detection, we evaluate the latest single-stage object detector, YOLOv8. We propose a fine-tuned model based on YOLOv8’s architecture, trained on a custom dataset with three object classes, i.e., towers, insulators, and conductors, resulting in an overall accuracy rate of 83.8% (mAP@0.5). The model was tested on a GeForce RTX 3070 (8 GB), as well as on a CPU, reaching 243 fps and 39 fps for video footage, respectively. We also verify that our model can serve as a baseline for other power line detection models; a defect detection model for insulators was trained using our model’s pre-trained weights on an open-source dataset, increasing precision and recall class predictions (F1-score). The model achieved a 99.5% accuracy rate in classifying defective insulators (mAP@0.5). | en_US |
dc.description.sponsorship | This research received no external funding. | en_US |
dc.format.extent | 1 - 17 | - |
dc.format.medium | Electronic | - |
dc.language | en | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | power lines | en_US |
dc.subject | unmanned aerial vehicles | en_US |
dc.subject | object detection | en_US |
dc.subject | YOLO | en_US |
dc.subject | custom dataset | en_US |
dc.title | Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-05-22 | - |
dc.identifier.doi | https://doi.org/10.3390/en17112535 | - |
dc.relation.isPartOf | Energies | - |
pubs.issue | 11 | - |
pubs.publication-status | Published online | - |
pubs.volume | 17 | - |
dc.identifier.eissn | 1996-1073 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | 2.55 MB | Adobe PDF | View/Open |
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