Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector

Elisavet Bellou (), Ioana Pisica and Konstantinos Banitsas
Additional contact information
Elisavet Bellou: Department of Electronic and Electrical Engineering, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK
Ioana Pisica: Department of Electronic and Electrical Engineering, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK
Konstantinos Banitsas: Department of Electronic and Electrical Engineering, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK

Energies, 2024, vol. 17, issue 11, 1-16

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).

Keywords: power lines; unmanned aerial vehicles; object detection; YOLO; custom dataset (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/11/2535/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/11/2535/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:11:p:2535-:d:1401056

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().