Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/29064
Title: Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector
Authors: Bellou, E
Pisica, I
Banitsas, K
Keywords: power lines;unmanned aerial vehicles;object detection;YOLO;custom dataset
Issue Date: 24-May-2024
Publisher: MDPI
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.
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).
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).
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).
URI: https://bura.brunel.ac.uk/handle/2438/29064
DOI: https://doi.org/10.3390/en17112535
Other Identifiers: ORCiD: Elisavet Bellou https://orcid.org/0000-0002-7088-5700
ORCiD: Ioana Pisica https://orcid.org/0000-0002-9426-3404
ORCiD: Konstantinos Banitsas https://orcid.org/0000-0003-2658-3032
2535
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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