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

Author

Listed:
  • 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)

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).

Suggested Citation

  • Elisavet Bellou & Ioana Pisica & Konstantinos Banitsas, 2024. "Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector," Energies, MDPI, vol. 17(11), pages 1-16, May.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2535-:d:1401056
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    References listed on IDEAS

    as
    1. Juping Gu & Junjie Hu & Ling Jiang & Zixu Wang & Xinsong Zhang & Yiming Xu & Jianhong Zhu & Lurui Fang, 2023. "Research on Object Detection of Overhead Transmission Lines Based on Optimized YOLOv5s," Energies, MDPI, vol. 16(6), pages 1-18, March.
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