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

Energies, ISSN: 1996-1073, Vol: 17, Issue: 11
2024
  • 4
    Citations
  • 0
    Usage
  • 10
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    4
  • Captures
    10
  • Mentions
    1
    • News Mentions
      1
      • 1

Most Recent News

Research from Brunel University London Provides New Data on Unmanned Aerial Vehicle (Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector)

2024 JUL 01 (NewsRx) -- By a News Reporter-Staff News Editor at Defense & Aerospace Daily -- Current study results on unmanned aerial vehicle have

Article Description

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).

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