IN-YOLO: Real-Time Detection of Outdoor High Voltage Insulators Using UAV Imaging

Diana Sadykova, Damira Pernebayeva, Mehdi Bagheri, Alex James

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The high voltage insulator requires continuous monitoring and inspection to prevent failures and emergencies. Manual inspections are costly as it requires covering a large geographical area where insulators are often subjected to harsh weather conditions. Automatic detection of insulators from aerial images is the first step towards performing real-time classification of insulator conditions using Unmanned Aerial Vehicle (UAV). In this paper, we provide a cost-effective solution for detecting insulators under the conditions of an uncluttered background, varied object resolution and illumination conditions using You Only Look Once (YOLO) deep learning neural network model from aerial images. We apply data augmentation to avoid overfitting with a training set size of 56000 image samples. It is demonstrated experimentally that this method can accurately locate insulator on UAV based real-time image data. The detected insulator images are then successfully subjected to insulator surface condition assessment for the presence of ice, snow and water using different classifiers.

Original languageEnglish
Article number8853298
Pages (from-to)1599-1601
Number of pages3
JournalIEEE Transactions on Power Delivery
Issue number3
Publication statusPublished - Jun 2020


  • data augmentation
  • detection
  • insulators
  • Unmanned Aerial Vehicle (UAV)
  • YOLOv2

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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