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Detection of Missing Insulator Caps Based on Machine Learning and Morphological Detection.

Zhaoyun Zhang1, Hefan Chen1, Shihong Huang1

  • 1Electronic Engineering and Intelligence College, Dongguan University of Technology, Dongguan 523000, China.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

This study introduces an automated method for detecting missing insulator caps on glass and porcelain insulators. The technique achieves high accuracy, improving transmission line safety and maintenance efficiency.

Keywords:
SVMmachine learningmissing insulator slicesmorphological detectionobject region detectionsmall-scale dataset

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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Computer Vision

Background:

  • Missing insulator caps in transmission lines compromise insulation and mechanical strength, leading to safety hazards.
  • Glass and porcelain insulators are particularly susceptible to missing cap issues.
  • Manual inspection is labor-intensive and prone to errors.

Purpose of the Study:

  • To develop an automated and accurate method for detecting missing insulator caps on glass and porcelain insulators.
  • To enhance the safety and efficiency of transmission line inspections.
  • To provide a tool for assessing the extent of insulator damage.

Main Methods:

  • Extraction of insulator characteristic regions based on grayscale and color properties from inspection images.
  • Generation of candidate boxes for insulator localization.
  • Classification and identification using a Support Vector Machine (SVM) classifier.
  • Morphological analysis to determine the presence or absence of insulator caps.

Main Results:

  • The SVM classifier achieved accuracy, recall, and average accuracy exceeding 90%.
  • The proposed method accurately identifies and locates insulators.
  • The system can determine the number of remaining insulator caps, quantifying damage.

Conclusions:

  • The developed method offers a highly accurate and efficient solution for detecting missing insulator caps.
  • This technology can significantly improve the safety and reliability of power transmission infrastructure.
  • The ability to assess insulator damage aids power companies in maintenance planning and risk management.