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Research on Insulator Defect Detection Based on an Improved MobilenetV1-YOLOv4.

Shanyong Xu1, Jicheng Deng1, Yourui Huang1,2

  • 1School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China.

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Summary

This study introduces an improved insulator defect detection algorithm for transmission lines, enhancing accuracy and speed. The new method significantly reduces model size and boosts real-time performance for safer infrastructure monitoring.

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Mobilenet-V1YOLOv4depthwise separable convolutioninsulator defect detectionscSE attention mechanism

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Transmission line safety relies on intact insulator devices.
  • Defects like insulator bursting and string loss pose significant risks.
  • Traditional insulator defect detection methods suffer from slow speeds and low efficiency.

Purpose of the Study:

  • To address the limitations of traditional insulator defect detection algorithms.
  • To improve the accuracy of insulator fault identification.
  • To enhance the convenience of daily maintenance work for transmission lines.

Main Methods:

  • Proposed an insulator defect detection algorithm based on an improved MobilenetV1-YOLOv4 framework.
  • Replaced YOLOv4's backbone with the lightweight Mobilenet-V1 module.
  • Integrated scSE attention mechanisms and depthwise separable convolutions for optimization.

Main Results:

  • Achieved a model weight of 57.9 MB, a 62.6% reduction compared to MobilenetV1-YOLOv4.
  • Improved average accuracy of insulator defect detection to 98.81% (a 0.26% increase).
  • Increased detection speed to 190 frames per second, an improvement of 37 frames per second.

Conclusions:

  • The improved MobilenetV1-YOLOv4 algorithm offers a more efficient and accurate solution for insulator defect detection.
  • The optimized model size and enhanced speed facilitate practical, real-time monitoring of transmission lines.
  • This advancement contributes to improved safety and reliability of electrical power transmission infrastructure.