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Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical

Xueli Liu1, Zhanlong Zhang1, Yuefeng Hao1

  • 1School of Electrical Engineering, Chongqing University, Chongqing 400044, China.

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|February 24, 2024
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Summary

This study optimizes infrared image processing for electrical fault diagnosis by enhancing target device segmentation using Gray Wolf Optimization (GWO) and improving temperature extraction accuracy with K-nearest neighbors (KNN). These methods accelerate analysis and provide more reliable fault detection.

Keywords:
infrared imagepower equipmentsegmentationtemperature feature extraction

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Image Processing

Background:

  • Infrared thermography is crucial for electrical equipment fault diagnosis.
  • Key steps include target device segmentation and temperature feature extraction.
  • Existing methods suffer from slow segmentation and inaccurate temperature readings due to computational complexity and non-linear grayscale-temperature relationships.

Purpose of the Study:

  • To accelerate infrared image segmentation for electrical equipment.
  • To improve the accuracy of temperature feature extraction from infrared images.
  • To enhance the overall reliability of infrared-based fault diagnosis.

Main Methods:

  • An optimized Otsu's method (OTSU) segmentation algorithm is proposed, enhanced by the Gray Wolf Optimization (GWO) algorithm.
  • A K-nearest neighbor (KNN) algorithm is developed for accurate temperature value extraction from infrared images.
  • The GWO-OTSU method optimizes threshold determination for faster segmentation.

Main Results:

  • The optimized segmentation method significantly increased threshold calculation speed by over 83.99% compared to non-optimized approaches, maintaining comparable segmentation quality.
  • The KNN-based temperature extraction method demonstrated substantial improvements, with a 73.68% increase in maximum residual absolute value and a 78.95% increase in average residual absolute value over linear methods.
  • These advancements lead to more efficient and accurate fault diagnosis in electrical equipment.

Conclusions:

  • The proposed GWO-optimized OTSU method effectively accelerates infrared image segmentation for electrical equipment.
  • The KNN-based temperature extraction method significantly enhances accuracy in identifying overheating and potential faults.
  • The integrated approach offers a more robust and efficient solution for electrical equipment fault diagnosis using infrared imaging.