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Two-Level Model for Detecting Substation Defects from Infrared Images.

Bing Li1, Tian Wang1, Zhedong Hu1

  • 1Department of Automation, North China Electric Power University, Baoding 071003, China.

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Summary

This study introduces a two-level defect detection model (TDDM) for identifying substation equipment issues using infrared images. The TDDM effectively detects defects despite limited data and complex backgrounds.

Keywords:
defect detectioninfrared imagesubstation equipmentsuperpixel segmentationtemperature probability density

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Deep convolutional neural networks (DCNNs) require extensive datasets for defect detection in substation equipment.
  • Acquiring large defect datasets is challenging.
  • Complex backgrounds in infrared images complicate defect identification.

Purpose of the Study:

  • To present a novel two-level defect detection model (TDDM) to address limitations in DCNN-based defect detection.
  • To improve the accuracy and efficiency of detecting defects in substation equipment from infrared images.

Main Methods:

  • An instance segmentation module was developed to extract target equipment.
  • Superpixel segmentation was applied to segment equipment for detailed information.
  • A temperature probability density distribution and defect determination strategy were employed for defect recognition.

Main Results:

  • The TDDM demonstrated effectiveness in defect detection tasks.
  • The model successfully identified defects in substation equipment using infrared imagery.
  • Experimental validation confirmed the model's performance.

Conclusions:

  • The TDDM offers a viable solution for defect detection in substation equipment, especially with limited datasets.
  • The proposed method enhances defect identification accuracy in complex infrared image backgrounds.
  • The TDDM contributes to improved maintenance and reliability of electrical infrastructure.