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A Preprocessing Method for Insulation Pull Rod Defect Dataset Based on the YOLOv5s Object Detection Network.

Xuetong Li1, Meng Cong2, Bo Liu2

  • 1Department of Electrical Engineering, Tsinghua University, Beijing 100084, China.

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Summary
This summary is machine-generated.

This study introduces a data preprocessing technique to improve defect detection in gas-insulated switchgear (GIS) components. By enhancing small defect visibility, the method significantly boosts the performance of intelligent identification systems for insulation faults.

Keywords:
MosaicYOLOv5sbounding boxcopy–pastedata augmentationdefect detectionpull rod

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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Computer Vision

Background:

  • Gas-insulated switchgear (GIS) components, specifically insulation pull rods, are prone to micro-defects from production.
  • Intelligent identification methods for insulation faults require large, balanced datasets, which are challenging to obtain due to limited defective samples and imbalanced defect types.
  • Existing methods struggle with poor recognition performance when faced with imbalanced defect data.

Purpose of the Study:

  • To propose an effective data preprocessing method for insulation pull rod defect feature datasets.
  • To enhance the recognition performance of intelligent identification systems for insulation pull rod defects.
  • To address the challenges of limited defective samples and imbalanced defect categories in actual production data.

Main Methods:

  • Utilized the YOLOv5s algorithm for defect detection in insulation pull rod images, establishing a dataset with five defect categories.
  • Introduced two preprocessing techniques: copy-paste augmentation within images and bounding box correction for hair-like impurities.
  • Integrated copy-paste augmentation with Mosaic data augmentation and refined bounding box corrections for hair-like impurities.

Main Results:

  • The proposed preprocessing methods effectively enhance small-sized defect targets (impurities and bubbles) while maintaining detection performance for other defect types.
  • The combined approach of copy-paste augmentation, Mosaic augmentation, and bounding box correction significantly improved the overall model performance.
  • Demonstrated a specific enhancement for small-sized defect targets, crucial for accurate fault identification.

Conclusions:

  • The developed data preprocessing method is effective in improving the performance of defect detection models for insulation pull rods in GIS.
  • The integration of specific augmentation and correction techniques addresses the issue of imbalanced defect data, leading to more robust identification systems.
  • This approach offers a viable solution for enhancing the reliability of intelligent fault identification in electrical equipment.