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Substation Equipment Defect Detection Based on Improved YOLOv8.

Yiwei Sun1, Xiangran Sun2, Ying Lin1

  • 1State Grid Shandong Electric Power Research Institute, Jinan 250003, China.

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|September 19, 2025
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Summary
This summary is machine-generated.

This study enhances substation equipment defect detection using an improved YOLOv8 object detection algorithm. The optimized model achieves higher accuracy, ensuring reliable power system operation.

Keywords:
YOLOv8defect detectionobject detectionsubstation equipment

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Substation equipment defects pose risks to power system stability.
  • Accurate and efficient defect detection is essential for preventative maintenance.

Purpose of the Study:

  • To develop an enhanced object detection algorithm for substation equipment defects.
  • To improve the performance of the YOLOv8 model for this specific application.

Main Methods:

  • Replaced YOLOv8 backbone with EfficientViT for better feature extraction.
  • Integrated Squeeze-and-Excitation (SE) attention mechanism for enhanced feature representation.
  • Substituted Bottleneck component with FasterBlock to accelerate inference speed.

Main Results:

  • Achieved a mean average precision (mAP) of 92.8% on the defect detection dataset.
  • Demonstrated a 1.8% improvement in mAP compared to the baseline YOLOv8 model.
  • Validated the effectiveness of EfficientViT, SE attention, and FasterBlock modifications.

Conclusions:

  • The proposed modifications significantly enhance YOLOv8 for substation equipment defect detection.
  • The improved algorithm offers a feasible and effective solution for maintaining power system reliability.
  • The optimized model balances accuracy and inference speed for practical deployment.