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Insulator defect detection in severe weather using improved YOLOv8.

Jia Li1, Yanjie Wu1, Shaojun Zhu1

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This study introduces YOLOv8-SSF, an improved algorithm for detecting insulator defects in power systems. The enhanced model significantly reduces misdetection and omission errors, improving power system reliability.

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Insulators are critical power system components.
  • Inclement weather causes insulator misdetection, leakage, and low accuracy.
  • Existing methods struggle with accurate defect detection.

Purpose of the Study:

  • To develop an advanced insulator defect detection algorithm.
  • To improve detection accuracy and reduce errors in adverse weather conditions.
  • To enhance the reliability of power transmission infrastructure.

Main Methods:

  • Proposed YOLOv8-SSF algorithm integrating SimAM attention mechanism for feature focus.
  • Incorporated SPDConv layer for enhanced small-target feature extraction.
  • Utilized Focal_EIOU loss function for improved detection and localization accuracy.

Main Results:

  • Reduced misdetection and omission rates for transmission conductor defects.
  • Achieved a mean average accuracy (mAP@0.5) of 87.2% on the dataset.
  • Demonstrated comprehensive simultaneous improvement in detection performance.

Conclusions:

  • YOLOv8-SSF effectively addresses insulator defect detection challenges.
  • The algorithm enhances power system safety and operational efficiency.
  • The proposed method offers a robust solution for real-world applications.