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Insulator Defect Detection Algorithm Based on Improved YOLOv11n.

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Summary
This summary is machine-generated.

This study introduces an advanced method for detecting defects in high-voltage insulators using a lightweight neural network. The approach improves accuracy and efficiency for safer electrical power transmission.

Keywords:
insulator defect detectionmultidimensional dynamic convolutions (ODConv)you only look once (YOLO)

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • High-voltage transmission line insulators are crucial for electrical isolation and mechanical support in power systems.
  • Environmental factors frequently cause insulator defects, necessitating reliable detection methods.
  • Efficient defect detection is vital for ensuring the safety and reliability of electrical power systems.

Purpose of the Study:

  • To propose an enhanced defect detection approach for high-voltage transmission line insulators.
  • To improve the accuracy and efficiency of insulator defect detection using deep learning.
  • To develop a lightweight neural network for real-time applications.

Main Methods:

  • A lightweight neural network based on the YOLOv11n architecture was developed.
  • Innovations include a redesigned C3k2 module with multidimensional dynamic convolutions (ODConv) for feature extraction.
  • Slimneck was introduced to reduce model complexity and computational cost, and WIoU loss function was applied for anchor box optimization.

Main Results:

  • The proposed method demonstrated superior performance compared to YOLOv8 and YOLOv10 in precision, recall, and mean average precision (mAP).
  • The model achieved high accuracy while maintaining low computational complexity.
  • Experimental results validate the effectiveness of the enhanced defect detection approach.

Conclusions:

  • The developed approach offers a promising solution for real-time, high-accuracy insulator defect detection.
  • This advancement contributes to enhancing the safety and reliability of power transmission systems.
  • The lightweight design makes the method suitable for practical deployment in power infrastructure monitoring.