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Aerial insulator defect detection method based on CWSP-YOLO.

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This study introduces an advanced YOLOv11 model for detecting insulator defects using Unmanned Aerial Vehicle (UAV) imagery. The improved model enhances accuracy and reduces errors in power equipment inspection.

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Unmanned Aerial Vehicle (UAV) technology is rapidly advancing, making aerial imagery crucial for intelligent power equipment inspection.
  • Traditional insulator defect detection methods suffer from high false/missed detection rates and insufficient multimodal data fusion.

Purpose of the Study:

  • To propose an improved YOLOv11-based model for enhanced insulator defect detection in power equipment.
  • To address the limitations of existing methods by integrating multimodal data and advanced deep learning techniques.

Main Methods:

  • An improved YOLOv11 model was developed, incorporating multimodal data fusion.
  • Key features include cross-modal collaboration, wavelet-optimized C3k2 modules, channel attention mechanisms, and PIoU v2-based dynamic gradient optimization.

Main Results:

  • The proposed model achieved a mean average precision (mAP) of 84.77% on a self-built dataset.
  • Performance metrics included 94.53% accuracy and 82.38% recall, with a processing speed of 24 FPS, meeting real-time requirements.

Conclusions:

  • The developed multimodal YOLOv11 model significantly improves insulator defect detection for UAV-based power inspection.
  • The system provides reliable and efficient support for real-time power equipment monitoring.