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A Lightweight Algorithm for Insulator Target Detection and Defect Identification.

Gujing Han1,2, Liu Zhao1,2, Qiang Li3

  • 1Department of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances UAV-based insulator defect detection by optimizing the YOLOv4 model with GhostNet and attention mechanisms. The improved model significantly reduces size while boosting accuracy and speed for edge deployment.

Keywords:
GhostNetYOLOv4attention mechanisminsulatorlightweight algorithm

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Transmission line inspection relies on Unmanned Aerial Vehicles (UAVs) for insulator defect identification.
  • Current UAV-based defect detection models face challenges in accuracy and edge-end deployment due to large model sizes.

Purpose of the Study:

  • To improve the accuracy and reduce the model size of UAV-based insulator defect detection algorithms.
  • To enhance the suitability of these algorithms for edge-end deployment in real-time applications.

Main Methods:

  • Reconstructed the YOLOv4 backbone using a lightweight GhostNet module.
  • Integrated depthwise separable convolution in the feature fusion layer.
  • Embedded ECA-Net channel attention mechanism into feature extraction and Path Aggregation Network (PANet).

Main Results:

  • Reduced model size from 244 MB to 42 MB (17.3% of original).
  • Increased mean Average Precision (mAP) by 0.77% to 95.4%, outperforming YOLOv5-s and YOLOX-s.
  • Achieved a processing speed of 8.8 FPS on Jetson Xavier NX, a 4.3 FPS improvement.

Conclusions:

  • The proposed lightweight YOLOv4 model significantly enhances accuracy and efficiency for UAV-based insulator defect detection.
  • The optimized model is well-suited for edge-end deployment, enabling faster and more accurate real-time inspections.