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This study introduces an improved YOLOv5s method for faster defective insulator detection in power line inspections. The enhanced model prioritizes speed, achieving quicker detection with acceptable precision for improved inspection efficiency.

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Defective insulator detection is critical for power transmission line safety.
  • Current artificial intelligence (AI)-based methods face challenges with detection speed, impacting inspection efficiency.
  • Unmanned aerial vehicle (UAV) inspections require rapid and accurate defect identification.

Purpose of the Study:

  • To enhance the detection speed of defective insulators using an improved YOLOv5s model.
  • To maintain acceptable precision while significantly increasing detection efficiency.
  • To optimize AI models for real-time power line inspection applications.

Main Methods:

  • Designed a novel ResNet unit with depthwise separable convolution and average pooling to replace original units in YOLOv5s's CSP1_X module, reducing parameters.
  • Integrated channel shuffle into the CSP1_X module to improve feature information flow.
  • Developed a new residual CBL module using depthwise separable and standard convolutions, replacing CBL modules in YOLOv5s's CSP2_X for parameter reduction and feature extraction.
  • Introduced a coordinated attention module with location information to enhance insulator location feature extraction.
  • Replaced the focus model with convolution to decrease computational load.

Main Results:

  • The proposed method demonstrates reduced parameters and floating-point operations per second (FLOPS).
  • Achieved higher frames per second (FPS), indicating a significant increase in detection speed.
  • Observed a slight decrease in mean precision (mAP) compared to existing methods.
  • The gain in detection speed outweighs the marginal loss in precision.

Conclusions:

  • The improved YOLOv5s method offers a faster and efficient solution for defective insulator detection in UAV-based power line inspections.
  • The architectural modifications successfully balance detection speed and precision for practical applications.
  • This approach contributes to enhanced power transmission safety through more efficient inspection processes.