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Insulator-Defect Detection Algorithm Based on Improved YOLOv7.

Jianfeng Zheng1,2, Hang Wu1, Han Zhang3

  • 1School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China.

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|November 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced YOLOv7 model for detecting minor insulator defects in transmission line images. The improved model achieves higher accuracy in complex backgrounds, ensuring safer operations.

Keywords:
HorBlockSIoUYOLOv7attention mechanisminsulator-defect detection

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Detecting minor insulator defects in transmission lines is crucial for operational safety.
  • Complex backgrounds in transmission line images pose significant challenges for existing detection methods.

Purpose of the Study:

  • To develop an improved YOLOv7 model for accurate detection of insulators with minor defects.
  • To enhance the performance of object detection models in complex visual environments.

Main Methods:

  • K-means++ clustering for optimized anchor box generation.
  • Integration of Coordinate Attention (CoordAtt) and HorBlock modules for feature enhancement.
  • Application of SCYLLA-IoU (SIoU) and focal loss for improved convergence and sample balancing.
  • Optimization of non-maximum suppression (NMS) to reduce detection errors.

Main Results:

  • The enhanced YOLOv7 model achieved a mean average precision (mAP) of 93.8%.
  • Performance improvements of 7.6%, 3.7%, and 4% over Faster R-CNN, YOLOv7, and YOLOv5s, respectively.
  • Effective detection of small objects against complex backgrounds was demonstrated.

Conclusions:

  • The proposed improved YOLOv7 model significantly enhances the accuracy of detecting minor insulator defects.
  • The model offers a robust solution for ensuring the safe operation of transmission lines through improved visual inspection.