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GRP-YOLOv5: An Improved Bearing Defect Detection Algorithm Based on YOLOv5.

Yue Zhao1, Bolun Chen1,2, Bushi Liu1

  • 1Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary

This study introduces GRP-YOLOv5, an enhanced algorithm for detecting bearing defects in chemical transmission equipment. The improved model achieves high accuracy but requires further optimization for real-time applications.

Keywords:
C2f structurePConv convolutionYOLOv5bearing defect detectiongamma transformation

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Materials Science

Background:

  • Bearing defects in chemical transmission equipment cause significant production inefficiencies.
  • Current detection methods may struggle with the visual similarity between defects and normal regions.

Purpose of the Study:

  • To develop an improved bearing defect detection algorithm for enhanced accuracy and reliability.
  • To address limitations in existing defect detection methodologies.

Main Methods:

  • An improved bearing defect detection algorithm based on YOLOv5 (GRP-YOLOv5) was proposed.
  • Image preprocessing involved gamma transformation to enhance contrast and grayscale.
  • Feature extraction utilized ResC2Net for improved detail and semantic information capture.
  • PConv convolution was integrated for feature fusion to increase network depth.

Main Results:

  • The GRP-YOLOv5 model achieved a mean Average Precision (mAP@0.5) of 93.5% and mAP@0.5:0.95 of 52.7%.
  • The model demonstrated excellent accuracy in bearing defect detection compared to other experimental models.
  • The model size was optimized to 25 MB, but Frames Per Second (FPS) performance was noted as a limitation.

Conclusions:

  • The GRP-YOLOv5 model offers a promising solution for accurate bearing defect detection.
  • Further research is needed to optimize the model's processing speed for real-time applications.