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Bearing defect detection based on the improved YOLOv5 algorithm.

Kangning Li1, Peigang Jiao1, Jiaming Ding1

  • 1School of Construction Machinery, Shandong Jiaotong University, Jinan, Shandong Province, China.

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Summary
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This study introduces an improved YOLOv5 model for efficient bearing defect detection. The enhanced method accurately identifies small and overlapping defects, improving upon existing techniques for practical applications.

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Area of Science:

  • Mechanical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Manual bearing inspection is inefficient and prone to missing small or overlapping defects.
  • Existing object detection methods struggle with the complexities of bearing defect identification.

Purpose of the Study:

  • To develop an improved YOLOv5 object detection method for enhanced bearing defect detection.
  • To address the challenges of detecting small, overlapping, and multiple coexisting defects in bearings.

Main Methods:

  • Replaced YOLOv5's C3 modules with Res2Block modules for superior feature extraction.
  • Integrated a Bidirectional Feature Pyramid Network (BiFPN) for improved feature fusion.
  • Conducted ablation and comparative experiments against existing defect detection algorithms.

Main Results:

  • The improved YOLOv5 algorithm demonstrated high mean Average Precision (mAP) and accuracy.
  • Achieved precise identification of small target defects on bearings in complex scenarios.
  • Outperformed existing methods, including those specifically designed for small target detection.

Conclusions:

  • The enhanced YOLOv5 model offers a more effective solution for automated bearing defect detection.
  • Provides a valuable reference for practical industrial applications requiring precise defect identification.
  • Significantly improves detection capabilities in scenarios with challenging defect characteristics.