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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.
Plos One
|October 28, 2024
Summary
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.
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.
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