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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
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.
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.
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