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Surface Defect Detection and Size Measurement of Bearings Based on Machine Vision
Research Article | Published: 09 February 2026
ICCK Transactions on Intelligent Cyber-Physical Systems Volume 1, Issue 1, 2025: 10-25
Volume 1, Issue 1, ICCK Transactions on Intelligent Cyber-Physical Systems
Volume 1, Issue 1, 2025
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ICCK Transactions on Intelligent Cyber-Physical Systems, Volume 1, Issue 1, 2025: 10-25

Free to Read | Research Article | 09 February 2026
Surface Defect Detection and Size Measurement of Bearings Based on Machine Vision
by
Liwei Lin Liwei Lin 1 *
1 School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
* Corresponding Author: Liwei Lin, [email protected]
DOI: 10.62762/TICPS.2025.819879
ARK: ark:/57805/ticps.2025.819879
Received: 05 December 2025, Accepted: 16 December 2025, Published: 09 February 2026  
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Abstract
Aiming at the problems of low efficiency, strong subjectivity in traditional bearing surface defect detection and insufficient dimensional measurement accuracy, this paper proposes an integrated detection scheme SimAM-YOLO that combines the improved YOLOv5 algorithm with size measurement technology. Based on YOLOv5, the scheme replaces the original C3 module with the C2F network structure and embeds the SimAM attention mechanism to enhance the model's ability to extract defect features. Combined with OpenCV, it realizes the real-time measurement of the key dimension of bearing radius and constructs a visual system for bearing size measurement. Experimental results show that the improved model achieves an average detection precision of 86.03%, a recall rate of 78%, and an mAP-0.5 of 82.17% for bearing defects such as cracks, scratches, and grooves, which are 14.8%, 8.77%, and 9.2% higher than the original YOLOv5 respectively. The dimensional measurement error is controlled within ±0.000061mm, meeting the requirements of industrial detection. The system has high automation and strong real-time performance, can adapt to the detection needs of bearings of different specifications, and provides an efficient and reliable technical support for bearing quality control.
Graphical Abstract
Surface Defect Detection and Size Measurement of Bearings Based on Machine Vision
Keywords
bearing detection
YOLOv5
C2F network
SimAM attention mechanism
machine vision
size measurement
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
AI Use Statement
The authors declare that no generative AI was used in the preparation of this manuscript.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Lin, L. (2026). Surface Defect Detection and Size Measurement of Bearings Based on Machine Vision. ICCK Transactions on Intelligent Cyber-Physical Systems, 1(1), 10–25. https://doi.org/10.62762/TICPS.2025.819879
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TY  - JOUR
AU  - Lin, Liwei
PY  - 2026
DA  - 2026/02/09
TI  - Surface Defect Detection and Size Measurement of Bearings Based on Machine Vision
JO  - ICCK Transactions on Intelligent Cyber-Physical Systems
T2  - ICCK Transactions on Intelligent Cyber-Physical Systems
JF  - ICCK Transactions on Intelligent Cyber-Physical Systems
VL  - 1
IS  - 1
SP  - 10
EP  - 25
DO  - 10.62762/TICPS.2025.819879
UR  - https://www.icck.org/article/abs/TICPS.2025.819879
KW  - bearing detection
KW  - YOLOv5
KW  - C2F network
KW  - SimAM attention mechanism
KW  - machine vision
KW  - size measurement
AB  - Aiming at the problems of low efficiency, strong subjectivity in traditional bearing surface defect detection and insufficient dimensional measurement accuracy, this paper proposes an integrated detection scheme SimAM-YOLO that combines the improved YOLOv5 algorithm with size measurement technology. Based on YOLOv5, the scheme replaces the original C3 module with the C2F network structure and embeds the SimAM attention mechanism to enhance the model's ability to extract defect features. Combined with OpenCV, it realizes the real-time measurement of the key dimension of bearing radius and constructs a visual system for bearing size measurement. Experimental results show that the improved model achieves an average detection precision of 86.03%, a recall rate of 78%, and an mAP-0.5 of 82.17% for bearing defects such as cracks, scratches, and grooves, which are 14.8%, 8.77%, and 9.2% higher than the original YOLOv5 respectively. The dimensional measurement error is controlled within ±0.000061mm, meeting the requirements of industrial detection. The system has high automation and strong real-time performance, can adapt to the detection needs of bearings of different specifications, and provides an efficient and reliable technical support for bearing quality control.
SN  - pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Lin2026Surface,
  author = {Liwei Lin},
  title = {Surface Defect Detection and Size Measurement of Bearings Based on Machine Vision},
  journal = {ICCK Transactions on Intelligent Cyber-Physical Systems},
  year = {2026},
  volume = {1},
  number = {1},
  pages = {10-25},
  doi = {10.62762/TICPS.2025.819879},
  url = {https://www.icck.org/article/abs/TICPS.2025.819879},
  abstract = {Aiming at the problems of low efficiency, strong subjectivity in traditional bearing surface defect detection and insufficient dimensional measurement accuracy, this paper proposes an integrated detection scheme SimAM-YOLO that combines the improved YOLOv5 algorithm with size measurement technology. Based on YOLOv5, the scheme replaces the original C3 module with the C2F network structure and embeds the SimAM attention mechanism to enhance the model's ability to extract defect features. Combined with OpenCV, it realizes the real-time measurement of the key dimension of bearing radius and constructs a visual system for bearing size measurement. Experimental results show that the improved model achieves an average detection precision of 86.03\%, a recall rate of 78\%, and an mAP-0.5 of 82.17\% for bearing defects such as cracks, scratches, and grooves, which are 14.8\%, 8.77\%, and 9.2\% higher than the original YOLOv5 respectively. The dimensional measurement error is controlled within ±0.000061mm, meeting the requirements of industrial detection. The system has high automation and strong real-time performance, can adapt to the detection needs of bearings of different specifications, and provides an efficient and reliable technical support for bearing quality control.},
  keywords = {bearing detection, YOLOv5, C2F network, SimAM attention mechanism, machine vision, size measurement},
  issn = {pending},
  publisher = {Institute of Central Computation and Knowledge}
}
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