Detection of steel defect using the image processing algorithms
Sharifzadeh, M., Amirfattahi, R., Sadri, S., Alirezaee, S., Ahmadi, M. (2008). Detection of steel defect using the image processing algorithms. EKB Journal Management System, 6(6th International Conference on Electrical Engineering ICEENG 2008), 1-7. doi: 10.21608/iceeng.2008.34372
M. Sharifzadeh; R. Amirfattahi; S. Sadri; S. Alirezaee; M. Ahmadi. "Detection of steel defect using the image processing algorithms". EKB Journal Management System, 6, 6th International Conference on Electrical Engineering ICEENG 2008, 2008, 1-7. doi: 10.21608/iceeng.2008.34372
Sharifzadeh, M., Amirfattahi, R., Sadri, S., Alirezaee, S., Ahmadi, M. (2008). 'Detection of steel defect using the image processing algorithms', EKB Journal Management System, 6(6th International Conference on Electrical Engineering ICEENG 2008), pp. 1-7. doi: 10.21608/iceeng.2008.34372
Sharifzadeh, M., Amirfattahi, R., Sadri, S., Alirezaee, S., Ahmadi, M. Detection of steel defect using the image processing algorithms. EKB Journal Management System, 2008; 6(6th International Conference on Electrical Engineering ICEENG 2008): 1-7. doi: 10.21608/iceeng.2008.34372

Detection of steel defect using the image processing algorithms

The International Conference on Electrical Engineering
Article 105, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Page 1-7  XML PDF (348.54 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2008.34372
View on SCiNiTO View on SCiNiTO
Authors
M. Sharifzadeh1; R. Amirfattahi1; S. Sadri1; S. Alirezaee2; M. Ahmadi3
1Electrical and Computer Department., Isfahan University of Technology, Isfahan, Iran.
2Engineering Department., University of Zanjan, Zanjan, Iran.
3Electrical & Computer Eng. Dept., University of Windsor, 401 Sunset Ave, Windsor, Ontario, Canada.
Abstract
Abstract:
In this paper, detection and classification of steel surface defects is investigated. Image
processing algorithms are applied for detecting four popular kind of steel defects, i.e.,
hole, scratch, Coil break and rust. The results show that the applied algorithms have a
good performance on steel defect detection. Numerical results indicate that the
implemented image processing algorithms have 88.4%, 78%, 90.4%, 90.3 % accuracy
respectively on the hole, scratch, Coil break and rust defect.
Keywords
Automatic inspection; Image processing; Steel defect
Statistics
Article View: 347
PDF Download: 1,644