Automatic inspection of steel surfaces is one of the basic processes in steel production. Most of steel producers have replaced the traditional human-based inspection methods with these new automatic and machine-based Methods. In this paper, a new approach has been proposed for detection and categorization of cold-rolled surface defects. The proposed algorithm is mainly based on artificial MLP neural networks and uncomplicated computational indicators. Experimental results show that the proposed method could detect up to 93.3% of prevalent defects. In addition, this method is appropriate for real-time implementation because of its satisfactory execution time on a modern computer.
Navidpanah,M. and Amirfattahi,R. (2012). Steel Surface Defect Categorization Using Artificial Neural Networks and Uncomplicated Computational Indicators. Journal of Steel & Structure, 6(12), 59-68. doi: 10.22034/jss.2012.238838
MLA
Navidpanah,M. , and Amirfattahi,R. . "Steel Surface Defect Categorization Using Artificial Neural Networks and Uncomplicated Computational Indicators", Journal of Steel & Structure, 6, 12, 2012, 59-68. doi: 10.22034/jss.2012.238838
HARVARD
Navidpanah M., Amirfattahi R. (2012). 'Steel Surface Defect Categorization Using Artificial Neural Networks and Uncomplicated Computational Indicators', Journal of Steel & Structure, 6(12), pp. 59-68. doi: 10.22034/jss.2012.238838
CHICAGO
M. Navidpanah and R. Amirfattahi, "Steel Surface Defect Categorization Using Artificial Neural Networks and Uncomplicated Computational Indicators," Journal of Steel & Structure, 6 12 (2012): 59-68, doi: 10.22034/jss.2012.238838
VANCOUVER
Navidpanah M., Amirfattahi R. Steel Surface Defect Categorization Using Artificial Neural Networks and Uncomplicated Computational Indicators. Journal of Steel & Structure, 2012; 6(12): 59-68. doi: 10.22034/jss.2012.238838
