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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29296


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5893
Enhanced Performance for Support Vector Machines as Multiclass Classifiers in Steel Surface Defect Detection
Abstract:
Steel surface defect detection is essentially one of pattern recognition problems. Support Vector Machines (SVMs) are known as one of the most proper classifiers in this application. In this paper, we introduce a more accurate classification method by using SVMs as our final classifier of the inspection system. In this scheme, multiclass classification task is performed based on the "one-againstone" method and different kernels are utilized for each pair of the classes in multiclass classification of the different defects. In the proposed system, a decision tree is employed in the first stage for two-class classification of the steel surfaces to "defect" and "non-defect", in order to decrease the time complexity. Based on the experimental results, generated from over one thousand images, the proposed multiclass classification scheme is more accurate than the conventional methods and the overall system yields a sufficient performance which can meet the requirements in steel manufacturing.
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References:

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[7] C. Shi, Y. Wang, H. Zhang, "Faults diagnosis based on support vector machines and particle swarm optimization", International Journal of Advancements in Computing Technology, Vol. 3, No. 5, pp. 70 ~ 79, 2011.
[8] J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.
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