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2012 (Vol. 4, Issue: 24)
Article Information:

Pavement Crack Classifiers: A Comparative Study

S. Siddharth, P.K. Ramakrishnan, G. Krishnamurthy, B. Santhi
Corresponding Author:  S. Siddharth 

Key words:  Gray Level Co-occurrence Matrix (GLCM) , linear classifier, neural networks, Support Vector Machine (SVM), , ,
Vol. 4 , (24): 5434-5437
Submitted Accepted Published
March 18, 2012 April 14, 2012 December 15, 2012

Non Destructive Testing (NDT) is an analysis technique used to inspect metal sheets and components without harming the product. NDT do not cause any change after inspection; this technique saves money and time in product evaluation, research and troubleshooting. In this study the objective is to perform NDT using soft computing techniques. Digital images are taken; Gray Level Co-occurrence Matrix (GLCM) extracts features from these images. Extracted features are then fed into the classifiers which classifies them into images with and without cracks. Three major classifiers: Neural networks, Support Vector Machine (SVM) and Linear classifiers are taken for the classification purpose. Performances of these classifiers are assessed and the best classifier for the given data is chosen.
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  Cite this Reference:
S. Siddharth, P.K. Ramakrishnan, G. Krishnamurthy, B. Santhi, 2012. Pavement Crack Classifiers: A Comparative Study.  Research Journal of Applied Sciences, Engineering and Technology, 4(24): 5434-5437.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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