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Abstract
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Article Information:
Pavement Crack Classifiers: A Comparative Study
S. Siddharth, P.K. Ramakrishnan, G. Krishnamurthy, B. Santhi
Corresponding Author: S. Siddharth
Submitted: March 18, 2012
Accepted: April 14, 2012
Published: December 15, 2012 |
Abstract:
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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.
Key words: Gray Level Co-occurrence Matrix (GLCM) , linear classifier, neural networks, Support Vector Machine (SVM), , ,
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Cite this Reference:
S. Siddharth, P.K. Ramakrishnan, G. Krishnamurthy, B. Santhi, . Pavement Crack Classifiers: A Comparative Study. Research Journal of Applied Sciences, Engineering and Technology, (24): 5434-5437.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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