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A texture-based method for classifying cracked concrete surfaces from digital images using neural networks

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4 Author(s)
Z. Chen ; Department of Civil and Mechanical Engineering, School of Computing and Engineering, University of Missouri-Kansas City, 64110 USA ; R. R. Derakhshani ; C. Halmen ; J. T. Kevern

Using a dSLR camera with macro LED light, 11 samples containing light and moderately cracked concrete surfaces were imaged with perpendicular and angled illumination. Textural features from gray level co-occurrence matrix statistics were derived, from which 3-6 salient features were selected. Cross validation accuracies were as high as 94% using neural network classifiers, indicating the feasibility of rapid, automatic concrete cracking assessment using COTS digital imaging.

Published in:

Neural Networks (IJCNN), The 2011 International Joint Conference on

Date of Conference:

July 31 2011-Aug. 5 2011