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

Author

Chen, Z. ; Derakhshani, R.R. ; Halmen, C. ; Kevern, J.T.

Author_Institution

Dept. of Civil & Mech. Eng., Univ. of Missouri-Kansas City, Kansas City, MO, USA

fYear

2011

fDate

July 31 2011-Aug. 5 2011

Firstpage

2632

Lastpage

2637

Abstract

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.

Keywords

concrete; crack detection; image texture; matrix algebra; mechanical engineering computing; neural nets; statistical analysis; COTS digital imaging; angled illumination; cracked concrete surface; dSLR camera; digital image; gray level cooccurrence matrix statistics; macro LED light; neural network; perpendicular illumination; textural feature; texture-based method; Artificial neural networks; Concrete; Feature extraction; Lighting; Surface cracks; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

San Jose, CA

ISSN

2161-4393

Print_ISBN

978-1-4244-9635-8

Type

conf

DOI

10.1109/IJCNN.2011.6033562

Filename

6033562