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Enhanced adaptive filter-bank-based automated pavement crack detection and segmentation system

[+] Author Affiliations
Clyde A. Lettsome

Georgia Institute of Technology School of Civil and Environmental Engineering, Atlanta, Georgia 30332

Yi-Chang Tsai

Georgia Institute of Technology School of Civil and Environmental Engineering, Atlanta, Georgia 30332

Vivek Kaul

Georgia Institute of Technology School of Civil and Environmental Engineering, Atlanta, Georgia 30332

J. Electron. Imaging. 21(4), 043008 (Oct 15, 2012). doi:10.1117/1.JEI.21.4.043008
History: Received December 9, 2010; Revised August 9, 2012; Accepted September 7, 2012
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Abstract.  We incorporate, evaluate, and assess the feasibility of using filter banks in automated pavement distress systems from a system level. We integrate a novel filter-bank-based distress segmentation method, which, unlike previously researched methods, does not depend on highpass data. In addition, we incorporate the standard Said Pearlman set partitioning in hierarchical trees compression coder into the automated pavement distress system, which is a first in this area of research. A third contribution of the research is a statistical detection algorithm that assists in overall system performance. Preliminary testing using images provided by the Georgia Department of Transportation demonstrate the promise of the proposed method.

© 2012 SPIE and IS&T

Citation

Clyde A. Lettsome ; Yi-Chang Tsai and Vivek Kaul
"Enhanced adaptive filter-bank-based automated pavement crack detection and segmentation system", J. Electron. Imaging. 21(4), 043008 (Oct 15, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.4.043008


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