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Analysis of pavement condition data employing Principal Component Analysis and sensor fusion techniques

by Rajan, Krithika

Abstract (Summary)
This thesis presents an automated pavement crack detection and classification system via image processing and pattern recognition algorithms. Pavement crack

detection is important to the Departments of Transportation around the country as it

is directly related to maintenance of pavement quality. Manual inspection and

analysis of pavement distress is the prevalent method for monitoring pavement quality. However, inspecting miles of highway sections and analyzing each is a cumbersome

and time consuming process. Hence, there has been research into automating the system of crack detection. In this thesis, an automated crack detection and classification

algorithm is presented. The algorithm is built around the statistical tool of Principal Component Analysis (PCA). The application of PCA on images yields the primary features of cracks based on which, cracked images are distinguished from non-cracked ones.

The algorithm consists of three levels of classification: a) pixel-level b)

subimage (32 X 32 pixels) level and c) image level. Initially, at the lowermost level,

pixels are classified as cracked/non-cracked using adaptive thresholding. Then the

classified pixels are grouped into subimages, for reducing processing complexity. Following the grouping process, the classification of subimages is validated based on the

decision of a Bayes classifier. Finally, image level classification is performed based

on a subimage profile generated for the image. Following this stage, the cracks are

further classified as sealed/unsealed depending on the number of sealed and unsealed subimages. This classification is based on the Fourier transform of each subimage. The proposed algorithm detects cracks aligned in longitudinal as well as transverse directions with respect to the wheel path with high accuracy. The algorithm can also be extended to detect block cracks, which comprise of a pattern of cracks in both

alignments.

Bibliographical Information:

Advisor:

School:Kansas State University

School Location:USA - Kansas

Source Type:Master's Thesis

Keywords:principal component analysis pavement distress image processing pattern recognition engineering electronics and electrical 0544

ISBN:

Date of Publication:01/01/2008

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