Statistical Selection and Interpretation of Imagery Features for Computer Vision-Based Pavement Crack–Detection Systems
Publication: Journal of Performance of Constructed Facilities
Volume 31, Issue 5
Abstract
This paper aims to explore the statistics of pavement cracks using computer-vision techniques. The knowledge discovered by mining the crack data can be used to avoid subjective crack feature selection for vision-based pavement evaluation systems. Moreover, the statistical evaluation of crack features can be used as fundamental data to justify pavement rehabilitation policies. For this purpose, surface images of flexible pavements in different deterioration stages were analyzed using a novel image-processing technique. Seven imagery features of the detected objects including area, length, width, orientation, intensity, texture roughness, and wheel-path position, which are commonly used in pavement applications, were extracted and analyzed. A comprehensive statistical analysis was performed using filter feature subset selection (FSS) methods to rank crack features based on their significance (relevance and redundancy) for the pavement crack–detection problem. Based on the results, length, intensity, and wheel-path position were identified as the optimal feature-set for the vision-based system. Statistical characteristics of crack features were also analyzed to extract accurate quantitative measures for pavement conditions assessment. The statistical characterization identified longitudinal cracks within the wheel path as the dominant defect of the validation data set. Such information can help management agencies make informed pavement maintenance policies.
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Acknowledgments
The authors thank International Cybernetics, Largo, Florida, for their technical communication and support in providing the pavement images used in this paper. The content of this paper reflects the views of the authors, who are solely responsible for the facts, data accuracy, opinions, findings, and conclusions presented herein. The contents do not necessarily reflect the official views or policies of the Florida Department of Transportation. This paper does not constitute a standard, specification, or regulation. In addition, the above-listed agency assumes no liability for the content of this paper or use thereof. This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20131520100720).
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Published In
Journal of Performance of Constructed Facilities
Volume 31 • Issue 5 • October 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Apr 15, 2016
Accepted: Oct 31, 2016
Published online: Mar 31, 2017
Discussion open until: Aug 31, 2017
Published in print: Oct 1, 2017
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