Primitive‐Based Classification of Pavement Cracking Images
Publication: Journal of Transportation Engineering
Volume 119, Issue 3
Abstract
Collection and analysis of pavement distress data are receiving attention for their potential to improve the quality of information on pavement condition. We present an approach for the automated classificaton of asphalt pavement distresses recorded on video or photographic film. Based on a model that describes the statistical properties of pavement images, we develop algorithms for image enhancement, segmentation, and distress classification. Image enhancement is based on subtraction of an “average” background: segmentation assigns one of four possible values to pixels based on their likelihood of belonging to the object. The classification approach proceeds in two steps: in the first step, the presence of primitives (building blocks of the various distresses) is identified, and in the second step, classification of images to a distress type (using the results from the first step) takes place. The system addresses the following distress types: longitudinal, transverse, block, alligator cracking, and plain. Application of the models to a set of asphalt pavement images gave promising results.
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References
1.
Ben‐Akiva, M., and Lerman, S. R. (1985). Discrete choice analysis. Theory and application to travel demand. MIT Press, Cambridge, Mass.
2.
Butler, B. C. (1989). “Pavement surface distress segmentation using real time imaging.” Proc., Int. Conf. on Applications of Advanced Technologies in Transportation Engineering, C. Hendrickson and K. Sinha, eds., San Diego, Calif.
3.
Chan, K. B., Soetandio, S., and Lytton, R. L. (1989). “Distress identification by an automatic thresholding technique.” Proc., Int. Conf. on Application of Advanced Technologies in Transportation Engineering, C. Hendrickson and K. Sinha, eds., San Diego, Calif.
4.
Duda, R. O., and Hart, P. E. (1973). Pattern classification and scene analysis. John Wiley & Sons, New York, N.Y.
5.
Jain, A. K., Dubes, R. C., and Chen, C. C. (1987). “Bootstrap techniques for error estimation.” IEEE transactions on pattern recognition and machine intelligence, Vol. PAMI‐9, 628–633.
6.
Kittler, J., Illingworth, J., and Foglein, J. (1985). “Threshold based on a simple image statistic.” Computer vision, graphics, and image processing, 30(2), 125–147.
7.
Kittler, J., and Illingworth, J. (1986). “Minimum error thresholding.” Pattern recognition, 19(1), 41–47.
8.
Koutsopoulos, H. N., and El Sanhouri, I. M. (1991). “Methods and algorithms for automated analysis of pavement images.” Transp. Res. Record 1311, 103–111.
9.
Maser, K. R. (1987). “Computational techniques for automating visual inspection.” Working Paper, Department of Civil Engineering, MIT, Cambridge, Mass.
10.
McFadden, D. (1976). “A comment on discriminant analysis ‘versus’ logit analysis.” Annals of Economic and Social Measurement, 5, 511–523.
11.
McFadden, D. (1979). “Quantitative methods for analysing travel behavior of individuals: some recent developments.” Behavioural tavel modelling, D. A. Hensher and P. R. Stopher eds., Croom Helm, London, England.
12.
Paterson, W. D. O. (1987). Road deterioration and maintenance effects: models for planning and management. The Johns Hopkins Univ. Press, Baltimore, Md.
13.
Proc., automated pavement distress data collection equipment seminar. (1990). Iowa State Univ., Ames, Iowa.
14.
Randys, S. J., and Jain, A. K. (1991). “Small sample size effects in statistical pattern recognition: recommendations for practitioners.” IEEE Trans. on Pattern Anal. and Machine Intelligence, 13(3), 252–265.
15.
Ritchie, S. G., Kaseko, M., and Bavarian, B. (1991). “Development of an intelligent system for automated pavement evaluation.” Transp. Res. Record 1311, 112–119.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 119 • Issue 3 • May 1993
Pages: 402 - 418
Copyright
Copyright © 1993 American Society of Civil Engineers.
History
Received: Aug 19, 1992
Published online: May 1, 1993
Published in print: May 1993
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