IMPACT OF IMAGE RESOLUTION ON PAVEMENT DISTRESS DETECTION USING PICUCHA METHODOLOGY
DOI:
https://doi.org/10.14311/CEJ.2016.04.0024Keywords:
Pavement Engineering, Automatic Distress Detection, PICUCHA MethodAbstract
An accurate and regular survey of the road surface distresses is a key factor for pavement
rehabilitation design and management, allowing public managers to maximize the value of the
continuously limited budgets for road improvements and maintenance. Manual pavement distress
surveys are labor-intensive, expensive and unsafe for highly-trafficked highways. Over the years,
automated surveys using various hardware devices have been developed and improved for
pavement field data collection to solve the problems associated with manual surveys. However,
the reliable distress detection software and the data analysis remain challenging. This study
focused on the analysis of a newly-developed pavement distress classification algorithm, called the
PICture Unsupervised Classification with Human Analysis (PICUCHA) method, particularly the
impact of image resolutions on its classification accuracy. The results show that a non-linear
relationship exists between the classification accuracy and the image resolution, suggesting that
images with a resolution around 1.24 mm/pixel may provide the optimal classification accuracy
when using the PICUCHA method. The findings of this study can help to improve more effective
uses of the specialize software for pavement distress classification, to support decision makers to
choose cameras according to their budgets and desired survey accuracy, and to evaluate how
existing cameras will perform if used with PICUCHA.
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References
Koutsopoulos, H.N., Downey, A.B. 1993. Primitive-Based Classification of Pavement Cracking
Images. J. Transp. Eng., 119 (3), p. 402–418.
Lin, J., Liu, Y. 2010. Potholes detection based on SVM in the pavement distress image.
Proc. - 9th Int. Symp. Distrib. Comput. Appl. to Business, Eng. Sci. DCABES 2010, p. 544–547.
Koch, C., Brilakis, I. 2011. Pothole detection in asphalt pavement images. Adv. Eng.
Informatics, 25 (3), p. 507–515.
Koch, C. et al. 2013. Automated Pothole Distress Assessment Using Asphalt Pavement
Video Data. J. Comput. Civ. Eng., 27 (4), p. 370–378.
Sun, L., Gu, W. 2011. Pavement Condition Assessment Using Fuzzy Logic Theory and
Analytic Hierarchy Process. J. Transp. Eng., 137 (9), p. 648–655.
Ouyang, A. et al. 2010. Surface Distresses Detection of Pavement Based on Digital Image
Processing. In: IFIP Advances in Information and Communication Technology. Nanchang, China,
p. 368–375.
Tsao, S. et al. 1994. Image-Based Expert-System Approach to Distress Detection on CRC
Pavement. J. Transp. Eng., 120 (1), p. 52–64.
Ho, T. et al. 2010. Pavement distress image recognition using k-means and classification
algorithms. In: Computing in Civil and Building Engineering, Proceedings of the International
Conference (Editor: W. Tizani). Nottingham, UK: Nottingham University Press, p. 73–78.
Rababaah, H. et al. 2005. Asphalt Pavement Crack Classification: A Comparison of GA,
MLP, and SOM. In: Genetic and Evolutionary Computation Conference (GECCO’2005) (Editor: F.
Rothlauf). Washington, D.C., USA.
Nguyen, T.S. et al. 2010. Pavement Cracking Detection Using an Anisotropy Measurement.
In: 11ème IASTED International Conference on Computer Graphics and Imaging (CGIM).
Innsbruck, Austria.
¨[11] Puan, O.C. et al. 2007. Automated Pavement Imaging Program (APIP) for Pavement
Cracks Classification and Quantification. Malaysian J. Civ. Eng., 19 (1), p. 1–16.
Tsai, Y. et al. 2010. Critical Assessment of Pavement Distress Segmentation Methods. J.
Transp. Eng., 136 (1), p. 11–19.
Salini, R. et al. 2015. Application of artificial intelligence for optimization in pavement
management. Int. J. Eng. Technol. Innov., 5 (3), p. 189–197.
Salini, R. 2010. INTELLIPave: Uma Abordagem Baseada em Inteligência Artificial para a
Modelagem de Pavimentos Asfálticos. University of Minho.
Salini, R. et al. 2009. INTELLIPave - Considering aside failure criteria and unknown
variables in evolutionary intelligence based models for asphalt pavement. In: Proceedings - 23rd
European Conference on Modelling and Simulation, ECMS 2009. Madrid, Spain, p. 624–629.
Salini, R. 2004. Salini Method 2004. Comput. Mech. Proc. Sixth World Congr. Comput.
Mech. Conjunction with Second Asian-Pacific Congr. Comput. Mech., 2, p. 148–155.
Keys, R.G. 1981. Cubic Convolution Interpolation for Digital Image Processing. Trans.
Acoust. Speech, Signal Process., ASSP-29 (6), p. 1153–1160.
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