Open Access
Issue
MATEC Web Conf.
Volume 271, 2019
2019 Tran-SET Annual Conference
Article Number 08004
Number of page(s) 5
Section Pavements
DOI https://doi.org/10.1051/matecconf/201927108004
Published online 09 April 2019
  1. Yun, H-B., Mokhtari. S., and Wu, L. (2015). Crack recognition and segmentation using morphological image-processing techniques for flexible pavements. Transportation Research Record: Journal of the Transportation Research Board 2523: 115–124. [CrossRef] [Google Scholar]
  2. Chambon, S., and Moliard, J-M., (2011). Automatic road pavement assessment with image processing: review and comparison. International Journal of Geophysics. [Google Scholar]
  3. Svasdisant, T., Schorch, M., and Baladi, G.Y. (2002). Mechanistic analysis of top-down cracks in asphalt pavements. Paper presented in Transportation Research Board Annual Meeting, January, 13–17. [Google Scholar]
  4. Myers, L.A., Roque, R., and Ruth, B.E., (1998). Mechanisms of surface-initiated longitudinal wheel path cracks in high-type bituminous pavements. Journal of the Association of Asphalt Paving Technologists, 67, 401–432. [Google Scholar]
  5. Uhlmeyer, J.S., Willoughby, K., Pierce, L.M., and Mahoney, J.P. (2000). Top-down cracking in Washington state asphalt concrete wearing course. In Transportation Research Record: Journal of Transportation Research Board, No. 1730, TRB, National Research Council, Washington, DC, 110–116. [CrossRef] [Google Scholar]
  6. Maser, K.R. (1987). Computational techniques for automating visual inspection. Massachusetts Institute of Technology, Report, Cambridge, MA. [Google Scholar]
  7. Georgopoulos, A., Loizos, A., and Flouda, A. (1995). Digital image processing as a tool for pavement distress evaluation. ISPRS Journal of Photogrammetry and Remote Sensing, 50(1), pp. 23–33. [CrossRef] [Google Scholar]
  8. Xu, B. and Huang, Y.R. (2003). Development of an automatic pavement surface distress inspection system. No. FHWA/TX-05/7-4975-1. [Google Scholar]
  9. Ying, L., and Salari, E. (2009). Beamlet transform based technique for pavement image processing and classification. In Electro/Information Technology, 2009. eit’09. IEEE International Conference, 141–145. [Google Scholar]
  10. Wu, L., Mokhtari, S., Nazef, A., Nam, B., and Yun, H-B. (2014). Improvement of crack-detection accuracy using a novel crack defragmentation technique in image-based road assessment. Journal of Computing in Civil Engineering, 30(1): 04014118. [CrossRef] [Google Scholar]
  11. Mokhtari, S., Wu, L., and Yun, H-B. (2017). Statistical selection and interpretation of imagery features for computer vision-based pavement crack–detection systems. Journal of Performance of Constructed Facilities, 31(5): 04017054. [CrossRef] [Google Scholar]
  12. Talab, A.M.A., Huang, Z., Xi, F., and HaiMing, L. (2016). Detection crack in image using Otsu method and multiple filtering in image processing techniques. Optik-International Journal for Light and Electron Optics 127(3): 1030–1033. [CrossRef] [Google Scholar]
  13. Hoang, N-D., Nguyen, Q-L., and Bui, D.T., (2018). Image processing–based classification of asphalt pavement cracks using support vector machine optimized by artificial bee colony. Journal of Computing in Civil Engineering, 32(5): 04018037. [CrossRef] [Google Scholar]
  14. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62–66. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.