Line-Structured Light Rut Detection of Asphalt Pavement with Pavement Markings Interference under Strong Light
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 148, Issue 2
Abstract
The construction of highways has been well-developed worldwide. Meanwhile, the heavy traffic flow brings huge pressure on highway maintenance. Pavement rutting is one of the major pavement distresses and its detection has been a research hot spot in pavement engineering. Despite the fruitful research outcomes, most of them were based on ideal circumstances and focused on how to improve the processing procedure to reduce the detection error of usual rutting measurement. Whereas some particular interference, such as pavement markings under strong light, usually occurs during the detection, and remains undetected. Pavement markings affect the accurate extraction of pavement transverse profiles and increase the detection error of rut depth. To fill this gap, this study proposed a line-structured rut detection method to improve the detecting accuracy of rut depth. The global gray scale correction algorithm and feature-based fusion segmentation algorithm are mainly used to eliminate pavement markings of the background. The centerline-based midpoint thinning algorithm, least square based curve correction method, and envelope model are applied to calculate the rut depth, and are applicable for different forms of rutting distress. A total of 600 of images collected from urban roads were classified into four categories and used to verify the proposed rut detection method with pavement markings interference under strong light. The experimental results indicate that the average relative detection error is 10.07% and the average proportion of detection accuracy is 87.65%. Meanwhile, the evaluation accuracy of the pavement condition assessed by the rut depth index reaches 83.87%. This manifests that the proposed method can not only deal with the rutting detection with interference, but can also apply to the situation without interference. Thus, the method could be used to evaluate pavement condition and offer a reliable data source for pavement maintenance. The work in the paper offers a vital reference for pavement rut detection methods worldwide.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all of the data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. (Data on the collected models and equipment hardware.)
Acknowledgments
This study is funded by the National Key Research and Development Program of China (No. 2017YFC0803902). The authors gratefully acknowledge their financial support.
References
Arezoumand, S., A. Mahmoudzadeh, A. Golroo, and B. Mojaradi. 2021. “Automatic pavement rutting measurement by fusing a high speed-shot camera and a linear laser.” Constr. Build. Mater. 283 (May): 122668. https://doi.org/10.1016/j.conbuildmat.2021.122668.
Arshadi, A. 2013. “Importance of asphalt binder properties on rut resistance of asphalt mixture.” M.S. thesis, Dept. of Civil and Environmental Engineering, College of Engineering, Univ. of Wisconsin–Madison.
Attoh-Okine, N., and O. Adarkwa. 2013. Pavement condition surveys–Overview of current practices. Newark, DE: Univ. of Delaware.
Bennett, C. R. 2002. Establishing reference transverse profiles for rut depth measurements in New Zealand. Motueka, New Zealand: Data Collection Limited.
Bennett, C. R., and H. Q. Wang. 2003. Harmonising automated rut depth measurements. Sacramento, CA: California Transit Association.
Ceravolo, R., G. Miraglia, C. Surace, and L. Zanotti Fragonara. 2016. “A computational methodology for assessing the time-dependent structural performance of electric road infrastructures.” Comput.-Aided Civ. Infrastruct. Eng. 31 (9): 701–716. https://doi.org/10.1111/mice.12199.
Cerni, G., F. Cardone, A. Virgili, and S. Camilli. 2012. “Characterisation of permanent deformation behaviour of unbound granular materials under repeated triaxial loading.” Constr. Build. Mater. 28 (1): 79–87. https://doi.org/10.1016/j.conbuildmat.2011.07.066.
Chen, X. Y., and B. Lei. 2013. “Fast and robust measurement of pavement ruts.” [In Chinese.] J. Appl. Sci. 31 (5): 512–518. https://doi.org/10.3969/j.issn.0255-8297.2013.05.011.
China Ministry of Transport. 2018. Highway performance assessment standards. [In Chinese.]. Beijing: China Ministry of Transport.
China Ministry of Transport. 2019. Field test methods of highway subgrade and pavement. [In Chinese.]. Beijing: China Ministry of Transport.
Chin, R. T., H.-K. Wan, D. L. Stover, and R. D. Iverson. 1987. “A one-pass thinning algorithm and its parallel implementation.” Comput. Vision Graph. Image Process. 40 (1): 30–40. https://doi.org/10.1016/0734-189X(87)90054-5.
Doyle, W. 1962. “Operations useful for similarity-invariant pattern recognition.” J. ACM 9 (2): 259–267. https://doi.org/10.1145/321119.321123.
Firoozi Yeganeh, S., A. Golroo, and M. R. Jahanshahi. 2019. “Automated rutting measurement using an inexpensive RGB-D sensor fusion approach.” J. Transp. Eng. Part B Pavements 145 (1): 04018061. https://doi.org/10.1061/JPEODX.0000095.
Guo, R., S. Ye, J. Zhang, X. Liu, and Y. Ji. 2018. “A novel laser stripe center extraction method for pavement rut detection.” Int. Symp. Optoelectron. Technol. Appl. 10846 (Dec): 1084629. https://doi.org/10.1117/12.2505409.
Han, C., H. Guo, and C. Wang. 2002. “Edge preservation evaluation of digital speckle filters.” In Vol. 4 of Proc., IEEE Int. Geoscience and Remote Sensing Symp. New York: IEEE. https://doi.org/10.1109/IGARSS.2002.1026581.
Hong, Z., Q. Ai, and K. Chen. 2018. “Line-laser-based visual measurement for pavement 3D rut depth in driving state.” Electron. Lett. 54 (20): 1172–1174. https://doi.org/10.1049/el.2018.5437.
Horé, A., and D. Ziou. 2010. “Image quality metrics: PSNR vs. SSIM.” In Proc., 2010 20th Int. Conf. on Pattern Recognition, 2366–2369. New York: IEEE.
Jia, Y., S. Wang, J. Peng, Y. Gao, M. Liu, and W. Zhou. 2021. “Characterization of rutting on asphalt pavement in terms of transverse profile shapes based on LTPP data.” Constr. Build. Mater. 269 (Feb): 121230. https://doi.org/10.1016/j.conbuildmat.2020.121230.
Ju, X. H., L. Bian, and L. Xu. 2016. “Rut detection model with two line lasers and error correcting technology.” In Proc., Int. Conf. on Mechanics and Mechanical Engineering 2015 (MME2015), 1039–1045. Singapore: World Scientific.
Kage, T., and K. Matsushima. 2015. “Method of rut detection using lasers and in-vehicle stereo camera.” In Proc., 2015 Int. Conf. on Intelligent Informatics and Biomedical Sciences (ICIIBMS), 48–53. New York: IEEE. https://doi.org/10.1109/ICIIBMS.2015.7439479.
Li, L., L. Sun, S. Tan, and G. Ning. 2013. “Simplified light center curve extraction method for pavement rut detection using line-structured light.” In Proc., ICTE 2013: Safety, Speediness, Intelligence, Low-Carbon, Innovation, 1031–1038. Reston, VA: ASCE.
Li, Q. Q., B. Lei, Q. Z. Mao, and Z. N. Fu. 2010. “A fast method for pavement ruts measuring with laser triangulation.” [In Chinese.] J. Wuhan Univ. 35 (3): 302–307. https://doi.org/10.13203/j.whugis2010.03.029.
Liu, Y., J. Song, W. Yuan, H. Xue, and S. Li. 2020. “Sub-pixel center extraction method for line structured light stripe.” IOP Conf. Ser.: Mater. Sci. Eng. 768 (7): 072045. https://doi.org/10.1088/1757-899X/768/7/072045.
Ma, J., X. M. Zhao, S. H. He, Y. Zhao, H. S. Song, and J. Zhang. 2017. “Review of pavement detection technology.” [In Chinese.] J. Traffic Transp. Eng. 17 (5): 121–137.
Ma, R. G., A. M. Sha, and H. X. Song. 2007. “Error analysis in road rut measurement with multi-sensors.” [In Chinese.] J. Chang’an Univ. 27 (3): 34–36. https://doi.org/10.3321/j.issn:1671-8879.2007.03.008.
Mahmoudzadeh, A., S. F. Yeganeh, M. Farazi, and A. Golroo. 2020a. “Inexpensive RGB-D sensors performance measurement in pavement data collection—Part I.” IEEE Sens. J. 20 (20): 11992–11996. https://doi.org/10.1109/JSEN.2020.2978395.
Mahmoudzadeh, A., S. F. Yeganeh, M. Farazi, and A. Golroo. 2020b. “Inexpensive RGB-D sensors performance measurement in pavement data collection—Part II.” IEEE Sens. J. 20 (20): 11997–12004. https://doi.org/10.1109/JSEN.2020.2985305.
Mallela, R., and H. Wang. 2006. “Harmonising automated rut depth measurements: Stage 2.” Land Transport New Zealand Research Report 277. Accessed August 13, 2020. https://www.nzta.govt.nz/assets/resources/research/reports /277/docs/277.pdf.
Meng, N., Y. Q. Zhou, H. J. Yang, J. Qu, and X. L. Chen. 2009. “Improved fairing algorithm with optimized modification amount of cloud data in reverse engineering.” In Proc., Int. Conf. on Measuring Technology & Mechatronics Automation, 230–233. New York: IEEE. https://doi.org/10.1109/ICMTMA.2009.570.
Perez, A., and R. C. Gonzalez. 1987. “An iterative thresholding algorithm for image segmentation.” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-9 (6): 742–751. https://doi.org/10.1109/TPAMI.1987.4767981.
Qiao, Y., A. Dawson, A. Huvstig, and L. Korkiala-Tanttu. 2015. “Calculating rutting of some thin flexible pavements from repeated load triaxial test data.” Int. J. Pavement Eng. 16 (6): 467–476. https://doi.org/10.1080/10298436.2014.943127.
Schnebele, E., B. F. Tanyu, G. Cervone, and N. Waters. 2015. “Review of remote sensing methodologies for pavement management and assessment.” Eur. Transport Res. Rev. 7 (2): 1–19. https://doi.org/10.1007/s12544-015-0156-6.
Tarefder, R. A., M. Zaman, and K. Hobson. 2003. “A laboratory and statistical evaluation of factors affecting rutting.” Int. J. Pavement Eng. 4 (1): 59–68. https://doi.org/10.1080/10298430310001593263.
Tsai, J. Y.-C., Z.-H. Wang, and F. Li. 2015. “Assessment of rut depth measurement accuracy of point-based rut bar systems using emerging 3D line laser imaging technology.” J. Mar. Sci. Technol. 23 (3): 322–330. https://doi.org/10.6119/JMST-014-0327-1.
Wang, C.-Y., Q.-C. Tan, and R.-H. Guo. 2014. “Design and optimization of a linear laser beam.” Lasers Eng. 27 (5): 373–381.
Wang, D., A. C. Falchetto, M. Goeke, W. Wang, T. Li, and M. P. Wistuba. 2017. “Influence of computation algorithm on the accuracy of rut depth measurement.” J. Traffic Transp. Eng. 4 (2): 156–164. https://doi.org/10.1016/j.jtte.2017.03.001.
Wei, Y., H. Hong, X. Zhang, and J. Yu. 2009. “A new method for automatic detection of rut feature based on road laser images.” In Vol. 7494 of Proc., MIPPR 2009: Multispectral Image Acquisition and Processing, 74941M. Bellingham, WA: International Society for Optics and Photonics.
Wu, F., J. Li, H. Yang, P. Yang, J. Liu, X. Liang, and B. Yuan. 2019. “Research of pavement topography based on NURBS reconstruction for 3D structured light.” Optik 194 (Oct): 163074. https://doi.org/10.1016/j.ijleo.2019.163074.
Yang, L., Y. Hu, and H. Zhang. 2020. “Comparative study on asphalt pavement rut based on analytical models and test data.” Int. J. Pavement Eng. 21 (6): 781–795. https://doi.org/10.1080/10298436.2018.1511781.
Yao, Y. S., W. Q. Zhao, and H. X. Song. 2007. “Discussion on detection technology of asphalt pavement rutting.” [In Chinese.] Road Mach. Constr. Mechanization 24 (6): 8–10. https://doi.org/10.3969/j.issn.1000-033X.2007.06.003.
Zhang, L., Y. Zhang, and B. Chen. 2020. “Improving the extracting precision of stripe center for structured light measurement.” Optik 207 (Apr): 163816. https://doi.org/10.1016/j.ijleo.2019.163816.
Zhang, T. Y., and C. Y. Suen. 1984. “A fast parallel algorithm for thinning digital patterns.” Commun. ACM 27 (3): 236–239. https://doi.org/10.1145/357994.358023.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Nov 21, 2020
Accepted: Nov 17, 2021
Published online: Feb 3, 2022
Published in print: Jun 1, 2022
Discussion open until: Jul 3, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Xiaodong Li, Chuanxi Luo, Shaohuai Wang, Xiang Long, Yan Wang, Jian Li, Mu He, Study of Low-Content Epoxy Asphalt Mixture Applied to the Road, Buildings, 10.3390/buildings14020443, 14, 2, (443), (2024).
- Hong Lang, Jinsong Qian, Ye Yuan, Jiang Chen, Yingying Xing, Aidi Wang, Automatic Pixel-Level Segmentation of Multiple Pavement Distresses and Surface Design Features with PDSNet II, Journal of Computing in Civil Engineering, 10.1061/JCCEE5.CPENG-5894, 38, 6, (2024).
- Ali Fares, Tarek Zayed, Sherif Abdelkhalek, Nour Faris, Muhammad Muddassir, Rutting measurement in asphalt pavements, Automation in Construction, 10.1016/j.autcon.2024.105358, 161, (105358), (2024).
- Hong Lang, Yuan Peng, Zheng Zou, Shengxue Zhu, Zhen Chen, Meng Zhang, Automated Bridgehead Settlement Detection on the Non-Staggered-Step Structures Based on Settlement Point Ratio Model, Applied Sciences, 10.3390/app13137888, 13, 13, (7888), (2023).