Automated shape-based pavement crack detection approach

    Teng Wang Affiliation
    ; Kasthurirangan Gopalakrishnan Affiliation
    ; Omar Smadi Affiliation
    ; Arun K. Somani Affiliation


Pavements are critical man-made infrastructure systems that undergo repeated traffic and environmental loadings. Consequently, they deteriorate with time and manifest certain distresses. To ensure long-lasting performance and appropriate level of service, they need to be preserved and maintained. Highway agencies routinely employ semiautomated and automated image-based methods for network-level pavement-cracking data collection, and there are different types of pavement-cracking data collected by highway agencies for reporting and management purposes. We design a shape-based crack detection approach for pavement health monitoring, which takes advantage of spatial distribution of potential cracks. To achieve this, we first extract Potential Crack Components (PCrCs) from pavement images. Next, we employ polynomial curve to fit all pixels within these components. Finally, we define a Shape Metric (SM) to distinguish crack blocks from background. We experiment the shape-based crack detection approach on different datasets, and compare detection results with an alternate method that is based on Support Vector Machines (SVM) classifier. Experimental results prove that our approach has the capability to produce higher detections and fewer false alarms. Additional research is needed to improve the robustness and accuracy of the developed approach in the presence of anomalies and other surface irregularities.

Keyword : pavement crack detection, local filtering, polynomial curve fitting, pavement imaging, pavement condition monitoring

How to Cite
Wang, T., Gopalakrishnan, K., Smadi, O., & Somani, A. K. (2018). Automated shape-based pavement crack detection approach. Transport, 33(3), 598-608.
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Jul 10, 2018
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AASHTO PP 68:2014. Standard Practice for Collecting Images of Pavement Surfaces for Distress Detection.

AASHTO PP 67:2016. Practice for Quantifying Cracks in Asphalt Pavement Surfaces from Collected Pavement Images Utilizing Automated Methods.

Adarkwa, O. A.; Attoh-Okine, N. 2013. Pavement crack classification based on tensor factorization, Construction and Building Materials 48: 853–857.

Ahuja, N.; Barkan, C. 2007. Machine Vision for Railroad Equipment Undercarriage Inspection Using Multi-Spectral Imaging. Final Report for High-Speed Rail IDEA Project 49. Transportation Research Board, Washington, DC, US. 37 p. Available from Internet:

ASCE. 2017. 2017 Infrastructure Report Card: Roads. American Society of Civil Engineers (ASCE). 5 p. Available from Internet:

Chen, L.; Zhang, J.; Ji, R. 2009. Identification algorithm for asphalt pavement cracks based on support vector machine, in International Conference on Transportation Engineering 2009, 25–27 July 2009, Chengdu, China, 3572–3577.

Elkrry, A. M.; Anderson, N. 2014. Non-Invasive Imaging and Assessment of Pavements. Report No NUTC R329. National University Transportation Center at Missouri University of Science and Technology, Rolla, MO, US. 56 p.

Flintsch, G.; McGhee, K. K. 2009. Quality Management of Pavement Condition Data Collection: a Synthesis of Highway Practice. NCHRP Synthesis 401. Transportation Research Board, Washington, DC. 153 p.

Gopalakrishnan, K.; Khaitan, S. K.; Choudhary, A.; Agrawal, A. 2017. Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection, Construction and Building Materials 157: 322–330.

Gopalakrishnan, K. 2016. Advanced Pavement Health Monitoring and Management: Video Lectures.

Laurent, J.; Lefebvre, D.; Samson, E. 2008. Development of a new 3D transverse laser profiling system for the automatic measurement of road cracks, in 6th Symposium on Pavement Surface Characteristics: Proceedings, 20–23 October 2008, Portorož, Slovenia, 1–17.

Liang, S.; Sun, B. 2010. Using wavelet technology for pavement crack detection, in ICLEM 2010: Logistics For Sustained Economic Development: Infrastructure, Information, Integration, 8–10 October 2010, Chengdu, China, 2479–2484.

Marques, A. G. 2012. Automatic Road Pavement Crack Detection Using SVM: Dissertation to Obtain a Master Degree in Electrical and Computer Engineering. Technical University of Lisbon, Portugal. 67 p.

McNeil, S.; Humplick, F. 1991. Evaluation of errors in automated pavement‐distress data acquisition, Journal of Transportation Engineering 117(2): 224–241.

McGhee, K. H. 2004. Automated Pavement Distress Collection Techniques: a Synthesis of Highway Practice. NCHRP Synthesis 334. Transportation Research Board, Washington, DC, US. 84 p.

McQueen, J.; Timm, D. 2005. Statistical analysis of automated versus manual pavement condition surveys, Transportation Research Record: Journal of the Transportation Research Board 1940: 55–62.

Miller, J. S.; Bellinger, W. Y. 2003. Distress Identification Manual for the Long-Term Pavement Performance Program. FHWA-RD-03-031. 4th Revised Edition. US Department of Transportation, Federal Highway Administration, Washington, DC, US. 169 p. Available from Internet:

Neubauer, S.; Todsen, M. 2014. Acoustic Imaging System Evaluation. RiP Project 35756. Iowa Department of Transportation, Ames, Iowa, US.

Oliveria, H.; Correia, P. L. 2010. Automatic crack detection on road imagery using anisotropic diffusion and region linkage, in 2010 18th European Signal Processing Conference, 23–27 August 2010, Aalborg, Denmark, 274–278.

Oliveria, H.; Correia, P. L. 2009. Automatic road crack segmentation using entropy and image dynamic thresholding, in 2009 17th European Signal Processing Conference, 24–28 August 2009, Glasgow, UK, 622–626.

Peng, B.; Wang, K. C. P.; Chen, C. 2014. Automatic crack detection by multi-seeding fusion on 1 mm resolution 3D pavement images, in T&DI Congress 2014: Planes, Trains, and Automobiles, 8–11 June 2014, Orlando, Florida, US, 543–552.

Pierce, L. M.; McGovern, G. 2014. Implementation of the AASHTO Mechanistic-Empirical Pavement Design Guide and Software: a Synthesis of Highway Practice. NCHRP Synthesis 457. Transportation Research Board, Washington, DC, US. 80 p.

Roque, R. 2014. Application of Imaging Techniques to Evaluate Polishing Characteristics of Aggregates. RiP Project 36638. University of Florida, Gainesville, FL, US.

Some, L. 2016. Automatic Image-Based Road Crack Detection Methods: MSc Thesis. School of Architecture and the Built Environment, KTH Royal Institute of Technology, Stockholm, Sweden. 61 p.

Sun, B.-C.; Qiu, Y.-J. 2007. Automatic identification of pavement cracks using mathematic morphology, in International Conference on Transportation Engineering 2007, 22–24 July 2007, Chengdu, China, 1783–1788.

Vaitkus, A.; Čygas, D.; Motiejūnas, A.; Pakalnis, P.; Miškinis, D. 2016. Improvement of road pavement maintenance models and technologies, The Baltic Journal of Road and Bridge Engineering 11(3): 242–249.

Vavrik, W.; Evans, L.; Sargand, S.; Stefanski, J. 2013. PCR Evaluation – Considering Transition from Manual to Semi-Automated Pavement Distress Collection and Analysis. Ohio Department of Transportation, Columbus, OH, US. 237 p. Available from Internet:

Wang, K. 2016. Safety Evaluation of Pavement Surface Characteristics with 1 mm 3D Laser Imaging. RiP Project 37465. Oklahoma State University, Stillwater, OK, US.

Wang, K. C. P.; Li, J. Q. 2014. 3D Laser Imaging for ODOT Interstate Network at True 1-mm Resolution. Final Report FHWA-OK-14-14. Oklahoma Department of Transportation Materials and Research Division, Oklahoma, OK, US. 151 p. Available from Internet:

Wang, K. C. P.; Smadi, O. 2011. Automated Imaging Technologies for Pavement Distress Surveys. Transportation Research Circular E-C156. Transportation Research Board, Washington, DC, US. 22 p. Available from Internet:

Wei, H.; Abrishami, H.; Xiao, X.; Karteek, A. 2015. Adaptive Video-based Vehicle Classification Technique for Monitoring Traffic. Report No FHWA/OH-2015/20. Ohio Department of Transportation, OH, US. 66 p. Available from Internet:

Zhang, J.; Sha, A.; Sun, Z. Y.; Gao, H. G. 2009. Pavement crack automatic recognition based on wiener filtering, in ICCTP 2009: Critical Issues in Transportation Systems Planning, Development, and Management, 5–9 August 2009, Harbin, China, 1–7.

Zhang, L.; Yang, F.; Zhang, Y. D.; Zhu, Y. J. 2016. Road crack detection using deep convolutional neural network, in 2016 IEEE International Conference on Image Processing (ICIP), 25–28 September 2016, Phoenix, AZ, US, 3708–3712.

Zimmerman, K.; Smadi, O.; Senesi, C.; Kebede, N.; Shah, K.; Rose, D. 2013. Increasing Consistency in the Highway Performance Monitoring System for Pavement Reporting. Final Report. NCHRP Project 20-24(82). 44 p. Available from Internet:

Zou, Q.; Cao, Y.; Li, Q.; Mao, Q.; Wang, S. 2012. CrackTree: Automatic crack detection from pavement images, Pattern Recognition Letters 33(3): 227–238.