Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

An Automatic Image Processing Algorithm Based on Crack Pixel Density for Pavement Crack Detection and Classification

  • Original Research Paper
  • Published:
International Journal of Pavement Research and Technology Aims and scope Submit manuscript

Abstract

Nowadays, there is a massive necessity to develop fully automated and efficient distress assessment systems to evaluate pavement conditions with the minimum cost. Due to having complex training processes, most of the current supervised learning-based practices in this area are not suitable for smaller, local-level projects with limited resources. This paper aims to develop an automatic crack assessment method to detect and classify cracks from 2-D and 3-D pavement images. A tile-based image processing method was proposed to apply a localized thresholding technique on each tile and detect the cracked ones (tiles containing cracks) based on crack pixels’ spatial distribution. For longitudinal and transverse cracking, a curve is then fitted on the cracked tiles to connect them. Next, cracks are classified, and their lengths are measured based on the orientation axes and length of the crack curves. This method is not limited to the pavement texture type, and it is cost-efficient as it takes less than 20 s per image for a commodity computer to generate results. The method was tested on 130 images of Portland Cement Concrete (PCC) and Asphalt Concrete (AC) surfaces; test results were found to be promising (Precision = 0.89, Recall = 0.83, F1 score = 0.86, and Crack length measurement accuracy = 80%).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Availability of data and material

The data for this study is available and can be accessed through: https://drive.google.com/file/d/1U8Ie-H-JYZUU5lSN0IXEiNf_S64g4kv8/view?usp=sharing.

Code availability

The custom code for this study is written in Matlab and will be provided upon request.

References

  1. Lekshmipathy, J., Samuel, N. M., & Velayudhan, S. (2020). Vibration vs vision: best approach for automated pavement distress detection. International Journal of Pavement Research and Technology. https://doi.org/10.1007/s42947-020-0302-y.

    Article  Google Scholar 

  2. Arezoumand, S., Mahmoudzadeh, A., Golroo, A., & Mojaradi, B. (2021). Automatic pavement rutting measurement by fusing a high speed-shot camera and a linear laser. Construction and Building Materials, 283, 122668.

    Article  Google Scholar 

  3. Kazemian, M., Sedighi, S., Ramezanianpour, A. A., Bahman‐Zadeh, F., & Ramezanianpour, A. M. (2021). Effects of cyclic carbonation and chloride ingress on durability properties of mortars containing Trass and Pumice natural pozzolans. Structural Concrete.

  4. Barri, K., Jahangiri, B., Davami, O., Buttlar, W. G., & Alavi, A. H. (2020). Smartphone-based molecular sensing for advanced characterization of asphalt concrete materials. Measurement, 151, 107212.

    Article  Google Scholar 

  5. Adlinge, S. S., & Gupta, A. K. (2013). Pavement deterioration and its causes. International Journal of Innovative Research and Development, 2(4), 437–450.

    Google Scholar 

  6. Aboudi, J. (1987). Stiffness reduction of cracked solids. Engineering Fracture Mechanics, 26(5), 637–650.

    Article  Google Scholar 

  7. Hamedi, G. H., Sahraei, A., & Esmaeeli, M. R. (2018). Investigate the effect of using polymeric anti-stripping additives on moisture damage of hot mix asphalt. European Journal of Environmental and Civil Engineering, 25, 1–14.

    Google Scholar 

  8. Daghighi, A. (2020). Full-Scale Field Implementation of Internally Cured Concrete Pavement Data Analysis for Iowa Pavement Systems. Creative Components. 638. https://lib.dr.iastate.edu/creativecomponents/638.

  9. Malakooti, A., Theh, W. S., Sadati, S. S., Ceylan, H., Kim, S., Mina, M., & Taylor, P. C. (2020). Design and full-scale implementation of the largest operational electrically conductive concrete heated pavement system. Construction and Building Materials, 255, 119229.

    Article  Google Scholar 

  10. Farhadmanesh, M., Cross, C., Mashhadi, A. H., Rashidi, A., & Wempen, J. (2021). Highway Asset and Pavement Condition Management using Mobile Photogrammetry. Transportation Research Record, 03611981211001855.

  11. Majidifard, H., Adu-Gyamfi, Y., & Buttlar, W. G. (2020). Deep machine learning approach to develop a new asphalt pavement condition index. Construction and building materials, 247, 118513.

    Article  Google Scholar 

  12. Justo-Silva, R., & Ferreira, A. (2019). Pavement maintenance considering traffic accident costs. International Journal of Pavement Research and Technology., 12, 562–573. https://doi.org/10.1007/s42947-019-0067-3.

    Article  Google Scholar 

  13. Cross, C., Farhadmanesh, M., & Rashidi, A. (2020). Assessing Close-Range Photogrammetry as an Alternative for LiDAR Technology at UDOT Divisions (No. UT-20.18). Utah. Dept. of Transportation. Division of Research.

  14. Khajehvand, M., Rassafi, A. A., & Mirbaha, B. (2021). Modeling traffic noise level near at-grade junctions: Roundabouts, T and cross intersections. Transportation Research Part D: Transport and Environment, 93, 102752.

  15. Dhital, D., & Lee, J. R. (2012). A fully non-contact ultrasonic propagation imaging system for closed surface crack evaluation. Experimental Mechanics, 52(8), 1111–1122.

    Article  Google Scholar 

  16. Hosseini, S., & Smadi, O. (2020). How prediction accuracy can affect the decision-making process in pavement management system. Infrastructures. https://doi.org/10.31224/osf.io/t28ue.

    Article  Google Scholar 

  17. Mahmoudzadeh, A., Golroo, A., Jahanshahi, M. R., & Firoozi Yeganeh, S. (2019). Estimating pavement roughness by fusing color and depth data obtained from an inexpensive RGB-D sensor. Sensors, 19(7), 1655.

    Article  Google Scholar 

  18. Abukhalil, Y. B. (2019). Cross asset resource allocation framework for pavement and bridges in Iowa. Graduate Theses and Dissertations. 16951. https://lib.dr.iastate.edu/etd/16951.

  19. Zahedian, S., Sadabadi, K. F., & Nohekhan, A. (2021). Localization of autonomous vehicles: Proof of concept for a computer vision approach. arXiv preprint arXiv:2104.02785.

  20. Aboah, A., Shoman, M., Mandal, V., Davami, S., Adu-Gyamfi, Y., & Sharma, A. (2021). A vision-based system for traffic anomaly detection using deep learning and decision trees. arXiv preprint arXiv:2104.06856.

  21. Zahedian, S., Sekuła, P., Nohekhan, A., & Vander Laan, Z. (2020). Estimating hourly traffic volumes using artificial neural network with additional inputs from automatic traffic recorders. Transportation Research Record, 2674(3), 272–282.

    Article  Google Scholar 

  22. Nejad, F. M., Motekhases, F. Z., Zakeri, H., & Mehrabi, A. (2015). An image processing approach to asphalt concrete feature extraction. Journal of Industrial and Intelligent Information, 3(1).

  23. Tsai, Y.-C.J., & Chatterjee, A. (2018). Pothole detection and classification using 3D technology and watershed method. Journal of Computing in Civil Engineering, 32(2), 04017078.

    Article  Google Scholar 

  24. Safaei, N., & Smadi, O., (2019). A tile-based image processing method based on crack density for detection and classification of pavement cracks. In: 98th Annual Meeting of Transportation Research Board. TRID. https://trid.trb.org/view/1595322.

  25. Hosseini, S. A., Ahmad, A., & Smadi, O. (2020). Use of deep learning to study modeling deterioration of pavements a case study in Iowa. Infrastructures, 5(11), 95.

    Article  Google Scholar 

  26. Federal Highway Administration Research and Technology, (2003). Distress Identification Manual for The LTPP. Fhwa-Rd-03-031, (May).

  27. Kargah-Ostadi, N., Nazef, A., Daleiden, J., & Zhou, Y. (2017). Evaluation framework for automated pavement distress identification and quantification applications. Transportation Research Record: Journal of the Transportation Research Board, 2639, 46–54.

    Article  Google Scholar 

  28. Albitres, C. M. C., Smith, R. E., & Pendleton, O. J. (2007). Comparison of automated pavement distress data collection procedures for local agencies in San Francisco Bay Area, California. Transportation Research Record: Journal of the Transportation Research Board, 1990(1), 119–126.

    Article  Google Scholar 

  29. Chambon, S., & Moliard, J.-M. (2011). Automatic road pavement assessment with image processing: review and comparison. International Journal of Geophysics, 2011, 1–20.

    Article  Google Scholar 

  30. Safaei, N. (2019). Pixel and region-based image processing algorithms for detection and classification of pavement cracks. Graduate Theses and Dissertations. 17555. https://lib.dr.iastate.edu/etd/17555.

  31. Safaei, N., Smadi, O., Safaei, B., & Masoud, A. (2021). Efficient road crack detection based on an adaptive pixel-level segmentation algorithm. Transportation Research Record, 03611981211002203.

  32. Shi, Y., Cui, L., Qi, Z., Meng, F., & Chen, Z. (2016). Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems, 17(12), 3434–3445.

    Article  Google Scholar 

  33. Oliveira, H. J. M. (2013). Crack detection and characterization in flexible road pavements using digital image processing. Ph.D. Dissertation, Universidade de Lisboa-Instituto Superior Técnico.

  34. Roli, F. (1996). Measure of texture anisotropy for crack detection on textured surfaces. Electronics Letters, 32(14), 1274–1275.

    Article  Google Scholar 

  35. Nguyen, T. S., Avila, M., & Begot, S. (2009). Automatic detection and classification of defect on road pavement using anisotropy measure. 17th European Signal Processing Conference, 2009, pp. 617–621.

  36. Koch, C., Doycheva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2016). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 30(2), 208–210.

    Article  Google Scholar 

  37. Tanaka, N., & Uematsu, K. (1998). A crack detection method in road surface images using morphology. MVA, 98, 17–19.

    Google Scholar 

  38. Dung, C. V. (2019). Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction, 99, 52–58.

    Article  Google Scholar 

  39. Cord, A., & Chambon, S. (2011). Automatic road defect detection by textural pattern recognition based on AdaBoost. Computer-Aided Civil and Infrastructure Engineering, 27(4), 244–259.

    Article  Google Scholar 

  40. Karballaeezadeh, N., Zaremotekhases, F., Shamshirband, S., Mosavi, A., Nabipour, N., Csiba, P., & Várkonyi-Kóczy, A. R. (2020). Intelligent road inspection with advanced machine learning; hybrid prediction models for smart mobility and transportation maintenance systems. Energies, 13(7), 1718.

    Article  Google Scholar 

  41. Liu, Z., Azmin, S., Ohashi, T., & Ejima, T. (2002). A tunnel crack detection and classification systems based on image processing. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 4664, 145–152.

    Google Scholar 

  42. Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision., 1(4), 321–331.

    Article  Google Scholar 

  43. Kaul, V., Yezzi, A., & Tsai, Y. (2012). Detecting curves with unknown endpoints and arbitrary topology using minimal paths. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(10), 1952–1965.

    Article  Google Scholar 

  44. Amhaz, R., Chambon, S., Idier, J., & Baltazart, V. (2014). A new minimal path selection algorithm for automatic crack detection on pavement images. IEEE International Conference on Image Processing (ICIP), 2014, 788–792.

    Google Scholar 

  45. Amhaz, R., Chambon, S., Idier, J., & Baltazart, V. (2015). Automatic crack detection on 2D pavement images: An algorithm based on minimal path selection. IEEE Transactions on Intelligent Transportation Systems.

  46. Paar, H. K., & Sidla, O. (2006). Optical crack following on tunnel surfaces. Proceedings of the SPIE International Society for Optical Engineering 6382.

  47. Yamaguchi, T., & Hashimoto, S. (2009). Fast crack detection method for large-size concrete surface images using percolation-based image processing. Machine Vision and Applications, 21(5), 797–809.

    Article  Google Scholar 

  48. Chaiyasarn, K. (2011). Damage detection and monitoring for tunnel inspection based on computer vision. Ph.D. Dissertation, Christ’s College, University of Cambridge, UK.

  49. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495.

    Article  Google Scholar 

  50. Ghosh, R., & Smadi, O. (2021). Automated Detection and Classification of Pavement Distresses Using 3D Pavement Surface Images and Deep Learning. Transportation Research Record, 03611981211007481.

  51. Hoang, N.-D. (2019). Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression. Automation in Construction, 105, 102843.

    Article  Google Scholar 

  52. Hadjidemetriou, G. M., & Christodoulou, S. E. (2019). Vision-and entropy-based detection of distressed areas for integrated pavement condition assessment. Journal of Computing in Civil Engineering, 33(3), 04019020.

    Article  Google Scholar 

  53. Yousaf, M. H., et al. (2018). Visual analysis of asphalt pavement for detection and localization of potholes. Advanced Engineering Informatics, 38, 527–537.

    Article  Google Scholar 

  54. Cheng, H. D., Chen, J.-R., Glazier, C., & Hu, Y. G. (1999). Novel approach to pavement cracking detection based on fuzzy set theory. Journal of Computing in Civil Engineering, 13(4), 270–280.

    Article  Google Scholar 

  55. Ying, L., & Salari, E. (2010). Beamlet transform-based technique for pavement crack detection and classification. Computer-Aided Civil and Infrastructure Engineering, 25(8), 572–580.

    Article  Google Scholar 

  56. Tsai, Y.-C., Kaul, V., & Mersereau, R. M. (2010). Critical assessment of pavement distress segmentation methods. Journal of Transportation Engineering, 136(1), 11–19.

    Article  Google Scholar 

  57. Lee, B. J., & David Lee, H. (2004). Position-invariant neural network for digital pavement crack analysis. Computer-Aided Civil and Infrastructure Engineering, 19(2), 105–118.

    Article  Google Scholar 

  58. Xu, B., & Huang, Y. (2005). Automatic inspection of pavement cracking distress. Applications of Digital Image Processing XXVIII.

  59. Oliveira, H., & Lobato, P. (2009). Supervised crack detection and classification in images of road pavement flexible surfaces. Recent Advances in Signal Processing.

  60. Oliveira, H., & Correia, P. L. (2013). Automatic road crack detection and characterization. IEEE Transactions on Intelligent Transportation Systems, 14(1), 155–168.

    Article  Google Scholar 

  61. Wang, T., Gopalakrishnan, K., Somani, A., Smadi, O., & Ceylan, H. (2016). Machine-Vision-Based Roadway Health Monitoring and Assessment: Development of a Shape-Based Pavement-Crack-Detection Approach. Final Report. Institute for Transportation, Iowa State University.

  62. Katakam, N. (2009). Pavement Crack Detection System Through Localized Thresholding. Master’s Thesis. The University of Toledo, The University of Toledo Digital Respiratory, pp. 36–36.

  63. Peng, L. (2004). Adaptive Median Filtering. Seminar Report, Machine Vision 140.429 Digital Image Processing.

  64. Image Filtering. Patrice’s Lectures. Department of Computer Science, University of Auckland, New Zealand. www.cs.auckland.ac.nz/courses/compsci373s1c/PatricesLectures/Image%20Filtering_2up.pdf. Accessed 10 Mar 2018.

  65. Detect Lines Using the Random Transform. Documentation in MathWorks. www.mathworks.com/help/images/detect-lines-using-the-radon-transform.html. Accessed 29 July 2018.

  66. Babashamsi, P., Yusoff, N. I. M., Ceylan, H., Nor, N. G. M., & Jenatabadi, H. S. (2016). Evaluation of pavement life cycle cost analysis: Review and analysis. International Journal of Pavement Research and Technology, 9(4), 241–254.

    Article  Google Scholar 

  67. Sasaki, Y. (2007) ‘The Truth of the F-measure’, Teach Tutor mater, 1–5.

  68. Derczynski, L. (2016). Complementarity, F-score, and NLP Evaluation. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16). 2016.

  69. Oliveira, H., & Correia, P. L. (2014). CrackIT—An image processing toolbox for crack detection and characterization. In: 2014 IEEE International Conference on Image Processing (ICIP).

  70. Zou, Q., et al. (2012). CrackTree: automatic crack detection from pavement images. Pattern Recognition Letters, 33(3), 227–238.

    Article  Google Scholar 

Download references

Funding

The research was conducted at the Institute for Transportation of Iowa State University and did not receive external funding directly.

Author information

Author notes

  1. Nima Safaei

    Present address: Tippie College of Business, University of Iowa, Iowa City, IA, USA

Authors and Affiliations

  1. Institute for Transportation, Iowa State University, Ames, IA, USA

    Nima Safaei & Omar Smadi

  2. Tippie College of Business, University of Iowa, Iowa City, IA, USA

    Arezoo Masoud

  3. Civil and Environmental Engineering Department, Michigan State University, East Lansing, MI, USA

    Babak Safaei

Authors
  1. Nima Safaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Omar Smadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arezoo Masoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Babak Safaei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nima Safaei.

Ethics declarations

Conflict of interest

There is no conflict of interest associated with this study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Safaei, N., Smadi, O., Masoud, A. et al. An Automatic Image Processing Algorithm Based on Crack Pixel Density for Pavement Crack Detection and Classification. Int. J. Pavement Res. Technol. 15, 159–172 (2022). https://doi.org/10.1007/s42947-021-00006-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42947-021-00006-4

Keywords

Search

Navigation