Engineering with Computers

, Volume 35, Issue 2, pp 487–498 | Cite as

A novel method for asphalt pavement crack classification based on image processing and machine learning

  • Nhat-Duc HoangEmail author
  • Quoc-Lam Nguyen
Original Article


This study constructs an automatic model for detecting and classifying asphalt pavement crack. Image processing techniques including steerable filters, projective integral of image, and an enhanced method for image thresholding are employed for feature extraction. Different scenarios of feature selection have been attempted to create data sets from digital images. These data sets are then employed to train and verify the performance of machine learning algorithms including the support vector machine (SVM), the artificial neural network (ANN), and the random forest (RF). The feature set that consists of the properties derived from the projective integral and the properties of crack objects can deliver the most desirable outcome. Experimental results supported by the Wilcoxon signed-rank test show that SVM has achieved the highest classification accuracy rate (87.50%), followed by ANN (84.25%), and RF (70%). Accordingly, the proposed automatic approach can be helpful to assist transportation agencies and inspectors in the task of pavement condition assessment.


Asphalt pavement Crack classification Image processing Machine learning Steerable filters 



  1. 1.
    Li S, Cao Y, Cai H (2017) Automatic pavement-crack detection and segmentation based on steerable matched filtering and an active contour model. J Comput Civil Eng 31:04017045CrossRefGoogle Scholar
  2. 2.
    Cubero-Fernandez A, Rodriguez-Lozano FJ, Villatoro R, Olivares J, Palomares JM (2017) Efficient pavement crack detection and classification. EURASIP J Image Video Process 2017:39CrossRefGoogle Scholar
  3. 3.
    Liu P, Otto F, Wang D, Oeser M, Balck H (2017) Measurement and evaluation on deterioration of asphalt pavements by geophones. Measurement 109:223–232CrossRefGoogle Scholar
  4. 4.
    Gopalakrishnan K, Khaitan SK, Choudhary A, Agrawal A (2017) Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection. Constr Build Mater 157:322–330CrossRefGoogle Scholar
  5. 5.
    Gavilán M, Balcones D, Marcos O, Llorca DF, Sotelo MA, Parra I, Ocaña M, Aliseda P, Yarza P, Amírola A (2011) Adaptive road crack detection system by pavement classification. Sensors 11:9628CrossRefGoogle Scholar
  6. 6.
    Ouma YO, Hahn M (2016) Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform. Adv Eng Inform 30:481–499CrossRefGoogle Scholar
  7. 7.
    Radopoulou SC, Brilakis I (2017) Automated detection of multiple pavement defects. J Comput Civil Eng 31:04016057CrossRefGoogle Scholar
  8. 8.
    Koch C, Jog GM, Brilakis I (2013) Automated pothole distress assessment using asphalt pavement video data. J Comput Civil Eng 27:370–378CrossRefGoogle Scholar
  9. 9.
    Tsai Y-C, Jiang C, Huang Y (2014) Multiscale crack fundamental element model for real-world pavement crack classification. J Comput Civil Eng 28:04014012CrossRefGoogle Scholar
  10. 10.
    Guan H, Li J, Yu Y, Chapman M, Wang H, Wang C, Zhai R (2015) Iterative tensor voting for pavement crack extraction using mobile laser scanning data. IEEE Trans Geosci Remote Sens 53:1527–1537CrossRefGoogle Scholar
  11. 11.
    Kaseko MS, Ritchie SG (1993) A neural network-based methodology for pavement crack detection and classification. Transp Res Part C Emerg Technol 1:275–291CrossRefGoogle Scholar
  12. 12.
    Bishop C (2006) Pattern recognition and machine learning. Springer Science + Business Media, SingaporezbMATHGoogle Scholar
  13. 13.
    Cheng HD, Miyojim M (1998) Automatic pavement distress detection system. Inf Sci 108:219–240CrossRefGoogle Scholar
  14. 14.
    Cheng HD, Chen J-R, Glazier C, Hu YG (1999) Novel approach to pavement cracking detection based on fuzzy set theory. J Comput Civil Eng 13:270–280CrossRefGoogle Scholar
  15. 15.
    Lee H, Kim J (2005) Development of a crack type index, transportation research record. J Transp Res Board 1940:99–109CrossRefGoogle Scholar
  16. 16.
    Jayaraman S, Veerakumar T, Esakkirajan S (2009) Digital image processing. Tata McGraw Hill Education, New YorkGoogle Scholar
  17. 17.
    Oliveira H, Correia PL (2009) Automatic road crack segmentation using entropy and image dynamic thresholding. In: Proceeding of the 17th European Signal Processing Conference. Glasgow, Scotland, European Association for Signal, Speech, and Image Processing, pp 622–626.
  18. 18.
    Kamaliardakani M, Sun L, Ardakani MK (2016) Sealed-crack detection algorithm using heuristic thresholding approach. J Comput Civil Eng 30:04014110CrossRefGoogle Scholar
  19. 19.
    Sun L, Kamaliardakani M, Zhang Y (2016) Weighted neighborhood pixels segmentation method for automated detection of cracks on pavement surface images. J Comput Civil Eng 30:04015021CrossRefGoogle Scholar
  20. 20.
    Nishikawa T, Yoshida J, Sugiyama T, Fujino Y (2012) Concrete crack detection by multiple sequential image filtering. Comput Aided Civil Infrastruct Eng 27:29–47CrossRefGoogle Scholar
  21. 21.
    Zalama E, Gómez-García-Bermejo J, Medina R, Llamas J (2014) Road crack detection using visual features extracted by Gabor filters. Comput Aided Civil Infrastruct Eng 29:342–358CrossRefGoogle Scholar
  22. 22.
    Jiang C, Tsai YJ (2016) Enhanced crack segmentation algorithm using 3D pavement data. J Comput Civil Eng 30:04015050CrossRefGoogle Scholar
  23. 23.
    Amhaz R, Chambon S, Idier J, Baltazart V (2016) Automatic crack detection on two-dimensional pavement images: an algorithm based on minimal path selection. IEEE Trans Intell Transp Syst 17:2718–2729CrossRefGoogle Scholar
  24. 24.
    Zhou J, Huang PS, Chiang F-P (2003) Wavelet-aided pavement distress image processing. In: Proceedings of the SPIE, vol. 5207, The International Society for Optical Engineering, pp 728–739.
  25. 25.
    Ying L, Salari E (2010) Beamlet transform-based technique for pavement crack detection and classification. Comput Aided Civil Infrastruct Eng 25:572–580CrossRefGoogle Scholar
  26. 26.
    Sun L, Qian Z (2016) Multi-scale wavelet transform filtering of non-uniform pavement surface image background for automated pavement distress identification. Measurement 86:26–40CrossRefGoogle Scholar
  27. 27.
    Mokhtari S, Wu L, Yun H-B (2016) Comparison of supervised classification techniques for vision-based pavement crack detection. Transp Res Rec J Transp Res Board 2595:119–127CrossRefGoogle Scholar
  28. 28.
    Koch C, Georgieva K, Kasireddy V, Akinci B, Fieguth P (2015) A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv Eng Inform 29:196–210CrossRefGoogle Scholar
  29. 29.
    Zakeri H, Nejad FM, Fahimifar A (2017) Image based techniques for crack detection, classification and quantification in asphalt pavement: a review. Arch Comput Methods Eng 24:935–977CrossRefzbMATHGoogle Scholar
  30. 30.
    Coenen TBJ, Golroo A (2017) A review on automated pavement distress detection methods. Cogent Eng 4:1374822CrossRefGoogle Scholar
  31. 31.
    Adelson EH, Freeman WT (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13:891–906CrossRefGoogle Scholar
  32. 32.
    Perona P (1995) Deformable kernels for early vision. IEEE Trans Pattern Anal Mach Intell 17:488–499CrossRefGoogle Scholar
  33. 33.
    Freeman WT, Adelson EH (1990) Steerable filters for early vision, image analysis, and wavelet decomposition. In: Proceedings Third International Conference on Computer Vision, Osaka, Japan, IEEE, pp 406–415.
  34. 34.
    Jacob M, Unser M (2004) Design of steerable filters for feature detection using canny-like criteria. IEEE Trans Pattern Anal Mach Intell 26:1007–1019CrossRefGoogle Scholar
  35. 35.
    Braz J, Ranchordas A, Araújo H, Jorge J (2007) Advances in computer graphics and computer vision. Springer, Berlin, HeidelbergCrossRefzbMATHGoogle Scholar
  36. 36.
    Otsu N (1979) A threshold selection method from Gray-Level histograms. IEEE Trans Syst Man Cybern 9:62–66CrossRefGoogle Scholar
  37. 37.
    Talab AMA, Huang Z, Xi F, HaiMing L (2016) Detection crack in image using Otsu method and multiple filtering in image processing techniques. Opt Int J Light Electron Opt 127:1030–1033CrossRefGoogle Scholar
  38. 38.
    Hoang N-D (2018) Detection of surface crack in building structures using image processing technique with an improved Otsu method for image thresholding. Adv Civil Eng 2018:10Google Scholar
  39. 39.
    MathWorks (2016) Image Processing Toolbox User’s Guide, The MathWorks, Inc. (Date of last access: 06/01/2017)
  40. 40.
    Pham BT, Tien Bui D, Prakash I, Dholakia MB (2017) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149(Part 1):52–63CrossRefGoogle Scholar
  41. 41.
    Breiman L (2001) Random Forests. Mach Learn 45(1):5–32. CrossRefzbMATHGoogle Scholar
  42. 42.
    Tapas N, Lone T, Reddy D, Kuppili V (2017) Prediction of cardiac arrest recurrence using ensemble classifiers. Sādhanā 42:1135–1141Google Scholar
  43. 43.
    Pham BT, Khosravi K, Prakash I (2017) Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal Area. Environmental Processes, UttarakhandCrossRefGoogle Scholar
  44. 44.
    Vapnik VN (1998). Statistical Learning Theory. Wiley, New York. ISBN-10: 0471030031Google Scholar
  45. 45.
    Hamel LH (2009) Knowledge discovery with support vector machines. Wiley, HobokenCrossRefGoogle Scholar
  46. 46.
    Hadjidemetriou GM, Vela PA, Christodoulou SE (2018) Automated pavement patch detection and quantification using support vector machines. J Comput Civil Eng 32:04017073CrossRefGoogle Scholar
  47. 47.
    Duan K-B, Keerthi SS (2005) Which Is the Best Multiclass SVM Method? An empirical study. In: Multiple classifier systems: 6th International Workshop, MCS 2005, Seaside, CA, USA, June 13–15, 2005. Proceedings. Springer, Berlin, Heidelberg, pp 278–285Google Scholar
  48. 48.
    Heaton J (2008) Introduction to neural networks for C#. Heaton Research, Inc., WashingtonGoogle Scholar
  49. 49.
    Hoang N-D, Tien D, Bui (2018) Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study. Bull Eng Geol Env 9:1077–1097Google Scholar
  50. 50.
    Tien Bui D, Hoang ND (2017) A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods. Geosci Model Dev 10:3391–3409CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Civil Engineering, Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  2. 2.Faculty of Civil EngineeringDuy Tan UniversityDa NangVietnam

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