Hybrid ABC-ANN for pavement surface distress detection and classification

  • Anan Banharnsakun
Original Article


Pavement condition assessment plays an important role in the process of road maintenance and rehabilitation. However, the traditional road inspection procedure is mostly performed manually, which is labor-intensive and time-consuming. The development of automated detection and classification of distress on the pavement surface system is thus necessary. In this paper, a pavement surface distress detection and classification system using a hybrid between the artificial bee colony (ABC) algorithm and an artificial neural network (ANN), called “ABC-ANN”, is proposed. In the proposed method, first, after the pavement image is captured, it will be segmented into distressed and non-distressed regions based on a thresholding method. The optimal threshold value used for segmentation in this step will be obtained from the ABC algorithm. Next, the features, including the vertical distress measure, the horizontal distress measure, and the total number of distress pixels, are extracted from a distressed region and used to provide the input to the ANN. Finally, based on these input features, the ANN will be employed to classify an area of distress as a specific type of distress, which includes transversal crack, longitudinal crack, and pothole. The experimental results demonstrate that the proposed approach works well for pavement distress detection and can classify distress types in pavement images with reasonable accuracy. The accuracy obtained by the proposed ABC-ANN method achieves 20 % increase compared with existing algorithms.


Pavement surface distress detection and classification Image segmentation Optimal threshold selection Maximization of entropy energy Artificial bee colony Artificial neural network 



This work is partially supported by the Faculty of Engineering at Si Racha, Kasetsart University Si Racha Campus.


  1. 1.
    Wang KCP (2000) Designs and implementations of automated systems for pavement surface distress survey. Journal of Infrastruct Syst 6:24–32CrossRefGoogle Scholar
  2. 2.
    Chambon S, Moliard J-M (2011) Automatic road pavement assessment with image processing: review and comparison. Int J Geophys 989354:20Google Scholar
  3. 3.
    Jing L, Aiqin Z (2010) Pavement crack distress detection based on image analysis. In: Proceedings of the international conference on machine vision and human-machine interface, pp 576–579Google Scholar
  4. 4.
    Ouyang A, Luo C, Zhou C (2011) Surface distresses detection of pavement based on digital image processing. In: Li D, Liu Y, Chen Y (eds) CCTA 2010, Part IV, IFIP AICT 347. Springer, Berlin, pp 368–375Google Scholar
  5. 5.
    Hu Y, Zhao C-X, Wang H-N (2010) Automatic pavement crack detection using texture and shape descriptors. IETE Tech Rev 27:398–405CrossRefGoogle Scholar
  6. 6.
    Salari E, Ouyang D (2012) An image-based pavement distress detection and classification. In: Proceedings of the IEEE international conference on electro/information technology, pp 1–6Google Scholar
  7. 7.
    Wang S, Tang W (2011) Pavement crack segmentation algorithm based on local optimal threshold of cracks density distribution. In: Huang D-S et al (eds) ICIC 2011, LNCS 6838. Springer, Heidelberg, pp 298–302Google Scholar
  8. 8.
    Sun L, Kamaliardakani M, Zhang Y (2015) Weighted neighborhood pixels segmentation method for automated detection of cracks on pavement surface images. J Comput Civ Eng. doi: 10.1061/(ASCE)CP.1943-5487.0000488 Google Scholar
  9. 9.
    Koch C, Brilakis I (2011) Pothole detection in asphalt pavement images. Adv Eng Inform 25:507–515CrossRefGoogle Scholar
  10. 10.
    Zhou J, Huang PS, Chiang F-P (2006) Wavelet-based pavement distress detection and evaluation. Opt Eng 45:027007.1Google Scholar
  11. 11.
    Huang Y, Xu B (2006) Automatic inspection of pavement cracking distress. J Electron Imaging 15:013017.1–013017.6CrossRefGoogle Scholar
  12. 12.
    Li Q, Liu X (2008) Novel approach to pavement image segmentation based on neighboring difference histogram method. In: Proceedings of the congress on image and signal processing, pp 792–796Google Scholar
  13. 13.
    Salari E, Yu X (2011) Pavement distress detection and classification using a genetic algorithm. In: Proceedings of the IEEE applied imagery pattern recognition workshop: imaging for decision making, pp 1–5Google Scholar
  14. 14.
    Omran MGH, Engelbrecht AP, Salman A (2005) Differential evolution methods for unsupervised image classification. In: Proceedings of the IEEE congress on evolutionary computation, pp 966–973Google Scholar
  15. 15.
    Yin P-Y (2007) Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl Math Comput 184:503–513MathSciNetzbMATHGoogle Scholar
  16. 16.
    Xu C, Duan H (2010) Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for low-altitude aircraft. Pattern Recogn Lett 31:1759–1772CrossRefGoogle Scholar
  17. 17.
    Bartolome LS, Bandala AA, Llorente C, Dadios EP (2012) Vehicle parking inventory system utilizing image recognition through artificial neural networks. Proc IEEE TENCON 2012:1–5Google Scholar
  18. 18.
    Gonzalez RC, Woods RE (2006) Digital image processing, 3rd edn. Prentice-Hall, New YorkGoogle Scholar
  19. 19.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66CrossRefGoogle Scholar
  20. 20.
    Rosin PL, Ioannidis E (2003) Evaluation of global image thresholding for change detection. Pattern Recogn Lett 24:2345–2356zbMATHCrossRefGoogle Scholar
  21. 21.
    Oliveira H, Correia PL (2009) Automatic road crack segmentation using entropy and image dynamic thresholding. In: Proceedings of the 17th European signal processing conference, pp 622–626Google Scholar
  22. 22.
    Kapur JN, Sahoo PK, Wong AKC (1985) A new method for grey-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29:273–285CrossRefGoogle Scholar
  23. 23.
    Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42:21–57CrossRefGoogle Scholar
  24. 24.
    Bishop CM (1995) Neural networks for pattern recognition. Clarendon Press, OxfordzbMATHGoogle Scholar
  25. 25.
    Serra J (1986) Introduction to mathematical morphology. Comput Vis Graph Image Process 35:283–305zbMATHCrossRefGoogle Scholar
  26. 26.
    Yin P-Y (1999) A fast scheme for optimal thresholding using genetic algorithms. Sig Process 72:85–95zbMATHCrossRefGoogle Scholar
  27. 27.
    Zhang R, Liu J (2006) Underwater image segmentation with maximum entropy based on particle swarm optimization (PSO). In: Proceedings of the first international multi-symposiums on computer and computational sciences (IMSCCS’06), pp 360–362Google Scholar
  28. 28.
    Pei Z, Zhao Y, Liu Z (2009) Image segmentation based on differential evolution algorithm. In: Proceedings of the international conference on image analysis and signal processing (IASP 2009), pp 48–51Google Scholar
  29. 29.
    Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13:146–165CrossRefGoogle Scholar
  30. 30.
    Banharnsakun A (2015) Data set of pavement surface distress.
  31. 31.
    Li NN, Hou XD, Yang XY, Dong YF (2009) Automation recognition of pavement surface distress based on support vector machine. In: Proceedings of the 2nd international conference on intelligent networks and intelligent systems, pp 346–349Google Scholar
  32. 32.
    Shi L, Gao C, Zhang J (2012) Pavement distress image recognition based on multilayer autoencoders. In: Proceedings of 4th international conference on artificial intelligence and computational intelligence, pp 666–673Google Scholar
  33. 33.
    Li L, Sun L, Ning G, Tan S (2014) Automatic pavement crack recognition based on bp neural network. PROMET-Traffic & Transp 26:11–22Google Scholar
  34. 34.
    Chang C-C, Lin C-J (2015) LIBSVM–a library for support vector machines.

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Computational Intelligence Research Laboratory (CIRLab), Computer Engineering Department, Faculty of Engineering at Si RachaKasetsart University Sriracha CampusChonburiThailand

Personalised recommendations