Skip to main content

Advertisement

SpringerLink
Log in

Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review

  • Original Paper
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Pavement condition information is a significant component in Pavement Management Systems. The labeling and quantification of the type, severity, and extent of surface cracking is a challenging area for weighing the asphalt pavements. This paper presents a widespread review on various platform and image processing approaches for asphalt surface interpretation. The main part of this study presents a comprehensive combination of the state of the art in image processing based on crack interpretation related to asphalt pavements. An attempt is made to study the existing methodologies from different points of views accompanied by extensive comparisons on three stages of methods—distress detection, classification, and quantification to facilitate further research studies. This paper presents a survey of the developed pavement inspection systems up to date. Additionally, emerging and evolution technologies considered to automate the processes are discussed.

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

Access this article

Log in via an institution

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29

Similar content being viewed by others

References

  1. Abdel-Qader I, Abudayyeh O, Kelly ME (2003) Analysis of edge-detection techniques for crack identification in bridges. J Comput Civ Eng 17(4):255–263

    Article  Google Scholar 

  2. Adu-Gyamfi Y, Okine NA, Garateguy G, Carrillo R, Arce GR (2011) Multiresolution information mining for pavement crack image analysis. J Comput Civ Eng 26(6):741–749

    Article  Google Scholar 

  3. Adu-Gyamfi YO, Kambhamettu C, Okine NA (2013) Performance assessment of flexible pavements using active contour models. In: 2013 Airfield and highway pavement conference: sustainable and efficient pavements. Los Angeles

  4. Adu-Gyamfi YO, Tienaah T, Attoh-Okine NO, Kambhamettu C (2014) Functional evaluation of pavement condition using a complete vision system. J Transp Eng 140(9):04014040

    Article  Google Scholar 

  5. Aghaeeyan A, Abdollahi F, Talebi HA (2015) UAV–UGVs cooperation: with a moving center based trajectory. Robot Auton Syst 63(Part 1):1–9

    Article  Google Scholar 

  6. Ahmed M, Haas CT, Haas R (2011) Toward low-cost 3D automatic pavement distress surveying: the close range photogrammetry approach. Can J Civ Eng 38(12):1301–1313

    Google Scholar 

  7. Amhaz R, Chambon S, Idier J, Baltazart V (2014) A new minimal path selection algorithm for automatic crack detection on pavement images. In: 2014 IEEE international conference on image processing, ICIP 2014. Institute of Electrical and Electronics Engineers Inc

  8. Ang JC, Haron H, Hamed HNA (2015) Semi-supervised SVM-based feature selection for cancer classification using microarray gene expression data. Current approaches in applied artificial intelligence. Springer, New York, pp 468–477

    Google Scholar 

  9. Asensio-Cubero J, Gan JQ, Palaniappan R (2016) Multiresolution analysis over graphs for a motor imagery based online BCI game. Comput Biol Med 68:21–26

    Article  Google Scholar 

  10. Atkinson J, Campos D (2016) Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Exp Syst Appl 47:35–41

    Article  Google Scholar 

  11. Attoh-Okine N, Barner K, Bentil D, Zhang R (2008) The empirical mode decomposition and the Hilbert-Huang transform. EURASIP J Adv Signal Process 2008(1):1–2

    Article  MATH  Google Scholar 

  12. Avdiyenko L, Bertschinger N, Jost J (2015) Adaptive information-theoretical feature selection for pattern classification. Computational intelligence. Springer, New York, pp 279–294

    MATH  Google Scholar 

  13. Avila M, Begot S, Duculty F, Nguyen TS (2014) 2D image based road pavement crack detection by calculating minimal paths and dynamic programming. In: 2014 IEEE international conference on image processing, ICIP 2014

  14. Ayenu-Prah A, Attoh-Okine N (2008) Evaluating pavement cracks with bidimensional empirical mode decomposition. EURASIP J Adv Signal Process 2008(1):861701

    Article  MATH  Google Scholar 

  15. Basavaprasad B, Ravi M (2014) A comparative study on classification of image segmentation methods with a focus on graph based techniques. Int J Res Eng Technol 3(03):310–314

    Google Scholar 

  16. Battiti R (1994) Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw 5(4):537–550

    Article  Google Scholar 

  17. Bayındır L (2016) A review of swarm robotics tasks. Neurocomputing 172:292–321

    Article  Google Scholar 

  18. Bermejo P, Gámez JA, Puerta JM (2011) A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets. Pattern Recognit Lett 32(5):701–711

    Article  Google Scholar 

  19. Bhardwaj A, Sam L, Akanksha F, Martín-Torres J, Kumar R (2016) UAVs as remote sensing platform in glaciology: present applications and future prospects. Remote Sens Environ 175:196–204

    Article  Google Scholar 

  20. Bianchini A, Bandini P (2010) Prediction of pavement performance through neuro-fuzzy reasoning. Comput Aided Civ Infrastruct Eng 25(1):39–54

    Article  Google Scholar 

  21. Bray J, Verma B, Li X, He W (2006) A neural network based technique for automatic classification of road cracks. In: International joint conference on neural networks, 2006. IJCNN’06. IEEE

  22. Burattini E, De Gregorio M, Rossi S (2010) An adaptive oscillatory neural architecture for controlling behavior based robotic systems. Neurocomputing 73(16–18):2829–2836

    Article  Google Scholar 

  23. Bursanescu L, Blais F (1997) Automated pavement distress data collection and analysis: a 3-D approach

  24. Cadenas J, Garrido M, Martínez R (2015) Selecting features from low quality datasets by a fuzzy ensemble. Computational intelligence. Springer, New York, pp 229–243

    Google Scholar 

  25. Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM

  26. Cai Y, Zhang Y (2011) Research on pavement crack recognition methods based on image processing. In: 3rd International conference on digital image processing, ICDIP 2011. Chengdu

  27. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698

    Article  Google Scholar 

  28. Ceylan H, Bayrak MB, Gopalakrishnan K (2014) Neural networks applications in pavement engineering: a recent survey. Int J Pavement Res Technol 7(6):434–444

    Google Scholar 

  29. Chambon S, Gourraud C, Moliard JM, Nicolle P (2010) Road crack extraction with adapted filtering and Markov model-based segmentation: introduction and validation. In: 5th International conference on computer vision theory and applications, VISAPP 2010. Angers

  30. Chambon S, Moliard J (2011) Automatic road pavement assessment with image processing: review and comparison. Int J Geophys 2011:20

    Article  Google Scholar 

  31. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28

    Article  Google Scholar 

  32. Chang-ping W (2008) Bayes discriminant analysis method of rock-mass quality classification. J China Coal Soc 33(4):395–399

    Google Scholar 

  33. Changxia M, Wenming W, Chunxia Z, Feng D, Zhengli Z (2009) Pavement cracks detection based on FDWT. In: International conference on computational intelligence and software engineering, 2009. CiSE 2009

  34. Chapeleau X, Blanc J, Hornych P, Gautier JL, Carroget J (2014) Use of distributed fiber optic sensors to detect damage in a pavement. In: Asphalt pavements: proceedings of the international conference on asphalt pavements, ISAP 2014

  35. Chen Y-C, Yang C-E, Kang S-C (2014) A lightweight bridge inspection system using a dual-cable suspension mechanism. Autom Constr 46:52–63

    Article  Google Scholar 

  36. Cheng H-D (1996) Automated real-time pavement distress detection using fuzzy logic and neural network. In: Nondestructive evaluation techniques for aging infrastructure and manufacturing. International Society for Optics and Photonics

  37. Cheng H-D, Chen J-R, Glazier C (1996) Novel fuzzy logic approach to pavement distress detection. In: Nondestructive evaluation techniques for aging infrastructure and manufacturing. International Society for Optics and Photonics

  38. Cheng H, Chen J-R, Glazier C, Hu Y (1999) Novel approach to pavement cracking detection based on fuzzy set theory. J Comput Civ Eng 13(4):270–280

    Article  Google Scholar 

  39. Cheng H, Wang J, Hu Y, Glazier C, Shi X, Chen X (2001) Novel approach to pavement cracking detection based on neural network. Trans Res Rec J Transp Res Board 1764:119–127

    Article  Google Scholar 

  40. Chotiprayanakul P, Liu DK, Dissanayake G (2012) Human–robot–environment interaction interface for robotic grit-blasting of complex steel bridges. Autom Constr 27:11–23

    Article  Google Scholar 

  41. Chou J, O’Neill WA, Cheng H (1994) Pavement distress classification using neural networks. In: IEEE international conference on systems, man, and cybernetics, 1994. Humans, information and technology, 1994. IEEE

  42. Chou J, O’Neill WA, Cheng H (1995) Pavement distress evaluation using fuzzy logic and moment invariants. Transp Res Rec 1505:39–46

    Google Scholar 

  43. Chua KM, Xu L (1994) Simple procedure for identifying pavement distresses from video images. J Transp Eng 120(3):412–431

    Article  Google Scholar 

  44. Chu B, Jung K, Lim M-T, Hong D (2013) Robot-based construction automation: an application to steel beam assembly (Part I). Autom Constr 32:46–61

    Article  Google Scholar 

  45. Chu J-W, Chu X-M, Wang R-B, Shi S-M (2003) Research on asphalt pavement surface distress image feature extraction method. J Image Graph 10:1211–1217

    Google Scholar 

  46. Colomina I, Molina P (2014) Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J Photogramm Remote Sens 92:79–97

    Article  Google Scholar 

  47. Cong F, Hautakangas H, Nieminen J, Mazhelis O, Perttunen M, Riekki J, Ristaniemi T (2013) Applying wavelet packet decomposition and one-class support vector machine on vehicle acceleration traces for road anomaly detection. Advances in neural networks–ISNN 2013. Springer, New York, pp 291–299

    Google Scholar 

  48. Cord A, Chambon S (2012) Automatic road defect detection by textural pattern recognition based on AdaBoost. Comput Aided Civ Infrastruct Eng 27(4):244–259

    Article  Google Scholar 

  49. Doquire G, Verleysen M (2013) Mutual information-based feature selection for multilabel classification. Neurocomputing 122:148–155

    Article  MATH  Google Scholar 

  50. Duerden T (2004) An aura of confusion Part 2: the aided eye—‘imaging the aura?’. ’Complement Ther Nurs Midwifery 10(2):116–123

    Article  Google Scholar 

  51. Duerden T (2004) An aura of confusion: ‘seeing auras—vital energy or human physiology?’Part 1 of a three part series. Complement Ther Nurs Midwifery 10(1):22–29

    Article  Google Scholar 

  52. Dy JG (2008) Unsupervised feature selection. Comput Methods Feature Sel 19–39

  53. Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. J Mach Learn Res 5:845–889

    MathSciNet  MATH  Google Scholar 

  54. ElAlami ME (2009) A filter model for feature subset selection based on genetic algorithm. Knowl Based Syst 22(5):356–362

    Article  Google Scholar 

  55. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874

    Article  MathSciNet  Google Scholar 

  56. Ferguson RA, Pratt DN, Turtle PR, MacIntyre IB, Moore DP, Kearney PD, Best MJ, Gardner JL, Berman M, Buckley MJ (2003) Road pavement deterioration inspection system. Google Patents

  57. Fernandez-Leon JA, Acosta GG, Rozenfeld A (2014) How simple autonomous decisions evolve into robust behaviours? A review from neurorobotics, cognitive, self-organized and artificial immune systems fields. Biosystems 124:7–20

    Article  Google Scholar 

  58. Files BT, Marathe AR (2016) A regression method for estimating performance in a rapid serial visual presentation target detection task. J Neurosci Methods 258:114–123

    Article  Google Scholar 

  59. Foithong S, Pinngern O, Attachoo B (2012) Feature subset selection wrapper based on mutual information and rough sets. Exp Syst Appl 39(1):574–584

    Article  Google Scholar 

  60. Gao X, Deng X, Chen N, Luo W, Hu L, Jackson T, Chen H (2011) Attentional biases among body-dissatisfied young women: an ERP study with rapid serial visual presentation. Int J Psychophysiol 82(2):133–142

    Article  Google Scholar 

  61. 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(10):9628–9657

    Article  Google Scholar 

  62. Gerson AD, Parra LC, Sajda P (2006) Cortically coupled computer vision for rapid image search. IEEE Trans Neural Syst Rehabil Eng 14(2):174–179

    Article  Google Scholar 

  63. Golparvar-Fard M, Balali V, De La Garza JM (2015) Segmentation and recognition of highway assets using image-based 3D point clouds and semantic Texton Forests. J Comput Civ Eng 29(1):04014023

    Article  Google Scholar 

  64. Grandsaert PJ (2015) Integrating pavement crack detection and analysis using autonomous unmanned aerial vehicle imagery. DTIC Document

  65. Graves SW (2013) Electro-optical sensor evaluation of airfield pavement. In: Airfield and highway pavement 2013: sustainable and efficient pavements—proceedings of the 2013 airfield and highway pavement conference

  66. Guan H, Li J, Yu Y, Chapman M, Wang C (2015) Automated road information extraction from mobile laser scanning data. IEEE Trans Intell Transp Syst 16(1):194–205

    Article  Google Scholar 

  67. Guan H, Li J, Yu Y, Chapman M, Wang H, Wang C, Zhai R (2014) Iterative tensor voting for pavement crack extraction using mobile laser scanning data. IEEE Trans Geosci Remote Sens 53(3):1527–1537

    Article  Google Scholar 

  68. Guenard A, Ciarletta L (2012) The AETOURNOS project: using a flock of UAVs as a cyber physical system and platform for application-driven research. Proced Comput Sci 10:939–945

    Article  Google Scholar 

  69. Guo W, Soibelman L, Garrett JH Jr (2009) Automated defect detection for sewer pipeline inspection and condition assessment. Autom Constr 18(5):587–596

    Article  Google Scholar 

  70. Haas RCG, Hudson WR, Falls LC (2015) Pavement asset management. Wiley, Hoboken

    Book  Google Scholar 

  71. Hall MA (1999) Correlation-based feature selection for machine learning. The University of Waikato, Hamilton

    Google Scholar 

  72. Han J, Sun Z, Hao H (2015) Selecting feature subset with sparsity and low redundancy for unsupervised learning. Knowl Based Syst 86:210–223

    Article  Google Scholar 

  73. Han Y, Yang Y, Yan Y, Ma Z, Sebe N, Zhou X (2015) Semisupervised feature selection via spline regression for video semantic recognition. IEEE Trans Neural Netw Learn Syst 26(2):252–264

    Article  MathSciNet  Google Scholar 

  74. Hsu H-H, Hsieh C-W, Lu M-D (2011) Hybrid feature selection by combining filters and wrappers. Exp Syst Appl 38(7):8144–8150

    Article  Google Scholar 

  75. Huang J-J, Cai Y-Z, Xu X-M (2008) A parameterless feature ranking algorithm based on MI. Neurocomputing 71(7–9):1656–1668

    Article  Google Scholar 

  76. Huang J, Cai Y, Xu X (2007) A hybrid genetic algorithm for feature selection wrapper based on mutual information. Pattern Recognit Lett 28(13):1825–1844

    Article  Google Scholar 

  77. Huang J, Liu W, Sun X (2014) A pavement crack detection method combining 2D with 3D information based on dempster-shafer theory. Comput Aided Civ Infrastruct Eng 29(4):299–313

    Article  Google Scholar 

  78. Huang W, Zhang N (2012) A novel road crack detection and identification method using digital image processing techniques. In: 2012 7th International conference on computing and convergence technology (ICCIT, ICEI and ICACT), ICCCT 2012. Seoul

  79. Huang Y, Tsai YJ (2011) Dynamic programming and connected component analysis for an enhanced pavement distress segmentation algorithm. Transp Res Rec 2225:89–98

    Article  Google Scholar 

  80. Huang Y, Xu B (2006) Automatic inspection of pavement cracking distress. J Electron Imaging 15(1):013017-013017-013016

    Article  Google Scholar 

  81. Hubacher J (2015) The phantom leaf effect: a replication, Part 1. J Altern Complement Med 21(2):83–90

    Article  Google Scholar 

  82. Huili Z, Guofeng Q, Xingjian W (2010) Improvement of canny algorithm based on pavement edge detection. In: 3rd International congress on image and signal processing (CISP), 2010

  83. Iacoviello D, Petracca A, Spezialetti M, Placidi G (2015) A real-time classification algorithm for EEG-based BCI driven by self-induced emotions. Comput Methods Progr Biomed 122(3):293–303

    Article  Google Scholar 

  84. Immerzeel WW, Kraaijenbrink PDA, Shea JM, Shrestha AB, Pellicciotti F, Bierkens MFP, de Jong SM (2014) High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles. Remote Sens Environ 150:93–103

    Article  Google Scholar 

  85. Jahanshahi MR, Jazizadeh F, Masri SF, Becerik-Gerber B (2012) Unsupervised approach for autonomous pavement-defect detection and quantification using an inexpensive depth sensor. J Comput Civ Eng 27(6):743–754

    Article  Google Scholar 

  86. Jahanshahi MR, Karimi FJ, Masri SF, Becerik-Gerber B (2013) Autonomous pavement condition assessment. Google Patents

  87. Jahanshahi MR, Kelly JS, Masri SF, Sukhatme GS (2009) A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures. Struct Infrastruct Eng 5(6):455–486

    Article  Google Scholar 

  88. Jahanshahi MR, Masri SF (2011) A novel crack detection approach for condition assessment of structures. In: ASCE international workshop on computing in civil engineering. Miami

  89. Jahanshahi MR, Masri SF (2012) Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures. Autom Constr 22:567–576

    Article  Google Scholar 

  90. Jahanshahi MR, Masri SF (2013) A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation. Smart Mater Struct 22(3):035019

    Article  Google Scholar 

  91. Jahanshahi MR, Masri SF, Padgett CW, Sukhatme GS (2013) An innovative methodology for detection and quantification of cracks through incorporation of depth perception. Mach Vis Appl 24(2):227–241

    Article  Google Scholar 

  92. Jain AK (1989) Fundamentals of digital image processing. Prentice-Hall, Inc., Upper Saddle River

    MATH  Google Scholar 

  93. Jiang J, Liu H, Ye H, Feng F (2015) Crack enhancement algorithm based on improved EM. J Inf Comput Sci 12(3):1037–1043

    Article  Google Scholar 

  94. Jie F, Licheng J, Fang L, Tao S, Xiangrong Z (2015) Mutual-information-based semi-supervised hyperspectral band selection with high discrimination, high information, and low redundancy. IEEE Trans Geosci Remote Sens 53(5):2956–2969

    Article  Google Scholar 

  95. Jiexian Z, Lili Z, Xiang F (2012) An improved beamlet tree-structured algorithm and its application in pavement crack detection. Sci Res Essays 7(10):1175–1184

    Google Scholar 

  96. Jiménez-González A, Martinez-de Dios JR, Ollero A (2013) Testbeds for ubiquitous robotics: a survey. Robot Auton Syst 61(12):1487–1501

    Article  Google Scholar 

  97. Jin C, Liu J, Guo J (2015) A hybrid model based on mutual information and support vector machine for automatic image annotation. Artificial intelligence perspectives and applications. Springer, New York, pp 29–38

    Google Scholar 

  98. Jin X, Davis CH (2005) An integrated system for automatic road mapping from high-resolution multi-spectral satellite imagery by information fusion. Inf Fus 6(4):257–273

    Article  Google Scholar 

  99. Jing L, Aiqin Z (2010) Pavement crack distress detection based on image analysis. In: 2010 International conference on machine vision and human–machine interface, MVHI 2010

  100. Jung K, Chu B, Hong D (2013) Robot-based construction automation: an application to steel beam assembly (Part II). Autom Constr 32:62–79

    Article  Google Scholar 

  101. Kaseko MS, Lo Z-P, Ritchie SG (1994) Comparison of traditional and neural classifiers for pavement-crack detection. J Transp Eng 120(4):552–569

    Article  Google Scholar 

  102. Kaseko MS, Ritchie SG (1993) A neural network-based methodology for pavement crack detection and classification. Transp Res C Emerg Technol 1(4):275–291

    Article  Google Scholar 

  103. Kaul V, Tsai Y, Mersereau RM (2010) Quantitative performance evaluation algorithms for pavement distress segmentation. Transp Res Rec 2153:106–113

    Article  Google Scholar 

  104. Kaul V, Tsai Y, Yezzi A (2010) Detection of curves with unknown endpoints using minimal path techniques. In: 2010 21st British machine vision conference, BMVC 2010, Aberystwyth, British Machine Vision Association, BMVA

  105. Kaul V, Yezzi A, Tsai YJ (2012) Detecting curves with unknown endpoints and arbitrary topology using minimal paths. IEEE Trans Pattern Anal Mach Intell 34(10):1952–1965

    Article  Google Scholar 

  106. Khan AM, Ravi S (2013) Image segmentation methods: a comparative study. Int J Soft Comput Eng (IJSCE) 3(4):84–92

    Google Scholar 

  107. Khoshgoftaar TM, Gao K, Napolitano A, Wald R (2014) A comparative study of iterative and non-iterative feature selection techniques for software defect prediction. Inf Syst Front 16(5):801–822

    Article  Google Scholar 

  108. Kim BH, Kim M, Jo S (2014) Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking. Comput Biol Med 51:82–92

    Article  Google Scholar 

  109. Kim JJ, Lee HD, Yun DG, Sung JG (2007) Development of laser pavement image processing system to enhance existing automated pavement distress detection process. In: Maintenance and rehabilitation of pavements and technological control: proceedings of the 5th international conference, MAIREPAV 2007

  110. Kim Y-S, Haas CT, Greer R (1998) Man-machine balanced crack sealing process for UT automated road maintenance machine. In: Proceedings of the international conference on applications of advanced technologies in transportation engineering

  111. Kim YS, Haas CT (2002) A man-machine balanced rapid object model for automation of pavement crack sealing and maintenance. Can J Civ Eng 29(3):459–474

    Article  Google Scholar 

  112. Kim YS, Haas CT, Greer R (1998) Path planning for machine vision assisted, teleoperated pavement crack sealer. J Transp Eng 124(2):137–143

    Article  Google Scholar 

  113. Koch C, Brilakis I (2011) Automated detection of potholes in visual data. In: ISEC 2011: 6th international structural engineering and construction conference—modern methods and advances in structural engineering and construction

  114. 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(2):196–210

    Article  Google Scholar 

  115. Kostavelis I, Gasteratos A (2015) Semantic mapping for mobile robotics tasks: a survey. Robot Auton Syst 66:86–103

    Article  Google Scholar 

  116. Koutsopoulos H, Downey A (1993) Primitive-based classification of pavement cracking images. J Transp Eng 119(3):402–418

    Article  Google Scholar 

  117. Koutsopoulos HN, El Sanhouri I, Downey AB (1993) Analysis of segmentation algorithms for pavement distress images. J Transp Eng 119(6):868–888

    Article  Google Scholar 

  118. Kruglova T, Sayfeddine D, Vitaliy K (2015) Robotic laser inspection of airplane wings using quadrotor. Procedia Eng 129:245–251

    Article  Google Scholar 

  119. Kumar V, Chhabra JK, Kumar D (2015) Automatic unsupervised feature selection using gravitational search algorithm. IETE J Res 61(1):22–31

    Article  Google Scholar 

  120. Kupferberg A, Glasauer S, Burkart JM (2013) Do robots have goals? How agent cues influence action understanding in non-human primates. Behav Brain Res 246:47–54

    Article  Google Scholar 

  121. Laurent J, Doucet M (2010) Vision system and a method for scanning a traveling surface to detect surface defects thereof. Google Patents

  122. LeBlanc J, Gennert MA, Wittels N, Gosselin D (1991) Analysis and generation of pavement distress images using fractals. Transp Res Rec (1311)

  123. Lee BJ, Lee H (2004) Position-invariant neural network for digital pavement crack analysis. Comput Aided Civ Infrastruct Eng 19(2):105–118

    Article  Google Scholar 

  124. Lee J, Kim D-W (2015) Mutual information-based multi-label feature selection using interaction information. Exp Syst Appl 42(4):2013–2025

    Article  Google Scholar 

  125. Leong BTM, Low SM, Ooi MP-L (2012) Low-cost microcontroller-based hover control design of a quadcopter. Procedia Eng 41:458–464

    Article  Google Scholar 

  126. Lettsome CA, Tsai Y (2012) An automated filter bank-based pavement crack detection system incorporating standard compression coders. Int J Pavement Res Technol 5(3):176–186

    Google Scholar 

  127. Li G (2012) Improved pavement distress detection based on contourlet transform and multi-direction morphological structuring elements. Appl Mech Mater 466–467:371–375

    Article  Google Scholar 

  128. Li G (2012) Improved pavement distress detection based on contour let transform and multi-direction morphological structuring elements. In: 2012 International conference on intelligent system and applied material, GSAM 2012, vol 466. Taiyuan. pp 371–375

  129. Li G (2013) New weighted mean filtering algorithm for surface image based on grey entropy. Sens Transducers 161(12):21–26

    Google Scholar 

  130. Li G, Xu Y, Li J (2013) Fuzzy contrast enhancement algorithm for road surface image based on adaptively changing index via grey entropy. Inf Technol J 12(19):5309–5314

    Article  Google Scholar 

  131. Li L, Sun LJ, Tan SG, Ning GB (2012) An efficient way in image preprocessing for pavement crack images. In: CICTP 2012: multimodal transportation systems—convenient, safe, cost-effective, efficient—proceedings of the 12th COTA international conference of transportation professionals

  132. Li L, Sun LJ, Tan SG, Ning GB (2012) An efficient way in image preprocessing for pavement crack images. In: 12th COTA international conference of transportation professionals: multimodal transportation systems—convenient, safe, cost-effective, efficient, CICTP 2012. Beijing

  133. Li M, Stein A, Bijker W, Zhan Q (2016) Region-based urban road extraction from VHR satellite images using binary partition tree. Int J Appl Earth Obs Geoinform 44:217–225

    Article  Google Scholar 

  134. Li N, Hou X, Yang X, Dong Y (2009) Automation recognition of pavement surface distress based on support vector machine. In: Second international conference on intelligent networks and intelligent systems, 2009. ICINIS’09. IEEE

  135. Li Q, Zou Q, Zhang D, Mao Q (2011) FoSA: F* seed-growing approach for crack-line detection from pavement images. Image Vis Comput 29(12):861–872

    Article  Google Scholar 

  136. Lin J, Liu Y (2010) Potholes detection based on SVM in the pavement distress image. In: Ninth international symposium on distributed computing and applications to business engineering and science (DCABES), 2010 IEEE

  137. Ling L, Peikang H, Xiaohu W, Xudong P (2009) Image edge detection based on beamlet transform. J Syst Eng Electron 20(1):1–5

    Google Scholar 

  138. Liu F, Xu G, Yang Y, Niu X, Pan Y (2008) Novel approach to pavement cracking automatic detection based on segment extending. In: International symposium on knowledge acquisition and modeling, 2008. KAM’08. IEEE

  139. Liu H, Sun J, Liu L, Zhang H (2009) Feature selection with dynamic mutual information. Pattern Recognit 42(7):1330–1339

    Article  MATH  Google Scholar 

  140. Lokeshwor H, Das LK, Goel S (2013) Robust method for automated segmentation of frames with/without distress from road surface video clips. J Transp Eng 140(1):31–41

    Article  Google Scholar 

  141. Lokeshwor H, Das LK, Goel S (2014) Robust method for automated segmentation of frames with/without distress from road surface video clips. J Transp Eng 140(1):31–41

    Article  Google Scholar 

  142. Luo W, Feng W, He W, Wang N-Y, Luo Y-J (2010) Three stages of facial expression processing: ERP study with rapid serial visual presentation. NeuroImage 49(2):1857–1867

    Article  Google Scholar 

  143. Ma C-X, Zhao C-X, Hou Y-K (2008) Pavement distress detection based on nonsubsampled contourlet transform. In: International conference on computer science and software engineering, 2008, IEEE

  144. Mahler DS, Kharoufa ZB, Wong EK, Shaw LG (1991) Pavement distress analysis using image processing techniques. Comput Aided Civ Infrastruct Eng 6(1):1–14

    Article  Google Scholar 

  145. Maini R, Aggarwal H (2009) Study and comparison of various image edge detection techniques. Int J Image Process (IJIP) 3(1):1–11

    Google Scholar 

  146. Maldonado S, Carrizosa E, Weber R (2015) Kernel Penalized K-means: a feature selection method based on Kernel K-means. Inf Sci 322:150–160

    Article  MathSciNet  Google Scholar 

  147. Mancini A, Malinverni ES, Frontoni E, Zingaretti P (2013) Road pavement crack automatic detection by MMS images. In: 2013 21st Mediterranean conference on control and automation, MED 2013—conference proceedings

  148. Marques AGCS, Correia PL (2012) Automatic road pavement crack detection using SVM. Lisbon, Portugal: Dissertation for the Master of Science Degree in Electrical and Computer Engineering at Instituto Superior Técnico

  149. Mathavan S, Kamal K, Rahman M (2015) A review of three-dimensional imaging technologies for pavement distress detection and measurements. IEEE Trans Intell Transp Syst 16(5):2353–2362

    Article  Google Scholar 

  150. McCane LM, Heckman SM, McFarland DJ, Townsend G, Mak JN, Sellers EW, Zeitlin D, Tenteromano LM, Wolpaw JR, Vaughan TM (2015) P300-based brain-computer interface (BCI) event-related potentials (ERPs): people with amyotrophic lateral sclerosis (ALS) vs. age-matched controls. Clin Neurophysiol 126(11):2124–2131

    Article  Google Scholar 

  151. McCune RR, Madey GR (2014) Control of Artificial Swarms with DDDAS. Procedia Comput Sci 29:1171–1181

    Article  Google Scholar 

  152. McGhee KH (2004) Automated pavement distress collection techniques. Transp Res Board 334

  153. McNeil S, Humplick F (1991) Evaluation of errors in automated pavement-distress data acquisition. J Transp Eng 117(2):224–241

    Article  Google Scholar 

  154. Mertz C, Varadharajan S, Jose S, Sharma K, Wander L, Wang J (2014) City-wide road distress monitoring with smartphones. In: 21st World congress on intelligent transport systems: reinventing transportation in our connected world, ITSWC 2014, intelligent transport systems (ITS)

  155. Metni N, Hamel T (2007) A UAV for bridge inspection: visual servoing control law with orientation limits. Autom Constr 17(1):3–10

    Article  Google Scholar 

  156. Michaelsen E, Meidow J (2014) Stochastic reasoning for structural pattern recognition: an example from image-based UAV navigation. Pattern Recognit 47(8):2732–2744

    Article  Google Scholar 

  157. Mitra P, Murthy C, Pal SK (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24(3):301–312

    Article  Google Scholar 

  158. Moazzam I, Kamal K, Mathavan S, Usman S, Rahman M (2013) Metrology and visualization of potholes using the microsoft kinect sensor. In: IEEE conference on intelligent transportation systems, proceedings, ITSC

  159. Nejad FM, Zakeri H (2011) A comparison of multi-resolution methods for detection and isolation of pavement distress. Exp Syst Appl 38(3):2857–2872

    Article  Google Scholar 

  160. Mohajeri MJH, Manning PJ (1991) Aria (trademark): an operating system of pavement distress diagnosis by image processing. Transp Res Rec 1311

  161. Mokhtari S (2015) Analytical study of computer vision-based pavement crack quantification using machine learning techniques. University of Central Florida Orlando, Florida

    Google Scholar 

  162. Montero R, Victores JG, Martínez S, Jardón A, Balaguer C (2015) Past, present and future of robotic tunnel inspection. Autom Constr 59:99–112

    Article  Google Scholar 

  163. Moussa G, Hussain K (2011) A new technique for automatic detection and parameters estimation of pavement crack. In: 4th International multi-conference on engineering and technological innovation, IMETI 2011. Orlando

  164. Na W, Tao W (2012) Proximal support vector machine based pavement image classification. In: 2012 IEEE 5th international conference on advanced computational intelligence, ICACI 2012. Nanjing

  165. Naidoo T, Joubert D, Chiwewe T, Tyatyantsi A, Rancati B, Mbizeni A (2014) Visual surveying platform for the automated detection of road surface distresses. In: Proceedings of SPIE: the international society for optical engineering

  166. Natalizio E, Di Caro G, Sekercioglu A, Yanmaz E (2013) A special issue of Ad Hoc Networks on “Theory, algorithms and applications of wireless networked robotics”. Ad Hoc Netw 11(7):1891–1892

    Article  Google Scholar 

  167. Nejad FM, Zakeri H (2011) An expert system based on wavelet transform and radon neural network for pavement distress classification. Exp Syst Appl 38(6):7088–7101

    Article  Google Scholar 

  168. Nejad FM, Zakeri H (2011) An optimum feature extraction method based on Wavelet–Radon transform and dynamic neural network for pavement distress classification. Exp Syst Appl 38(8):9442–9460

    Article  Google Scholar 

  169. Nejad FM, Zakeri H (2012) The hybrid method and its application to smart pavement management. Metaheuristics Water Geotech Transp Eng 439

  170. Nguyen T, Khosravi A, Creighton D, Nahavandi S (2015) EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems. Exp Syst Appl 42(9):4370–4380

    Article  Google Scholar 

  171. Nguyen TS, Avila M, Begot S (2009) Automatic detection and classification of defect on road pavement using anisotropy measure. In: Signal processing conference, 2009 17th European. IEEE, pp 617–621

  172. Ni Z, Tang P, Xi Y (2012) A new method to pavement cracking detection based on the biological inspired model. In: 2012 International conference on computer science and information processing, CSIP 2012. Xi’an

  173. Nishikawa T, Yoshida J, Sugiyama T, Fujino Y (2012) Concrete crack detection by multiple sequential image filtering. Comput Aided Civ Infrastruct Eng 27(1):29–47

    Article  Google Scholar 

  174. Oh J-K, Jang G, Oh S, Lee JH, Yi B-J, Moon YS, Lee JS, Choi Y (2009) Bridge inspection robot system with machine vision. Autom Constr 18(7):929–941

    Article  Google Scholar 

  175. Oliveira H, Caeiro J, Correia PL (2014) Accelerated unsupervised filtering for the smoothing of road pavement surface imagery. In: European signal processing conference

  176. Oliveira H, Caeiro JJ, Correia PL (2010) Improved road crack detection based on one-class Parzen density estimation and entropy reduction. In: 2010 17th IEEE international conference on image processing, ICIP 2010. Hong Kong

  177. Oliveira H, Correia PL (2008) Supervised strategies for cracks detection in images of road pavement flexible surfaces. In: Signal processing conference, 2008 16th European, IEEE

  178. Oliveira H, Correia PL (2010) Automatic crack detection on road imagery using anisotropic diffusion and region linkage. In: 18th European signal processing conference, EUSIPCO 2010. Aalborg

  179. Oliveira H, Correia PL (2013) Automatic road crack detection and characterization. IEEE Trans Intell Transp Syst 14(1):155–168

    Article  Google Scholar 

  180. Oliveira H, Correia PL (2014) CrackIT: an image processing toolbox for crack detection and characterization. In: 2014 IEEE international conference on image processing, ICIP 2014

  181. Ouyang A, Dong Q, Wang Y, Liu Y (2014) The classification of pavement crack image based on beamlet algorithm. In: 7th IFIP WG 5.14 international conference on computer and computing technologies in agriculture, CCTA 2013

  182. Ouyang A, Wang Y (2012) Edge detection in pavement crack image with beamlet transform. In: 2012 2nd International conference on electronic and mechanical engineering and information technology, EMEIT 2012. Shenyang

  183. Ouyang W, Xu B (2013) Pavement cracking measurements using 3D laser-scan images. Meas Sci Technol 24(10):105204

    Article  Google Scholar 

  184. Paraforos DS, Griepentrog HW, Vougioukas SG (2016) Country road and field surface profiles acquisition, modelling and synthetic realisation for evaluating fatigue life of agricultural machinery. J Terramech 63:1–12

    Article  Google Scholar 

  185. Peng K, Cai G, Chen BM, Dong M, Lum KY, Lee TH (2009) Design and implementation of an autonomous flight control law for a UAV helicopter. Automatica 45(10):2333–2338

    Article  MathSciNet  MATH  Google Scholar 

  186. Prakash S, Chowdhury AR, Gupta A (2015) Monitoring the human health by measuring the biofield “aura”: an overview. Int J Appl Eng Res 10(2765427658)

  187. Pynn J, Wright A, Lodge R (1999) Automatic identification of cracks in road surfaces. In: Image processing and its applications, 1999. Seventh International Conference on (Conf. Publ. No. 465)

  188. Qureshi B, Koubâa A (2014) Five traits of performance enhancement using cloud robotics: a survey. Procedia Comput Sci 37:220–227

    Article  Google Scholar 

  189. Rababaah H (2005) Asphalt pavement crack classification: a comparative study of three ai approaches—multilayer perceptron, genetic algorithms, and self-organizing maps. Citeseer

  190. Rababaah H, Vrajitoru D, Wolfer J (2005) Asphalt pavement crack classification: a comparison of GA, MLP, and SOM. In: Proceedings of genetic and evolutionary computation conference. Late-Breaking Paper

  191. Radopoulou SC, Brilakis I (2015) Patch detection for pavement assessment. Autom Constr 53:95–104

    Article  Google Scholar 

  192. Rai R, Deshpande AV (2016) Fragmentary shape recognition: a BCI study. Comput Aided Des 71:51–64

    Article  Google Scholar 

  193. Raman M, Hossain M, Miller R, Cumberledge G, Lee H, Kang K (2004) Assessment of image-based data collection and the AASHTO provisional standard for cracking on asphalt-surfaced pavements. Transp Res Rec J Transp Res Board 1889:116–125

    Article  Google Scholar 

  194. Reeves B (2011) High speed photometric stereo pavement scanner. Google Patents

  195. Roca D, Lagüela S, Díaz-Vilariño L, Armesto J, Arias P (2013) Low-cost aerial unit for outdoor inspection of building façades. Autom Constr 36:128–135

    Article  Google Scholar 

  196. Rouillard J, Duprès A, Cabestaing F, Leclercq S, Bekaert M-H, Piau C, Vannobel J-M, Lecocq C (2015) Hybrid BCI coupling EEG and EMG for severe motor disabilities. Procedia Manuf 3:29–36

    Article  Google Scholar 

  197. Saar T, Talvik O (2010) Automatic asphalt pavement crack detection and classification using neural networks. In: Electronics conference (BEC), 2010 12th Biennial Baltic, IEEE

  198. Salari E, Bao G (2010) Pavement distress detection and classification using feature mapping. In: 2010 IEEE international conference on electro/information technology, EIT2010. Normal

  199. Salari E, Bao G (2011) Pavement distress detection and severity analysis. In: Proceedings of SPIE. The International Society for Optical Engineering

  200. Salari E, Ouyang D (2012) An image-based pavement distress detection and classification. In: 2012 IEEE international conference on electro/information technology, EIT 2012. Indianapolis

  201. Salari E, Yu X (2011) Pavement distress detection and classification using a Genetic Algorithm. In: Applied imagery pattern recognition workshop (AIPR), 2011 IEEE, IEEE

  202. Salman M, Mathavan S, Kamal K, Rahman M (2013) Pavement crack detection using the Gabor filter. In: 2013 16th international IEEE conference on intelligent transportation systems: intelligent transportation systems for all modes, ITSC 2013. The Hague

  203. Schnebele E, Tanyu BF, Cervone G, Waters N (2015) Review of remote sensing methodologies for pavement management and assessment. Eur Transp Res Rev 7(2):1–19

    Article  Google Scholar 

  204. Senanayake M, Senthooran I, Barca JC, Chung H, Kamruzzaman J, Murshed M (2016) Search and tracking algorithms for swarms of robots: a survey. Robot Auton Syst 75(Part B):422–434

    Article  Google Scholar 

  205. Senthilkumaran N, Rajesh R (2009) Edge detection techniques for image segmentation–a survey of soft computing approaches. Int J Recent Trends Eng 1(2)

  206. Senthilkumaran N, Rajesh R (2009) Image segmentation-a survey of soft computing approaches. In: International conference on advances in recent technologies in communication and computing, 2009. ARTCom’09. IEEE, pp 844–846

  207. Sharifi M, Fathy M, Mahmoudi MT (2002) A classified and comparative study of edge detection algorithms. In: Proceedings international conference on information technology: coding and computing, 2002. IEEE

  208. Shen Y, Dang JW, Wang YP, Feng X (2014) A compressed sensing pavement distress image filtering algorithm based on NSCT domain. J Optoelectron Laser 25(8):1620–1626

    Google Scholar 

  209. Shi C, Ruan Q, An G, Zhao R (2015) Hessian semi-supervised sparse feature selection based on-matrix norm. IEEE Trans Multimed 17(1):16–28

    Article  Google Scholar 

  210. Shukla A, Karki H (2016) Application of robotics in offshore oil and gas industry: a review Part II. Robot Auton Syst 75(Part B):508–524

    Article  Google Scholar 

  211. Shukla A, Karki H (2016) Application of robotics in onshore oil and gas industry: a review Part I. Robot Auton Syst 75(Part B):490–507

    Article  Google Scholar 

  212. Siebert S, Teizer J (2014) Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Autom Constr 41:1–14

    Article  Google Scholar 

  213. Solla M, Lagüela S, González-Jorge H, Arias P (2014) Approach to identify cracking in asphalt pavement using GPR and infrared thermographic methods: preliminary findings. NDT E Int 62:55–65

    Article  Google Scholar 

  214. Song B, Wei N (2013) Statistics properties of asphalt pavement images for cracks detection. J Inf Comput Sci 10(9):2833–2843

    Article  Google Scholar 

  215. Song H, Wang W, Wang F, Wu L, Wang Z (2015) Pavement crack detection by ridge detection on fractional calculus and dual-thresholds. Int J Multimed Ubiquitous Eng 10(4):19–30

    Article  Google Scholar 

  216. Sridevi M, Mala C (2012) A survey on monochrome image segmentation methods. Procedia Technol 6:548–555

    Article  Google Scholar 

  217. Stamatescu V, Wong S, Kearney D, Lee I, Milton A (2015) Mutual information for enhanced feature selection in visual tracking. In: SPIE defense + security. International Society for Optics and Photonics

  218. Su YS, Kang SC, Chang JR, Hsieh SH (2013) Dual-light inspection method for automatic pavement surveys. J Comput Civ Eng 27(5):534–543

    Article  Google Scholar 

  219. Sun X, Huang J, Liu W, Xu M (2012) Pavement crack characteristic detection based on sparse representation. EURASIP J Adv Signal Process 2012(1):1–11

    Article  Google Scholar 

  220. Sun ZY, Hao XL, Li W, Yuan MX (2015) Research of pavement 3D data denoising algorithm. J Chang Univ (Nat Sci Ed) 35(1):20–25

    Google Scholar 

  221. Sun ZY, Zhao HW, Li W, Hao XL, Huyan J (2015) 3D pavement crack identification method based on dual-phase scanning detection. China J Highway Transp 28(2):26–32

    Google Scholar 

  222. Sundin S, Braban-Ledoux C (2001) Artificial intelligence–based decision support technologies in pavement management. Comput Aided Civ Infrastruct Eng 16(2):143–157

    Article  Google Scholar 

  223. Tan Y, Zheng Z-Y (2013) Research advance in swarm robotics. Def Technol 9(1):18–39

    Article  Google Scholar 

  224. Tang J, Gu Y (2013) Automatic crack detection and segmetnation using a hybrid algorithm for road distress analysis. In: 2013 IEEE international conference on systems, man, and cybernetics, SMC 2013. Manchester

  225. Terzi S (2013) Modeling for pavement roughness using the ANFIS approach. Adv Eng Softw 57:59–64

    Article  Google Scholar 

  226. Timm DH, McQueen JM (2004) A study of manual vs. automated pavement condition surveys. Auburn University, Alabama

    Google Scholar 

  227. Tomiyama K, Kawamura A, Ishida T (2013) Automatic detection method of localized pavement roughness using quarter car model by lifting wavelet filters. Int J Pavement Res Technol 6(5):627–632

    Google Scholar 

  228. Tong H, Chao WW, Qiang HC, Bo XY (2012) Path planning of UAV based on voronoi diagram and DPSO. Procedia Eng 29:4198–4203

    Article  Google Scholar 

  229. Tsai Y-C, Kaul V, Mersereau RM (2009) Critical assessment of pavement distress segmentation methods. J Transp Eng 136(1):11–19

    Article  Google Scholar 

  230. Tsai Y, Kaul V, Yezzi A (2013) Automating the crack map detection process for machine operated crack sealer. Autom Constr 31:10–18

    Article  Google Scholar 

  231. Tsai YC, Jiang C, Huang Y (2014) Multiscale crack fundamental element model for real-world pavement crack classification. J Comput Civ Eng 28(4):04014012

    Article  Google Scholar 

  232. Tsai YC, Kaul V, Lettsome CA (2012) Enhanced adaptive filter-bank-based automated pavement crack detection and segmentation system. J Electron Imaging 21(4):043008-043008

    Google Scholar 

  233. Tsai YC, Kaul V, Mersereau RM (2010) Critical assessment of pavement distress segmentation methods. J Transp Eng 136(1):11–19

    Article  Google Scholar 

  234. Tsai YJ, Li F (2012) Critical assessment of detecting asphalt pavement cracks under different lighting and low intensity contrast conditions using emerging 3D laser technology. J Transp Eng 138(5):649–656

    Article  Google Scholar 

  235. Tseng Y-H, Kang S-C, Chang J-R, Lee C-H (2011) Strategies for autonomous robots to inspect pavement distresses. Autom Constr 20(8):1156–1172

    Article  Google Scholar 

  236. Uncu Ö, Türkşen IB (2007) A novel feature selection approach: combining feature wrappers and filters. Inf Sci 177(2):449–466

    Article  MathSciNet  MATH  Google Scholar 

  237. Varadharajan S, Jose S, Sharma K, Wander L, Mertz C (2014) Vision for road inspection. In: IEEE winter conference on applications of computer vision (WACV), 2014 IEEE

  238. Varela G, Caamaño P, Orjales F, Deibe Á, López-Peña F, Duro RJ (2014) Autonomous UAV based search operations using constrained sampling evolutionary algorithms. Neurocomputing 132:54–67

    Article  Google Scholar 

  239. Vasuki Y, Holden E-J, Kovesi P, Micklethwaite S (2014) Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: an image analysis approach. Comput Geosci 69:22–32

    Article  Google Scholar 

  240. Velinsky SA, Feng X, Bennett DA (2003) Operator controlled, vehicle-based highway crack-sealing machine. Heavy Veh Syst 10(3):145–166

    Article  Google Scholar 

  241. Verma SK, Chauhan R (2014) Nanorobotics in dentistry: a review. Indian J Dent 5(Supplement):62–70

    Article  Google Scholar 

  242. Victores JG, Martínez S, Jardón A, Balaguer C (2011) Robot-aided tunnel inspection and maintenance system by vision and proximity sensor integration. Autom Constr 20(5):629–636

    Article  Google Scholar 

  243. Wang C, Sha A, Sun Z (2010) Pavement crack classification based on chain code. In: Seventh international conference on fuzzy systems and knowledge discovery (FSKD), 2010, IEEE

  244. Wang J, Gao RX (2012) Pavement distress analysis based on dual-tree complex wavelet transform. Int J Pavement Res Technol 5(5):283–288

    Google Scholar 

  245. Wang JJ-Y, Huang JZ, Sun Y, Gao X (2015) Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization. Exp Syst Appl 42(3):1278–1286

    Article  Google Scholar 

  246. Wang K (2000) Designs and implementations of automated systems for pavement surface distress survey. J Infrastruct Syst 6(1):24–32

    Article  MathSciNet  Google Scholar 

  247. Wang K, Li Q, Gong W (2008) Wavelet-based pavement distress image edge detection with a trous algorithm. Transp Res Rec J Transp Res Board 2024:73–81

    Article  Google Scholar 

  248. Wang KCP, Li Q, Gong W (2007) Wavelet-based pavement distress image edge detection with a trous algorithm. Transp Res Rec 2024:73–81

    Article  Google Scholar 

  249. Wang S, Pedrycz W, Zhu Q, Zhu W (2015) Unsupervised feature selection via maximum projection and minimum redundancy. Knowl Based Syst 75:19–29

    Article  MATH  Google Scholar 

  250. Wang S, Tang W (2011) Pavement crack segmentation algorithm based on local optimal threshold of cracks density distribution. In: 7th International conference on intelligent computing, ICIC 2011. Zhengzhou. 6838 LNCS, pp 298–302

  251. Wang S, Tang W (2011) Pavement crack segmentation algorithm based on local optimal threshold of cracks density distribution. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). 6838 LNCS, pp 298–302

  252. Wang X, Feng X (2011) Pavement distress detection and classification with automated image processing. In: Proceedings 2011 international conference on transportation, mechanical, and electrical engineering, TMEE 2011

  253. Wang Z (2000) Formulation and assessment of a customizable procedure for pavement distress index

  254. Wang Z, Li M, Li J (2015) A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure. Inf Sci 307:73–88

    Article  MathSciNet  Google Scholar 

  255. Wei H, Li N, Liu M, Tan J (2013) A novel autonomous self-assembly distributed swarm flying robot. Chin J Aeronaut 26(3):791–800

    Article  Google Scholar 

  256. Wei N, Zhao X, Dou X, Song H, Wang T (2010) Beamlet transform based pavement image crack detection. In: International conference on intelligent computation technology and automation (ICICTA), 2010, IEEE

  257. Wirkner J, Löw A, Hamm AO, Weymar M (2015) New learning following reactivation in the human brain: targeting emotional memories through rapid serial visual presentation. Neurobiol Learn Memory 119:63–68

    Article  Google Scholar 

  258. Wu C, Lu B, Chen D, Wang L (2011) Pavement image denoising based on shearlet treansform. In: International conference on electronics and optoelectronics (ICEOE), 2011, IEEE

  259. Wu S, Liu Y (2012) A segment algorithm for crack dection. In: 2012 IEEE symposium on electrical and electronics engineering, EEESYM 2012. Kuala Lumpur

  260. Xiao W-X, Zhang X, Huang W (2004) Preliminary study of pavement surface distress automation recognition based on wavelet neural network. Shanghai Highw 2:008

    Google Scholar 

  261. Xu G, Ma J, Liu F, Niu X (2008) Automatic recognition of pavement surface crack based on Bp neural network. In: International conference on computer and electrical engineering, 2008. ICCEE 2008. IEEE

  262. Xu K, Wei N, Ma R (2013) Pavement crack image detection algorithm under nonuniform illuminance. In: 2013 IEEE 3rd international conference on information science and technology, ICIST 2013, Yangzhou, Jiangsu, IEEE Computer Society

  263. Xu W, Tang Z, Xu D, Wu G (2015) Integrating multi-features fusion and gestalt principles for pavement crack detection. J Comput Aided Des Comput Graph 27(1):147–156

    Google Scholar 

  264. Xu W, Tang Z, Zhou J, Ding J (2013) Pavement crack detection based on saliency and statistical features. In: 2013 20th IEEE international conference on image processing, ICIP 2013. Melbourne

  265. Yan WY, Shaker A, El-Ashmawy N (2015) Urban land cover classification using airborne LiDAR data: a review. Remote Sens Environ 158:295–310

    Article  Google Scholar 

  266. Yao J, Mao Q, Goodison S, Mai V, Sun Y (2015) Feature selection for unsupervised learning through local learning. Pattern Recognit Lett 53:100–107

    Article  Google Scholar 

  267. Yao M, Zhao Z, Yao X, Xu B (2015) Fusing complementary images for pavement cracking measurements. Meas Sci Technol 26(2):025005

    Article  Google Scholar 

  268. Yao P, Wang H, Su Z (2015) UAV feasible path planning based on disturbed fluid and trajectory propagation. Chin J Aeronaut 28(4):1163–1177

    Article  Google Scholar 

  269. Yassi M, Moattar MH (2014) Robust and stable feature selection by integrating ranking methods and wrapper technique in genetic data classification. Biochem Biophys Res Commun 446(4):850–856

    Article  Google Scholar 

  270. Ying H, Zhu Q, Tan Z, Wei J (2013) Faulting estimation method based on vertical acceleration of cement concrete pavement. J Harbin Inst Technol 45(2):96–100

    Google Scholar 

  271. Ying L (2009) Beamlet transform based technique for pavement image processing and classification. The University of Toledo, Toledo

    Book  Google Scholar 

  272. Ying L, Salari E (2010) Beamlet transform-based technique for pavement crack detection and classification. Comput Aided Civ Infrastruct Eng 25(8):572–580

    Article  Google Scholar 

  273. Yoo H-S, Kim Y-S (2015) Development of a crack recognition algorithm from non-routed pavement images using artificial neural network and binary logistic regression. KSCE J Civ Eng 20:1–12

    Google Scholar 

  274. Youquan H, Hanxing Q, Jian W, Wei Z, Jianfang X (2011) Studying of road crack image detection method based on the mathematical morphology. In: 4th International congress on image and signal processing, CISP 2011. Shanghai

  275. Yu S-N, Jang J-H, Han C-S (2007) Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel. Autom Constr 16(3):255–261

    Article  Google Scholar 

  276. Yuwono M, Guo Y, Wall J, Li J, West S, Platt G, Su SW (2015) Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in heating ventilation and air conditioning systems. Appl Soft Comput 34:402–425

    Article  Google Scholar 

  277. Zakeri H, Nejad FM, Fahimifar A, Torshizi AD, Zarandi MHF (2013) A multi-stage expert system for classification of pavement cracking. In: IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), 2013 Joint

  278. Zakeri H, Nejad FM, Fahimifar A, Torshizi AD, Zarandi MHF (2014) A new automatic MF generator (AMFG) for general 3D type-ii fuzzy in the polar frame. In: 2014 IEEE conference on norbert wiener in the 21st century: driving technology’s future, 21CW 2014—incorporating the proceedings of the 2014 North American fuzzy information processing society conference, NAFIPS 2014, conference proceedings

  279. Zalama E, Gómez-García-Bermejo J, Medina R, Llamas J (2014) Road crack detection using visual features extracted by gabor filters. Comput Aided Civ Infrastruct Eng 29(5):342–358

    Article  Google Scholar 

  280. Zarco-Tejada PJ, González-Dugo V, Berni JAJ (2012) Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens Environ 117:322–337

    Article  Google Scholar 

  281. Zarrinpanjeh N, Samadzadegan F, Schenk T (2013) A new ant based distributed framework for urban road map updating from high resolution satellite imagery. Comput Geosci 54:337–350

    Article  Google Scholar 

  282. Zhang A, Li Q, Wang KCP, Qiu S (2013) Matched filtering algorithm for pavement cracking detection. Transp Res Rec 2367:30–42

    Article  Google Scholar 

  283. Zhang C-K, Hu H (2005) Feature selection using the hybrid of ant colony optimization and mutual information for the forecaster. In: Proceedings of 2005 international conference on machine learning and cybernetics, 2005. IEEE

  284. Zhang C, Elaksher A (2012) An unmanned aerial vehicle-based imaging system for 3D measurement of unpaved road surface distresses1. Comput Aided Civ Infrastruct Eng 27(2):118–129

    Article  Google Scholar 

  285. Zhang D-Q, Qu S-R, Li W-B, He L (2009) Image enhancement algorithm on ridgelet domain in detection of road cracks. China J Highway Transp 22(2):26–31

    Google Scholar 

  286. Zhang D, Qu S, He L, Shi S (2009) Automatic ridgelet image enhancement algorithm for road crack image based on fuzzy entropy and fuzzy divergence. Opt Lasers Eng 47(11):1216–1225

    Article  Google Scholar 

  287. Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: a survey of unsupervised methods. Comput Vis Image Underst 110(2):260–280

    Article  Google Scholar 

  288. Zhang J, Sha A, Sun Z, Gao H (2009). Pavement crack automatic recognition based on wiener filtering. In: Critical issues in transportation system planning, development, and management proceedings of the ninth international conference of chinese transportation professionals

  289. Zhang Y, Zhou H (2012) Automatic pavement cracks detection and classification using radon transform. J Inf Comput Sci 9(17):5241–5247

    Google Scholar 

  290. Zhao J, Lu K, He X (2008) Locality sensitive semi-supervised feature selection. Neurocomputing 71(10):1842–1849

    Article  Google Scholar 

  291. Zhao Z, Liu H (2007) Semi-supervised feature selection via spectral analysis. SDM, SIAM

  292. Zhibiao S, Yanqing G (2013) Algorithm on contourlet domain in detection of road cracks for pavement images. J Algorithms Comput Technol 7(1):15–26

    Article  MathSciNet  MATH  Google Scholar 

  293. Zhou H, Yang S, Zhu J (2010) Illumination invariant enhancement and threshold segmentation algorithm for asphalt pavement crack image. In: 2010 6th International conference on wireless communications, networking and mobile computing, WiCOM 2010. Chengdu

  294. Zhou HL, Jiang YL, Wan X (2011) Evolving fuzzy neural network for highway subsurface condition evaluation using ground penetrating radar. Adv Inf Sci Serv Sci 3(9):176–182

    Google Scholar 

  295. Zhou J, Huang P, Chiang F-P (2005) Wavelet-based pavement distress classification. Transp Res Rec J Transp Res Board 1940(1):89–98

    Article  Google Scholar 

  296. Zhou J, Huang PS, Chiang F-P (2006) Wavelet-based pavement distress detection and evaluation. Opt Eng 45(2):027007-027007-027010

    Article  Google Scholar 

  297. Zhu Z, German S, Brilakis I (2010) Detection of large-scale concrete columns for automated bridge inspection. Autom Constr 19(8):1047–1055

    Article  Google Scholar 

  298. Zou Q, Cao Y, Li Q, Mao Q, Wang S (2012) CrackTree: automatic crack detection from pavement images. Pattern Recognit Lett 33(3):227–238

    Article  Google Scholar 

  299. Zuo YX, Wang GQ, Zuo CC (2013) The segmentation algorithm for pavement cracking images based on the improved fuzzy clustering. In: 2013 2nd International conference on chemical, mechanical and materials engineering, CMME 2013, vol 319. Melbourne. pp 362–366

  300. 长安大学 (2011) Pavement crack detection system based on image and detection method thereof. Google Patents

Download references

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, Iran

    H. Zakeri, Fereidoon Moghadas Nejad & Ahmad Fahimifar

Authors
  1. H. Zakeri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fereidoon Moghadas Nejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmad Fahimifar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fereidoon Moghadas Nejad.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zakeri, H., Nejad, F.M. & Fahimifar, A. Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review. Arch Computat Methods Eng 24, 935–977 (2017). https://doi.org/10.1007/s11831-016-9194-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-016-9194-z

Keywords

Search

Navigation