Adaptive road crack detection system by pavement classification

Sensors

Volumn 11, Issue 10, 2011, Pages 9628-9657

  Gavilán, Miguel ^a   Balcones, David ^a   Marcos, Oscar ^a   Llorca, David F ^a   Sotelo, Miguel A ^a   Parra, Ignacio ^a   Ocaña, Manuel ^a   Aliseda, Pedro ^b   Yarza, Pedro ^b   Amírola, Alejandro ^b  

^a UNIVERSITY OF ALCALÁ   (Spain)

^b Acciona Agua   (Spain)

Author keywords

Gray level co occurrence matrix; Linear features; Local binary pattern; Multi class svm; Road distress detection; Road surface classification

Indexed keywords

ALGORITHM; ARTICLE; BUILDING MATERIAL; IMAGE PROCESSING; MECHANICAL STRESS; METHODOLOGY; MOTOR VEHICLE; REPRODUCIBILITY; SURFACE PROPERTY; TRAFFIC AND TRANSPORT;

ALGORITHMS; CONSTRUCTION MATERIALS; IMAGE PROCESSING, COMPUTER-ASSISTED; MOTOR VEHICLES; REPRODUCIBILITY OF RESULTS; STRESS, MECHANICAL; SURFACE PROPERTIES; TRANSPORTATION;

EID: 80055064510     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s111009628     Document Type: Article

References (63)

1. ERF. European Road Statistics 2010; Available online: http://www.erf.be/images/...n_Road_Statistics_2010.pdf (accessed on 27 August 2011).
2. Ministerio de Fomento de España. Available online: http://www.fomento.gob.es (accessed on 27 August 2011). Research Board of the National Academies: Washington, DC, USA, 2004.
3. ERF. Sustainable Roads and Optimal Mobility; Available online: http://www.erf.be/media/wg_sustainableroads/SUSTAINABLE%20ROADS_Final%20 Version_Version%20to%20Print.pdf (accessed on 27 August 2011).
4. Rough Roads Ahead: Fix Them or Pay for It Later; American Association of State Highway and Transportation Officials (AASHTO): Washington, DC, USA, 2009.
5. Pavement Management Report for Merced County; Merced County Association of Governments (MCAG): Merced, CA, USA, 2009.
6. Bennett, C.R.; Chamorro, A.; Chen, C.; de Solminihac, H.; Flintsch, G.W. Data Collection Technologies for Road Management; World Bank East-Asia Transport Unit: Washington, DC, USA, 2007.
7. NCHRP (National Cooperative Highway Research Program). Automated Pavement Distress Collection Techniques; Transport Research Board of the National Academies: Washington, DC, USA, 2004.
8. Wang, K.C.P.; Elliot, R.P. Investigation of Image Archiving for Pavement Surface Distress Survey. Final Report; Mack-Blackwell Transportation Center, Department of Civil Engineering, University of Arkansas, Fayetteville, NC, USA, 1999.
9. CSIRO. Roadcrack; Available online: http://www.csiro.au/solutions/RoadCrack.html (accessed on 27 August 2011).
10. Fugro. Automatic Road Analyzer (ARAN); Available online: http://www.roadware.com/products/9000 (accessed on 27 August 2011).
11. WayLink. Digital Highway Data Vehicle (DHDV); Available online: http://www.waylink.com/DHDV.htm (accessed on 27 August 2011).
12. Ramboll. Rst Pavue-Crack Recognition System; Available online: http://rst.ramboll.se/produkter/rst%20pavue (accessed on 27 August 2011).
13. Transport Research Laboratory (TRL). Highways Agency Road Research Information System 2 (Harris2); Available online: http://www.trl.co.uk/facilities/mobile_test_equipment/(accessed on 27 August 2011).
14. GIE Technologies. Laservision; Available online: http://www.gieinc.ca/main_en.html (accessed on 27 August 2011).
15. ARCADIS. Advanced Inspections Project Innovative Technologies to Improve Highway Condition Monitoring. Report of the Innovation Projects Highway Maintenance RWS-DVS; Available online: http://www.rijkswaterstaat.nl/images/Advanced%20Inspection%20Project%20F inal%20Report_tcm174-283988.pdf (accessed on 27 August 2011).
16. Automated Pavement Cracking Assessment Equipment: State of the Art; World Road Association-PIARC: London, UK, 2003.
17. McRobbie, S.; Wright, A. TTS Research-Crack Detection on Local Roads-Phase 1; Transport Research Laboratory: Washington, DC, USA, 2005.
18. Furness, G.; Barnes, S.; Wright, A. Crack Detection on Local Roads-Phase 2; Available online: http://www.ukroadsliaisongroup.org/pdfs/07-03-19%20TTS_Crack_Report_Fina l_190307.pdf (accessed on 27 August 2011).
19. Wimsatt, A.; Hurlebaus, S.; Scullion, T.; Fernando, E. Promising existing and emerging technologies and techniques. In Proceedings of the International Symposium on Nondestructive Testing for Design Evaluation and Construction Inspection, Wahshington, DC, USA, 18 January 2008.
20. Subirats, P.; Fabrre, O.; Dumoulin, J.; Legeay, V.; Barba, D. A combined wavelet-based image processing method for emergent crack detection on pavement surface images. In Proceedings of the XII European Signal Processing Conference, Vienna, Austria, 6-10 September 2004.
21. Sy, N.T.; Avila, M.; Begot, S.; Bardet, J.C. Detection of defects in road surface by a vision system. In Proceedings of the 14th IEEE Mediterranean Electrotechnical Conference, Ajaccio, France, 5-7 May 2008.
22. Lee, B.Y.; Kim, J.K.; Kim, Y.Y.; Yi, S.T. A technique based on image processing for measuring cracks in the surface of concrete structures. In Proceedings of the 19th International Conference on Structural Mechanics in Reactor Technology (SMiRT-19), Toronto, ON, Canada, 12-17 August 2007.
23. Wang, Z.-Z.; Yong, J.-H. Texture analysis and classification with linear regression model based on wavelet transform. IEEE Trans. Image Process. 2008, 17, 1421-1430.
24. Chambon, S.; Gourraud, C.; Moliard, J.-M.; Nicolle, P. Road crack extraction with adapted filtering and Markov model-based segmentation-Introduction and validation. In Proceedings of the International Conference on Computer Vision Theroy and Applications, Angers, France, 17-21 May 2010.
25. Li, H.; Lavin, M.A.; Master, R.J.L. Fast hough transform: A hierarchical approach. Comput. Graph. Image Process. 1986, 36, 139-161.
26. Xu, L.; Oja, E.; Kultanen, P. A new curve detection method: Randomized hough transform (rht). Patten Recognit. Lett. 1990, 11, 331-338.
27. Chang, C.C; Lin, C.J. LIBSVM: A Library for Support Vector Machines; Available online: http://www.csie.ntu.edu.tw/~cjlin/libsvm (accessed on 27 August 2011).
28. Jain, A.K.; Farrokhnia, F. Unsupervised texture segmentation using Gabor filters. Pattern Recognit. 1991, 24, 1167-1186.
29. Mallat, S.G. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 1989, 11, 674-693.
30. Haralick, R.M.; Bosley, R. Texture features for image classification. In Proceedings of the Third ERTS NASA Goddard Space Flight Center, Greenbelt, MD, USA, 5-9 March 1973; pp. 1929-1969.
31. Matas, J.; Chum, O.; Urban, M.; Pajdla, T. Robust wide baseline stereo from maximally stable extremal regions. In Proceedings of the BMVC, Cardiff, UK, 2-5 September 2002; pp. 384-393.
32. Ojala, T.; PietikaÈinen, M.; Harwood, D. A comparative study of texture measures with classification based on feature distributions. Pattern Recognit. 1996, 29, 51-59.
33. Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 1979, 9, 62-66.
34. Battiato, S.; Stanco, F.; Cafiso, S.; di Graziano, A. Adaptive imaging techniques for pavement distress analysis. Commun. SIMAI Congr. 2007, doi: 10.1685/CSC06016.
35. Zuo, Y.; Wang, G.; Zuo, C. A novel image segmentation method of pavement surface cracks based on fractal theory. In Proceedings of the International Conference on Computational Intelligence and Security, Suzhou, China, 13-17 December 2008; Volume 2, pp. 485-488.
36. Wei, W.; Liu, B.; Bai, P. Automatic road crack image preprocessing for detection and identification. In Proceedings of the 2nd International Conference on Intelligent Networks and Intelligent Systems (ICINIS09), Tianjin, China, 1-3 November 2009; pp. 319-322.
37. Salari, E.; Bao, G. Pavement distress detection and classification using feature mapping. In Proceedings of the IEEE International Conference on Electro/Information Technology, Normal, IL, USA, 20-22 May 2010.
38. Jing, L.; Aiqin, Z. Pavement crack distress detection based on image analysis. In Proceedings of the 2010 International Conference on Machine Vision and Human-machine Interface, Kaifeng, China, 24-25 April 2010.
39. Iyer, S.; Sinha, S.K. A robust approach for automatic detection and segmentation of cracks in underground pipeline images. Image Vision Comput. 2005, 23, 921-933.
40. Bai, X.; Zhou, F. Multi structuring element top-hat transform to detect linear features. In Proceedings of the IEEE 10th International Conference on Signal Processing (ICSP), Beijing, China, 24-28 October 2010.
41. Maode, Y.; Shaobo, B.; Kun, X.; Yuyao, H. Pavement crack detection and analysis for high-grade highway. In Proceedings of the 8th International Conference on Electronic Measurement and Instruments ICEMI07, Xi'an, China, 16-18 August 2007.
42. Subirats, P.; Dumoulin, J.; Legeay, V.; Barba, D. Automation of pavement surface crack detection using the continuous wavelet transform. In Proceedings of the IEEE International Conference on Image Processing, Atlanta, GA, USA, 8-11 October 2006.
43. Chambon, S.; Subirats, P.; Dumoulin, J. Introduction of a wavelet transform based on 2D matched filter in a Markov random field for fine structure extraction: Application on road crack detection. Proc. SPIE 2009, doi: 10.1117/12.805437.
44. Zhou, J.; Huang, P.; Chiang, F.-P. Wavelet-based pavement distress classification. Trans. Res. Rec. J. Trans. Res. Board 2005, 1940, 89-98.
45. Ma, C.; Wang, W.; Zhao, C.; Di, F.; Zhu, Z. Pavement cracks detection based on FDWT. In Proceedings of the International Conference on Computational Intelligence and Software Engineering (CiSE09), Wuhan, China, 11-13 December 2009.
46. Ma, C.-X.; Zhao, C.-X.; Hou, Y.-K. Pavement distress detection based on nonsubsampled contourlet transform. In Proceedings of the International Conference on Computer Science and Software Engineering, Wuhan, China, 12-14 December 2008.
47. Zhibiao, S.; Yanqing, G. Algorithm on contourlet domain in detection of road cracks for pavement images. In Proceedings of the Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, Hong Kong, 10-12 August 2010.
48. Ying, L.; Salari, E. Beamlet transform based technique for pavement image processing and classification. In Proceedings of the International Conference on Electro/Information Technology, EIT09, Windsor, ON, Canada, 7-9 June 2009.
49. Wei, N.; Zhao, X.; Dou, X.-Y.; Song, H.; Wang, T. Beamlet transform based pavement image crack detection. In Proceedings of the International Conference on Intelligent Computation Technology and Automation ICICTA10, Changsha, China, 11-12 May 2010.
50. Kaseko, M.S.; Lo, Z.P.; Ritchie, S.G. Comparison of traditional and neural classifiers for automated pavement crack detection. J. Transp. Eng. 1994, 120, 552-569.
51. Lee, B.J.; Lee, H.D. A robust position invariant artificial neural network for digital pavement crack analysis. In Proceedings of the TRB Annual Meeting, Washington, DC, USA, 12-16 January 2003.
52. Bray, J.; Verma, B.; Li, X.; He, W. A neural network based technique for automatic classification of road cracks. In Proceedings of the International Joint Conference on Neural Networks (IJCNN06), Vancouver, BC, Canada, 16-21 July 2006; pp. 907-912.
53. Xu, G.; Ma, J.; Liu, F.; Niu, X. Automatic recognition of pavement surface crack based on Bp neural network. In Proceedings of the International Conference on Computer and Electrical Engineering (ICCEE08), Phuket, Thailand, 20-22 December 2008.
54. Saar, T.; Talvik, O. Automatic asphalt pavement crack detection and classification using neural networks. In Proceedings of the 12th Biennial Baltic Electronics Conference (BEC10), Tallinn, Estonia, 4-6 October 2010.
55. Li, N.; Hou, X.; Yang, X.; Dong, Y. Automation recognition of pavement surface distress based on support vector machine. In Proceedings of the Second International Conference on Intelligent Networks and Intelligent Systems (ICINIS09), Tianjin, China, 1-3 November 2009.
56. Younes, G.; Hadda, C.; Attia, N.; Djelloul, Z. Supervised learning and automatic recognition of asphalt pavement deteriorations. In Proceedings of the IEEE/ACS International Conference on Computer Systems and Applications (AICCSA09), Rabat, Morocco, 10-13 May 2009.
57. Sun, C.; Vallotton, P. Fast linear feature detection using multiple directional non-maximum suppression. In Proceedings of the 18th IEEE International Conference on Pattern Recognition (ICPR06), Hong Kong, 20-24 August 2006.
58. Sun, C.; Vallotton, P. Priority-based path growing for linear feature detection. In Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA07), Glenelg, Australia, 3-5 December 2007.
59. Nguyem, T.-S.; Avila, M.; Stephane, B. Automatic detection and classification of defect on road pavement using anisotropy measure. In Proceedings of the 17th European Signal Processing Conference (EURIPCO09), Glasgow, UK, 24-28 August 2009.
60. Huang, Y.; Xu, B. Automatic inspection of pavement cracking distress. J. Electron. Imaging 2006, doi: 10.1117/1.2177650.
61. Sorncharean, S.; Phiphobmongkol, S. Crack detection on asphalt surface image using enhanced grid cell analysis. In Proceedings of the 4th IEEE International Symposium on Electronic Design, Test and Applications (Delta08), Hong Kong, 23-25 January 2008; pp. 49-54.
62. Yamaguchi, T.; Hashimoto, S. Automated crack detection for concrete surface image using percolation model and edge information. In Proceedings of the 32nd Annual Conference of the IEEE Industrial Electronics Society (IECON2006), Paris, France, 7-10 November 2006; pp. 3355-3360.
63. Amírola, A.; Aliseda, P.; Crespo, R.; Yarza, P., Pavement surface defects: Classification and quantification over a road network. In Proceedings of the 6th Symposium on Pavement Surface Characteristics (SURF2008), Portoroz, Slovenia, 20-22 October 2008.