Smart Structures and Systems

  • 제14권4호
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  • Pages.719-741
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  • 2014
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Automated assessment of cracks on concrete surfaces using adaptive digital image processing

  • Liu, Yufei (Key Laboratory of Civil Engineering Safety and Durability of Ministry of Education, Department of Civil Engineering, Tsinghua University) ;
  • Cho, Soojin (School of Urban and Environmental Engineering, U/san National Institute of Science and Technology) ;
  • Spencer, Billie F. Jr (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Fan, Jiansheng (Key Laboratory of Civil Engineering Safety and Durability of Ministry of Education, Department of Civil Engineering, Tsinghua University)
  • 투고 : 2014.05.30
  • 심사 : 2014.08.30
  • 발행 : 2014.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

Monitoring surface cracks is important to ensure the health of concrete structures. However, traditional visual inspection to monitor the concrete cracks has disadvantages such as subjective inspection nature, associated time and cost, and possible danger to inspectors. To alter the visual inspection, a complete procedure for automated crack assessment based on adaptive digital image processing has been proposed in this study. Crack objects are extracted from the images using the subtraction with median filter and the local binarization using the Niblack's method. To adaptively. determine the optimal window sizes for the median filter and the Niblack's method without distortion of crack object an optimal filter size index (OFSI) is proposed. From the extracted crack objects using the optimal size of window, the crack objects are decomposed to the crack skeletons and edges, and the crack width is calculated using 4-connected normal line according to the orientation of the local skeleton line. For an image, a crack width nephogram is obtained to have an intuitive view of the crack distribution. The proposed procedure is verified from a test on a concrete reaction wall with various types of cracks. From the crack images with different crack widths and patterns, the widths of cracks in the order of submillimeters are calculated with high accuracy.

키워드

참고문헌

  1. Abdel-Qader, I., Abudayyeh,O. and Kelly, M.E. (2003), "Analysis of edge-detection techniques for crack identification in bridges", J. Comput. Civil Eng.-ASCE,17(4), 255-263. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(255)
  2. Boughet, J.Y. (2013), Website: Camera Calibration Toolbox for Matlab: available at: http://www.vision.caltech.edu/bouguetj/calib_doc/.
  3. Canny,J. (1986), "A computational approach to edge", IEEE T. Pattern Anal., 8(6), 679-698.
  4. Chanda, B. and Majumder, D.D. (2004), Digital Image Processing and Analysis, PHI Learning Pvt. Ltd., New Delhi, India.
  5. Chock, G. (2007), ATC-20 Post-Earthquake Building Safety Evaluations Performed after the October 15, 2006 Hawaii Earthquakes Summary and Recommendations for Improvements (updated). available at: https://www.eeri.org/wp-content/uploads/United%20States/SEAOH_EarthquakeSafetyEvalRec.pdf
  6. Duncan, J. (1984), "Selective attention and the organization of visual information", J. Exp. Psychol. Gen. 113(4), 501-517. https://doi.org/10.1037/0096-3445.113.4.501
  7. Ehrig, K., Goebbels, J., Meinel,D., Paetsch, O., Prohaska, S. and Zobel, V. (2011), "Comparison of crack detection methods for analyzing damage processes in concrete with computed tomography", Proceedings of theInternational Symposium on Digital Industrial Radiology and Computed Tomography, Berlin, Germany, June.
  8. Fujita, Y. and Hamamoto, Y. (2011), "A robust automatic crack detection method from noisy concrete surfaces", Mach. Vision Appl., 22(2), 245-254. https://doi.org/10.1007/s00138-009-0244-5
  9. Huang, T.S. (1981), Two-dimensional Digital Signal Processing II: Transforms and Median Filters, Springer-Verlag New York, Inc, New-York, USA.
  10. Jahanshahi, M.R., Kelly, J.S., Masri, S.F. and Sukhatme, G.S. (2009), "A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures", Struct. Infrasstruct. E., 5(6), 455-486. https://doi.org/10.1080/15732470801945930
  11. Johnson, K. (2004), San Simeon Earthquake, City of Paso Robles Emergency Response Report, available at:http://www.prcity.com/government/pdf/EQResponseRpt.pdf.
  12. Kefali, A., Sari, T. and Sellami, M. (2010), "Evaluation of several binarization techniques for old Arabic documents images", Proceedings of the1stInternational Symposium on Modeling and Implementing Complex Systems, Constantine, Algeria, May.
  13. Khurshid, K., Siddiqi, I., Faure, C. and Vincent, N. (2009), "Comparison of Niblack inspired binarization methods for ancient documents", IS&T/SPIE ElectronicImaging, International Society for Optics and Photonics, San Francisco, CA, USA.
  14. Kim, W.J., Lee, J.M., Kim, J.S. and Lee, C.J. (2012), "Measuring high speed crack propagation in concrete fracture test using mechanoluminescent material", Smart Struct. Syst., 10(6), 547-555. https://doi.org/10.12989/sss.2012.10.6.547
  15. Lam, L., Lee, S.W. and Suen, C.Y. (1992), "Thinning methodologies-a comprehensive survey", IEEE T. Pattern Anal., 14(9), 869-885. https://doi.org/10.1109/34.161346
  16. Lee, B.Y., Kim, Y.Y., Yi, S.T. and Kim, J.K. (2013), "Automated image processing technique for detecting and analysing concrete surface cracks", Struct. Infrastruct. E., 9(6), 567-577. https://doi.org/10.1080/15732479.2011.593891
  17. Lu, Z.R. and Liu, J.K. (2012), "Vibration analysis of a cracked beam with axialforce and crack identification", Smart Struct. Syst., 9(4), 355-371. https://doi.org/10.12989/sss.2012.9.4.355
  18. Niblack, W. (1985), An introduction to digital image processing, Strandberg Publishing Company, Kobenhavn, Denmark.
  19. Nishikawa, T., Yoshida, J., Sugiyama, T. and Fujino, Y. (2012), "Concrete crack detection by multiple sequential image filtering", Comput‐Aided Civ. Inf., 27(1), 29-47. https://doi.org/10.1111/j.1467-8667.2011.00716.x
  20. Otsu, N. (1975), "A threshold selection method from gray-level histograms", Automatica, 11(285-296), 23-27.
  21. Peng, J.Y. and Hsu, C.N. (2009), Adaptive local thresholding for fluorescence cell micrographs, Inst. of Info. Sci., Academia Sinica, Tech. Rep. TR-IIS-09-008.
  22. Prieto, R. and Brinckerhoff, P. (2002), The 3Rs: Lessons Learned from September 11th, Royal Academy of Engineering, London, UK.
  23. Rathinam, S., Kim, Z.W. and Sengupta, R. (2008), "Vision-based monitoring of locally linear structures using an unmanned aerial vehicle 1", J. Infrastruct. Syst. - ASCE, 14(1), 52-63. https://doi.org/10.1061/(ASCE)1076-0342(2008)14:1(52)
  24. Shokri, T. and Nanni, A. (2014), "Crack source location by acoustic emission monitoring method in RC strips during in-situ load test", Smart Struct. Syst., 13(1), 155-171. https://doi.org/10.12989/sss.2014.13.1.155
  25. Tabatabaei, S.A. and Bohlool, M. (2010), "A novel method for binarization of badly illuminated document images", Proceedings of the17th IEEE International Conference onImage Processing (ICIP), Hong Kong, China, September.
  26. Tanaka, N. and Uematsu, K. (1998), "A crack detection method in road surface images using morphology", IAPR Workshop on Machine Vision Applications, Makuhari, Chiba, Japan.
  27. Torok, M.M., Golparvar-Fard, M. and Kochersberger, K.B. (2013), "Image-based automated 3D crack detection for post-disaster building assessment", J. Comput. Civil Eng. - ASCE, doi:10.1061/(ASCE)CP.1943-5487.0000334.
  28. Trier, O.D. and Jain, A.K. (1995), "Goal-directed evaluation of binarization methods", IEEE T. Pattern Anal., 17(12), 1191-1201. https://doi.org/10.1109/34.476511
  29. Wang, C., Chen, T. and Qu, Z. (2010), "A novel improved median filter for salt-and-pepper noise from highly corrupted images", Proceedings of the3rd IEEE International Symposium on Systems and Control in Aeronautics and Astronautics (ISSCAA), Harbin, China, June.
  30. Xiang, J., Matsumoto, T., Long, J., Wang, Y. and Jiang, Z. (2012), "A simple method to detectcracks in beam-like structures", Smart Struct. Syst., 9(4), 335-353. https://doi.org/10.12989/sss.2012.9.4.335
  31. Yamaguchi, T. and Hashimoto, S. (2010), "Fast crack detection method for large-size concrete surface images using percolation-based image processing", Mach. Vision Appl., 21(5), 797-809. https://doi.org/10.1007/s00138-009-0189-8
  32. Zhang, J. and Hu, J. (2008), "Image segmentation based on 2D Otsu method with histogram analysis", Proceedingsof the2008 International IEEE Conference on Computer Science and Software Engineering, Wuhan, Hubei, China, December.
  33. Ziou, D. and Tabbone, S. (1998), "Edge detection techniques-an overview", Int. J. Pattern Recogn., 8, 537-559.

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  9. A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications vol.2016, 2016, https://doi.org/10.1155/2016/7103039
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  20. A robotics and computer-aided procedure for defect evaluation in bridge inspection vol.10, pp.3, 2014, https://doi.org/10.1007/s13349-020-00395-3
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  26. Methodology for Quantifying Features of Early-Age Concrete Cracking from Laser Scanned 3D Data vol.33, pp.7, 2021, https://doi.org/10.1061/(asce)mt.1943-5533.0003774
  27. Deep convolutional neural networks for semantic segmentation of cracks vol.29, pp.1, 2022, https://doi.org/10.1002/stc.2850