Multimedia Tools and Applications

, Volume 77, Issue 20, pp 26581–26599 | Cite as

A novel automatic dam crack detection algorithm based on local-global clustering

  • Xinnan Fan
  • Jingjing Wu
  • Pengfei ShiEmail author
  • Xuewu Zhang
  • Yingjuan Xie


Dam crack detection is necessary to ensure the safety of dams. However, traditional detection methods always perform poorly, with a low detection rate and high false alarm rate, due to the complex underwater environment. In this paper, a novel automatic dam crack detection algorithm (CrackLG) is proposed based on local-global clustering analysis that can find cracks on dam surfaces accurately and quickly using images as well as reduce human subjectivity. First, an image shot of an underwater dam surface is divided into non-overlapping image blocks after pre-processing. Then, image blocks containing crack pixels are identified by local clustering analysis. Second, the image is binarized by adaptive bi-level thresholding based on the local gray intensity. Meanwhile, some noise is removed based on the computed optimal threshold. After extracting global 3-D features, final crack regions are obtained by global clustering analysis. The advantage of CrackLG is that the threshold for realizing image binarization is self-adaptive. Additionally, it can automatically perform crack detection without human supervision. The simulation and comparison show that the proposed CrackLG method is more effective for underwater dam crack detection.


Crack detection Dam CrackLG Feature extraction K-means clustering Threshold 



This paper has a clear division of labor. Fan Xinnan contributed to the conception and design of the study. Wu Jingjing wrote and performed the simulation. Shi Pengfei and Zhang Xuewu revised the manuscript. All authors have read and approved the final manuscript. The authors also wish to thank the National Natural Science Foundation of China (No. 61573128 and No. 61671202), the National Key Research Program of China (No. 2016YFC0401606), the Jiangsu Province Natural Science Foundation (grant number BK20170305), and the Fundamental Research Funds for the Central Universities (No. 2015B25214), which provide financial aid and assistance for this paper.


  1. 1.
    Adhikari RS, Moselhi O, Bagchi A (2014) Image-based retrieval of concrete crack properties for bridge inspection. Autom Construct 39:180–194CrossRefGoogle Scholar
  2. 2.
    Ahmed NBC, Lahouar S, Souani C, Besbes K (2017) Automatic crack detection from pavement images using fuzzy thresholding. In: International conference on control, automation and diagnosis, pp 528–537Google Scholar
  3. 3.
    Amhaz R, Chambon S, Idier J, Baltazart V (2015) Automatic crack detection on 2D pavement images: an algorithm based on minimal path selection. IEEE Trans Intell Transport Syst, 24pGoogle Scholar
  4. 4.
    Anzai Y (2012) Pattern recognition & machine learning. ElsevierGoogle Scholar
  5. 5.
    Boutsidis C, Magdon-Ismail M (2013) Deterministic feature selection for k-means clustering. IEEE Trans Inf Theory 59(9):6099–6110MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chambon S, Subirats P, Dumoulin J (2009) Introduction of a wavelet transform based on 2D matched filter in a Markov random field for fine structure extraction: application on road crack detection. In: IS&T/SPIE Electronic imaging. International Society for Optics and Photonics, pp 72510A–72510AGoogle Scholar
  7. 7.
    Dewan S, Bajaj S, Prakash S (2015) Using ant’s colony algorithm for improved segmentation for number plate recognition. In: 2015 IEEE/ACIS 14th International conference on computer and information science (ICIS). IEEE, pp 313–318Google Scholar
  8. 8.
    Dorafshan S (2016) Automatic surface crack detection in concrete structures using OTSU thresholding and morphological operations (Doctoral dissertation, Utah State University)Google Scholar
  9. 9.
    Fasel TR, Sohn H, Park G, Farrar CR (2005) Active sensing using impedance-based ARX models and extreme value statistics for damage detection. Earthquake Eng Struct Dyn 34(7):763–785CrossRefGoogle Scholar
  10. 10.
    Hu D, Tian T, Yang H, Xu S, Wang X (2012) Wall crack detection based on image processing. In: 2012 Third, International conference on intelligent control and information processing (ICICIP). IEEE, pp 597–600. 2Google Scholar
  11. 11.
    Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis, vol 344. WileyGoogle Scholar
  12. 12.
    Kumar M, Patel NR (2007) Clustering data with measurement errors. Comput Statist Data Anal 51(12):6084–6101MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lee BY, Kim YY, Yi ST, Kim JK (2013) Automated image processing technique for detecting and analysing concrete surface cracks. Struct Infrastruct Eng 9 (6):567–577CrossRefGoogle Scholar
  14. 14.
    Li QQ, Liu X (2008) A model for segmentation and distress statistic of massive pavement images based on multi-scale strategies. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37Google Scholar
  15. 15.
    Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016) Underwater image de-scattering and classification by deep neural network. Comput Electric Eng 54:68–77CrossRefGoogle Scholar
  16. 16.
    Lins RG, Givigi SN (2016) Automatic crack detection and measurement based on image analysis. IEEE Trans Instrum Measur 65(3):583–590CrossRefGoogle Scholar
  17. 17.
    Liu QY, Tan Q (2005) Concrete crack detection based on image processing. J Wuhan Univ TechnolGoogle Scholar
  18. 18.
    Nishikawa T, Yoshida J, Sugiyama T, Fujino Y (2012) Concrete crack detection by multiple sequential image filtering. Comput-Aided Civil Infrastruct Eng 27(1):29–47CrossRefGoogle Scholar
  19. 19.
    Noboyuki O (1979) A threshold selection method from gray level histogram. IEEE Trans Syst Man Cybern 9:62–66CrossRefGoogle Scholar
  20. 20.
    Noh Y, Koo D, Kang YM, Park D, Lee D (2017) Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering. In: 2017 International conference on applied system innovation (ICASI). IEEE, pp 877–880Google Scholar
  21. 21.
    Oliveira H, Correia PL (2013) Automatic road crack detection and characterization. IEEE Trans Intell Transp Syst 14(1):155–168CrossRefGoogle Scholar
  22. 22.
    Oliveira H, Correia PL (2017) Road surface crack detection: improved segmentation with pixel-based refinement. In: 2017 25th European on signal processing conference (EUSIPCO). IEEE, pp 2026–2030Google Scholar
  23. 23.
    Panetta K, Chen G, Agaian S (2015) Human-visual-system-inspired underwater image quality measures. IEEE J Ocean Eng 41(3):1–11Google Scholar
  24. 24.
    Qiao M, Xiaoying W, Yu-an Z (2016) Research on a least squares thresholding algorithm for pavement crack detection. In: 2016 Sixth International conference on information science and technology (ICIST). IEEE, pp 465–469Google Scholar
  25. 25.
    Sevim B, Altunisik AC, Bayraktar A (2012) Experimental evaluation of crack effects on the dynamic characteristics of a prototype arch dam using ambient vibration tests. Comput Concrete 10(3):277–294CrossRefGoogle Scholar
  26. 26.
    Sevim B, Altunisik AC, Bayraktar A (2013) Structural identification of concrete arch dams by ambient vibration tests. Adv Concrete Construct 1(3):227–237CrossRefGoogle Scholar
  27. 27.
    Shi Y, Cui L, Qi Z, Meng F, Chen Z (2016) Automatic road crack detection using random structured forests. IEEE Trans Intell Transp Syst 17:1–12CrossRefGoogle Scholar
  28. 28.
    Shi P, Fan X, Ni J et al (2016) A detection and classification approach for underwater dam cracks[J]. Structural Health Monitoring, 1475921716651039Google Scholar
  29. 29.
    Wang G-r, Fan X-n, Shi P-f, Chen W (2015) Underwater dam crack image enhancement algorithm based on rough set. Comput Modern 9:008Google Scholar
  30. 30.
    Xiangan W, Xingxin X, Jin W, Dong L, Qizhen R, Shunming H, Jinyin S (1998) Research on the GPR exploration for various hidden dangers in water conservancy projects [J]. Geol Prospect, 3Google Scholar
  31. 31.
    Xu W, Tang Zh M, Lv JY (2013) Pavement crack detection based on image saliency. J Image Graph 16(1):69–77Google Scholar
  32. 32.
    Zhang Y (2002) The application of opening-closing operation to eliminate lmage noise [J]. J Weifang Univ, 2Google Scholar
  33. 33.
    Zhang G, Liu Y, Zhou Q (2008) Study on real working performance and overload safety factor of high arch dam. Sci Chin Series E: Technol Sci 51:48–59CrossRefGoogle Scholar
  34. 34.
    Zhang L, He B, Song Y, Yan T (2016) Underwater image feature extraction and matching based on visual saliency detection. InOCEANS 2016-Shanghai. IEEE, pp 1–4Google Scholar
  35. 35.
    Zarfl C, Lumsdon AE, Berlekamp J, Tydecks L, Tockner K (2015) A global boom in hydropower dam construction. Aqua Sci 77(1):161–170CrossRefGoogle Scholar
  36. 36.
    Zhu BF (2006) Current situation and prospect of temperature control and cracking prevention technology for concrete dam. J Hydraul Eng 5(4):415–428Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xinnan Fan
    • 1
  • Jingjing Wu
    • 1
  • Pengfei Shi
    • 1
    Email author
  • Xuewu Zhang
    • 1
  • Yingjuan Xie
    • 1
  1. 1.College of Internet of Things EngineeringHohai UniversityChangzhouChina

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