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, 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 Shi
  • Xuewu Zhang
  • Yingjuan Xie
Article
  • 61 Downloads

Abstract

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.

Keywords

Crack detection Dam CrackLG Feature extraction K-means clustering Threshold 

Notes

Acknowledgements

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.

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Copyright information

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

Authors and Affiliations

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

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