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A Robust Method for Automatically Detecting Cracks on Noisy Concrete Surfaces

  • Conference paper
Next-Generation Applied Intelligence (IEA/AIE 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5579))

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Abstract

Automatic crack detection from concrete surface images is very effective for nondestructive testing. In our previous work, we proposed two preprocessing methods for automatic crack detection from noisy concrete surfaces. In this paper, we propose an automatic crack detection method after the preprocessings. The proposed method consists of two steps. One is relaxation process to prevent noises, and the other is a improved locally adaptive thresholding to detect cracks exactly. We evaluate the performance of the proposed method using 60 actual noisy concrete surface images, compared to those of the conventional thresholding techniques. Experimental results show that the robustness and the accuracy of the proposed method are good.

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Author information

Authors and Affiliations

  1. Graduate School of Medicine, Yamaguchi University, 2-16-1 Tokiwadai, Ube, 755-8611, Japan

    Yusuke Fujita & Yoshihiko Hamamoto

Authors
  1. Yusuke Fujita
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  2. Yoshihiko Hamamoto
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Tainan, 700, Tainan, Taiwan

    Been-Chian Chien

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Computer Science, Texas State University-San Marcos, 601 University Drive, 78666-4616, San Marcos, TX, USA

    Moonis Ali

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© 2009 Springer-Verlag Berlin Heidelberg

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Fujita, Y., Hamamoto, Y. (2009). A Robust Method for Automatically Detecting Cracks on Noisy Concrete Surfaces. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_8

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  • DOI: https://doi.org/10.1007/978-3-642-02568-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02567-9

  • Online ISBN: 978-3-642-02568-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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