• DocumentCode
    2394374
  • Title

    A novel crack detection algorithm of underwater dam image

  • Author

    Cong-ping Chen ; Jian Wang ; Lei Zou ; Jun Fu ; Cong-ji Ma

  • Author_Institution
    Sch. of Mech. & Mater. Eng., Three Gorges Univ., Yichang, China
  • fYear
    2012
  • fDate
    19-20 May 2012
  • Firstpage
    1825
  • Lastpage
    1828
  • Abstract
    A novel algorithm is introduced for the deficiencies of underwater dam image crack detection. The algorithm makes use of the intensity values of 2D image to generate a 3D spatial surface, which is regarded as a concave-convex ground with “pits” and “ditches”. The “pits” represent the noise pixels and the “ditches” represent the crack pixels. The cracks that are difficult to describe in 2D image can be regarded well as ditches in the 3D spatial surface. Then by analyzing the characteristics of ditches space curvatures, the space detected method is used to get the ditches information, which is mapped to 2D surface as the crack. Because the detected result contains some noise and fake cracks, so BP neural network is adopted to identify crack object. As a result, the crack information is detected successfully.
  • Keywords
    backpropagation; crack detection; dams; image processing; neural nets; object detection; structural engineering computing; 2D image; 3D spatial surface; BP neural network; concave-convex ground; crack detection algorithm; crack object identification; crack pixels; ditches space curvatures; noise pixels; underwater dam image; Detection algorithms; Feature extraction; Hydroelectric power generation; Neural networks; Noise; Surface cracks; Surface morphology; BP neural network; crack recognition; space curvature; underwater dam image;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems and Informatics (ICSAI), 2012 International Conference on
  • Conference_Location
    Yantai
  • Print_ISBN
    978-1-4673-0198-5
  • Type

    conf

  • DOI
    10.1109/ICSAI.2012.6223399
  • Filename
    6223399