• DocumentCode
    2075772
  • Title

    Research on Image Recognition Method of In-Service Pipeline Corrosion Fault

  • Author

    Yuan Peixin ; Tan Jun

  • Author_Institution
    Northeastern Univ., Shenyang, China
  • fYear
    2009
  • fDate
    17-19 Oct. 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    In this paper, for the practical demand of in-service pipeline detection, a new way called mathematical morphology wavelet de-noising innovative method has been developed, based on separate defect points. This method needs to extract the edge of defective parts by wavelet transform modulus maximum method, select some key characteristic parameters in favor of defects identification like fine length, moment invariant, gray energy and so on, and recognize patterns by means of single-output BP neural network. This method has been successfully applied to differentiate the weld joints and corrosion defects of pipelines, and quantitatively recognize the corrosion defects.
  • Keywords
    edge detection; mathematical morphology; neural nets; defect points; defects identification; image recognition method; in-service pipeline corrosion fault; mathematical morphology wavelet de-noising innovative method; patterns recognition; single-output BP neural network; wavelet transform modulus maximum method; Character recognition; Corrosion; Image edge detection; Image recognition; Morphology; Neural networks; Noise reduction; Pattern recognition; Pipelines; Wavelet transforms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-1-4244-4129-7
  • Electronic_ISBN
    978-1-4244-4131-0
  • Type

    conf

  • DOI
    10.1109/CISP.2009.5301140
  • Filename
    5301140