Detecting Shape of Weld Defect Image on X-ray Film by Image Processing Applied Genetic Algorithm Detecting Shape of Weld Defect Image on X-ray Film by Image Processing Applied Genetic Algorithm

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Abstract

・rights:日本機械学会・rights:本文データは学協会の許諾に基づきCiNiiから複製したものである・relation:isVersionOf:http://ci.nii.ac.jp/naid/110004225643/

Several types of non-destructive testing methods are used for detecting weld defects. Because the X-ray radiographic testing method is particularly useful in inspecting the inside of a weld metal, it is often used in industry. However, since the number of skilled inspectors for X-ray radiographic testing has been gradually decreasing, recently, several methods to detect weld defects from films automatically have been investigated to improve the quality of the detection results. However, X-ray film images contain much noise, and defect images show very low contrast and various shapes in spite of the same kind of defect. Moreover, boundaries between a defect image and the background are unclear, making it difficult to automate the inspection of X-ray films. If the type of defect image were to be judged by an expert system or a neural network which learns the rules of professional inspectors, the boundaries of the defect image would have to be detected in a manner similar to recognition by a human's (or an inspector's) sense of vision. Therefore, in this study, a new image processing method applied genetic algorithms that were a method of optimization, was constructed and applied to the detection of defect boundaries in detail.

Journal

  • JSME International Journal Series C

    JSME International Journal Series C 45(2), 534-542, 2002-06-15

    The Japan Society of Mechanical Engineers

References:  10

Codes

  • NII Article ID (NAID)
    110004225643
  • NII NACSIS-CAT ID (NCID)
    AA11179487
  • Text Lang
    ENG
  • Article Type
    ART
  • ISSN
    13447653
  • NDL Article ID
    6190098
  • NDL Source Classification
    ZN11(科学技術--機械工学・工業)
  • NDL Call No.
    Z53-Y272
  • Data Source
    CJP  NDL  NII-ELS  IR  J-STAGE 
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