• DocumentCode
    3065394
  • Title

    Image Processing Technology for Pipe Weld Visual Inspection

  • Author

    Liao, Gaohua ; Xi, Junmei

  • Author_Institution
    Nanchang Inst. of Technol., Nanchang, China
  • Volume
    1
  • fYear
    2009
  • fDate
    10-11 July 2009
  • Firstpage
    173
  • Lastpage
    176
  • Abstract
    To the pipeline welding defect detection, robot tracking weld reliably, the welds original image acquired by visual sensor need pretreatment to eliminate the impact of noise. A pipeline welding machine vision detection method proposed in this paper. First of all, the use of neighborhood mean filter for smoothing, the largest variance threshold method selecting adaptive threshold to segmentation image. Image after smooth will be the binarization processing. Then remove the small area noise with labeling method, get a clear image of the weld. The level of projection method to the recognition of weld image and determine the location of weld. The experiments show that the pretreatment method solute the prevailing situation of uneven illumination, and also reducing the size of the processing image, reducing the amount of data, saving time, meeting the needs of the pipeline weld tracking real-time detection, laying a solid foundation for follow-up quality inspection.
  • Keywords
    image segmentation; inspection; mechanical engineering computing; pipelines; reliability; welding; adaptive image thresholding; binarization processing; image processing; image segmentation; pipe weld visual inspection; pipeline welding defect detection; pipeline welding machine vision detection; robot tracking weld reliably; visual sensor; Adaptive filters; Image processing; Image segmentation; Image sensors; Inspection; Machine vision; Pipelines; Robot sensing systems; Smoothing methods; Welding; Image processing; Object recognition; computer vision; robot;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Engineering, 2009. ICIE '09. WASE International Conference on
  • Conference_Location
    Taiyuan, Shanxi
  • Print_ISBN
    978-0-7695-3679-8
  • Type

    conf

  • DOI
    10.1109/ICIE.2009.262
  • Filename
    5210863