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Computer Optics, 2023, Volume 47, Issue 1, Pages 112–117
DOI: https://doi.org/10.18287/2412-6179-CO-1137
(Mi co1108)
 

This article is cited in 5 scientific papers (total in 5 papers)

IMAGE PROCESSING, PATTERN RECOGNITION

Detection of surface defects in welded joints during visual inspections using machine vision methods

M. G. Yemelyanova, S. S. Smailova, O. E. Baklanova

East Kazakhstan State Technical University named after D. Serikbayev, Ust-Kamenogorsk
References:
Abstract: We discuss a problem of automatic defect detection in welded joints of stainless steel pipes in the production process. Possible defects that occur during tungsten inert gas welding are shown. The substantiation of the choice of the method for solving the problem based on modeling and background subtraction is given. An algorithm for defect detection in welded joints on frames of video sequences is proposed, taking into account the features of a specific area. The background models are built using the methods of averaging and a mixture of Gaussians. Experimental studies of the algorithm are carried out using examples of processing frames of video sequences received from a static camera. The obtained results confirm that the background modeling method based on frame averaging is suitable for the automatic detection of welding defects since the defects are different and have characteristic features. The proposed algorithm makes it possible to detect and highlight the defective area in a welded joint on frames of video sequences. The experimental results show that the algorithm satisfies the requirements for con-tinuous rapid detection of surface defects.
Keywords: visual inspection, welded joints, defect, machine vision, background subtraction
Received: 31.03.2022
Accepted: 08.11.2022
Document Type: Article
Language: Russian
Citation: M. G. Yemelyanova, S. S. Smailova, O. E. Baklanova, “Detection of surface defects in welded joints during visual inspections using machine vision methods”, Computer Optics, 47:1 (2023), 112–117
Citation in format AMSBIB
\Bibitem{YemSmaBak23}
\by M.~G.~Yemelyanova, S.~S.~Smailova, O.~E.~Baklanova
\paper Detection of surface defects in welded joints during visual inspections using machine vision methods
\jour Computer Optics
\yr 2023
\vol 47
\issue 1
\pages 112--117
\mathnet{http://mi.mathnet.ru/co1108}
\crossref{https://doi.org/10.18287/2412-6179-CO-1137}
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  • https://www.mathnet.ru/eng/co1108
  • https://www.mathnet.ru/eng/co/v47/i1/p112
  • This publication is cited in the following 5 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
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