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Detection of surface defects in welded joints during visual inspections using machine vision methods
M.G. Yemelyanova 1, S.S. Smailova 1, O.E. Baklanova 1

1 D. Serikbayev East Kazakhstan Technical University,
070004, Ust-Kamenogorsk, Kazakhstan, Serikbayev 19

 PDF, 1369 kB

DOI: 10.18287/2412-6179-CO-1137

Pages: 112-117.

Full text of article: Russian language.

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 continuous rapid detection of surface defects.

Keywords:
visual inspection, welded joints, defect, machine vision, background subtraction.

Citation:
Yemelyanova MG, Smailova SS, Baklanova OE. Detection of surface defects in welded joints during visual inspections using machine vision methods. Computer Optics 2023; 47(1): 112-117. DOI: 10.18287/2412-6179-CO-1137.

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