Automatic Segmentation by the Method of Interval Fusion with Preference Aggregation When Recognizing Weld Defects

Quality control in welding is usually carried out during the visual inspection process and is highly dependent on an operator experience. In this paper, an approach to automatic detection and classification of a defective region is proposed, in which the segmentation of the analyzed photographic ima...

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Bibliographic Details
Published inRussian journal of nondestructive testing Vol. 59; no. 12; pp. 1280 - 1290
Main Authors Muravyov, S. V., Nguyen, D. C.
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 01.12.2023
Springer Nature B.V
Subjects
Characterization and Evaluation of Materials
Chemistry and Materials Science
Image segmentation
Materials Science
Optical Methods
Quality control
Structural Materials
Weld defects
image processing
histogram
interval fusion
welding joint
segmentation
preference aggregation
defect region
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Summary:Quality control in welding is usually carried out during the visual inspection process and is highly dependent on an operator experience. In this paper, an approach to automatic detection and classification of a defective region is proposed, in which the segmentation of the analyzed photographic image of a weld (i.e., its division into defective and defect-free regions) is performed using the region growing procedure. The starting points for this procedure are selected by the authors’ robust method of interval fusion with preference aggregation (IF&PA) on the base of image histogram analysis. Testing the proposed approach for real life photographic images showed its ability to detect different types of weld defects with higher accuracy compared to traditional methods, such as the Otsu method and k -means.
ISSN:1061-8309
1608-3385
DOI:10.1134/S1061830923600855