Computer-Aided Recognition of Defects in Welded Joints during Visual Inspections Based on Geometric Attributes

S. V. Muravyov, E. Yu Pogadaeva

Research output: Contribution to journalArticle

Abstract

Abstract: An automated defect recognition algorithm is presented for detecting and classifying weld defects by photographic images. The proposed recognition algorithm selects a defective domain in a segmented image, extracts geometric features from the image, and relates the defect to one of six classes: no defect, cavity, longitudinal crack, transverse crack, burn-through, or multiple defect. The algorithm is implemented in the Matlab 2018b MathWorks environment and has been tested on 60 photographs of defects of various classes; the accuracy of recognition was 85%.

Original languageEnglish
Pages (from-to)259-267
Number of pages9
JournalRussian Journal of Nondestructive Testing
Volume56
Issue number3
DOIs
Publication statusPublished - 1 Mar 2020

Keywords

  • classification
  • defect
  • image processing
  • segmentation
  • visual inspection
  • weld

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

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