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

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 language	English
Pages (from-to)	259-267
Number of pages	9
Journal	Russian Journal of Nondestructive Testing
Volume	56
Issue number	3
DOIs	https://doi.org/10.1134/S1061830920030055
Publication status	Published - 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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10.1134/S1061830920030055

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

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%.",

keywords = "classification, defect, image processing, segmentation, visual inspection, weld",

author = "Muravyov, {S. V.} and Pogadaeva, {E. Yu}",

year = "2020",

month = mar,

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doi = "10.1134/S1061830920030055",

language = "English",

volume = "56",

pages = "259--267",

journal = "Russian Journal of Nondestructive Testing",

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T1 - Computer-Aided Recognition of Defects in Welded Joints during Visual Inspections Based on Geometric Attributes

AU - Muravyov, S. V.

AU - Pogadaeva, E. Yu

PY - 2020/3/1

Y1 - 2020/3/1

N2 - 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%.

AB - 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%.

KW - classification

KW - defect

KW - image processing

KW - segmentation

KW - visual inspection

KW - weld

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