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 | |
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