Computer-Aided Recognition of Defects in Welded Joints during Visual Inspections Based on Geometric Attributes
| Parent link: | Russian Journal of Nondestructive Testing Vol. 56, iss. 3.— 2020.— [P. 259-267] |
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| Sumari: | Title screen 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%. Режим доступа: по договору с организацией-держателем ресурса |
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2020
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| Accés en línia: | https://doi.org/10.1134/S1061830920030055 |
| Format: | Electrònic Capítol de llibre |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=662251 |
| Sumari: | Title screen 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%. Режим доступа: по договору с организацией-держателем ресурса |
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| DOI: | 10.1134/S1061830920030055 |