Computer-Aided Recognition of Defects in Welded Joints during Visual Inspections Based on Geometric Attributes; Russian Journal of Nondestructive Testing; Vol. 56, iss. 3
| Parent link: | Russian Journal of Nondestructive Testing Vol. 56, iss. 3.— 2020.— [P. 259-267] |
|---|---|
| मुख्य लेखक: | |
| निगमित लेखक: | |
| अन्य लेखक: | |
| सारांश: | 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 |
| भाषा: | अंग्रेज़ी |
| प्रकाशित: |
2020
|
| विषय: | |
| ऑनलाइन पहुंच: | https://doi.org/10.1134/S1061830920030055 |
| स्वरूप: | MixedMaterials इलेक्ट्रोनिक पुस्तक अध्याय |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=662251 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 662251 | ||
| 005 | 20250408135610.0 | ||
| 035 | |a (RuTPU)RU\TPU\network\33388 | ||
| 090 | |a 662251 | ||
| 100 | |a 20200619d2020 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 102 | |a US | ||
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i | |
| 182 | 0 | |a b | |
| 200 | 1 | |a Computer-Aided Recognition of Defects in Welded Joints during Visual Inspections Based on Geometric Attributes |f S. V. Muravyov (Murav’ev), E. Yu. Pogadaeva | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 21 tit.] | ||
| 330 | |a 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%. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Russian Journal of Nondestructive Testing | ||
| 463 | |t Vol. 56, iss. 3 |v [P. 259-267] |d 2020 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a weld | |
| 610 | 1 | |a defect | |
| 610 | 1 | |a segmentation | |
| 610 | 1 | |a classification | |
| 610 | 1 | |a image processing | |
| 610 | 1 | |a visual inspection | |
| 610 | 1 | |a сварные швы | |
| 610 | 1 | |a дефекты | |
| 610 | 1 | |a сегментация | |
| 610 | 1 | |a обработка изображений | |
| 700 | 1 | |a Muravyov (Murav’ev) |b S. V. |c specialist in the field of control and measurement equipment |c Professor of Tomsk Polytechnic University,Doctor of technical sciences |f 1954- |g Sergey Vasilyevich |3 (RuTPU)RU\TPU\pers\31262 |9 15440 | |
| 701 | 1 | |a Pogadaeva |b E. Yu. |g Ekaterina Yurjevna | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Отделение автоматизации и робототехники |3 (RuTPU)RU\TPU\col\23553 |
| 801 | 2 | |a RU |b 63413507 |c 20200619 |g RCR | |
| 856 | 4 | |u https://doi.org/10.1134/S1061830920030055 | |
| 942 | |c CF | ||