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|a Weld Defects Automatic Visual Recognition by Combined Application of Canny Edge Detector and Interval Fusion with Preference Aggregation
|f S. V. Muravyov (Murav’ev), Nguyen Duc C.
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| 203 |
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|a Текст
|c электронный
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| 300 |
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|a Title screen
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| 320 |
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|a [References: 14 tit.]
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|a Checking the surface quality of the weld is an important step in nondestructive testing. This inspection can be performed visually, but the accuracy of the results is highly operator-dependent and time-consuming. This paper proposes a new approach to automatic visual recognition of welding defect edges from photographic images. The approach is a combination of the Canny edge detector (CED) and the method of interval fusion with preference aggregation (IF&PA). CED uses first-order derivatives to detect pixels with sudden changes in color intensity and to recognize the contours of objects in the image. For this aim, the CED needs a threshold value to recognize real edges and remove false ones. The IF&PA algorithm is proposed to detect this threshold, where the image of the weld surface is divided into equal bands. For each band, a color intensity histogram is constructed, using which the lower and upper bounds, and the average intensity values are defined for an intensity interval, which characterizes the band. All the intervals are represented by rankings forming a preference profile. Kemeny ranking rule is applied to the profile to determine the best color intensity value, which then acts as the CED threshold. The proposed approach has been tested in detecting the edge of some typical weld defects. The test results show that the proposed approach accurately recognizes the edges of weld defects and eliminates most of the noise in the input image.
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| 463 |
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|t Information, Control, and Communication Technologies (ICCT)
|o Proceedings of the 6th International Scientific Conference, Astrakhan, Russian Federation, 03-07 October 2022
|v [4 p.]
|d 2022
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| 610 |
1 |
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|a электронный ресурс
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| 610 |
1 |
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|a труды учёных ТПУ
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| 610 |
1 |
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|a weld defects
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| 610 |
1 |
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|a image segmentation
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| 610 |
1 |
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|a Canny edge
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| 610 |
1 |
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|a detector
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| 610 |
1 |
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|a interval fusion
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| 610 |
1 |
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|a preference aggregation
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| 610 |
1 |
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|a дефекты
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| 610 |
1 |
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|a сварка
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| 610 |
1 |
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|a детекторы
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| 700 |
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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
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| 701 |
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0 |
|a Nguyen Duc C.
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| 712 |
0 |
2 |
|a Национальный исследовательский Томский политехнический университет
|b Инженерная школа информационных технологий и робототехники
|c 2017-
|x TPU
|7 ca
|8 rus
|9 28330
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| 801 |
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2 |
|a RU
|b 63413507
|c 20221221
|g RCR
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| 850 |
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|a 63413507
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| 856 |
4 |
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|u https://doi.org/10.1109/ICCT56057.2022.9976559
|z https://doi.org/10.1109/ICCT56057.2022.9976559
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| 942 |
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|c CF
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