Please use this identifier to cite or link to this item:
http://elar.urfu.ru/handle/10995/92377
Title: | Semantic segmentation in flaw detection |
Authors: | Kotyuzanskiy, L. A. Ryzhkova, N. G. Chetverkin, N. V. |
Issue Date: | 2020 |
Publisher: | Institute of Physics Publishing |
Citation: | Kotyuzanskiy L. A. Semantic segmentation in flaw detection / L. A. Kotyuzanskiy, N. G. Ryzhkova, N. V. Chetverkin. — DOI 10.1088/1757-899X/862/3/032056 // IOP Conference Series: Materials Science and Engineering. — 2020. — Vol. 3. — Iss. 862. — 32056. |
Abstract: | The paper presents a review of study on detection and classification of defects using semantic image segmentation based on convolutional neural networks. Taking into account the revealed general features of flaw detection tasks of various industries related to the lack of a large marked data set and the need to detect defects of small sizes. The convolutional neural network of the u-net architecture was chosen as the basis for the decision support system. Testing of this architecture on several datasets yielded positive results regardless of the area of use. © 2020 IOP Publishing Ltd. All rights reserved. |
URI: | http://elar.urfu.ru/handle/10995/92377 |
Access: | info:eu-repo/semantics/openAccess |
SCOPUS ID: | 85086226041 |
PURE ID: | 13161991 |
ISSN: | 17578981 |
DOI: | 10.1088/1757-899X/862/3/032056 |
Appears in Collections: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
Files in This Item:
File | Description | Size | Format | |
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10.1088-1757-899X-862-3-032056.pdf | 690,26 kB | Adobe PDF | View/Open |
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