Open AccessSemantic segmentation in flaw detection
Author(s) -
L. A. Kotyuzanskiy,
Natalia G. Ryzhkova,
N. V. Chetverkin
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/862/3/032056
Subject(s) - convolutional neural network , segmentation , computer science , artificial intelligence , pattern recognition (psychology) , set (abstract data type) , architecture , data set , basis (linear algebra) , machine learning , data mining , mathematics , geography , geometry , archaeology , programming language
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.