10.14489/td.2024.06.pp.060-071

2024, 06 June

DOI: 10.14489/td.2024.06.pp.060-071

Kleizer M. P., Kuvshinnikov V. S., Kovshov E. E.
CONVOLUTIONAL NEURAL NETWORKS USAGE FOR RASTER IMAGE PROCESSING IN NON-DESTRUCTIVE TESTING
(pp. 60-71)

Abstract. An overview of Russian and foreign literature sources on the use of computer vision technologies and convolutional artificial neural networks in technological operations of visual control is presented. Methods and algorithms review of computer vision and artificial neural network models used by the authors of scientific articles, methods of preprocessing and expanding the initial data set for models training under consideration and verifying the correctness of machine vision algorithms is carried out. Possible approaches for automating the assessment of visual quality control in various fields of industry are highlighted, research results, results of machine vision algorithms, neural network models, their accuracy and efficiency indicators are presented, as well as areas and possible scenarios for the application of the presented algorithmic solutions. Some results of machine vision algorithms, neural network models, their accuracy and efficiency indicators are presented.

Keywords: testing automation, defect detection, convolutional artificial neural networks, non-destructive testing, machine vision, NDE 4.0.

+ - About the Authors Click to collapse

M. P. Kleizer (Federal State Budget Education Institution of Higher Education “MIREA – Russian Technical University”, Moscow, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.
V. S. Kuvshinnikov, E. E. Kovshov (Joint-Stock Company “Research and Development Institute of Construction Technology – Atomstroy”, Moscow, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра. , Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.

+ - References Click to collapse

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