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Документ Відкритий доступ Application of YOLOX deep learning model for automated object detection on thermograms(2022-01-26) Skladchykov, Ivan; Momot, Andrii; Galagan, Roman; Bohdan, Halyna; Trotsiuk, KaterynaДокумент Відкритий доступ Automation of ultrasound breast cancer images classification using deep neural networks(Sciences of Europe, 2022) Momot, Andrii; Galagan, Roman; Zaboluieva, MartaДокумент Відкритий доступ Deep Learning Automated System for Thermal Defectometry of Multilayer Materials(2021) Momot, Andrii; Galagan, Roman; Gluhovskii, VictorДокумент Відкритий доступ Influence of architecture and training dataset parameters on the neural networks efficiency in thermal nondestructive testing(2019-10) Momot, Andrii; Galagan, RomanDescribes the perspective of the use of artificial neural networks in automated thermal non-destructive testing and defectometry systems. The influence of backpropagation neural networks architecture on the efficiency of defect classification and accuracy of determining their depth and thickness are analyzed. Considered the influence of volume and quality of training dataset on the efficiency of defect classification and accuracy of defectometry. Performance of neural networks is evaluated by quantitative indicators, such as MSE, relative error and Tanimoto criterion. The optimal neural network architecture for using in active thermal testing was established on the basis of experimental researches.