Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products

The mathematical statement of the problem of recognizing rivet joint defects in aircraft products is given. A computational method for the recognition of rivet joint defects in aircraft equipment based on video images of aircraft joints has been proposed with the use of neural networks YOLO-V5 for d...

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Autores principales: Amosov, Oleg Semenovich, Amosova, Svetlana Gennadievna, Iochkov, Ilya Olegovich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105654/
https://www.ncbi.nlm.nih.gov/pubmed/35591107
http://dx.doi.org/10.3390/s22093417
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author Amosov, Oleg Semenovich
Amosova, Svetlana Gennadievna
Iochkov, Ilya Olegovich
author_facet Amosov, Oleg Semenovich
Amosova, Svetlana Gennadievna
Iochkov, Ilya Olegovich
author_sort Amosov, Oleg Semenovich
collection PubMed
description The mathematical statement of the problem of recognizing rivet joint defects in aircraft products is given. A computational method for the recognition of rivet joint defects in aircraft equipment based on video images of aircraft joints has been proposed with the use of neural networks YOLO-V5 for detecting and MobileNet V3 Large for classifying rivet joint states. A novel dataset based on a real physical model of rivet joints has been created for machine learning. The accuracy of the result obtained during modeling was 100% in both binary and multiclass classification.
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spelling pubmed-91056542022-05-14 Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products Amosov, Oleg Semenovich Amosova, Svetlana Gennadievna Iochkov, Ilya Olegovich Sensors (Basel) Article The mathematical statement of the problem of recognizing rivet joint defects in aircraft products is given. A computational method for the recognition of rivet joint defects in aircraft equipment based on video images of aircraft joints has been proposed with the use of neural networks YOLO-V5 for detecting and MobileNet V3 Large for classifying rivet joint states. A novel dataset based on a real physical model of rivet joints has been created for machine learning. The accuracy of the result obtained during modeling was 100% in both binary and multiclass classification. MDPI 2022-04-29 /pmc/articles/PMC9105654/ /pubmed/35591107 http://dx.doi.org/10.3390/s22093417 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Amosov, Oleg Semenovich
Amosova, Svetlana Gennadievna
Iochkov, Ilya Olegovich
Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products
title Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products
title_full Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products
title_fullStr Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products
title_full_unstemmed Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products
title_short Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products
title_sort deep neural network recognition of rivet joint defects in aircraft products
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105654/
https://www.ncbi.nlm.nih.gov/pubmed/35591107
http://dx.doi.org/10.3390/s22093417
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