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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Detalles Bibliográficos
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
Descripción
Sumario: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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