70049

Автор(ы): 

Автор(ов): 

3

Параметры публикации

Тип публикации: 

Статья в журнале/сборнике

Название: 

Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products

Электронная публикация: 

Да

ISBN/ISSN: 

14248220

DOI: 

10.3390/s22093417

Наименование источника: 

  • Sensors

Обозначение и номер тома: 

Vol. 22, Iss. 9

Город: 

  • Basel, Switzerland

Издательство: 

  • MDPI

Год издания: 

2022

Страницы: 

https://www.mdpi.com/1424-8220/22/9/3417
Аннотация
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.

Библиографическая ссылка: 

Амосов О.С., Амосова С.Г., Иочков И.О. Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products // Sensors. 2022. Vol. 22, Iss. 9. С. https://www.mdpi.com/1424-8220/22/9/3417.