Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products.

Journal: Sensors (Basel, Switzerland)
Published Date: Apr 29, 2022

Abstract

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.

Authors

  • Oleg Semenovich Amosov
    Laboratory of Intellectual Control Systems and Modeling, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 117997 Moscow, Russia.
  • Svetlana Gennadievna Amosova
    Laboratory of Cyber-Physical Systems, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 117997 Moscow, Russia.
  • Ilya Olegovich Iochkov
    Department of Postgraduate Studies, Komsomolsk-on-Amur State University, 681013 Komsomolsk-on-Amur, Russia.

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