Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products

• Main Authors: Amosov, O.S (Author), Amosova, S.G (Author), Iochkov, I.O (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Aircraft; Aircraft detection; aircraft equipment; Binary classification; classification; computer vision; Computer vision; deep neural network; Deep neural networks; defect; Defects; detection; Detection; Mathematical statement; Multi-class classification; Neural network recognition; Neural-networks; pattern recognition; Physical modelling; rivet joint; Rivet joint; Rivets; Video image
• Online Access: View Fulltext in Publisher

 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.