Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products
Oleg Semenovich Amosov, Svetlana G. Amosova, Ilya Olegovich Iochkov
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.
Topics & Concepts
RivetJoint (building)Artificial neural networkBinary classificationArtificial intelligenceStructural engineeringComputer scienceEngineeringBinary numberPattern recognition (psychology)Support vector machineMathematicsArithmeticEngineering Diagnostics and ReliabilityIndustrial Engineering and TechnologiesAdvanced Measurement and Detection Methods