ArticleSensors (Basel, Switzerland)2022
Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Research on Multi-Type Rivet Head Defect Extraction and Classification Based on PointGhost Lightweight Network.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Based Smart Quality Inspection for Manufacturing.Micromachines · 2023Article
- Inspection of Underwater Hull Surface Condition Using the Soft Voting Ensemble of the Transfer-Learned Models.Sensors (Basel, Switzerland) · 2022Article
- Enhancing Precision with an Ensemble Generative Adversarial Network for Steel Surface Defect Detectors (EnsGAN-SDD).Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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