Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data
Abstract
1. Introduction
2. Background
3. Materials and Methods
3.1. OOD Detector Model
Residual Image Analysis
3.2. Binary Classifier Model Trained with Supervised-Learning
3.3. Datasets
4. Results
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Original Sample Count | Augmented Sample Count |
---|---|---|
Train weld okay | 27,454 | 164,724 |
Train defects | 11,705 | 70,230 |
Train synthetic natural image indications | 112,000 | |
Train synthetic, circular indication | 5000 | |
Train synthetic, partial circle inclusion | 5000 | |
Test weld okay | 3480 | |
Test defect, high contrast | 3396 | |
Test defect, mid-contrast | 2898 | |
Test defect, low contrast | 1830 | |
Test, synthetic, five different types | 200 |
TPR Average and Spread | ||||
---|---|---|---|---|
Training Data | D | D + SC | D + SC + SPC | D + SNI |
Test Dataset | ||||
Defects high contrast | ||||
Defects mid-contrast | ||||
Defects low contrast | ||||
Synthetic circular hollow inclusion | ||||
Synthetic dogbone inclusion | ||||
Synthetic elongated inclusion | ||||
Synthetic partial circle inclusion | ||||
Synthetic raster |
TPR Average and Spread | |||
---|---|---|---|
Perturbation Dataset | None | D | SNI |
Test Dataset | |||
Defects high contrast | |||
Defects mid-contrast | |||
Defects low contrast | |||
Synthetic circular hollow inclusion | |||
Synthetic dogbone inclusion | |||
Synthetic elongated inclusion | |||
Synthetic partial circle inclusion | |||
Synthetic raster |
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Lindgren, E.; Zach, C. Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data. Metals 2022, 12, 1963. https://doi.org/10.3390/met12111963
Lindgren E, Zach C. Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data. Metals. 2022; 12(11):1963. https://doi.org/10.3390/met12111963
Chicago/Turabian StyleLindgren, Erik, and Christopher Zach. 2022. "Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data" Metals 12, no. 11: 1963. https://doi.org/10.3390/met12111963
APA StyleLindgren, E., & Zach, C. (2022). Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data. Metals, 12(11), 1963. https://doi.org/10.3390/met12111963