Synthetic data augmentation for surface defect detection and classification using deep learning
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DOI: 10.1007/s10845-020-01710-x
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- Maciej Grzenda & Andres Bustillo, 2019. "Semi-supervised roughness prediction with partly unlabeled vibration data streams," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 933-945, February.
- Te-Hsiu Sun & Fang-Cheng Tien & Fang-Chih Tien & Ren-Jieh Kuo, 2016. "Automated thermal fuse inspection using machine vision and artificial neural networks," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 639-651, June.
- Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
- Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
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- Li Wei & Mahmud Iwan Solihin & Sarah ‘Atifah Saruchi & Winda Astuti & Lim Wei Hong & Ang Chun Kit, 2024. "Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review," SN Operations Research Forum, Springer, vol. 5(3), pages 1-71, September.
- Songling Huang & Lisha Peng & Hongyu Sun & Shisong Li, 2023. "Deep Learning for Magnetic Flux Leakage Detection and Evaluation of Oil & Gas Pipelines: A Review," Energies, MDPI, vol. 16(3), pages 1-27, January.
- Erica Espinosa & Alvaro Figueira, 2023. "On the Quality of Synthetic Generated Tabular Data," Mathematics, MDPI, vol. 11(15), pages 1-18, July.
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Keywords
Surface defects; Classification; Convolutional neural network; Generative adversarial network; Deep learning;All these keywords.
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