Page 175 - Read Online
P. 175
Wang et al. J. Mater. Inf. 2026, 6, 13 Page 19 of 19
27. Bosnar, L.; Hagen, H.; Gospodnetic, P. Procedural defect modeling for virtual surface inspection environments. IEEE. Comput. Graph.
Appl. 2023, 43, 13-22. DOI PubMed
28. Manyar, O. M.; Cheng, J.; Levine, R.; Krishnan, V.; Barbič, J.; Gupta, S. K. Physics informed synthetic image generation for deep
learning-based detection of wrinkles and folds. J. Comput. Inform. Sci. Eng. 2023, 23, 030903. DOI
29. Abou Akar, C.; Tekli, J.; Khalil, J.; et al. SORDI.ai: large-scale synthetic object recognition dataset generation for industries. Multimed.
Tools. Appl. 2024, 84, 18263-304. DOI
30. Gutierrez, P.; Luschkova, M.; Cordier, A.; Shukor, M.; Schappert, M.; Dahmen, T. Synthetic training data generation for deep learning
based quality inspection. arXiv 2021, arXiv:2104.02980. Available online: https://doi.org/10.48550/arXiv.2104.02980. (accessed 4 Mar
2026).
31. Fulir, J.; Bosnar, L.; Hagen, H.; Gospodnetić, P. Synthetic data for defect segmentation on complex metal surfaces. In 2023 IEEE/CVF
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, Canada, June 17-24, 2023. IEEE; 2023. pp.
4424-34. DOI
32. Schraml, D.; Notni, G. Synthetic training data in AI-driven quality inspection: the significance of camera, lighting, and noise parameters.
Sensors 2024, 24, 649. DOI PubMed PMC
33. Bosch, C.; Pueyo, X.; Mérillou, S.; Ghazanfarpour, D. A physically-based model for rendering realistic scratches. Comput. Graph.
Forum. 2004, 23, 361-70. DOI
34. He, L.; Gao, F.; Hou, W.; Hao, L. Objective image quality assessment: a survey. Int. J. Comput. Math. 2013, 91, 2374-88. DOI
35. Xie, E.; Wang, W.; Yu, Z.; Anandkumar, A.; Alvarez, J. M.; Luo, P. SegFormer: simple and efficient design for semantic segmentation
with transformers. arXiv 2021, arXiv:2105.15203. Available online: https://doi.org/10.48550/arXiv.2105.15203. (accessed 4 Mar 2026).
36. Weng, W.; Zhu, X. INet: convolutional networks for biomedical image segmentation. IEEE. Access. 2021, 9, 16591-603. DOI
37. Chen, L. C.; Zhu, Y.; Papandreou, G.; Schroff, F.; Adam, H. Encoder-decoder with atrous separable convolution for semantic image
segmentation. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, Editors. Computer Vision - ECCV 2018. Cham: Springer International
Publishing; 2018. pp. 833-51. DOI
38. Badrinarayanan, V.; Kendall, A.; Cipolla, R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE.
Trans. Pattern. Anal. Mach. Intell. 2017, 39, 2481-95. DOI
39. Mirza, M.; Osindero, S. Conditional generative adversarial nets. arXiv 2014, arXiv:1411.1784. Available online: https://doi.org/10.4855
/arXiv.1411.1784. (accessed 4 Mar 2026).
40. Niu, T.; Li, B.; Li, W.; Qiu, Y.; Niu, S. Positive-sample-based surface defect detection using memory-augmented adversarial
autoencoders. IEEE/ASME. Trans. Mechatron. 2022, 27, 46-57. DOI
Disclaimer/Publisher’s Note: All statements, opinions, and data contained in this publication are solely those of the individual author(s) and
contributor(s) and do not necessarily reflect those of OAE and/or the editor(s). OAE and/or the editor(s) disclaim any responsibility for harm to
persons or property resulting from the use of any ideas, methods, instructions, or products mentioned in the content.
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License
(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and
reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and
the source, provide a link to the Creative Commons license, and indicate if changes were made.

