Generative Adversarial Network Based Deep Learning Method for Machine Vision Inspection
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-46238-2_10
Reference19 articles.
1. Lu, H. P., & Su, C. T. (2021). CNNs combined with a conditional GAN for Mura defect classification in TFT-LCDs[J]. IEEE Transactions on Semiconductor Manufacturing, 34(1), 25–33.
2. Kim, M., Lee, M., & An, M. (2020). Effective automatic defect classification process based on CNN with stacking ensemble model for TFT-LCD panel[J]. Journal of Intelligent Manufacturing, 31(5), 1165–1174.
3. Xie, X. (2008). A review of recent advances in surface defect detection using texture analysis techniques[J]. ELCVIA: Electronic Letters on Computer Vision and Image Analysis, 7(3), 1–22.
4. Li, Y., Luo, H., & Yu, M. (2019). Fabric defect detection algorithm using RDPSO-based optimal Gabor filter[J]. The Journal of the Textile Institute, 110(4), 487–495.
5. Zhou, X., Wang, Y., & Zhu, Q. (2019). A surface defect detection framework for glass bottle bottom using visual attention model and wavelet transform[J]. IEEE Transactions on Industrial Informatics, 16(4), 2189–2201.
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