Open Access
ITM Web Conf.
Volume 43, 2022
The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
Article Number 01012
Number of page(s) 3
Published online 14 March 2022
  1. T. Czimmermann et al., ‘Visual-based defect detection and classification approaches for industrial applications—a survey’, Sensors, vol. 20, no. 5, p. 1459, 2020.DOI: [CrossRef] [Google Scholar]
  2. N. Neogi, D. K. Mohanta, and P. K. Dutta, ‘Defect detection of steel surfaces with global adaptive percentile thresholding of gradient image’, J. Inst. Eng. India Ser. B, vol. 98, no. 6, pp. 557–565, 2017. DOI: [CrossRef] [Google Scholar]
  3. T. Shi, J. Kong, X. Wang, Z. Liu, and G. Zheng, ‘Improved Sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy’, J. Cent. South Univ., vol. 23, no. 11, pp. 2867–2875, 2016.DOI: [CrossRef] [Google Scholar]
  4. W.-Y. Lin, C.-Y. Lin, G.-S. Chen, and C.-Y. Hsu, ‘Steel surface defects detection based on deep learning’, 2018, pp. 141–149.DOI: 10.1007/978-3-319-94484-5_15 [Google Scholar]
  5. C.-Y. Lin, C.-H. Chen, C.-Y. Yang, F. Akhyar, C.-Y. Hsu, and H.-F. Ng, ‘Cascading convolutional neural network for steel surface defect detection’, 2019, pp. 202–212. DOI: 10.1007/978-3-030-20454-9_20 [Google Scholar]
  6. Q. Luo, X. Fang, L. Liu, C. Yang, and Y. Sun, ‘Automated visual defect detection for flat steel surface: A survey’, IEEE Trans. Instrum. Meas., vol. 69, no. 3, pp. 626–644, 2020. DOI: 10.1109/TIM.2019.2963555 [CrossRef] [Google Scholar]
  7. G. H. Nguyen, A. Bouzerdoum, and S. L. Phung, ‘Learning pattern classification tasks with imbalanced data sets’, Pattern Recognit., pp. 193–208, 2009. [Google Scholar]
  8. Y.-J. Han and H.-J. Yu, ‘Fabric defect detection system using stacked convolutional denoising auto-encoders trained with synthetic defect data’, Appl. Sci., vol. 10, no. 7, p. 2511, 2020.DOI: [CrossRef] [Google Scholar]
  9. S. Jain, G. Seth, A. Paruthi, U. Soni, and G. Kumar, ‘Synthetic data augmentation for surface defect detection and classification using deep learning’, J. Intell. Manuf., pp. 1–14, 2020.DOI: [Google Scholar]
  10. H. S. Shon, E. Batbaatar, W.-S. Cho, and S. G. Choi, ‘Unsupervised Pre-Training of Imbalanced Data for Identification of Wafer Map Defect Patterns’, IEEE Access, vol. 9, pp. 52352–52363, 2021.DOI: 10.1109/ACCESS.2021.3068378 [CrossRef] [Google Scholar]
  11. X. Zheng, H. Wang, J. Chen, Y. Kong, and S. Zheng, ‘A generic semi-supervised deep learning-based approach for automated surface inspection’, IEEE Access, vol. 8, pp. 114088–114099, 2020. DOI: 10.1109/ACCESS.2020.3003588 [CrossRef] [Google Scholar]
  12. S. Alam, S. K. Sonbhadra, S. Agarwal, and P. Nagabhushan, ‘One-class support vector classifiers: A survey’, Knowl.-Based Syst., vol. 196, p. 105754, 2020.DOI: [CrossRef] [Google Scholar]
  13. W. Liu et al., ‘Towards visually explaining variational autoencoders’, 2020, pp. 8642–8651. [Google Scholar]
  14. L. Wang, D. Zhang, J. Guo, and Y. Han, ‘Image Anomaly Detection Using Normal Data Only by Latent Space Resampling’, Appl. Sci., vol. 10, no. 23, p. 8660, 2020.DOI: [CrossRef] [Google Scholar]
  15. A. Mujeeb, W. Dai, M. Erdt, and A. Sourin, ‘One class based feature learning approach for defect detection using deep autoencoders’, Adv. Eng. Inform., vol. 42, p. 100933,2019. DOI: [CrossRef] [Google Scholar]
  16. P. Bergmann, S. Löwe, M. Fauser, D. Sattlegger, and C. Steger, ‘Improving unsupervised defect segmentation by applying structural similarity to autoencoders’, ArXiv Prepr.ArXiv180702011,2018. DOI: 10.5220/0007364503720380 [Google Scholar]
  17. D. Gong et al., ‘Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection’, in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 1705–1714. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.