Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data
Source abstract
Abstract
As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image classification. These methods are effective but require a large number of actual defect data. However, it is very difficult to get a large amount of actual defect data in industrial areas. To overcome this problem, we propose a method for defect detection using stacked convolutional autoencoders. The autoencoders we proposed are trained by using only non-defect data and synthetic defect data generated by using the characteristics of defect based on the knowledge of the experts. A key advantage of our approach is that actual defect data is not required, and we verified that the performance is comparable to the systems trained using real defect data.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Integrated Circuits and Semiconductor Failure Analysis · Surface Roughness and Optical Measurements
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
R&D Center, Vieworks, Anyang-si 14055, Korea
School of Computer Science, University of Seoul, Seoul 02504, Korea
References (22)
Show source-provided references
1. Yu, Z., Wu, X., and Gu, X. (2017). Fully Convolutional Networks for Surface Defect Inspection in Industrial Environment. International Conference on Computer Vision System, Springer.10.1007/978-3-319-68345-4_37 ↗
2. Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., and De Schutter, B. (2016, January 24–29). Deep convolutional neural networks for detection of rail surface defects. Proceedings of the IJCNN, Vancouver, BC, Canada.10.1109/IJCNN.2016.7727522 ↗
3. Li, Y., Huang, H., Xie, Q., Yao, L., and Chen, Q. (2018). Research on a surface defect detection algorithm based on MobileNet-SSD. Appl. Sci., 8.10.3390/app8091678 ↗
4. Stacked autoencoder method for fabric defect detection
5. Mei, S., Wang, Y., and Wen, G. (2018). Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors, 18.10.3390/s18041064 ↗
6. Deformable patterned fabric defect detection with fisher criterion-based deep learning10.1109/TASE.2016.2520955 ↗
7. Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., and Steger, C. (2018). Improving unsupervised defect segmentation by applying structural similarity to autoencoders. arXiv.10.5220/0007364500002108 ↗
8. Seam detection of inhomogeneously textured fabrics based on wavelet transform10.1177/0040517514555796 ↗
9. Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method10.1080/00405000.2014.929790 ↗
10. Automated defect detection in textured materials using wavelet-domain hidden Markov models10.1117/1.OE.53.9.093107 ↗
11. Automated defect detection in textured surfaces using optimal elliptical Gabor filters10.1016/j.ijleo.2015.04.017 ↗
12. Automated defect detection in uniform and structured fabrics using Gabor filters and PCA10.1016/j.jvcir.2013.05.011 ↗
13. Pérez, P., Gangnet, M., and Blake, A. (2003). Poisson image editing. ACM SIGGRAPH 2003 Papers, Association for Computing Machinery.10.1145/1201775.882269 ↗
14. Wikipedia (2020, January 14). Alpha Compositing. Available online: https://en.wikipedia.org/wiki/Alpha_compositing.
15. Wikipedia (2020, January 14). Feature Scaling. Available online: https://en.wikipedia.org/wiki/Feature_scaling.
16. Mao, X., Shen, C., and Yang, Y.B. (2016). Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Advances in Neural Information Processing Systems, Curran Associates Inc.
17. Stacked convolutional auto-encoders for hierarchical feature extraction
18. Vieworks (2020, January 14). TDI Line Scan Cameras. Available online: https://vision.vieworks.com/en/camera/tdi_line_scan.
19. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
20. Glorot, X., and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR.org.
21. Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.10.1007/978-3-319-24574-4_28 ↗
22. Effective Classification using a Small Training Set based on Discretization and Statistical Analysis10.1109/TKDE.2015.2416727 ↗