[
Alipour, M. and Harris, D.K. (2020). Increasing the robustness of material-specific deep learning models for crack detection across different materials, Engineering Structures 206: 110157.10.1016/j.engstruct.2019.110157
]Search in Google Scholar
[
Bergmann, P., Fauser, M., Sattlegger, D. and Steger, C. (2019). MVTec AD—A comprehensive real-world dataset for unsupervised anomaly detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, USA, pp. 9592–9600.
]Search in Google Scholar
[
Bhattad, A., Rock, J. and Forsyth, D. (2018). Detecting anomalous faces with ‘no peeking’ autoencoders, arXiv 1802.05798.
]Search in Google Scholar
[
da Costa, A.Z., Figueroa, H.E. and Fracarolli, J.A. (2020). Computer vision based detection of external defects on tomatoes using deep learning, Biosystems Engineering 190: 131–144.10.1016/j.biosystemseng.2019.12.003
]Search in Google Scholar
[
He, T., Liu, Y., Xu, C., Zhou, X., Hu, Z. and Fan, J. (2019). A fully convolutional neural network for wood defect location and identification, IEEE Access 7: 123453–123462.10.1109/ACCESS.2019.2937461
]Search in Google Scholar
[
He, T., Liu, Y., Yu, Y., Zhao, Q. and Hu, Z. (2020). Application of deep convolutional neural network on feature extraction and detection of wood defects, Measurement 152: 107357.10.1016/j.measurement.2019.107357
]Search in Google Scholar
[
Hu, G., Huang, J., Wang, Q., Li, J., Xu, Z. and Huang, X. (2019). Unsupervised fabric defect detection based on a deep convolutional generative adversarial network, Textile Research Journal 90(3–4): 247–270, DOI:10.1177/0040517519862880.10.1177/0040517519862880
]Search in Google Scholar
[
Jia, L., Chen, C., Xu, S. and Shen, J. (2020). Fabric defect inspection based on lattice segmentation and template statistics, Information Sciences 512: 964–984.10.1016/j.ins.2019.10.032
]Search in Google Scholar
[
Kang, X. and Zhang, E. (2019). A universal defect detection approach for various types of fabrics based on the Elo-rating algorithm of the integral image, Textile Research Journal 89(21–22): 4766–4793.10.1177/0040517519840636
]Search in Google Scholar
[
Li, C., Gao, G., Liu, Z., Huang, D. and Xi, J. (2019a). Defect detection for patterned fabric images based on GHOG and low-rank decomposition, IEEE Access 7: 83962–83973.10.1109/ACCESS.2019.2925196
]Search in Google Scholar
[
Li, F., Yuan, L., Zhang, K. and Li, W. (2019b). A defect detection method for unpatterned fabric based on multidirectional binary patterns and the gray-level co-occurrence matrix, Textile Research Journal 90(7–8): 776–796, DOI: 10.1177/0040517519879904.10.1177/0040517519879904
]Search in Google Scholar
[
Li, Y., Zhao, W. and Pan, J. (2016). Deformable patterned fabric defect detection with fisher criterion-based deep learning, IEEE Transactions on Automation Science and Engineering 14(2): 1256–1264.10.1109/TASE.2016.2520955
]Search in Google Scholar
[
Lian, J., Jia, W., Zareapoor, M., Zheng, Y., Luo, R., Jain, D.K. and Kumar, N. (2019). Deep learning based small surface defect detection via exaggerated local variation-based generative adversarial network, IEEE Transactions on Industrial Informatics 16(2): 1343–1351.10.1109/TII.2019.2945403
]Search in Google Scholar
[
Lizarraga-Morales, R.A., Correa-Tome, F.E., Sanchez-Yanez, R.E. and Cepeda-Negrete, J. (2019). On the use of binary features in a rule-based approach for defect detection on patterned textiles, IEEE Access 7: 18042–18049.10.1109/ACCESS.2019.2896078
]Search in Google Scholar
[
Luo, Q., Fang, X., Sun, Y., Liu, L., Ai, J., Yang, C. and Simpson, O. (2019). Surface defect classification for hot-rolled steel strips by selectively dominant local binary patterns, IEEE Access 7: 23488–23499.10.1109/ACCESS.2019.2898215
]Search in Google Scholar
[
MVTEC AD (2019). MVTEC Anomaly Detection Dataset, MVTec Software GmbH, Munich, https://www.mvtec.com/company/research/datasets/mvtec-ad.
]Search in Google Scholar
[
Ouyang, W., Xu, B., Hou, J. and Yuan, X. (2019). Fabric defect detection using activation layer embedded convolutional neural network, IEEE Access 7: 70130–70140.10.1109/ACCESS.2019.2913620
]Search in Google Scholar
[
Sun, J., Wang, P., Luo, Y.-K. and Li, W. (2019). Surface defects detection based on adaptive multiscale image collection and convolutional neural networks, IEEE Transactions on Instrumentation and Measurement 68(12): 4787–4797.10.1109/TIM.2019.2899478
]Search in Google Scholar
[
Wang, H., Zhang, J., Tian, Y., Chen, H., Sun, H. and Liu, K. (2018). A simple guidance template-based defect detection method for strip steel surfaces, IEEE Transactions on Industrial Informatics 15(5): 2798–2809.10.1109/TII.2018.2887145
]Search in Google Scholar
[
Wang, J., Li, Q., Gan, J., Yu, H. and Yang, X. (2019a). Surface defect detection via entity sparsity pursuit with intrinsic priors, IEEE Transactions on Industrial Informatics 16(1): 141–150.10.1109/TII.2019.2917522
]Search in Google Scholar
[
Wang, R., Guo, Q., Lu, S. and Zhang, C. (2019b). Tire defect detection using fully convolutional network, IEEE Access 7: 43502–43510.10.1109/ACCESS.2019.2908483
]Search in Google Scholar
[
Wei, B., Hao, K., Tang, X.-s. and Ding, Y. (2019). A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes, Textile Research Journal 89(17): 3539–3555.10.1177/0040517518813656
]Search in Google Scholar
[
Xie, H., Zhang, Y. and Wu, Z. (2019). Fabric defect detection method combing image pyramid and direction template, IEEE Access 7: 182320–182334.10.1109/ACCESS.2019.2959880
]Search in Google Scholar
[
Yang, H., Chen, Y., Song, K. and Yin, Z. (2019). Multiscale feature-clustering-based fully convolutional autoencoder for fast accurate visual inspection of texture surface defects, IEEE Transactions on Automation Science and Engineering 16(3): 1450–1467.10.1109/TASE.2018.2886031
]Search in Google Scholar
[
Yu, H., Li, Q., Tan, Y., Gan, J., Wang, J., Geng, Y.-a. and Jia, L. (2018). A coarse-to-fine model for rail surface defect detection, IEEE Transactions on Instrumentation and Measurement 68(3): 656–666.10.1109/TIM.2018.2853958
]Search in Google Scholar
[
Zhang, Z., Wen, G. and Chen, S. (2019). Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding, Journal of Manufacturing Processes 45: 208–216.10.1016/j.jmapro.2019.06.023
]Search in Google Scholar
