Abstract
Surface defect inspection is a crucial step to ensure the quality of magnetic tiles. Recently, deep learning methods have shown excellent performance on many vision tasks. Some deep learning-based methods have been applied to the surface defect inspection of magnetic tiles as well. However, related methods are based on supervised learning, which requires plenty of labeled samples to train deep neural networks. In industrial application scenarios, the annotation of large labeled datasets is extremely expensive, time-consuming, and error-prone. A semi-supervised learning method based on pseudo-labeling is proposed in this paper to address the problem of surface defect classification of magnetic tiles with limited labeled samples. The proposed method consists of two models: the teacher model and the student model. The training procedure is divided into two stages: pseudo-label generation and student model training. In the pseudo-label generation stage, the teacher model parameters and the pseudo-labels of unlabeled samples are alternatively optimized based on the idea of transductive learning. Curriculum learning is employed to reduce the impact of label noise so that high-quality pseudo-labels can be obtained. In the student model training stage, labeled samples and unlabeled samples with pseudo-labels are jointly used to train the classifier, with mixup to achieve information fusion and regularization. The experimental results show that the proposed method outperforms the supervised-only and semi-supervised baselines. With only 4.4% of labeled samples in the training set, the proposed method can still achieve the defect classification accuracy of 90.13%.
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Tao J., Wang Y., Wang K.: Defect detection for end surface of ferrite magnetic tile. In: 8th Int. Symp. AOMATT, 9684 513–520, https://doi.org/10.1117/12.2241044. Sep. 2016
Yang, C., Liu, P., Yin, G., Jiang, H., Li, X.: Defect detection in magnetic tile images based on stationary wavelet transform. NDT E Int. 83, 78–87 (2016). https://doi.org/10.1016/j.ndteint.2016.04.006
Li, X., Jiang, H., Yin, G.: Detection of surface crack defects on ferrite magnetic tile. NDT E Int. 62, 6–13 (2014). https://doi.org/10.1016/j.ndteint.2013.10.006
Xie, L., Lin, L., Yin, M., Meng, L., Yin, G.: A novel surface defect inspection algorithm for magnetic tile. Appl. Surf. Sci. 375, 118–126 (2016). https://doi.org/10.1016/j.apsusc.2016.03.013
Huang, Y., Qiu, C., Yuan, K.: Surface defect saliency of magnetic tile. Vis. Comput. 36(1), 85–96 (2020). https://doi.org/10.1007/s00371-018-1588-5
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. MICCAI 9351, 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Xie, L., Xiang, X., Huining, X., Wang, L., Lin, L., Yin, G.: FFCNN: A deep neural network for surface defect detection of magnetic tile. IEEE Trans. Ind. Electron. 68(4), 3506–3516 (2021). https://doi.org/10.1109/TIE.2020.2982115
Cui, L., Jiang, X., Xu, M., Li, W., Lv, P., Zhou, B.: SDDNet: a fast and accurate network for surface defect detection. IEEE Trans. Instrum. Meas. 70, 1–13 (2021). https://doi.org/10.1109/TIM.2021.3056744
Hajizadeh, S., Núñez, A., Tax, D.M.J.: Semi-supervised rail defect detection from imbalanced image data. IFAC-Pap. 49(3), 78–83 (2016). https://doi.org/10.1016/j.ifacol.2016.07.014
Chun, C., Ryu, S.-K.: Road surface damage detection using fully convolutional neural networks and semi-supervised learning. Sensors 19(24), 5501 (2019). https://doi.org/10.3390/s19245501
Gao, Y., Gao, L., Li, X., Yan, X.: A semi-supervised convolutional neural network-based method for steel surface defect recognition. Robot. Comput.-Integr. Manuf. 61, 101825 (2020). https://doi.org/10.1016/j.rcim.2019.101825
He, Y., Song, K., Dong, H., Yan, Y.: Semi-supervised defect classification of steel surface based on multi-training and generative adversarial network. Opt. Lasers Eng. 122, 294–302 (2019). https://doi.org/10.1016/j.optlaseng.2019.06.020
Di, H., Ke, X., Peng, Z., Dongdong, Z.: Surface defect classification of steels with a new semi-supervised learning method. Opt. Lasers Eng. 117, 40–48 (2019). https://doi.org/10.1016/j.optlaseng.2019.01.011
Tao, X., et al.: Bearing defect diagnosis based on semi-supervised kernel Local fisher discriminant analysis using pseudo labels. ISA Trans. 110, 394–412 (2021). https://doi.org/10.1016/j.isatra.2020.10.033
Zhang, S., Ye, F., Wang, B., Habetler, T.G.: Semi-supervised bearing fault diagnosis and classification using variational autoencoder-based deep generative models. IEEE Sens. J. 21(5), 6476–6486 (2021). https://doi.org/10.1109/JSEN.2020.3040696
Liu, J., Song, K., Feng, M., Yan, Y., Tu, Z., Zhu, L.: Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection. Opt. Lasers Eng. 136, 106324 (2021). https://doi.org/10.1016/j.optlaseng.2020.106324
Wang, Y., Gao, L., Gao, Y., Li, X.: A new graph-based semi-supervised method for surface defect classification. Robot. Comput.-Integr. Manuf. 68, 102083 (2021). https://doi.org/10.1016/j.rcim.2020.102083
Danlei, X., Fei, W., Ying, S., Xiao-Yuan, J.: Cross-project defect prediction via semi-supervised discriminative feature learning. IEICE Trans. Inf. Syst. E103.D(10), 2237–2240 (2020). https://doi.org/10.1587/transinf.2020EDL8044
Ouali Y., Hudelot C., Tami M.: An overview of deep semi-supervised learning. https://arxiv.org/abs/2006.05278, Accessed: Jun. 23, 2020. [Online]. http://arxiv.org/abs/2006.05278. (2020)
Lee, D.-H.: Pseudo-label : the simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop Chall. Represent. Learn 3(2), 896 (2013)
Chapelle O., Zien A.: Semi-supervised classification by low density separation. In: Proc. 10th Int. Workshop AISTATS, Jan. 2005, [Online]. http://www.gatsby.ucl.ac.uk/aistats/fullpapers/198.pdf. (2005)
Yves, G., Yoshua, B.: Entropy regularization. In: Chapelle, O., Scholkopf, B., Zien, A. (eds.) Semi-Supervised Learning, pp. 151–168. The MIT Press (2006)
Zhang C., Bengio S., Hardt M., Recht B., Vinyals O.: Understanding deep learning requires rethinking generalization. [Online]. Available: https://openreview.net/forum?id=Sy8gdB9xx. (2017)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: ICML, pp. 41–48. NY, USA, New York (2009)
Zhang H., CisseM., Dauphin Y. N., Lopez-Paz D.: Mixup: Beyond empirical risk minimization. ICLR, Accessed: Mar. 12, 2020. [Online]. https://openreview.net/forum?id=r1Ddp1-Rb. (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, pp. 770–778. CVPR (2016)
Huang, G., Liu, Z., Maaten, L.V.D., Weinberger, K.Q.: Densely connected convolutional networks, pp. 2261–2269. CVPR (2017)
A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” in NIPS, Feb. 2017, Accessed: Mar. 10, 2020. [Online]. https://openreview.net/forum?id=ry8u21rtl.
Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: Inverted residuals and linear bottlenecks, pp. 4510–4520. CVPR, Salt Lake City UT, USA (2018)
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Liu, T., Ye, W. A semi-supervised learning method for surface defect classification of magnetic tiles. Machine Vision and Applications 33, 35 (2022). https://doi.org/10.1007/s00138-022-01286-x
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DOI: https://doi.org/10.1007/s00138-022-01286-x