Abstract
In modern manufacturing, defect recognition is an important technology, and using recent advances, such as convolutional neural networks (CNNs) to help defect recognition have addressed many attentions. However, CNN requires large-scale samples for training. In industries, large-scale samples are usually unavailable, and this impedes the wide application of CNNs. Ensemble learning might be a feasible manner for the small-scale-sample problem, But the weight for different CNNs needs explicit selection, and this is complex and time-consuming. To overcome this problem, this paper proposes a genetic algorithm (GA)-based ensemble CNNs for small-scale sample defect recognition problem. The proposed method uses an ensemble strategy to combinate several CNN models to solve the small-scale-sample problem in defect recognition, and use GA to optimize the ensemble weights with 5-fold cross-validation. With these improvements, the proposed method can find the optimal ensemble weight automatically, and it avoids the complex and explicit parameter selection. The experimental results with different trainable samples indicate that the proposed method outperforms the other defect recognition methods, which indicates that the proposed method is effective for small-scale sample defect recognition tasks. Furthermore, the discussion results also suggest that the proposed method is robust for noise, and it indicates that the proposed method has good potential in defect recognition tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Liu, Y.C., Hsu, Y.L., Sun, Y.N., et al.: A computer vision system for automatic steel surface inspection. In: Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010, pp. 1667–1670 (2010)
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)
Xie, X.: A review of recent advances in surface defect detection using texture analysis techniques. ELCVIA Electron. Lett. Comput. Vis. Image Anal. 7, 1–22 (2008)
Xu, K., Liu, S., Ai, Y.: Application of shearlet transform to classification of surface defects for metals. Image Vis. Comput. 35, 23–30 (2015)
Liu, T., Bao, J., Wang, J., Zhang, Y.: A coarse-grained regularization method of convolutional kernel for molten pool defect identification. ASME J Comput Inf Sci Eng 20, 021005 (2020)
Bi, M., Sun, Z.: Fabric defect detection using undecimated wavelet transform. Inf. Technol. J. 10, 1701–1708 (2011)
Leng, J., Zhang, H., Yan, D., Liu, Q., Chen, X., Zhang, D.: Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. J. Ambient Intell. Hum. Comput. 10(3), 1155–1166 (2018). https://doi.org/10.1007/s12652-018-0881-5
Xiao, M., Jiang, M., Li, G., Xie, L., Yi, L.: An evolutionary classifier for steel surface defects with small sample set. EURASIP J. Image Video Process. 2017(1), 1–13 (2017). https://doi.org/10.1186/s13640-017-0197-y
Gaja, H., Liou, F.: Defect classification of laser metal deposition using logistic regression and artificial neural networks for pattern recognition. Int. J. Adv. Manuf. Technol. 94(1–4), 315–326 (2017). https://doi.org/10.1007/s00170-017-0878-9
Luo, Q., Sun, Y., Li, P., et al.: Generalized completed local binary patterns for time-efficient steel surface defect classification. IEEE Trans. Instr. Meas. 68, 667–679 (2019)
Yang, S.-W., Lin, C.-S., Lin, S.-K., Chiang, H.-T.: Automatic defect recognition of TFT array process using gray level co-occurrence matrix. Optik (Stuttg) 125, 2671–2676 (2014)
Hu, H., Peng, G., Wang, X., Zhou, Z.: Weld defect classification using 1-D LBP feature extraction of ultrasonic signals. Nondestruct. Test Eval. 33, 92–108 (2018)
Niu, S., Li, B., Wang, X., Lin, H.: Defect image sample generation with GAN for improving defect recognition. IEEE Trans. Autom. Sci. Eng. 17(3), 1611–1622 (2020)
Jiang, H., Hu, Q., Zhi, Z., et al.: Convolution neural network model with improved pooling strategy and feature selection for weld defect recognition. Weld World (2020)
Chen, W., Gao, Y., Gao, L., Li, X.: A new ensemble approach based on deep convolutional neural networks for steel surface defect classification. Proc. CIRP 72, 1069–1072 (2018)
Song, K., Yan, Y.: A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl. Surf. Sci. 285, 858–864 (2013)
Gao, Y., Gao, L., Li, X., Wang, X.V.: A multilevel information fusion-based deep learning method for vision-based defect recognition. IEEE Trans. Instr. Meas. 69, 3980–3991 (2020)
Ren, R., Hung, T., Tan, K.C.: A generic deep-learning-based approach for automated surface inspection. IEEE Trans. Cybern. 48, 929–940 (2018)
Acknowledgements
This research work is supported by the National Key R&D Program of China under Grant No. 2018AAA0101700, and the Program for HUST Academic Frontier Youth Team under Grant No. 2017QYTD04.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
� 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, Y., Gao, L., Li, X., Wang, C. (2021). A Genetic Algorithm-Based Ensemble Convolutional Neural Networks for Defect Recognition with Small-Scale Samples. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-78743-1_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78742-4
Online ISBN: 978-3-030-78743-1
eBook Packages: Computer ScienceComputer Science (R0)