A nested autoencoder approach to automated defect inspection on textured surfaces

• Autorzy: Oz Muhammed Ali Nur , Kaymakci Ozgur Turay , Mercimek Muharrem

Identyfikatory

DOI

10.34768/amcs-2021-0035

• Języki publikacji: EN

Abstrakty

EN

In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition, emerging technologies must be developed and incorporated. Automated visual inspection systems, which improve the overall mass production quantity and quality in lines, are crucial. The modifications of the inspection system involve excessive time and money costs. Therefore, these systems should be flexible in terms of fulfilling the changing requirements of high capacity production support. A coherent defect detection model as a primary application to be used in a real-time intelligent visual surface inspection system is proposed in this paper. The method utilizes a new approach consisting of nested autoencoders trained with defect-free and defect injected samples to detect defects. Making use of two nested autoencoders, the proposed approach shows great performance in eliminating defects. The first autoencoder is used essentially for feature extraction and reconstructing the image from these features. The second one is employed to identify and fix defects in the feature code. Defects are detected by thresholding the difference between decoded feature code outputs of the first and the second autoencoder. The proposed model has a 96% detection rate and a relatively good segmentation performance while being able to inspect fabrics driven at high speeds.

Słowa kluczowe

EN

autoencoder; defect detection; automatic visual inspection; deep learning

PL

autoenkoder; wykrywanie defektów; inspekcja wizyjna; inspekcja automatyczna; uczenie głębokie

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2021
• Tom: Vol. 31, no. 3
• Strony: 515--523
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Oz Muhammed Ali Nur

• Department of Control and Automation Engineering, Yildiz Technical University, Cifte Havuzlar, Davutpasa Campus, 34220 Esenler, Istanbul, Turkey

autor

Kaymakci Ozgur Turay

• Department of Electronics Engineering, Canakkale Onsekiz Mart University, Barbaros Mahallesi Terzioglu Campus, Prof. Dr Sevim Bulut Street, No. 20, Canakkale, Turkey

autor

Mercimek Muharrem

• Department of Control and Automation Engineering, Yildiz Technical University, Cifte Havuzlar, Davutpasa Campus, 34220 Esenler, Istanbul, Turkey

Bibliografia

• [1] 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.
• [2] 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.
• [3] Bhattad, A., Rock, J. and Forsyth, D. (2018). Detecting anomalous faces with ‘no peeking’ autoencoders, arXiv 1802.05798.
• [4] 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.
• [5] 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.
• [6] 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.
• [7] 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.
• [8] 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.
• [9] 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] 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.
• [11] 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.
• [12] 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.
• [13] 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.
• [14] 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.
• [15] 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.
• [16] MVTEC AD (2019). MVTEC Anomaly Detection Dataset, MVTec Software GmbH, Munich, https://www.mvtec.com/company/research/datasets/mvtec-ad.
• [17] 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.
• [18] 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.
• [19] 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.
• [20] 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.
• [21] Wang, R., Guo, Q., Lu, S. and Zhang, C. (2019b). Tire defect detection using fully convolutional network, IEEE Access 7: 43502–43510.
• [22] 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.
• [23] Xie, H., Zhang, Y. and Wu, Z. (2019). Fabric defect detection method combing image pyramid and direction template, IEEE Access 7: 182320–182334.
• [24] 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.
• [25] 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.
• [26] 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.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).