access icon openaccess Adversarial semi-supervised learning method for printed circuit board unknown defect detection

Due to the lack of training data and fuzziness of unknown defects, unknown defect detection, which aims to identify no clearly defined defects, is still a challenging task. In practical industrial scenarios, defects on a printed circuit board account generally for a small proportion, so the data sets are highly biased towards no defect class. To this end, unknown defect detection can be treated as an anomaly detection problem. According to this, a semi-supervised learning method is proposed in this study to solve the above-mentioned problems. Inspired by the conditional generative adversarial network, the authors propose an improved end-to-end architecture for detecting unknown defects. The designed architecture is composed of three networks: a generator, a discriminator, and an encoder. Among them, the generator and the discriminator are trained by competing with each other, while collaborating to learn the distribution of underlying concepts in the target class. During training, the authors only train normal samples, and unknown defects do not appear in the process. In the testing phase, unknown defects are detected by calculating the distance between generated samples and real samples under the feature space. Experimental results over several benchmark data sets show the effectiveness of the model and superiority on state-of-the-art approaches.

Inspec keywords: automatic optical inspection; learning (artificial intelligence); neural net architecture; printed circuit manufacture

Other keywords: adversarial semisupervised learning method; unknown defect detection; discriminator; generator; anomaly detection problem; printed circuit board; improved end-to-end architecture; conditional generative adversarial network; defect class; feature space

Subjects: Computer vision and image processing techniques; Production engineering computing; Electronic engineering computing; Printed circuit manufacture; Inspection and quality control; Printed circuit manufacture; Neural computing techniques; Inspection and quality control; Industrial applications of IT; Knowledge engineering techniques

References

    1. 1)
      • 18. Miyato, T., Kataoka, T., Koyama, M., et al: ‘Spectral normalization for generative adversarial networks’, arXiv preprint arXiv, 1802.05957, 2018.
    2. 2)
      • 4. Xiaojun, C.X.L.: ‘A novel algorithm for inspecting and classifying the defects of printed circuit boards’, J. Huazhong Univ. Sci. Technol., Nat. Sci. Ed., 2010, 7, p. 19.
    3. 3)
      • 15. Schlegl, T., Seebock, P., Waldstein, S.M., et al: ‘Unsupervised anomaly detection with generative adversarial networks to guide marker discovery’. Int. Conf. on Information Processing in Medical Imaging, Boone, USA, 2017, pp. 146157.
    4. 4)
      • 14. An, J., Cho, S.: ‘Variational autoencoder based anomaly detection using reconstruction probability’, Spec. Lect. IE, 2015, 2, pp. 118.
    5. 5)
      • 12. Zenati, H., Foo, C.S., Lecouat, B., et al: ‘Efficient GAN-based anomaly detection’, arXiv preprint arXiv, 1802.06222, 2018.
    6. 6)
      • 21. Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al: ‘Generative adversarial nets’. Advances in Neural Information Processing Systems, Montreal, Quebec, Canada, 2014, pp. 26722680.
    7. 7)
      • 20. Nair, V., Hinton, G.E.: ‘Rectified linear units improve restricted Boltzmann machines’. Proc. 27th Int. Conf. on Machine Learning, Haifa, Israel, 2010, pp. 807814.
    8. 8)
      • 24. LeCun, Y.: ‘The MNIST database of handwritten digits’, 1998. Available at http://yann.lecun.com/exdb/mnist/.
    9. 9)
      • 13. Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: ‘GANomaly: semi-supervised anomaly detection via adversarial training’, arXiv preprint arXiv, 1805.06725, 2018.
    10. 10)
      • 8. Idé, T., Papadimitriou, S., Vlachos, M.: ‘Computing correlation anomaly scores using stochastic nearest neighbors’. Seventh IEEE Int. Conf. on Data Mining, 2007. ICDM 2007, Omaha, Nebraska, USA, 2007, pp. 523528.
    11. 11)
      • 19. Armijo, L.: ‘Minimization of functions having Lipschitz continuous first partial derivatives’, Pac. J. Math., 1966, 16, (1), pp. 13.
    12. 12)
      • 16. Ioffe, S., Szegedy, C.: ‘Batch normalization: accelerating deep network training by reducing internal covariate shift’. Int. Conf. on Machine Learning, Lille, France, 2015, pp. 448456.
    13. 13)
      • 17. He, K., Zhang, X., Ren, S., et al: ‘Deep residual learning for image recognition’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, 2016, pp. 770778.
    14. 14)
      • 10. Cong, Y., Yuan, J., Liu, J.: ‘Sparse reconstruction cost for abnormal event detection’. 2011 IEEE Conf. on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, 2011, pp. 34493456.
    15. 15)
      • 3. Cha, Y.J., Choi, W., Suh, G., et al: ‘Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types’, Comput.-Aided Civ. Infrastruct. Eng., 2018, 33, (9), pp. 731747.
    16. 16)
      • 6. Benedek, C.: ‘Detection of soldering defects in printed circuit boards with hierarchical marked point processes’, Pattern Recognit. Lett., 2011, 32, (13), pp. 15351543.
    17. 17)
      • 25. Krizhevsky, A., Nair, V., Hinton, G.: ‘The CIFAR-10 dataset’, 2014. Available at http://www.cs.toronto.edu/kriz/cifar.html.
    18. 18)
      • 11. Xia, Y., Cao, X., Wen, F., et al: ‘Learning discriminative reconstructions for unsupervised outlier removal’. Proc. IEEE Int. Conf. on Computer Vision, Santiago, Chile, 2015, pp. 15111519.
    19. 19)
      • 9. Sabokrou, M., Fathy, M., Hoseini, M.: ‘Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder’, Electron. Lett., 2016, 52, (13), pp. 11221124.
    20. 20)
      • 1. Soukup, D., Huber-Mork, R.: ‘Convolutional neural networks for steel surface defect detection from photometric stereo images’. Int. Symp. on Visual Computing, Las Vegas, Nevada, USA, 2014, pp. 668677.
    21. 21)
      • 22. Kingma, D.P., Ba, J.: ‘Adam: a method for stochastic optimization’, arXiv preprint arXiv, 1412.6980, 2014.
    22. 22)
      • 2. Masci, J., Meier, U., Ciresan, D., et al: ‘Steel defect classification with max-pooling convolutional neural networks’. The 2012 Int. Joint Conf. on Neural Networks, Brisbane, Australia, 2012, pp. 16.
    23. 23)
      • 5. Chauhan, A.P.S., Bhardwaj, S.C.: ‘Detection of bare PCB defects by image subtraction method using machine vision’. Proc. World Congress on Engineering, London, UK, 2011, vol. 2, pp. 68.
    24. 24)
      • 7. Lu, Z., He, Q., Xiang, X., et al: ‘Defect detection of PCB based on Bayes feature fusion’, J. Eng., 2018, 16, pp. 17411745.
    25. 25)
      • 23. Hanley, J.A., McNeil, B.J.: ‘The meaning and use of the area under a receiver operating characteristic (ROC) curve’, Radiology, 1982, 143, (1), pp. 2936.
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