Abstract
In this paper, a design tool for deep convolutional neural network (DCNN) is considered and developed. As a test trial, a DCNN designed by using the tool is applied to visual inspection system of resin molded articles. The defects to be inspected are crack, burr, protrusion and chipping phenomena that occur in the manufacturing process of resin molded articles. An image generator is also developed to systematically generate many similar images for training. Similar images are easily produced by rotating, translating, scaling and transforming an original image. The designed DCNN is trained using the produced images and is evaluated through classification experiments. The usefulness of the proposed design tool has been confirmed through the test trial.
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Nagi, J., Ducatelle, F., Caro, G.A.D., Ciresan, D., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J., Gambardella, L.M.: Max-pooling convolutionalneural networks for vision-based hand gesture recognition. In: Proceedings of 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA2011), pp. 342–347, Kuala Lumpur (2011)
Weimer, D., Scholz-Reiter, B., Shpitalni, M.: Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann. Manuf. Technol. 65(1), 417–420 (2016)
Faghih-Roohi, S., Hajizadeh, S., Nunez, A., Babuska, R., Schutter, B.D.: Deep convolutional neural networks for detection of rail surface defects. In: Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN 2016), pp. 2584–2589, Vancouver (2016)
Zhou, S., Chen, Y., Zhang, D., Xie, J., Zhou, Y.: Classification of surface defects on steel sheet using convolutional neural networks. Mater. Technol. 51(1), 123–131 (2017)
Nagata, F., Tokuno, K., Tamano, H., Nakamura, H, Tamura, M., Kato, K., Otsuka, A., Ikeda, T., Watanabe, K., Habib, M.K.: Basic application of deep convolutional neural network to visual inspection. In: Proceedings of International Conference on Industrial Application Engineering (ICIAE2018), 5 p. Okinawa (2018)
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Nagata, F. et al. (2018). Design Tool of Deep Convolutional Neural Network for Visual Inspection. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_57
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DOI: https://doi.org/10.1007/978-3-319-93803-5_57
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