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2019 | OriginalPaper | Chapter

Deep Learning for Deflectometric Inspection of Specular Surfaces

Authors : Daniel Maestro-Watson, Julen Balzategui, Luka Eciolaza, Nestor Arana-Arexolaleiba

Published in: International Joint Conference SOCO’18-CISIS’18-ICEUTE’18

Publisher: Springer International Publishing

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Abstract

Deflectometric techniques provide abundant information useful for aesthetic defect inspection in specular and glossy/shinny surfaces. A series of light patterns is observed indirectly through their reflection on the surface under inspection, and different geometrical or texture information about the surface can be extracted. In this paper, we present a deep learning based approach for the automated defect identification in deflectometric recordings. The proposed learning framework automatically learns features used for classification. Although the method is in an early stage of development, the experiments with industrial parts show promising results, and a very direct application if compared to hand-crafted feature definition approaches.

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Literature
1.
Angelova, A., Krizhevsky, A., Vanhoucke, V., Ogale, A.S., Ferguson, D.: Real-time pedestrian detection with deep network cascades. In: Proceedings of British Machine Vision Conference (BMVC), vol. 2, p. 4 (2015)
2.
Burke, J., Li, W., Heimsath, A., von Kopylow, C., Bergmann, R.: Qualifying parabolic mirrors with deflectometry. J. Eur. Opt. Soc. Rapid Publ. 8 (2013)
3.
Burke, J.: Inspection of reflective surfaces with deflectometry. In: Proceedings of the International Conference on Processes in Combined Digital Optical & Imaging Methods, pp. 108–111 (2016)
4.
Caulier, Y.: Inspection of complex surfaces by means of structured light patterns. Opt. Express 18(7), 6642–6660 (2010)CrossRef
5.
Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., Schutter, B.D.: Deep convolutional neural networks for detection of rail surface defects. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2584–2589, July 2016
6.
Fan, Z., Wu, Y., Lu, J., Li, W.: Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network. ArXiv, February 2018
7.
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)MATH
8.
Häusler, G., Faber, C., Olesch, E., Ettl, S.: Deflectometry vs. interferometry. In: Proceedings of the SPIE 8788, Optical Measurement Systems for Industrial Inspection (2013)
9.
Kofler, C., Spöck, G., Muhr, R.: Classifying defects in topography images of silicon wafers. In: 2017 Winter Simulation Conference (WSC), pp. 3646–3657, December 2017
10.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, USA, pp. 1097–1105 (2012)
11.
Le, T.T., Ziebarth, M., Greiner, T., Heizmann, M.: Systematic design of object shape matched wavelet filter banks for defect detection. In: 39th International Conference on Telecommunications and Signal Processing (TSP), pp. 470–473, June 2016
12.
LeCun, Y., et al.: Generalization and network design strategies. Connectionism in perspective, pp. 143–155 (1989)
13.
Malekzadeh, T., Abdollahzadeh, M., Nejati, H., Cheung, N.M.: Aircraft Fuselage Defect Detection using Deep Neural Networks. ArXiv e-prints, December 2017
14.
Masci, J., Meier, U., Ciresan, D., Schmidhuber, J., Fricout, G.: Steel defect classification with max-pooling convolutional neural networks. In: 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2012)
15.
Muniategui, A., Hériz, B., Eciolaza, L., Ayuso, M., Iturrioz, A., Quintana, I., Álvarez, P.: Spot welding monitoring system based on fuzzy classification and deep learning. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6 (2017)
16.
Nagato, T., Fuse, T., Koezuka, T.: Defect inspection technology for a gloss-coated surface using patterned illumination. In: Proceedings of the SPIE, vol. 8661 (2013)
17.
Ren, R., Hung, T., Tan, K.C.: A generic deep-learning-based approach for automated surface inspection. IEEE Trans. Cybern. 48(3), 929–940 (2018)CrossRef
18.
Saldner, H.O., Huntley, J.M.: Temporal phase unwrapping: application to surface profiling of discontinuous objects. Appl. Opt. 36(13), 2770–2775 (1997)CrossRef
19.
Schreiber, H., Bruning, J.H.: Phase Shifting Interferometry. In: Optical Shop Testing, pp. 547–666. John Wiley & Sons (2007)
20.
Soukup, D., Huber-Mörk, R.: Convolutional neural networks for steel surface defect detection from photometric stereo images. In: International Symposium on Visual Computing, pp. 668–677. Springer (2014)
21.
Tarry, C., Stachowsky, M., Moussa, M.: Robust detection of paint defects in moulded plastic parts. In: Canadian Conference on Computer and Robot Vision, pp. 306–312, May 2014
22.
Tutsch, R., Petz, M., Fischer, M.: Optical three-dimensional metrology with structured illumination. Opt. Eng. 50, 101507–101510 (2011)CrossRef
23.
Werling, S., Mai, M., Heizmann, M., Beyerer, J.: Inspection of specular and partially specular surfaces. Metrol. Measur. Syst. 16(3), 415–431 (2009)
24.
Zhang, Z., Wang, Y., Huang, S., Liu, Y., Chang, C., Gao, F., Jiang, X.: Three-dimensional shape measurements of specular objects using phase-measuring deflectometry. Sensors 17(12), 2835 (2017)CrossRef
25.
Ziebarth, M.: Empirical comparison of defect classifiers on specular surfaces. In: Proceedings of the 2013 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory (2014)
Metadata
Title
Deep Learning for Deflectometric Inspection of Specular Surfaces
Authors
Daniel Maestro-Watson
Julen Balzategui
Luka Eciolaza
Nestor Arana-Arexolaleiba
Copyright Year
2019
Book
International Joint Conference SOCO’18-CISIS’18-ICEUTE’18

Print ISBN: 978-3-319-94119-6

Electronic ISBN: 978-3-319-94120-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-94120-2_27

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