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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Acknowledgments
This work has been developed by the intelligent systems for industrial systems group supported by the Department of Education, Language policy and Culture of the Basque Government. The authors would also like to thank the collaboration partner Ekide S.L. for their contributions.
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Maestro-Watson, D., Balzategui, J., Eciolaza, L., Arana-Arexolaleiba, N. (2019). Deep Learning for Deflectometric Inspection of Specular Surfaces. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_27
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DOI: https://doi.org/10.1007/978-3-319-94120-2_27
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