Abstract
Automating optical-inspection systems using machine learning has become an interesting and promising area of research. In particular, the deep-learning approaches have shown a very high and direct impact on the application domain of visual inspection. This paper presents a complete inspection system for automated quality control of a specific industrial product. Both hardware and software part of the system are described, with machine vision used for image acquisition and pre-processing followed by a segmentation-based deep-learning model used for surface-defect detection. The deep-learning model is compared with the state-of-the-art commercial software, showing that the proposed approach outperforms the related method on the specific domain of surface-crack detection. Experiments are performed on a real-world quality-control case and demonstrate that the deep-learning model can be successfully used even when only 33 defective training samples are available. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited.
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Acknowledgements
This work was supported in part by the following research programs: GOSTOP program C3330-16-529000 co-financed by the Republic of Slovenia and the ERDF, ARRS research project J2-9433 (DIVID), and ARRS research programme P2-0214.
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Tabernik, D., Šela, S., Skvarč, J., Skočaj, D. (2019). Deep-Learning-Based Computer Vision System for Surface-Defect Detection. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_44
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DOI: https://doi.org/10.1007/978-3-030-34995-0_44
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