Abstract
This paper describes an approach for automatic classification of multi-class printing defects based on semantic segmentation models. Classification of current printing defects strongly depends on visual inspection of skilled workers. Therefore, we developed an application that captures the expert’s perception and knowledge directly into the teaching image data, and classify the data automatically using semantic segmentation. We compared U-Net, SegNet, and PSPNet by benchmarking to find the best model for our situation where the number of input images for every defect type is set in the range of 10–120 by applying data augmentation. As the result, we found SegNet is the best model for our tiny and faint images. Finally, we added another grayscale channel to the input layer of SegNet to improve sensitivity to obscurity and show the effect.
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Acknowledgments
This research is supported by Cross-ministerial Strategic Innovation Promotion Program (SIP), “Big-data and AI-enabled Cyberspace Technologies” (Funding Agency: NEDO). We appreciate the support. The authors also appreciate all reviewers’ constructive comments.
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Tsuji, T., Arima, S. (2021). Automatic Multi-class Classification of Tiny and Faint Printing Defects Based on Semantic Segmentation. In: Zimmermann, A., Howlett, R., Jain, L. (eds) Human Centred Intelligent Systems. Smart Innovation, Systems and Technologies, vol 189. Springer, Singapore. https://doi.org/10.1007/978-981-15-5784-2_9
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DOI: https://doi.org/10.1007/978-981-15-5784-2_9
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