Visual inspection on paper by machine vision

Heikki Kalviainen; Pasi Saarinen; Petja Salmela; Albert Sadovnikov; Alexander Drobchenko

doi:10.1117/12.518668

30 September 2003 Visual inspection on paper by machine vision

Heikki Kalviainen, Pasi Saarinen, Petja Salmela, Albert Sadovnikov, Alexander Drobchenko

Proceedings Volume 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision; (2003) https://doi.org/10.1117/12.518668
Event: Photonics Technologies for Robotics, Automation, and Manufacturing, 2003, Providence, RI, United States

Abstract

There are several important standard laboratory experiments for determining the quality of produced paper in the paper making industry. To know the quality is essential since it defines the use of paper for various purposes. Moreover, customers are expecting a certain degree of quality. Many of paper printability tests are based on off-line visual inspection. Currently these tests are done by printing test marks on a piece of paper and then observing the quality by a human evaluator. In this report visual inspection on paper by machine vision is discussed from a point of off-line industrial measurements. The work focuses on the following paper printability problems: missing dots (Heliotest), print dot density, unevenness of printing image, surface strength (IGT), ink setting, linting, fiber counting, and digital printing. Compared to visual inspection by human evaluation, automated machine vision systems could offer several useful advantages: less deviations in measurements, better measurement accuracy, new printability parameters, shorter measurement times, less manpower to monotonic measurements, many quality parameters by one system, and automatic data transfer to mill level information systems. Current results with paper and board samples indicate that human evaluators could be replaced. However, further research is needed since the printability problems vary mill by mill, there is a large number of various paper and board samples, and the relationships between off-line and on-line measurements must be considered.

Citation Download Citation

Heikki Kalviainen, Pasi Saarinen, Petja Salmela, Albert Sadovnikov, and Alexander Drobchenko "Visual inspection on paper by machine vision", Proc. SPIE 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision, (30 September 2003); https://doi.org/10.1117/12.518668

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