Machine Vision AI, Self-learning Process Disturbance Monitoring Device, TAPPICon22
In the paper industry, there are numerous places where it would be beneficial to monitor and measure certain
quantities, like issues on product quality, dirtiness on process equipment and process disturbances. Traditionally this
has been done by operators, but continuous awareness is very tiring and practically impossible. Some of these
locations are also dangerous to be in, the process is too fast to follow, or important events may be very random and
rare.
Applications have been developed based on machine vision, where computers analyze video feed from cameras and
alarm users to do corrective action when certain quantities exceed pre-set limits. These applications are typically
very tightly bound to one position, sensitive to external disturbances and very time consuming and costly to develop,
so their usage is quite limited.
During the past ten years new techniques have evolved, which allow self-learning from video feed and raising an
alarm if there is something which does not fit to learned pattern. If the process or products changes, application can
adapt to the changed situation. This paper presents research on detecting defects in products on corrugator machine
using deep learning technology. The presented results and the experiments show potential of the methods in paper
industry.
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