TRANSFORMATIVE TECH: ARTIFICIAL INTELLIGENCE
classical machine vision system to inspect for every defect. ‘Manufacturing companies sometimes struggle with traditional visual inspection systems and software, because they are hard coded on certain specific defects,’ he said. Te advantage of Covision’s software, according to Tschimben, is that it can handle varying inspection conditions and changing parts easily. ‘In the long run, we help our customers be faster and more accurate,’ he said. Te software is trained on
around 100 to 200 images of mostly good parts. An operator then decides whether the predictions the software makes as to possible defects are accurate and retrains where necessary. Transfer learning can be used where the customer has similar defects, like dents, burrs, missing geometry, or changing colour. Tere’s also a continuous learning approach, where the software learns to account for changes in the production process over time. Along with finding defects,
the system has an aggregate function to give high-level statistics on how the lines are running. Tschimben said the system
does not save large amounts of data, but that Covision installs workstations on the production site – on average, one workstation would cover four production lines – to handle the data generated by real-time requirements of running at speeds of up to one part every 200ms. Covision collaborates with Nvidia on the workstations. At the moment, Covision Lab
has two PhD students: Tsung Ming Tai, in collaboration with Nvidia, whose work is on video understanding and forecasting of actions and activities; and Cynthia Ugwu, who is focusing on anomaly detection for visual inspection. ‘Most of the PhDs are working on shaping the status quo of research and solving large industrial challenges,’ Tschimben stated. ‘In the computer vision and machine learning space, research and applications are
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closely linked, as this year’s CVPR 2022 has shown once again.’ Tsung Ming Tai was awarded second place for one of the challenges at the Computer Vision and Pattern Recognition (CVPR) conference. ‘Tis will benefit our manufacturing use cases in the long run,’ Tschimben continued. ‘We have
many companies reaching out to us already to start research collaborations, beyond the mere use of our products. ‘Our close collaboration
with research departments at universities will guarantee we stay at the forefront of [machine learning] and continue to push the state-of-the-art ourselves,’
he added. ‘Our unsupervised machine learning approach to visual inspection is a very novel approach to machine learning… at industrial scale.’ Tschimben concluded:
‘Tere’s a high acceptance rate for machine learning at the moment, so the time is right [for Covision to grow].’O
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