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or security personnel. Integration of ML models
into these legacy systems allows evaluation against
pre-defined scenarios. “Is that human wearing the
required PPE gear?” may be the typical application.
Technology that achieve this functionality is already
well established, but cost effective integration into
existing systems that have grown organically and
consists of a wide variety of multi generational
hardware components remains challenging.
-Standardization and integration: as with
everything else, scalable vision deployments will
benefit from, and also contribute to standardization.
From software protocols to hardware interfaces,
vision integration into modern PLC’s and industrial
robots are already on the rise in the form of
augmented or assisted production processes. Various
levels and systems are in use today, and out of the
current experimental environment standards and
norms will emerge.
10 TUBE NEWS November 2019
Using neural networks in the interpretation of digital
images rapidly increases the levels of functionality,
but is significantly more complex than most legacy
systems that rely on comparison with a predefined
data set, and as such, training of the neural nets
to achieve the required level of accuracy is both
challenging and critical for success.
The vast improvements in quality and accuracy seen
when applying neural networks to vision systems
in the manufacturing and industrial sectors have
rendered classic computer vision techniques almost
obsolete. In particular, the use of convolutional
neural networks - neural networks more apt for
processing and classifying images - has made
classifying products, detecting the presence or
absence of specific items in complicated contexts,
and the detection of visible defects in products far
more precise and reliable than was possible before.
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