Tech Briefs Magazine - March 2023 - 23

Instead of an autonomous robot in
route stopping because an object is in
proximity, the robot is able to understand
more precisely what is in its path and determine
if it can go around it. In a case
where a robot detects a box in its path, it
can then confidently adjust its path plan
to go around the stationary box, virtually
losing no time. In more advanced use cases,
drones are used to inspect surfaces of
critical assets for damage or defects. CV
is used to detect and classify defects and
provide further analytics on what defects
were found and where.
The factory of the future is connected,
intelligent, and needs to be able to
ensure highly productive production
environments. One of the key enablers
is CV. The technology is empowering
AR tools, advanced quality inspections,
smarter robots, and intelligent safety
and security systems. Design engineers
are integrating cameras into machine
designs and production lines at the design
phase.
Quality and production effectiveness
is ensured when intelligent tools are integrated
in critical areas. For instance,
in assembly and joining processes where
two major parts are joined in a welding
process, cameras can monitor the process
and CV can determine the weld quality.
In an instance where porosity is detected,
the welding engineer for the production
line or even a machine itself can make adjustments
to the process in near real-time.
By doing so productivity and quality is improved
and risks are mitigated.
A Unique Imaging Solution
CV AI is much more powerful than
smart AI image sensors or rules-based
vision as a whole. Usually " smart " vision
sensors leverage pre-trained models that
are not retrainable from the perspective
of the camera. Even " edge " training
smart AI sensors are highly limited to the
most basic applications. Some sensors or
AI systems rely on open-source tools that
are trained on datasets that could be subject
to biases or inconsistent labels.
Rules-based vision uses decades old
rules that are based on the specific object,
lighting, camera, lens, and ambient
conditions. These rules are good
for simple detection applications but
fail when contending with detecting
things in changing or challenging backgrounds,
shapes, colors, sizes, lighting,
and environments.
Because CV AI allows for changes and
updates, it can evolve with a user's needs.
For example, if new variables need to be
detected, the computer vision models
can be retrained based on new data, new
variables, and feedback. It can also learn
to ignore variables or conditions that
change but do not matter when making
detections and give feedback to improve
the confidence in the predictions.
CV AI can also eliminate the risk of
valuable knowledge leaving with a person
because it learns and retains visual
inspection
requirements in complex
production environments. Retaining
that tribal knowledge is also impactful
when onboarding new employees, as it
reduces weeks and even perhaps months
of training time.
One of the most appealing attributes
of CV AI is that it is camera agnostic and
can work with any type of imaging technologies
on the market, including camera
technologies such as resolution imager,
visible light, invisible (NIR, SWIR),
X-ray, thermal, industrial cameras, CCTV
Cameras, microscopes, and borescopes.
Tech Briefs, March 2023
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Tech Briefs Magazine - March 2023

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