Considerations for Computer Vision
Quality Control in Manufacturing
Computer vision systems are installed in manufacturing plants to assist with a wide
variety of industrial quality assurance and control processes, and to a lesser extent in
applications of process control.
Typical applications usually fall into the following
categories: “Yes” or “No” with a degree of certainty for objects
that it had never seen before.
• Dimensional: inspections of shapes, sizes,
orientations of objects
Surface blemish or defect: inspection or detection
of gauges, scratches, pits or marks
Assembly or completeness: inspection for
presence or status of sub components
Operational status: inspection or detection of
erroneous operational events With ever faster computers and the rise of industrial
use of machine learning techniques, probabilistic
approaches utilizing convolutional neural networks
to determine outcomes is fast becoming the
technology of choice.
The main purpose in all applications is to distill a
complex photo, or series of photos, into a binary
outcome as measured against a predefined set of
references or rules. In essence, the camera is asked
a complex question, for example “Is this component
satisfactory” and is required to answer “Yes” or “No”. Vision inspection and evaluation systems that are
integrally tied to convolutional neural networks are
capable of effectively addressing a wide variety of
problems, but there are some hard limits to consider
before venturing down this road.
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Considerations for the application of a
computer vision system
Problem definition
The underlying architecture of how this “Yes” or
“No” answer is inferred can further be divided into
two distinct categories. Absolute comparison and
probabilistic comparison. Absolute comparison is
based on the concept of an image being compared to
a database of images and the system recognizes the
image as one of the provided samples, or in the case
of deviation, does not.
Probabilistic comparison utilizes deep neural
networks to assign a probability that an image
contains features that are comparable to that of a
provided training set, allowing the system to answer
8 TUBE NEWS November 2019
Although neural net vision systems can infer and
evaluate scenarios that it has not seen before,
training towards specific problem sets or applications
greatly increases the accuracy and rapidly reduces
time required to deploy. It is more effective to define
multiple aspects for the system to consider, and
aim to narrow the definition, rather than provide
it with a broad and ill defined problem set that
encompasses a wide spectrum of evaluations. During
communication with clients, significant time is spent
asking the question: “What exactly are we looking
for?”