STEPHANIE WONG: Hi.
I'm Stephanie.
And today, I'll show you how Google Cloud's
visual inspection products can help
you improve the efficiency of the manufacturing
visual quality control processes.
In the world of manufacturing, current market volatility
leads to huge operational pressures.
You're first in line to respond quickly
to decreased demand, disruption in the supply chain,
and interrupted retail channels.
Business continuity becomes P zero,
and the weight of reducing costs falls on your shoulders.
Naturally, leveraging AI to improve work efficiency
becomes the light at the end of the tunnel
as you start to explore more ways to advance automation,
control manufacturing processes remotely,
and enable a remote workforce.
In the last several years, visual inspection technologies
have been helping manufacturers ease
these operational pressures.
Image recognition is being used for electronic part
classification, retail quality assurance, and even food
inspection.
Yet, it can be difficult and costly to implement
these technologies.
Traditional inspection machinery can cost
millions of dollars per device.
Setting up and deploying AI for inspection
not only needs a reliable IT infrastructure and expert
resources, but there's usually a limited amount of sample data
to start with.
On top of that, training ML models
can be extremely challenging, time consuming,
and can require a specialized skill set that not
all companies have access to.
Take these defective parts, for instance.
Factory workers are often tasked with finding
these discrete defects on the production line.
These small marks can be extremely subtle,
making it hard for even a human eye to be able to see.
It's no surprise that this process can be
time consuming and inefficient.
This is why Google Cloud has a rich offering
for visual inspection products, which
reduces the pressure on ground workers and AI developers
and increases time to value.
It optimizes for several steps at the visual inspection
process from data ingestion to production model optimization.
Google Cloud's visual inspection products do this
by enabling customers to efficiently
pre-process minimal images for model training,
then it augments the data and trains automatically,
so you can quickly deploy the model locally
and run inferencing at the production line.
Better yet, you can continuously improve the model
by using fresh data collected from production lines
to retrain the model in the cloud giving you
increased accuracy as you discover new use cases.
Here's what an end-to-end architecture would look like.
Once a camera is set up on the production line,
users can collect images and store them
on the factory floor server.
From here, the images are uploaded
to Google Cloud using the visual inspection AI products to begin
model training.
Let's say you want to inspect an assembled product
to find defective components like damaged glass on devices.
Labeling thousands of devices is probably
the last thing you want to do.
Instead, using Google Cloud's visual inspection AI products,
you can upload images for a new product component,
and the solution can automatically
extract this component after you label a few images.
The model will analyze all loaded images
to automatically identify the subset of the data with defects
or anomalies.
Along the way, you have a chance to review current model
performance for defect detection using
built-in metrics, like average precision, precision,
and recall.
After downloading and deploying the trained model,
inferencing can be efficiently done locally
on your factory floor for rapid edge performance.
And when an unlabeled image comes in,
the deployed model can predict new defects automatically.
Results can then be viewed in the central cloud
dashboard giving prediction details and a place
to correct inspections.
Using Google Cloud's visual inspection products
you can train customized ML models
in the cloud with minimum amount of label images,
leverage scalable cloud infrastructure
for distributed training and optimize costs,
enable production workloads within weeks versus months,
efficiently deploy models, and run
inferencing at production lines on-premise for low latency.
And it's not just useful for automating the detection
of defective devices.
Google Cloud's visual inspection products
can support a wide range of discrete manufacturing use
cases, such as cosmetic defect detection and Edge
detection for electronics, surface quality
control for automotives, optical wave for a defect
detection for semiconductors, and fabric or packaging quality
control for consumer packaged goods.
Using Google Cloud's visual inspection products,
you can increase the throughput and efficiency of your ML
models, improve overall operations,
and augment your workers ability to guarantee
the highest quality products land
in the hands of your customer.
Reach out to your Google Cloud rep
to learn how you can deploy visual inspection solutions
using cloud AI products.
And visit the Google Cloud Vision AI page to learn more
about our products.
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