Object detection helps automate tedious tasks, increase
production environment safety, and find everything from manufacturing defects
to a parking spot. It has a ways to go for more nuanced tasks, but its
prospects are bright.
Seagate Technology inspects tens of millions of computer
storage drive parts daily. The media that goes into them. The heads. The
wafers. The completed assemblies and outer shells.
For much of the company’s history, this inspection was a
time-consuming and costly process. To achieve the quality the global company’s
customers expect, hard disk and solid-state drives would be tested once, twice,
or even three times before human beings gave them a final blessing in what’s
known as sequential inspections, which involves looking intently at parts
through electron microscopes.
But with the rise of AI-enabled object detection technology,
Seagate is automating inefficiency out of its production processes and bringing
in high-speed consistency. To achieve this increased throughput, the company is
now installing high-powered edge servers closer to the point of production to
run its machine learning models and expand its Fourth Industrial Revolution
technology to assembly lines in factories around the world.
AI technologies at
the edge
“Our quality systems use a multitude of different types of
measurements that are image-based,” says Bruce King, a data science
technologist at Seagate. “It’s a rules-based system that is programmed by
engineers. The opportunity that we have with artificial intelligence is that we
can have the machine learn all the rules so we don’t have to program them. This
saves the engineers quite a bit of time and effort. And we’re finding that the
machine learning tools are very flexible. They can find all the same things
that a rule-based system can. They self-learn, so to speak. And their accuracy
is as good or better in all cases that we’ve analyzed so far.”
Seagate is one of hundreds—maybe thousands—of companies that
are either planning, piloting, or deploying machine learning with deep neural
networks, algorithms patterned after the human brain, to solve real-world
business challenges. The good news is that technology techniques like deep
learning, and a treasure trove of publicly available frameworks and libraries,
have made advanced analytics commercially viable and able to scale. The
technologies may vary, and opinions may differ, but like any emerging
technologies and standards, arriving at a clear consensus regarding platforms
and approaches will continue to evolve. In the meantime, companies like Seagate
are getting an early-mover advantage over their competition.
Some of the common debates are around what to sensor, which
high-resolution camera to use, which aggregation and data transport software to
use, and how to manage and secure the devices at the edge. Organizations must
also consider what edge platform is best suited for a majority of their use
cases, to minimize complexity and sprawl, as well as the need to hire, versus
contract, super-smart data scientists to make object detection work, as demand
for this scarce talent pool grows.
Whatever the case, there is clear momentum behind object
detection technology. Manufacturers, local governments, transportation
authorities, marinas, schools, hospitals, and other vertical industries are
increasingly embracing projects built on it.
Factories of the
future
In manufacturing, for example, Foxconn, a Taiwanese
multinational electronics contract manufacturing company, is about to launch an
object detection program to inspect every Hewlett Packard Enterprise server and
component it produces for distribution in Europe. Indeed, its Kutná Hora plant
in the Czech Republic is designed to inspect multiple products coming down the
line and can also examine any variety of configurations rolling along,
regardless of their order. The system will examine the objects, compare them
with images in the company’s database, and with the help of video analytics and
AI, determine whether anything looks amiss. In short, the system enables Foxconn
to identify any potential failures in real time, which is critical given the
company’s efforts to ship orders to customers like HPE as rapidly as possible,
while ensuring consistent high quality across the products produced.
“The system compares what’s been assembled to what it
believes should be assembled,” says John Gallagher, operations manager at
Foxconn CZ. “It will look to see if everything is where it should be. Are the
processors in the right spots? Are the cables in the right sockets? Are the drives
correcting? And it will go through the whole configuration that we ask it to
identify. The benefit of all of this to us is that we try to ship 50 percent of
the orders we receive within 24 hours. This [object detection technology]
allows us to be more predictable for customers. It allows us to have shorter
manufacturing times for them as well. And it helps remove failures from the
process.”
Gallagher says the company has high hopes for the pilot
project, which kicked off in late March. If all goes well, Foxconn could expand
the technology to other continents.
Smart transportation
Object detection technology is also showing great promise in
the world of transportation, most notably with how people park their cars and
moor their boats.
If you drive to work, you may dread the usual slog of trying
to find parking on a city street or in a garage. In the near future, this might
not be so difficult, thanks to smart parking solutions.
Instead of driving around for 10, 15, or even 30 minutes
trying to find a parking space, you whip out a mobile app that locates a
parking spot for you. On the back end, some combination of IoT parking meters,
sensors, video cameras, radar, analytics software, DNN programs, edge servers,
and mobile payment systems might come together to find that lone spot at the
corner of 5th and Market—and even reserve it for you. In fact, you might
eventually be able to sign up for an AI-driven service that knows your typical
driving habits—when you go to work, when you stop off at the neighborhood bar,
and when you go home—and prearranges everything before you even think to do it
yourself.
Sure, that’s all pretty futuristic. But the technology is
evolving so quickly that it’s not beyond the realm of possibility. And we are
already seeing startups and local governments as well as transportation
authorities working together to try and make something happen before too long.
In fact, one research firm says the smart parking
technologies market is expected to eclipse $5.51 billion by 2024, with a compound
annual growth rate of 19 percent.
“The provision and management of parking within cities is
critical for the city’s ability to function and thrive,” notes Mark Zannoni,
IDC research director for smart cities and transportation. “Parking
impacts the urban economy and quality of life, as it provides access to
businesses and events, affects traffic, and provides sizable revenues to local
governments. Accordingly, more and more cities worldwide are looking at smart
parking as a service offering while they transform themselves into smart cities.”
Los Angeles could be considered one such smart city. Since
2012, its LA Express Park program has sought to make more parking available in
a 4.5-square-mile downtown area by giving commuters several ways to find
parking spots. This has always been a huge issue for the City of Angels, where
much of downtown peak traffic congestion is thought to be caused by drivers
hunting for parking.
Through LA Express and work being done with a San Francisco
Bay Area startup called Streetline, the city has slowly expanded the use of
smart parking meters, parking space vehicle sensors, real-time parking guidance
systems, and an integrated parking management system to other districts.
Streetline has partnered with other California cities, including San Carlos and
San Mateo, to save people nearly 713,000 hours of driving, 3 million miles, and
178,00 gallons of gas, according to its website.
“We are still in the position that hard, real data is
required to power real-time availability of parking information,” says Mark
Noworolski, CTO at Streetline. “Connected cameras with an object detection back
end are a natural fit for that, as they are less invasive than on-street
sensors and can be deployed in a wide variety of environments.”
The same technology that may save you time on the road could
also help you accurately anchor your boat. At the recent Dusseldorf Boat Show
in Germany, FLIR Systems, a Wilsonville, Oregon-based imaging and surveillance
systems company, announced what may be the marine industry’s first intelligent
object recognition and motion sensing “assisted docking solution.” Called
Raymarine DocSense, the company’s vision camera technology and video analytics
integrate intelligence gathered from surrounding imagery with the vessel’s
propulsion and steering system to assist boat owners in tight-quarter docking
maneuvering.
Campus security
One of the more profound and meaningful applications of
object detection technology will be how it helps protect children.
Since the Columbine school massacre in 1999, more than
187,000 students have been exposed to gun violence, according to a Washington
Post study. It’s a tragedy that continues to grow and intensify with few good
solutions. For example, some suggest deploying armed guards while others
advocate hoisting security gates on campuses. Neither approach has much
support.
But what if you could almost invisibly identify whether
guns, knives, or other weapons—concealed or visible—are coming into schools? If
you could do so without violating privacy rights, wouldn’t it be interesting?
And wouldn’t it be worth discussing possibly expanding such technology to other
threatened areas, such as airports, bus stations, hotels, casinos, movie
theaters and shopping malls?
Numerous startups not only believe it’s possible but that they
have a calling to do so.
“This is a relatively new field. But given the urgency of
the [mass shooting] problem, there are a number of players looking at how to
address the challenge,” says Martin Cronin, CEO of Canada’s Patriot One
Technologies, which offers a concealed weapons detection system built around a
cognitive microwave radar device and custom AI software. “There is no question
that embedded, discreet sensors, powered by AI, will become more ubiquitous for
public safety.”
Cronin acknowledges that any discussion about monitoring
people to see what they’re carrying—even if it’s guns—is always controversial.
For that reason, he says it is imperative to involve the public in all
decisions around security measures.
“There are understandably public fears about slipping
quietly into a mass surveillance society,” Cronin says. “Our technologies focus
on the threat objects (guns, knives, bombs, etc.). The person is of no interest
to the system unless they are carrying a threat. That is important to maintaining
public confidence and for assuring that there is no discrimination involved. We
also generate no body image in the process of scanning for weapons with our
radar-based systems, which is important for privacy. This is an approach which
we feel can maintain the important public consensus. People want to feel safe,
but there is a limit to what they are prepared to give up in their civil
liberties to enjoy that safety.”
Careful advancement
Object recognition technology is in its early stages. While
it has begun to transform the manufacturing floor because the system works with
known objects, the long-term vision for the technology is to infer things about
objects and learn from those experiences. AI and machine learning techniques
using multilayered applications of deep neural networks will help it to
eventually be able to pick out almost any object in a crowd. But it’s not quite
there yet. A team of cognitive psychologists from the University of California,
Los Angeles, challenged some of the best deep learning networks to look at
color images of animals and objects, with the images altered. For example, the
surface of a golf ball was displayed on a teapot; zebra stripes were placed on
a camel; and the pattern of a blue and red argyle sock was shown on an
elephant. The learning networks ranked their top choices and chose the correct
item as the first choice for only five of 40 objects.
“Deep learning systems process information very differently
from humans,” notes Philip Kellman, a UCLA distinguished professor of
psychology and senior author of the study. “The biggest misunderstanding is
that AI devices are ‘seeing’ the way we are. That’s not a comment about having
consciousness. When a deep network is shown a picture and says ‘horse,’ it does
not have a description of what the shape of the object is [and] does not
perceive that it is detachable from the surfaces behind it. Conversely, if you
view a certain glass ornament on my desk, you will immediately see that it is a
goose, because you see its shape. Deep learning systems aren’t seeing that
shape at all. And shape is not the only abstract property that’s being
missed—but it is among the most crucial. In human vision and learning, the
fascinating fact is that you don’t need to exhaustively show all possible views
and versions of the objects that you want the network to learn because when we
acquire abstract descriptions, like shape, we have a more powerful way of
generalizing.”
