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 3. AI Rides the Rails to Automate Track Inspection


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AI RIDES THE RAILS TO AUTOMATE TRACK INSPECTION

Published Date December 16, 2022 Author Brandon Lewis



Rail travel is one of the most popular modes of transportation in the world,
especially in high-density urban centers where throngs of people pack into
trains to commute across town. As you can imagine, sending locomotives and rail
cars weighing hundreds or thousands of tons down two steel beams makes ongoing
safety inspections the highest priority.

But did you know that most of these inspections, including those of train
tracks, are still conducted manually?

That’s right. Most track inspections, even in the U.S., are still performed
visually by a human who walks or drives down a track and stops periodically to
examine conditions, measure the gauge or distance between rails, and check that
track fasteners are properly secured. The time consumed worldwide by this
exercise is difficult to comprehend.

With all the recent advances in AI and computer vision technology, automated
inspection solutions can replace human inspectors to save time, cut costs, and
improve the coverage of visual track inspections. That is, if the AI system is
engineered well enough to withstand the rigors of the rails.


AI INSPECTION AUTOMATES TRACK SAFETY

Take track fastener inspection, for instance: an AI inspection system comprising
an HD camera mounted under the locomotive, AI models that classify the angle of
rail fasteners, and an edge computer that can monitor the integrity of every
single rail fasteners, then relay abnormal inferences back to an operations
center so technicians can be dispatched if necessary (Figure 1).

Figure 1. A straightforward track fastener inspection system can eliminate the
inefficiency of having humans visually inspect train tracks. (Source: Moxa Inc.)

This is far more efficient than sending a human to check intermittent rail
fasteners and, best of all, the train carrying the automated inspection system
is already headed that way to begin with. And not only does this accurately
detect any issues, but it also helps ensure passengers and cargo are traveling
on the safest route possible.

Of course, all that sounds much simpler than it really is. A system like this
requires a highly accurate, custom AI model; sufficient processing power to run
it at the edge; enough efficiency to run on limited resources; and the ability
to operate for years in harsh environments in accordance with standards like EN
50155.

Solutions that meet all those requirements don’t just fall off the back of a
train.

With all the recent advances in #AI and #ComputerVision #technology, automated
inspection solutions can replace human inspectors to save time, cut costs, and
improve the coverage of visual track inspections. @MoxaInc via @insightdottech


EYES ON THE RAILS

Realizing the potential benefits, a train operator in Asia enlisted the help of
Moxa Inc., a leading provider in industrial computing and maker of the V2406C
Series Multi-WWAN Rail Computer.

The V2406C is an EN 50155:2017- and EN 50121-4-compliant onboard and wayside
railway computer designed for heavy-duty data processing tasks that was already
deployed in trains as an onboard CCTV and NVR storage and video processing
platform. Based on 7th generation Intel® Core™ processor technology, the V2406C
has the wide -40ºC to +70ºC temperature support needed to subsist in rolling
stock environments for up to 15 years as well as the power efficiency to operate
constantly without draining resources.

But to achieve the 90-percent-plus accuracies required by the track fastener use
case, the V2406C needed an extra boost. The nudge it needed was delivered via
the Intel® Distribution of OpenVINO™ Toolkit and Intel® Movidius™ Vision
Processing Unit (VPU) acceleration modules.

The OpenVINO toolkit automatically optimizes AI models developed in frameworks
like Caffe, TensorFlow, PyTorch, or MXNet for deployment on a range of Intel
silicon, including Movidius VPUs. In the track fastener inspection system, these
VPUs were integrated by plugging two acceleration modules featuring them into
mPCIe slots on the V2406C Series. The OpenVINO suite was then used to compress a
custom track fastener classification algorithm designed by Moxa and a system
integrator to fit on the V2406C Series’ 32 GB of onboard DDR4 RAM.

The resulting AI inspection algorithms execute on the Movidius VPUs to identify
track fasteners with abnormal angles of greater than 18 degrees in real time,
yielding more than 90% classification accuracy.


BACK FROM THE WAYSIDE

Today, the train operator logs inferences from the V2406C AI inspection system
on two hot-swappable HDD/SSD storage expansion drives, then reviews trip data at
an operations center when trains return from their routes. But there are plans
to make use of redundant LTE/Wi-Fi connectivity afforded by the V2406C’s two
mPCIe wireless expansion slots and four SIM card slots to wirelessly transmit
inference outputs and positioning data back to control centers from wayside
trains.

By tying this edge intelligence back to a central hub, train operators can
convert human inspectors into maintenance technicians who are dispatched only to
specific sections of track that require immediate attention. The result will be
better track conditions, fewer delays, and lower operational costs, all thanks
to automated inspection via the AIoT.

 

This article was edited by Christina Cardoza, Associate Editorial Director for
insight.tech.


ABOUT THE AUTHOR

Brandon brings more than a decade of high-tech journalism and media experience
to his current role as Editor-in-Chief of the electronics engineering
publication Embedded Computing Design. His coverage focuses on artificial
intelligence and machine learning, the Internet of Things, cybersecurity,
embedded processors, edge computing, prototyping kits, and safety-critical
systems, but extends to any topic of interest to the electronic design
community. Brandon leads interactive YouTube communities around platforms like
the Embedded Toolbox video interview series and Dev Kit Weekly hardware reviews,
and co-hosts the Embedded Insiders Podcast. Drop him a line at
brandon.lewis@opensysmedia.com or DM him on Twitter @techielew.

Follow on Twitter Follow on Linkedin Visit Website More Content by Brandon Lewis
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