Aerospace & Defense Technology - October 2021 - 4

Rugged Computing
An autonomous underwater
vehicle (AUV) detecting a
mine on the seafloor.
Developing Object
Detection Systems for
Autonomous Underwater Vehicles
T
he bottom of a lake or an
ocean is an ever-changing
place. Water flows back and
forth in shifting currents. Sunlight
heats the sand and darkness cools
it back down. The sand itself moves, unveiling
rocks and man-made objects of
peculiar shape underneath.
Two key technologies-sonar and autonomous
underwater vehicles (AUVs)-
have provided us with relatively ubiquitous
access to this complex and remote
landscape. Commercial and academic
participants are now figuring out how to
apply advanced computer vision technology
to undersea surveying. When
these vision systems are based on biologically
inspired deep neural networks,
they are commonly considered a form of
artificial intelligence (AI).
Object Detection and Classification
For ground and air vehicles, computer
vision technologies are now widely used
to identify and classify objects from
video. Identification and classification
promise two key benefits for undersea
exploration. First, they promise to automate
the analysis of the seafloor. Current
workflows rely on AUVs taking
sonar data and physically transporting it
back to the surface for expert analysis.
Automated object detection will allow
experts to save hours of time and focus
mainly on validating an AI's findings.
Second, object detection should enable
greater autonomy for AUVs, which
are largely preprogrammed. Future
AUVs will be expected to not just collect
seafloor images but to perceive what
those images mean and take immediate
action, such as moving closer to an interesting
object for reinspection.
The development of object detection
for undersea applications has lagged behind
those for air and land, but it is catching
up. Charles River Analytics recently
released an object detection software
product called AutoTRap Onboard™,
which joins a select field of undersea object
detection solutions. The current version
of AutoTRap Onboard was developed
in partnership with Teledyne Gavia and
has been tailored towards use on their
Gavia AUVs. It has been shown in sea
tests to detect and classify certain types of
mine-like objects with a high degree of accuracy,
and to do so in real time. Importantly,
the software platform is readily extensible
to detect other objects and to run
on other platforms and sonar systems.
(The current release version is optimized
for side scan sonar systems manufactured
by EdgeTech.)
Modular AUV components like AutoTRap
Onboard are a relatively recent development,
as are AUVs themselves.
Ocean science and industry stakeholders
began to widely adopt commercially
available AUVs in the early-to-mid 2000s,
according to Arjuna Balasuriya, Principal
Scientist at Charles River and product lead
for AutoTRap Onboard. AUVs emerged as
a viable technology at that time due to advances
in onboard computer processing
power and battery lifetime.
A Teledyne Gavia AUV with AutoTRap Onboard in preparation for testing.
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Aerospace & Defense Technology - October 2021

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