The 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
the 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.