Attention-enhanced and integrated deep learning approach for fishing vessel classification based on multiple features
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 13, 2025
Effective
fisheries
management
is
the
key
to
achieve
sustainable
globally,
while
accurate
monitoring
of
fishing
vessels
essential
improve
effectiveness
measures.
Self-reported
information
on
vessel
types
often
limited
and
may
not
cover
all
operating
vessels,
causing
incomplete
in
management.
Therefore,
a
novel
way
objectively
identify
large
quantity
needed.
In
this
study,
we
presented
an
innovative
integrated
deep
learning
model
by
using
automatic
identification
system
(AIS)
data
classify
five
including
gillnetter,
hook
liner,
trawler,
fish
carrier,
stow
net
vessel,
further
improving
performance
classification.
First,
preprocessed
removing
erroneous
information,
dividing
trajectories
day
obtain
complete
reliable
dataset.
Then,
multidimensional
feature
vector
was
constructed
combining
geometric,
static
dynamic
characteristics
explain
behavioral
differences
various
more
effectively.
Finally,
fed
into
ensemble
two-dimensional
bidirectional
long
short-term
memory
network
convolutional
neural
with
attention
mechanism
for
training,
prediction
results
were
obtained
through
fully
connected
layer.
The
accuracy
91.90%,
which
higher
than
other
single
classifiers.
experimental
demonstrated
that
method
remarkable
could
be
adopted
precision
classification
based
AIS
data.
Язык: Английский
Incorporating prior knowledge of collision risk into deep learning networks for ship trajectory prediction in the maritime Internet of Things industry
Engineering Applications of Artificial Intelligence,
Год журнала:
2025,
Номер
146, С. 110311 - 110311
Опубликована: Фев. 20, 2025
Язык: Английский
A framework for ship semantic behavior representation and indexing
Ocean Engineering,
Год журнала:
2025,
Номер
329, С. 121023 - 121023
Опубликована: Апрель 7, 2025
Язык: Английский
Machine learning applications for risk assessment in maritime transport: Current status and future directions
Engineering Applications of Artificial Intelligence,
Год журнала:
2025,
Номер
155, С. 110959 - 110959
Опубликована: Май 9, 2025
Язык: Английский
Deep Learning Methods to Mitigate Human-Factor-Related Accidents in Maritime Transport
Journal of Marine Science and Engineering,
Год журнала:
2024,
Номер
12(10), С. 1819 - 1819
Опубликована: Окт. 12, 2024
Artificial
intelligence
aims
to
be
the
solution
multiple
engineering
problems
by
trying
emulate
human
learning
process.
In
this
sense,
maritime
transport
standards
have
clearly
evolved,
which
are
based
on
two
principal
pillars:
International
Convention
for
Safety
of
Life
at
Sea
(SOLAS)
and
Prevention
Pollution
from
Ships
(MARPOL).
Based
a
formal
safety
assessment
research
process,
these
pillars
try
solve
most
accidents,
which,
in
their
final
steps,
associated
with
factors.
research,
an
original
methodology
employing
deep
process
image
recognition
during
mooring
line
operation,
dangerous
ships,
is
developed.
The
main
results
indicate
that
proposed
method
excellent
tool
advising
ship
officers
watch
and,
consequently,
provides
new
way
prevent
factors
onboard
causing
future
must
considered
international
standards.
Язык: Английский