2018 Winter Simulation Conference (WSC),
Journal Year:
2023,
Volume and Issue:
unknown, P. 2710 - 2721
Published: Dec. 10, 2023
This
study
introduces
a
deep
learning-based
method
for
indoor
3D
object
detection
and
localization
in
healthcare
facilities.
incorporates
spatial
channel
attention
mechanisms
into
the
YOLOv5
architecture,
ensuring
balance
between
accuracy
computational
efficiency.
The
network
achieves
an
AP50
of
67.6%,
mAP
46.7%,
real-time
rate
with
FPS
67.
Moreover,
proposes
novel
mechanism
estimating
coordinates
detected
objects
projecting
them
onto
maps,
average
error
0.24
m
0.28
x
y
directions,
respectively.
After
being
tested
validated
real-world
data
from
university
campus,
proposed
shows
promise
improving
disinfection
efficiency
facilities
by
enabling
robot
navigation.
Journal of Imaging,
Journal Year:
2024,
Volume and Issue:
10(4), P. 81 - 81
Published: March 28, 2024
Computer
vision
(CV),
a
type
of
artificial
intelligence
(AI)
that
uses
digital
videos
or
sequence
images
to
recognize
content,
has
been
used
extensively
across
industries
in
recent
years.
However,
the
healthcare
industry,
its
applications
are
limited
by
factors
like
privacy,
safety,
and
ethical
concerns.
Despite
this,
CV
potential
improve
patient
monitoring,
system
efficiencies,
while
reducing
workload.
In
contrast
previous
reviews,
we
focus
on
end-user
CV.
First,
briefly
review
categorize
other
(job
enhancement,
surveillance
automation,
augmented
reality).
We
then
developments
hospital
setting,
outpatient,
community
settings.
The
advances
monitoring
delirium,
pain
sedation,
deterioration,
mechanical
ventilation,
mobility,
surgical
applications,
quantification
workload
hospital,
for
events
outside
highlighted.
To
identify
opportunities
future
also
completed
journey
mapping
at
different
levels.
Lastly,
discuss
considerations
associated
with
outline
processes
algorithm
development
testing
limit
expansion
healthcare.
This
comprehensive
highlights
ideas
expanded
use
European Journal of Electrical Engineering and Computer Science,
Journal Year:
2023,
Volume and Issue:
7(4), P. 39 - 45
Published: July 30, 2023
Object
detection,
a
fundamental
task
in
computer
vision,
involves
identifying
and
localizing
objects
within
images
or
videos.
This
paper
provides
comprehensive
review
of
traditional
deep
learning-based
object
detection
techniques
their
applications,
challenges,
future
directions.
We
first
discuss
methods,
which
rely
on
handcrafted
features
classical
machine
learning
algorithms.
then
explore
the
advancements
brought
by
learning,
including
convolutional
neural
networks
(CNNs)
transformer-based
architectures,
have
significantly
improved
accuracy
efficiency
tasks.
A
thorough
comparison
evaluation
different
are
presented,
considering
performance
metrics,
speed,
robustness
to
size,
orientation,
occlusion
variations.
also
examine
diverse
applications
across
various
domains,
such
as
robotics,
autonomous
vehicles,
surveillance,
medical
imaging,
augmented
reality.
outline
open
challenges
research
directions,
emphasizing
need
combine
with
other
tasks,
develop
few-shot
zero-shot
approaches,
address
issues
related
fairness,
accountability,
transparency.
aims
comprehensively
most
prominent
techniques,
evolution,
domains.
discussed
methods
recent
strengths
limitations.
Concrete
cracking
in
bridges
significantly
endangers
their
safety
and
integrity.
Traditional
crack
detection
methods,
reliant
on
human
visual
inspection,
are
labor-intensive
prone
to
errors.
This
paper
introduces
a
unique
framework
for
bridge
integration
with
building
information
models
(BIM),
trialed
423-ft
Atlanta,
Georgia.
The
comprises
two
main
stages:
(1)
creating
BIM
model
using
drone-captured
images
structure
from
motion
(SFM)
photogrammetry,
(2)
utilizing
deep
learning-based
encoder-decoder
network
segment
cracks
orthomosaic
superimpose
these
segmented
onto
the
model.
suggested
method
showed
robust
performance,
achieving
mean
intersection
over
union
(mIoU)
of
0.787,
precision
0.751,
recall
0.742.
These
results
underline
potential
proposed
improve
efficiency
inspection
processes.
Journal of Imaging,
Journal Year:
2024,
Volume and Issue:
10(10), P. 253 - 253
Published: Oct. 14, 2024
The
early
detection
of
the
acute
deterioration
escalating
illness
severity
is
crucial
for
effective
patient
management
and
can
significantly
impact
outcomes.
Ambient
sensing
technology,
such
as
computer
vision,
may
provide
real-time
information
that
could
recognition
response.
This
study
aimed
to
develop
a
vision
model
quantify
number
type
(clinician
vs.
visitor)
people
in
an
intensive
care
unit
(ICU)
room,
trajectory
their
movement,
preliminarily
explore
its
relationship
with
delirium
marker
severity.
To
present,
we
implemented
counting-by-detection
supervised
strategy
using
images
from
ICU
rooms.
was
accomplished
through
developing
three
methods:
single-frame,
multi-frame,
tracking-to-count.
We
then
explored
how
person
distribution
room
corresponded
presence
delirium.
Our
designed
pipeline
tested
different
set
models.
report
performance
statistics
preliminary
insights
into
between
persons
evaluated
our
method
compared
it
other
approaches,
including
density
estimation,
counting
by
detection,
regression
methods,
adaptability
environments.
