Helmet
detection
is
crucial
for
advancing
protection
levels
in
public
road
traffic
dynamics.
This
problem
statement
translates
to
an
object
task.
Therefore,
this
paper
compares
recent
You
Only
Look
Once
(YOLO)
models
the
context
of
helmet
terms
reliability
and
computational
load.
Specifically,
YOLOv8,
YOLOv9,
newly
released
YOLOv11
have
been
used.
Besides,
a
modified
architectural
pipeline
that
remarkably
improves
overall
performance
has
proposed
manuscript.
hybridized
YOLO
model
(h-YOLO)
pitted
against
independent
analysis
proves
h-YOLO
preferable
over
plain
models.
The
were
tested
using
range
standard
benchmarks
such
as
recall,
precision,
mAP
(Mean
Average
Precision).
In
addition,
training
testing
times
recorded
provide
scope
real-time
scenario.
AI,
Journal Year:
2025,
Volume and Issue:
6(3), P. 57 - 57
Published: March 13, 2025
Background:
Bird
species
identification
and
classification
are
crucial
for
biodiversity
research,
conservation
initiatives,
ecological
monitoring.
However,
conventional
techniques
used
by
biologists
time-consuming
susceptible
to
human
error.
The
integration
of
deep
learning
models
offers
a
promising
alternative
automate
enhance
recognition
processes.
Methods:
This
study
explores
the
use
bird
in
city
Zacatecas.
Specifically,
we
implement
YOLOv8
Small
real-time
detection
MobileNet
V3
classification.
were
trained
tested
on
dataset
comprising
five
species:
Vermilion
Flycatcher,
Pine
Mexican
Chickadee,
Arizona
Woodpecker,
Striped
Sparrow.
evaluation
metrics
included
precision,
recall,
computational
efficiency.
Results:
findings
demonstrate
that
both
achieve
high
accuracy
identification.
excels
detection,
making
it
suitable
dynamic
monitoring
scenarios,
while
provides
lightweight
yet
efficient
solution.
These
results
highlight
potential
artificial
intelligence
ornithological
research
improving
reducing
manual
efforts.
IET Image Processing,
Journal Year:
2025,
Volume and Issue:
19(1)
Published: Jan. 1, 2025
ABSTRACT
Drones,
due
to
their
high
efficiency
and
flexibility,
have
been
widely
applied.
However,
small
objects
captured
by
drones
are
easily
affected
various
conditions,
resulting
in
suboptimal
surveying
performance.
While
the
YOLO
series
has
achieved
significant
success
detecting
large
targets,
it
still
faces
challenges
target
detection.
To
address
this,
we
propose
an
innovative
model,
AMFE‐YOLO,
aimed
at
overcoming
bottlenecks
Firstly,
introduce
AMFE
module
focus
on
occluded
thereby
improving
detection
capabilities
complex
environments.
Secondly,
design
SFSM
merge
shallow
spatial
information
from
input
features
with
deep
semantic
obtained
neck,
enhancing
representation
ability
of
reducing
noise.
Additionally,
implement
a
novel
strategy
that
introduces
auxiliary
head
identify
very
targets.
Finally,
reconfigured
head,
effectively
addressing
issue
false
positives
small‐object
precision
object
AMFE‐YOLO
outperforms
methods
like
YOLOv10
YOLOv11
terms
mAP
VisDrone2019
public
dataset.
Compared
original
YOLOv8s,
average
improved
5.5%,
while
model
parameter
size
was
reduced
0.7
M.
Journal of Marine Science and Engineering,
Journal Year:
2025,
Volume and Issue:
13(5), P. 925 - 925
Published: May 8, 2025
The
accurate
detection
of
small
ships
based
on
images
or
vision
is
critical
for
many
scenarios,
like
maritime
surveillance,
port
security,
and
navigation
safety.
However,
achieving
a
challenge
cost-efficiency
models;
while
the
models
could
meet
this
requirement,
they
have
unacceptable
computation
costs
real-time
surveillance.
We
propose
YOLO-LPSS,
novel
model
designed
to
significantly
improve
ship
accuracy
with
low
cost.
characteristics
YOLO-LPSS
are
as
follows:
(1)
Strengthening
backbone’s
ability
extract
emphasize
features
relevant
objects,
particularly
in
semantic-rich
layers.
(2)
A
sophisticated,
learnable
method
up-sampling
processes
employed,
taking
into
account
both
deep
image
information
semantic
information.
(3)
Introducing
post-processing
mechanism
final
output
resampling
process
restore
missing
local
region
high-resolution
feature
map
capture
global-dependence
features.
experimental
results
show
that
outperforms
known
YOLOv8
nano
baseline
other
works,
number
parameters
increases
by
only
0.33
M
compared
original
YOLOv8n
0.796
0.831
AP50:95
classes
consisting
mainly
targets
(the
bounding
box
target
area
less
than
5%
resolution),
which
3–5%
higher
vanilla
recent
SOTA
models.
Buildings,
Journal Year:
2025,
Volume and Issue:
15(11), P. 1836 - 1836
Published: May 27, 2025
Monitoring
worker
safety
during
ladder
operations
at
construction
sites
is
challenging
due
to
occlusion,
where
workers
are
partially
or
fully
obscured
by
objects
other
workers,
and
overlapping,
which
makes
individual
tracking
difficult.
Traditional
object
detection
models,
such
as
YOLOv8,
struggle
maintain
continuity
under
these
conditions.
To
address
this,
we
propose
an
integrated
framework
combining
YOLOv8
for
initial
the
SAMURAI
algorithm
enhanced
occlusion
handling.
The
system
was
evaluated
across
four
scenarios:
non-occlusion,
minor
major
multiple
overlap.
results
indicate
that,
while
performs
well
in
non-occluded
conditions,
accuracy
declines
significantly
severe
occlusions.
integration
of
improves
stability,
identity
preservation,
robustness
against
occlusion.
In
particular,
achieved
a
success
rate
94.8%
91.2%
overlap
scenarios—substantially
outperforming
alone
maintaining
continuity.
This
study
demonstrates
that
YOLOv8-SAMURAI
provides
reliable
solution
real-time
monitoring
complex
environments,
offering
foundation
improved
compliance
risk
mitigation.
In
aerial
image
object
detection,
small
targets
present
significant
challenges
due
to
limited
pixel
information,
complex
backgrounds,
and
sensitivity
bounding
box
perturbations.
To
tackle
these
issues,
we
propose
SO-RTDETR
for
detection.
The
model
introduces
a
Cross-Scale
Feature
Fusion
with
S2
(S2-CCFF)
module,
Parallelized
Patch-Aware
attention
(PPA)
the
Normalized
Wasserstein
Distance
(NWD)
loss
function,
leading
performance
improvements.
Specifically,
S2-CCFF
module
enhances
information
by
incorporating
an
additional
layer,
while
SPDConv
downsampling
maintains
key
details
reduces
computational
cost.
CSPOK-Fusion
mechanism
integrates
global,
local,
large
branch
features,
capturing
multi-scale
representations
effectively
mitigating
interference
from
backgrounds
occlusions,
thereby
enhancing
spatial
representation
of
features
across
scales.
PPA
embedded
in
Backbone
network,
leverages
multi-level
feature
fusion
mechanisms
retain
strengthen
addressing
issue
loss.
NWD
focusing
on
relative
positioning
shape
differences
boxes,
increases
robustness
minor
perturbations,
detection
accuracy.
Experimental
results
VisDrone
NWPU
VHR-10
datasets
demonstrate
that
our
approach
outperforms
state-of-the-art
detectors.
Biomimetics,
Journal Year:
2024,
Volume and Issue:
9(11), P. 711 - 711
Published: Nov. 19, 2024
Despite
the
implementation
of
numerous
interventions
to
enhance
urban
traffic
safety,
estimation
risk
crashes
resulting
in
life-threatening
and
economic
costs
remains
a
significant
challenge.
In
light
above,
an
online
inference
method
for
crash
based
on
self-developed
TAR-DETR
WOA-SA-SVM
methods
is
proposed.
The
method's
robust
data
capabilities
can
be
applied
autonomous
mobile
robots
vehicle
systems,
enabling
real-time
road
condition
prediction,
continuous
monitoring,
timely
roadside
assistance.
First,
dataset
object
detection,
named
TAR-1,
created
by
extracting
information
from
major
roads
around
Hainan
University
China
incorporating
Russian
car
news.
Secondly,
we
develop
innovative
Context-Guided
Reconstruction
Feature
Network-based
Urban
Traffic
Objects
Detection
Model
(TAR-DETR).
model
demonstrates
detection
accuracy
76.8%
objects,
which
exceeds
performance
other
state-of-the-art
models.
employed
TAR-1
extract
features,
feature
was
designated
as
TAR-2.
TAR-2
comprises
six
features
three
categories.
A
new
algorithm
proposed
optimize
parameters
(C,
g)
SVM,
thereby
enhancing
robustness
inference.
developed
combining
Whale
Optimization
Algorithm
(WOA)
Simulated
Annealing
(SA),
Hybrid
Bionic
Intelligent
Algorithm.
inputted
into
Support
Vector
Machine
(SVM)
optimized
using
hybrid
used
infer
crashes.
achieves
average
80%