Review on computer vision-based inspection and monitoring for bridge cables
Measurement,
Год журнала:
2025,
Номер
unknown, С. 116892 - 116892
Опубликована: Фев. 1, 2025
Язык: Английский
An Advanced Computer Vision Method for Noncontact Vibration Measurement of Cables in Cable‐Stayed Bridges
Structural Control and Health Monitoring,
Год журнала:
2025,
Номер
2025(1)
Опубликована: Янв. 1, 2025
With
the
development
of
computer
and
image
processing
technologies,
vision
(CV)
has
been
attracting
increasing
attention
in
field
civil
engineering
measurement
monitoring.
Cables
slender
structures
have
unique
challenges
for
CV‐based
vibration
methods,
such
as
low
pixel
proportion
sensitivity
to
environmental
conditions.
This
study
proposes
a
noncontact
method
based
on
line
tracking
algorithm
(LTA).
The
robustness
applicability
proposed
under
varying
resolutions,
signal‐to‐noise
ratios,
cable
inclination
angles
were
systematically
evaluated
through
experimental
test
specimen.
To
validate
effectiveness
practical
detection
applications,
scaled
cable‐stayed
bridge
model
was
carried
out.
numerical
result
indicates
that
LTA
provides
high
reliability
accuracy
values
force.
maximum
errors
first‐order
self‐vibration
frequency
force
is
0.99%
2%,
respectively.
maintains
strong
stability
across
various
conditions,
which
reference
long‐term
structural
health
monitoring
bridges.
Язык: Английский
Bayesian continuous wavelet transform for time-varying damping identification of cables using full-field measurement
Automation in Construction,
Год журнала:
2024,
Номер
168, С. 105791 - 105791
Опубликована: Сен. 21, 2024
Язык: Английский
Vision-based identification of cable tensions and finite element model verification of a cable-stayed bridge
Journal of Civil Structural Health Monitoring,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 20, 2024
Язык: Английский
Bridge component segmentation for health monitoring an enhanced DeepLabV3+ model with lightweight network and multi-scale channel attention mechanism
Advances in Structural Engineering,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 5, 2024
Due
to
the
influence
of
various
factors,
such
as
complex
environments
and
sustained
load
effects,
long-term
service
life
bridge
structures
will
lead
a
gradual
deterioration
in
performance.
Therefore,
health
monitoring
is
utmost
importance,
component
identification
crucial
step
evaluating
overall
structural
integrity
bridges.
With
advancement
deep
learning
algorithms,
semantic
segmentation
methods
can
effectively
classify
identify
components
environments,
thereby
facilitating
assessment
their
state.
Nevertheless,
conventional
for
segmenting
suffer
from
drawbacks
intensive
computation,
inadequate
feature
extraction,
low
accuracy,
failing
meet
requirements
current
monitoring.
Consequently,
this
paper
proposes
method
based
on
an
improved
DeepLabV3
+
model,
named
DeepLabV3-MS,
which
enhanced
model.
This
utilizes
MobileNetV2
backbone
network
reduce
parameter
count
improve
computational
speed
The
Strip
Pooling
(SP)
also
integrated
into
ASPP,
known
SP_ASPP,
enhance
capture
more
comprehensive
contextual
information.
Additionally,
Multi-scale
Channel
Attention
Mechanism
(MS-CAM)
incorporated
integration
efficiency
multi-semantic
multi-scale
features.
results
indicate
that
compared
with
original
Mean
Intersection
over
Union
Pixel
Accuracy
DeeplabV3-MS
model
increased
by
5.90%,
4.92%,
respectively.
Furthermore,
comparison
classic
models
PSPNet
U-Net,
demonstrated
increase
19.50%
8.88%
MIoU
MPA,
respectively,
well
13.50%
5.34%,
proposed
has
superior
performance
across
evaluation
metrics,
exerting
significant
impact
safety
components.
it
offers
valuable
technical
support
research
applications
related
fields.
Язык: Английский
An advanced method for surface damage detection of concrete structures in low-light environments based on image enhancement and object detection networks
Advances in Bridge Engineering,
Год журнала:
2024,
Номер
5(1)
Опубликована: Дек. 13, 2024
Abstract
Surface
damage
detection
in
concrete
structures
is
critical
for
maintaining
structural
integrity,
yet
current
object
algorithms
often
struggle
low-light
environments.
To
address
this
challenge,
study
proposed
a
methodology
that
integrates
image
enhancement
and
networks
to
improve
identification
such
conditions.
Specifically,
we
employ
the
self-calibrated
illumination
(SCI)
model
reconstruct
images,
which
are
then
processed
by
an
improved
YOLOv5-based
network,
YOLOv5-GAM-ASFF,
incorporating
global
attention
mechanism
(GAM)
adaptive
spatial
feature
fusion
(ASFF).
The
performance
of
YOLOv5-GAM-ASFF
evaluated
on
dataset
structure
demonstrating
its
superiority
over
YOLOv5s,
YOLOv6s,
YOLOv7-tiny.
results
show
achieves
[email protected]
79.1%,
surpassing
other
models
1.3%,
3.3%,
5.8%,
respectively.
This
approach
provides
reliable
solution
surface
environments,
advancing
field
health
monitoring
improving
accuracy
under
challenging
Язык: Английский