A DSF-net-based approach to dual-branch instance segmentation of weak bridge defects
Engineering Structures,
Год журнала:
2025,
Номер
327, С. 119583 - 119583
Опубликована: Янв. 4, 2025
Язык: Английский
Application of machine learning to leakage detection of fluid pipelines in recent years: A review and prospect
Measurement,
Год журнала:
2025,
Номер
unknown, С. 116857 - 116857
Опубликована: Янв. 1, 2025
Язык: Английский
Inner wall defect detection in oil and gas pipelines using point cloud data segmentation
Automation in Construction,
Год журнала:
2025,
Номер
173, С. 106098 - 106098
Опубликована: Март 4, 2025
Язык: Английский
Corrosion Depth Prediction of a Buried Pipeline Based on TEM and MSDBO-BiLSTM
Journal of Pipeline Systems Engineering and Practice,
Год журнала:
2025,
Номер
16(2)
Опубликована: Март 12, 2025
Язык: Английский
Research of Highway Bridge Settlement Monitoring Technology based on Machine Vision
Journal of Research in Science and Engineering,
Год журнала:
2024,
Номер
6(7), С. 29 - 32
Опубликована: Июль 28, 2024
In
view
of
the
significant
impact
deep
foundation
pit
excavation
on
surface
surrounding
roads
and
bridges,
widely
used
monitoring
technology
still
relies
manual
detection
means,
which
leads
to
consumption
a
large
number
human
material
resources,
efficiency
is
relatively
low.
Therefore,
this
paper
provides
method
system
highway
bridge
pile
displacement
based
machine
vision.
Through
real-time
automatic
settlement
changes,
it
targeted
suggestions
guidance
for
maintenance
during
excavation.
At
same
time,
new
type
marker
module
provided
enhance
accuracy
feature
point
recognition
in
image
processing.
The
results
show
that
vision
can
automatically
monitor
real
time
with
high
accuracy,
improve
safety
stability
bridges
construction.
Язык: Английский
A New Hybrid Velocity Prediction Model for Pipeline Detectors Based on Bo-Ssa, Bilstm, and the Attention Mechanism
Опубликована: Янв. 1, 2024
Язык: Английский
Identification of Coating Layer Pipeline Defects Based on the GA-SENet-ResNet18 Model
International Journal of Pressure Vessels and Piping,
Год журнала:
2024,
Номер
unknown, С. 105327 - 105327
Опубликована: Сен. 1, 2024
Язык: Английский
Anomaly triplet-net: progress recognition model using deep metric learning considering occlusion for manual assembly work
Advanced Robotics,
Год журнала:
2024,
Номер
39(2), С. 89 - 101
Опубликована: Ноя. 11, 2024
In
this
paper,
we
propose
a
method
for
recognizing
progress
in
product
assembly
considering
occlusion
using
deep
metric
learning.
Visualizing
the
process
factories
is
crucial
enhancing
work
efficiency
and
minimizing
disposal
costs.
However,
there
problem
that
products
are
managed
by
pasting
them
on
paper
with
status
written
them.
We
solve
of
having
to
manage
manually.
First,
target
detected
from
images
acquired
fixed-point
camera
installed
factory
learning-based
object
detection
method.
Next,
area
cropped
image.
Finally,
classification
based
learning
image,
estimated
as
rough
step.
As
specific
estimation
model,
an
Anomaly
Triplet-Net
which
improved
model
existing
Triplet
Loss.
This
considers
anomaly
samples.
experiments,
82.9
[%]
success
rate
achieved
Triplet-Net.
also
experimented
practicality
sequence
detection,
cropping,
progression
estimation,
confirmed
effectiveness
overall
system.
Язык: Английский