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15
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3
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Sep
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20
26
:
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3
3
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3
3
9
1332
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n
tellig
en
t
r
o
a
d
m
o
n
ito
r
in
g
s
o
lu
tio
n
s
th
at
in
v
o
lv
e
UAV
im
ag
es,
L
i
DAR
s
en
s
o
r
s
,
G
PS
lo
ca
lizatio
n
,
an
d
m
u
ltimo
d
al
d
ata
f
u
s
io
n
in
o
r
d
er
to
p
r
o
v
id
e
lar
g
e
s
ca
le
m
o
n
ito
r
in
g
an
d
s
m
ar
t
city
ap
p
licatio
n
s
[
2
1
]
–
[
2
5
]
.
I
n
s
p
ite
o
f
r
ec
en
t
p
r
o
g
r
ess
in
th
is
f
ield
,
m
o
s
t
o
f
th
e
ex
is
tin
g
s
o
lu
tio
n
s
c
o
n
ce
n
tr
ate
o
n
d
am
ag
e
d
etec
tio
n
ac
cu
r
ac
y
a
n
d
b
en
ch
m
a
r
k
ed
d
at
asets
,
wh
er
ea
s
v
er
y
f
ew
s
o
lu
tio
n
s
h
av
e
c
o
n
s
id
e
r
ed
r
ea
l
-
tim
e
im
p
lem
en
tatio
n
alo
n
g
with
GPS
-
en
ab
led
v
is
u
aliza
tio
n
f
o
r
in
f
r
a
s
tr
u
ctu
r
e
m
a
n
ag
em
e
n
t.
T
h
u
s
,
th
is
wo
r
k
co
n
s
id
er
s
a
s
o
lu
tio
n
b
ased
o
n
YOL
Ov
8
in
o
r
d
er
to
d
etec
t
v
a
r
io
u
s
k
in
d
s
o
f
r
o
ad
d
am
ag
es a
n
d
p
r
o
v
id
es r
ea
l
-
tim
e
GPS
-
en
ab
led
v
is
u
aliza
tio
n
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
tu
d
y
u
tili
ze
s
th
e
YOL
Ov
8
d
ee
p
lear
n
in
g
f
r
a
m
ewo
r
k
to
en
ab
le
r
ea
l
-
tim
e
id
en
tific
ati
o
n
o
f
r
o
a
d
s
u
r
f
ac
e
p
r
o
b
lem
s
,
i
n
clu
d
in
g
p
o
th
o
les,
cr
ac
k
s
,
an
d
s
u
r
f
ac
e
ir
r
eg
u
lar
ities
.
YOL
Ov
8
is
a
s
o
p
h
is
ticated
co
n
v
o
l
u
tio
n
al
n
e
u
r
al
n
etwo
r
k
(
C
NN)
ar
ch
itectu
r
e
en
g
in
ee
r
e
d
to
attain
ele
v
ated
p
r
ec
is
io
n
a
n
d
m
in
im
al
laten
cy
in
o
b
jec
t
d
etec
tio
n
,
r
e
n
d
er
i
n
g
it
ex
ce
p
tio
n
ally
ap
p
r
o
p
r
iate
f
o
r
r
ea
l
-
tim
e
s
u
r
v
eillan
ce
o
f
r
o
ad
way
co
n
d
itio
n
s
.
Fig
u
r
e
1
s
h
o
ws in
d
etail
h
o
w
t
h
e
s
u
g
g
ested
s
y
s
tem
is
b
u
ilt
.
Fig
u
r
e
1
.
B
lo
ck
d
iag
r
am
o
f
p
r
o
p
o
s
ed
s
y
s
tem
2
.
1
.
Da
t
a
s
et
c
o
llect
i
o
n
A
d
ataset
co
n
s
is
tin
g
o
f
3
,
1
7
8
r
ea
l
-
wo
r
l
d
im
ag
es
was
g
ath
er
ed
f
r
o
m
th
e
NH
-
4
4
s
p
u
r
r
o
ad
(
Hy
d
er
ab
a
d
–
Niza
m
ab
a
d
–
Nag
p
u
r
s
eg
m
en
t)
.
T
h
ese
im
ag
es
s
h
o
w
p
r
o
b
lem
s
with
th
e
r
o
a
d
s
u
r
f
ac
e
in
m
a
n
y
k
in
d
s
o
f
lig
h
t
an
d
wea
th
er
.
T
o
m
ak
e
th
e
d
ataset
m
o
r
e
d
iv
er
s
e,
f
u
r
th
er
an
n
o
tated
ex
am
p
les
f
r
o
m
th
e
cr
o
wd
s
en
s
in
g
-
b
ased
r
o
ad
d
am
ag
e
d
etec
tio
n
c
h
allen
g
e
2
0
2
2
wer
e
ad
d
ed
.
T
h
e
co
m
b
in
ed
d
a
taset
wa
s
ca
r
ef
u
lly
lab
eled
to
s
h
o
w
th
e
m
an
y
k
in
d
s
o
f
r
o
ad
d
am
ag
e
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
S
ma
r
t ro
a
d
ma
in
ten
a
n
ce
:
r
ea
l
-
time
s
u
r
fa
ce
d
a
ma
g
e
d
e
tectio
n
a
n
d
ma
p
p
i
n
g
w
ith
YOLOv8
(
P
r
ee
ty
S
in
g
h
)
1333
2
.
2
.
YO
L
O
v
8
a
rc
hite
ct
ure
T
h
e
YOL
Ov
8
-
s
m
o
d
el
was
ch
o
s
en
to
attain
a
n
o
p
tim
al
eq
u
il
ib
r
iu
m
b
etwe
e
n
d
etec
tio
n
p
r
ec
is
io
n
an
d
co
m
p
u
tatio
n
al
p
er
f
o
r
m
an
ce
.
T
h
e
ar
ch
itectu
r
e
co
m
p
r
is
es
th
r
e
e
p
r
im
ar
y
c
o
m
p
o
n
en
ts
:
th
e
b
a
ck
b
o
n
e
,
n
ec
k
,
a
n
d
h
ea
d
n
etwo
r
k
s
.
2
.
2
.
1
.
B
a
ck
bo
ne
net
wo
r
k
T
h
e
b
ac
k
b
o
n
e
n
etwo
r
k
o
f
YOL
Ov
8
p
lay
s
a
cr
u
cial
r
o
le
in
f
e
atu
r
e
ex
tr
ac
tio
n
b
y
m
ea
n
s
o
f
co
n
v
o
l
u
tio
n
al
lay
er
s
d
ir
ec
t
ly
ap
p
lied
to
th
e
in
p
u
t
im
ag
e.
T
h
is
p
r
o
ce
d
u
r
e
is
ex
p
r
ess
ed
as
f
o
llo
ws
m
ath
em
atica
lly
:
F
(
i
,
j
)
=
∑
∑
I
(
m
,
n
)
⋅
K
(
m
,
n
)
(
1
)
T
h
e
p
ix
el
v
alu
e
at
(
m
,
n
)
in
th
e
in
p
u
t
im
ag
e
is
r
e
p
r
esen
ted
b
y
th
e
n
o
tatio
n
w
h
er
e
I
(
m
,
n
)
,
K(
m
,
n
)
,
an
d
F(i,
j)
r
esp
ec
tiv
ely
s
tan
d
f
o
r
th
e
k
er
n
el
weig
h
t,
th
e
o
u
tp
u
t f
ea
tu
r
e
at
(
i,j)
o
f
th
e
s
ec
o
n
d
lay
er
an
d
s
o
o
n
.
R
e
L
U
(
x
)
=
ma
x
(
0
,
x
)
(
2
)
Up
g
r
ad
in
g
th
e
f
ea
tu
r
e
ex
t
r
ac
tio
n
in
YOL
Ov
8
is
r
ea
lized
with
cr
o
s
s
s
tag
e
p
ar
tial
f
u
s
io
n
(
C
2
f
)
,
wh
ich
is
a
n
ex
t
-
g
en
er
atio
n
ar
c
h
itectu
r
e
th
at
u
n
ites
s
h
allo
w
a
n
d
d
ee
p
f
ea
tu
r
es
in
a
n
ef
f
ec
tiv
e
m
an
n
er
.
R
ath
er
th
a
n
d
ea
lin
g
with
all
f
ea
tu
r
e
m
ap
s
at
ea
ch
lay
er
.
T
h
e
f
o
ll
o
win
g
e
q
u
atio
n
d
escr
ib
es th
is
m
er
g
in
g
m
eth
o
d
:
F
me
r
ge
d
=
F
s
ha
l
l
ow
+
F
de
e
p
(
3
)
2
.
2
.
2
.
Nec
k
net
w
o
r
k
T
h
e
n
ec
k
m
o
d
u
le
o
f
th
e
Y
OL
Ov
8
ar
ch
itectu
r
e
is
r
esp
o
n
s
ib
le
f
o
r
r
ef
in
in
g
an
d
im
p
r
o
v
in
g
th
e
ex
tr
ac
ted
f
ea
t
u
r
es
b
e
f
o
r
e
th
e
y
r
ea
c
h
th
e
d
etec
tio
n
h
ea
d
.
T
h
e
n
ec
k
d
o
es
th
is
b
y
t
h
e
u
s
e
o
f
two
p
o
wer
f
u
l
ar
ch
itectu
r
es:
f
ea
tu
r
e
p
y
r
am
i
d
n
etwo
r
k
(
FP
N)
an
d
p
ath
a
g
g
r
eg
atio
n
n
etwo
r
k
(
PAN)
,
th
is
p
r
o
ce
s
s
ca
n
b
e
r
ep
r
esen
ted
as
:
F
c
omb
in
e
d
=
F
l
ow
+
Up
(
F
high
)
(
4
)
T
h
e
p
ath
a
g
g
r
e
g
atio
n
n
etwo
r
k
(
PAN)
f
u
r
th
er
r
ef
in
es
th
e
f
ea
tu
r
e
f
u
s
io
n
p
r
o
ce
s
s
b
y
en
ab
lin
g
b
id
ir
ec
tio
n
al
in
f
o
r
m
atio
n
f
lo
w
ac
r
o
s
s
m
u
ltip
le
s
ca
les.
F
fin
a
l
=
Dow
n
(
F
c
omb
in
e
d
)
+
Up
(
F
c
omb
in
e
d
)
(
5
)
wh
er
e
Do
wn
(
Fco
m
b
in
ed
)
r
ef
e
r
s
to
th
e
p
r
o
ce
s
s
o
f
d
o
wn
s
am
p
lin
g
,
wh
ich
ex
t
r
ac
ts
m
o
r
e
co
n
t
ex
tu
al
in
f
o
r
m
atio
n
f
r
o
m
th
e
lo
wer
lay
e
r
s
wh
ile
Up
(
Fco
m
b
in
ed
)
is
th
e
p
r
o
ce
s
s
o
f
u
p
s
am
p
lin
g
th
at
allo
ws
th
e
r
ef
in
ed
f
ea
tu
r
es
to
b
e
p
r
o
p
ag
ated
b
ac
k
to
th
e
h
ig
h
er
r
eso
lu
tio
n
s
.
2
.
2
.
3
.
H
ea
d
net
w
or
k
I
n
YOL
Ov
8
,
th
e
h
ea
d
n
etwo
r
k
is
cr
u
cial
to
t
h
e
p
r
o
ce
s
s
o
f
o
b
tain
in
g
th
e
f
in
al
o
b
ject
d
etec
tio
n
p
r
ed
ictio
n
s
b
ased
o
n
th
e
r
ef
in
ed
f
ea
tu
r
e
m
a
p
s
th
at
h
a
v
e
b
ee
n
p
ass
ed
o
n
.
T
h
e
b
o
u
n
d
in
g
b
o
x
es
ar
e
d
e
f
in
ed
b
y
th
e
f
o
u
r
p
ar
am
eter
s
:
t
h
e
ce
n
t
er
co
o
r
d
in
ates
(
x
,
y
)
o
f
th
e
b
o
x
an
d
its
wid
th
w
an
d
h
eig
h
t
h
.
W
h
en
d
ep
icted
m
ath
em
atica
lly
,
th
e
b
o
u
n
d
in
g
b
o
x
is
as f
o
llo
ws:
B
=
(
x
,
y
,
w
,
h
)
(
6
)
T
h
e
co
n
f
id
e
n
ce
s
co
r
e
is
ca
l
cu
lated
b
ased
o
n
th
e
I
n
ter
s
ec
tio
n
o
v
er
Un
io
n
(
I
o
U)
m
etr
ic,
wh
ic
h
in
d
icate
s
h
o
w
m
u
ch
th
e
p
r
ed
i
cted
b
o
x
o
v
er
lap
s
with
th
e
ac
t
u
al
b
o
x
.
T
h
e
f
o
r
m
u
la
f
o
r
th
e
co
n
f
id
en
ce
s
co
r
e
is
as f
o
llo
ws:
C
=
Po
×
IoU
(
7
)
I
n
th
is
co
n
tex
t,
Po
r
e
f
er
s
to
th
e
lik
elih
o
o
d
o
f
an
o
b
ject
b
ein
g
p
r
esen
t
with
in
th
e
b
o
u
n
d
in
g
b
o
x
p
r
ed
icted
,
wh
ile
I
o
U
s
ig
n
if
ies
th
e
r
atio
o
f
th
e
o
v
er
la
p
ar
ea
to
th
e
co
m
b
i
n
ed
ar
ea
o
f
th
e
p
r
ed
icted
an
d
ac
t
u
al
b
o
u
n
d
in
g
b
o
x
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
3
,
Sep
tem
b
er
20
26
:
1
3
3
1
-
1
3
3
9
1334
2
.
3
.
M
o
del
t
ra
i
ning
T
h
e
m
o
d
el
was
tr
ain
ed
o
v
er
1
0
0
ep
o
c
h
s
u
s
in
g
a
g
r
o
u
p
s
ize
o
f
1
6
an
d
an
in
p
u
t
r
eso
lu
tio
n
o
f
6
4
0
×
6
4
0
p
ix
els.
T
h
e
Ad
am
o
p
tim
i
ze
r
was
em
p
lo
y
ed
with
a
lear
n
in
g
r
ate
o
f
0
.
0
0
1
.
T
h
e
tr
ain
e
d
m
o
d
el
attain
ed
a
n
o
v
er
all
d
etec
tio
n
ac
cu
r
ac
y
o
f
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1
.
2
%,
ass
ess
ed
ac
r
o
s
s
co
n
f
id
en
ce
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esh
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ld
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s
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m
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6
0
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o
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.
2
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4
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Rea
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im
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deplo
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ent
a
nd
s
pa
t
ia
l m
a
pp
ing
Peo
p
le
co
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ld
u
p
lo
ad
p
ictu
r
es,
m
o
v
ies,
o
r
watc
h
liv
e
we
b
c
am
s
tr
ea
m
in
g
th
r
o
u
g
h
a
web
ap
p
m
a
d
e
with
Stre
am
lit.
T
h
e
tr
ain
ed
Y
OL
Ov
8
m
o
d
el
is
u
s
ed
to
ev
al
u
ate
ea
ch
f
r
am
e
in
r
e
al
tim
e
t
o
f
in
d
a
n
d
class
if
y
o
b
jects.
GPS
in
teg
r
atio
n
m
a
k
es
it
p
o
s
s
ib
le
to
au
to
m
atica
lly
f
in
d
r
o
ad
f
laws,
an
d
a
n
i
n
ter
ac
tiv
e
in
ter
f
ac
e
s
h
o
ws in
f
o
r
m
atio
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a
b
o
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t t
h
e
s
o
r
t o
f
d
am
ag
e,
h
o
w
s
u
r
e
y
o
u
a
r
e
ab
o
u
t it,
a
n
d
wh
e
r
e
it is
o
n
a
m
ap
.
2
.
5
.
P
er
f
o
r
m
a
nce
ev
a
lu
a
t
io
n m
et
rics
Ass
es
s
in
g
th
e
p
er
f
o
r
m
an
ce
o
f
an
o
b
ject
d
etec
tio
n
m
o
d
el
lik
e
YOL
Ov
8
,
ev
alu
atio
n
m
etr
ics
p
lay
a
cr
u
cial
r
o
le.
T
h
e
m
etr
ics u
s
ed
in
th
is
s
tu
d
y
ar
e:
2
.
5
.
1
.
P
re
cisi
o
n
Pre
cisi
o
n
an
d
r
ec
all
ar
e
f
u
n
d
am
en
tal
m
etr
ics
u
s
ed
to
ev
alu
ate
o
b
ject
d
etec
tio
n
p
er
f
o
r
m
an
ce
.
I
t
is
d
ef
in
ed
as:
=
+
(
8
)
T
r
u
e
p
o
s
itiv
es
(
TP
)
r
ep
r
esen
ts
co
r
r
ec
tly
id
en
tifie
d
o
b
ject
s
,
an
d
f
alse
p
o
s
itiv
es
(
FP
)
r
ep
r
esen
ts
in
co
r
r
ec
tly
d
etec
ted
o
b
jects th
at
d
o
n
o
t c
o
r
r
esp
o
n
d
t
o
ac
tu
al
o
b
jects in
th
e
im
ag
e.
2
.
5
.
2
.
Rec
a
ll
I
t
m
ea
s
u
r
es
th
e
p
r
o
p
o
r
tio
n
o
f
co
r
r
ec
tly
d
etec
ted
o
b
jects
o
u
t
o
f
all
ac
tu
al
o
b
jects
p
r
esen
t
in
th
e
g
r
o
u
n
d
tr
u
t
h
.
R
ec
all
is
g
iv
en
b
y
:
=
+
(
9
)
wh
er
e
f
alse n
eg
ativ
es
(
FN
)
r
e
p
r
esen
ts
o
b
jects th
at
wer
e
p
r
es
en
t in
th
e
im
ag
e
b
u
t
n
o
t d
etec
t
ed
b
y
t
h
e
m
o
d
el.
2
.
5
.
3
.
F1
-
s
co
re
T
h
e
F1
-
s
co
r
e
r
ep
r
esen
ts
th
e
h
ar
m
o
n
ic
m
ea
n
o
f
b
o
th
p
r
ec
is
io
n
an
d
r
ec
all
wh
ich
g
u
ar
a
n
tees
o
n
e
m
ea
s
u
r
e
o
f
m
o
d
el
p
e
r
f
o
r
m
an
c
e.
T
h
e
f
o
r
m
u
la
f
o
r
ca
lcu
latin
g
it is
as f
o
llo
ws:
1
=
2
×
×
+
(
10
)
T
h
e
F1
-
s
co
r
e
r
an
g
es
f
r
o
m
0
t
o
1
,
with
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ig
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r
v
alu
es
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d
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etter
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alan
ce
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etwe
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r
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io
n
an
d
r
ec
all.
2
.
5
.
4
.
M
ea
n
a
v
er
a
g
e
prec
is
io
n
Me
an
av
er
a
g
e
p
r
ec
is
io
n
(
m
A
P)
is
th
e
m
ain
m
ea
s
u
r
e
f
o
r
co
m
p
ar
in
g
o
b
ject
d
etec
tio
n
m
o
d
els
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d
is
d
eter
m
in
ed
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y
ad
d
in
g
u
p
th
e
p
r
ec
is
io
n
-
r
ec
all
cu
r
v
e
at
v
ar
i
o
u
s
co
n
f
id
en
ce
th
r
esh
o
ld
s
.
I
n
itially
,
th
e
av
e
r
ag
e
p
r
ec
is
io
n
(
AP)
f
o
r
ea
ch
ca
te
g
o
r
y
is
f
o
u
n
d
as th
e
ar
ea
u
n
d
er
t
h
e
p
r
ec
is
io
n
-
r
ec
all
cu
r
v
e:
=
∫
(
)
1
0
(
)
(
11
)
wh
er
e
p
r
ec
is
io
n
is
m
ea
s
u
r
ed
at
d
if
f
er
en
t
r
ec
all
lev
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an
d
th
e
in
teg
r
al
r
ep
r
esen
ts
th
e
s
u
m
o
f
p
r
ec
is
io
n
v
alu
es
o
v
er
v
a
r
y
in
g
r
ec
all
th
r
esh
o
l
d
s
.
=
1
/
∑
=
1
(1
2
)
w
h
er
e
C
is
th
e
to
tal
n
u
m
b
er
o
f
o
b
ject
class
es,
an
d
APi
is
th
e
Av
er
ag
e
Pre
cisi
o
n
f
o
r
class
iii.
A
h
ig
h
er
m
AP
v
alu
e
s
ig
n
if
ies th
at
th
e
m
o
d
el
p
er
f
o
r
m
s
well
ac
r
o
s
s
d
if
f
er
en
t
o
b
ject
ca
teg
o
r
ies
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
S
ma
r
t ro
a
d
ma
in
ten
a
n
ce
:
r
ea
l
-
time
s
u
r
fa
ce
d
a
ma
g
e
d
e
tectio
n
a
n
d
ma
p
p
i
n
g
w
ith
YOLOv8
(
P
r
ee
ty
S
in
g
h
)
1335
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
o
u
tco
m
e
s
h
o
ws
th
at
th
e
ef
f
icac
y
o
f
th
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p
r
o
p
o
s
ed
YOL
Ov
8
-
b
ased
r
o
ad
s
u
r
f
ac
e
d
am
ag
e
d
etec
tio
n
f
r
am
ewo
r
k
in
p
r
ec
is
ely
id
en
tif
y
in
g
m
u
ltip
le
ca
teg
o
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ies
o
f
r
o
ad
d
ef
ec
ts
,
s
u
ch
as
p
o
th
o
les,
lo
n
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cr
ac
k
s
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tr
an
s
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er
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e
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allig
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cr
ac
k
s
.
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h
e
tr
ain
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m
o
d
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attain
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o
v
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all
d
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ac
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r
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f
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co
n
f
id
en
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s
co
r
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p
r
im
ar
ily
f
allin
g
b
etwe
en
0
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6
0
an
d
0
.
9
5
,
d
em
o
n
s
tr
atin
g
s
tr
o
n
g
an
d
d
e
p
en
d
a
b
le
p
er
f
o
r
m
an
ce
ac
r
o
s
s
d
iv
er
s
e
r
o
ad
tex
tu
r
es
an
d
illu
m
in
atio
n
en
v
ir
o
n
m
en
ts
.
T
h
e
r
o
b
u
s
t
p
er
f
o
r
m
an
ce
o
f
th
e
YOL
O
v
8
m
o
d
el
ca
n
b
e
ascr
ib
ed
to
its
an
ch
o
r
-
f
r
ee
d
etec
tio
n
ap
p
r
o
ac
h
,
i
t
u
s
es
th
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FP
N
–
PAN
ar
ch
itectu
r
e
t
o
co
m
b
in
e
f
ea
tu
r
es
at
d
if
f
er
en
t
s
ca
les
an
d
h
as
a
cr
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s
s
s
tag
e
p
ar
tial
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b
ased
b
ac
k
b
o
n
e
in
s
tead
.
All
o
f
th
ese
tr
ait
s
h
elp
it
f
in
d
f
is
s
u
r
e
p
atter
n
s
th
at
ar
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m
icr
o
s
co
p
ic,
ir
r
eg
u
lar
,
an
d
h
ar
d
to
s
ee
.
T
h
ese
ar
ch
itectu
r
al
f
ea
tu
r
es
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ak
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it
ea
s
ier
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f
in
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th
in
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ev
en
wh
en
th
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r
ea
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wo
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ld
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ar
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.
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e
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m
m
en
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d
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tem
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etter
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ak
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el
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lik
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YOL
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,
Fas
ter
R
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NN,
an
d
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NN
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ased
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eth
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s
.
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h
is
is
esp
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tr
u
e
wh
e
n
th
e
tech
n
o
lo
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y
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n
r
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wo
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ld
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ty
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es
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f
p
av
em
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t
an
d
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r
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atter
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s
.
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e
p
r
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p
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s
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s
tem
m
ain
tain
s
h
ig
h
ac
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r
ac
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h
ile
f
ac
ilit
atin
g
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ea
l
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tim
e
d
ep
lo
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m
en
t,
co
n
tr
as
tin
g
with
ea
r
lier
tech
n
iq
u
es
th
at
o
cc
asio
n
ally
en
co
u
n
ter
is
s
u
es with
r
ea
l
-
tim
e
in
f
er
en
ce
.
3.
1
.
Resul
t
a
na
ly
s
is
Fig
u
r
e
2
illu
s
tr
ates
th
e
m
ain
i
n
ter
f
ac
e
o
f
th
e
GPS
-
en
ab
led
YOL
Ov
8
r
o
ad
d
am
a
g
e
d
etec
tio
n
s
y
s
tem
.
I
m
ag
es
(
.
p
n
g
,
.
j
p
g
)
,
v
id
eo
s
(
.
m
p
4
)
,
a
n
d
liv
e
ca
m
er
a
s
tr
ea
m
s
m
ay
b
e
p
r
o
v
id
e
d
to
f
ac
ilit
at
e
f
lex
ib
le
r
ea
l
-
tim
e
an
aly
s
is
.
B
o
u
n
d
in
g
b
o
x
es,
co
n
f
id
en
ce
lev
els,
an
d
p
o
s
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n
a
l
m
etad
ata
ar
e
allo
ca
ted
to
ea
ch
r
o
ad
d
ef
ec
t
an
d
Fig
u
r
e
3
illu
s
tr
ates
th
e
r
ea
l
-
ti
m
e
o
n
lin
e
in
te
r
f
ac
e
d
e
v
elo
p
ed
with
Stre
am
lit.
T
h
is
in
ter
f
ac
e
en
ab
les
au
to
m
ated
d
etec
tio
n
an
d
g
e
o
lo
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tio
n
o
f
r
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ad
d
am
a
g
e,
f
a
cilitatin
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s
tr
aig
h
tf
o
r
war
d
f
a
u
lt
v
is
u
aliza
tio
n
.
User
s
h
a
v
e
th
e
ab
ilit
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to
d
y
n
am
ically
a
d
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s
t
co
n
f
id
e
n
ce
th
r
esh
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ld
s
to
o
p
t
im
ize
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e
tr
ad
e
-
o
f
f
b
etwe
en
f
alse
p
o
s
itiv
es
an
d
m
is
s
ed
d
etec
tio
n
s
.
Fig
u
r
e
2
.
Ho
m
e
p
ag
e
s
cr
ee
n
1
Fig
u
r
e
3
.
Ho
m
e
p
ag
e
s
cr
ee
n
2
Fig
u
r
e
4
illu
s
tr
ates
th
e
v
id
e
o
-
b
ased
d
etec
tio
n
m
o
d
u
le,
w
h
er
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u
n
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ter
r
u
p
ted
v
id
e
o
s
tr
ea
m
s
ar
e
ex
am
in
ed
f
r
am
e
b
y
f
r
am
e
f
o
r
r
ea
l
-
tim
e
id
e
n
tific
atio
n
o
f
r
o
ad
d
am
ag
e.
L
o
ca
tio
n
id
en
t
if
icatio
n
,
r
ea
l
-
tim
e
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r
ev
iew,
a
n
d
m
ac
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e
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d
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n
p
r
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ce
s
s
in
g
en
h
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ce
th
e
e
f
f
icie
n
cy
o
f
r
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ad
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s
p
ec
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io
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o
o
tag
e
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aly
s
is
with
th
is
m
o
d
u
le.
T
h
e
an
aly
tical
in
ter
f
ac
e
d
ep
icted
in
Fig
u
r
e
5
s
u
m
m
ar
izes
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e
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tc
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icate
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m
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c
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.
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