I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
11
,
No
.
4
,
A
u
g
u
s
t
2021
,
p
p
.
3
3
6
5
~
3
3
7
3
I
SS
N:
2088
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v
11
i
4
.
pp
3
3
6
5
-
3
3
7
3
3365
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
Adv
a
nces i
n
l
a
ne
m
a
r
k
ing
d
etec
tio
n
a
lg
o
rith
m
s
f
o
r
a
ll
-
w
ea
ther
c
o
ndition
s
H
a
dh
ra
m
i A
b.
G
ha
ni
1
,
Ro
s
li
B
esa
r
2
,
Z
a
m
a
ni M
d Sa
ni
3
,
M
o
hd
Na
ze
ri
K
a
m
a
rudd
i
n
4
,
Sy
a
beela
Sy
a
ha
li
5
,
At
iqu
lla
h
M
o
ha
m
ed
Da
ud
6
,
Aer
un
M
a
rt
in
7
1
De
p
a
rtme
n
t
o
f
Da
ta S
c
ien
c
e
,
Un
iv
e
rsit
y
M
a
la
y
sia
Ke
lan
tan
,
M
a
lay
sia
2,
4,
5,
6
,
7
F
a
c
u
l
ty
o
f
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
,
M
u
lt
im
e
d
ia Un
iv
e
rsi
tt
y
,
M
a
la
y
si
a
3
F
a
c
u
lt
y
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Un
iv
e
rsiti
T
e
k
n
ik
a
l
M
a
la
y
si
a
M
e
lak
a
,
M
a
la
y
sia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Au
g
25
,
2
0
20
R
ev
i
s
ed
Dec
18
,
2
0
2
0
A
cc
ep
ted
J
an
1
3
,
2
0
2
1
Driv
in
g
v
e
h
icle
s
in
a
ll
-
w
e
a
th
e
r
c
o
n
d
it
io
n
s
is
c
h
a
ll
e
n
g
in
g
a
s
th
e
la
n
e
m
a
rk
e
rs
ten
d
to
b
e
u
n
c
lea
r
to
th
e
d
riv
e
rs
f
o
r
d
e
tec
ti
n
g
th
e
lan
e
s.
M
o
r
e
o
v
e
r,
th
e
v
e
h
icle
s
w
il
l
m
o
v
e
slo
w
e
r
h
e
n
c
e
in
c
re
a
sin
g
th
e
ro
a
d
traff
ic
c
o
n
g
e
s
ti
o
n
w
h
ich
c
a
u
se
s
d
iff
icu
lt
ies
in
d
e
tec
ti
n
g
th
e
lan
e
m
a
rk
e
rs
e
sp
e
c
iall
y
f
o
r
a
d
v
a
n
c
e
d
d
riv
in
g
a
ss
istan
c
e
s
y
ste
m
s
(
AD
A
S
).
T
h
e
re
f
o
re
,
th
is
p
a
p
e
r
c
o
n
d
u
c
ts
a
th
o
r
o
u
g
h
re
v
iew
o
n
v
isio
n
-
b
a
se
d
lan
e
m
a
r
k
in
g
d
e
tec
ti
o
n
a
lg
o
rit
h
m
s
d
e
v
e
lo
p
e
d
f
o
r
a
ll
-
w
e
a
th
e
r
c
o
n
d
it
io
n
s.
T
h
e
re
v
ie
w
m
e
th
o
d
o
l
o
g
y
c
o
n
sists
o
f
tw
o
m
a
jo
r
a
re
a
s,
w
h
ich
a
re
a
re
v
i
e
w
o
n
th
e
g
e
n
e
ra
l
s
y
st
e
m
m
o
d
e
ls
e
m
p
lo
y
e
d
in
th
e
la
n
e
m
a
r
k
in
g
d
e
tec
ti
o
n
a
lg
o
rit
h
m
s
a
n
d
a
re
v
ie
w
o
n
th
e
ty
p
e
s
o
f
w
e
a
th
e
r
c
o
n
d
it
i
o
n
s
c
o
n
sid
e
re
d
f
o
r
th
e
a
lg
o
rit
h
m
s.
T
h
ro
u
g
h
o
u
t
t
h
e
re
v
ie
w
p
ro
c
e
ss
,
it
is
o
b
se
rv
e
d
th
a
t
th
e
lan
e
m
a
r
k
i
n
g
d
e
tec
ti
o
n
a
lg
o
rit
h
m
s
in
li
tera
tu
re
h
a
v
e
m
o
stl
y
c
o
n
sid
e
re
d
w
e
a
th
e
r
c
o
n
d
i
ti
o
n
s
su
c
h
a
s
f
o
g
,
r
a
in
,
h
a
z
e
a
n
d
sn
o
w
.
A
n
e
w
c
o
n
to
u
r
-
a
n
g
le
m
e
th
o
d
h
a
s
a
lso
b
e
e
n
p
r
o
p
o
se
d
f
o
r
la
n
e
m
a
r
k
e
r
d
e
tec
ti
o
n
.
M
o
st
o
f
th
e
re
se
a
rc
h
w
o
rk
f
o
c
u
s
o
n
lan
e
d
e
tec
ti
o
n
,
b
u
t
t
h
e
c
las
si
f
ica
ti
o
n
o
f
th
e
ty
p
e
s
o
f
lan
e
m
a
r
k
e
rs
re
m
a
in
s
a
sig
n
if
ica
n
t
re
se
a
rc
h
g
a
p
th
a
t
is w
o
rth
t
o
b
e
a
d
d
re
ss
e
d
f
o
r
A
D
A
S
a
n
d
in
telli
g
e
n
t
tran
s
p
o
rt
s
y
ste
m
s
K
ey
w
o
r
d
s
:
A
ll
-
w
ea
th
er
co
n
d
itio
n
s
I
m
ag
e
p
r
e
-
p
r
o
ce
s
s
i
n
g
L
a
n
e
d
etec
tio
n
L
a
n
e
m
ar
k
in
g
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Had
h
r
a
m
i
A
b
.
Gh
a
n
i
Dep
ar
t
m
en
t o
f
Data
Scie
n
ce
Un
i
v
er
s
it
y
Ma
la
y
s
ia
Kela
n
ta
n
Kela
n
ta
n
,
Ma
la
y
s
ia
E
m
ail:
h
ad
h
r
a
m
i.a
g
@
u
m
k
.
ed
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
I
n
d
if
f
er
en
t
w
ea
t
h
er
co
n
d
itio
n
s
l
ik
e
r
ai
n
y
an
d
f
o
g
g
y
co
n
d
itio
n
s
,
t
h
e
p
o
s
s
ib
ilit
y
o
f
r
o
ad
cr
ash
e
s
in
cr
ea
s
es.
T
h
e
lac
k
i
n
g
clar
it
y
in
t
h
e
d
r
iv
er
’
s
v
i
s
io
n
s
i
n
cr
ea
s
es
th
e
r
is
k
o
f
ac
cid
en
t
s
w
h
ic
h
ar
e
lik
el
y
to
ca
u
s
e
in
j
u
r
ies
a
n
d
ca
s
u
al
ties
.
B
ased
o
n
a
s
tu
d
y
ca
r
r
ied
o
u
t
in
P
en
a
n
g
Ma
la
y
s
ia,
o
n
e
o
f
t
h
e
m
ai
n
ca
u
s
e
s
o
f
ac
cid
en
t
s
is
th
e
r
ain
w
h
ic
h
ca
u
s
es
t
h
e
im
ag
e
o
f
th
e
la
n
e
m
ar
k
er
s
to
b
e
b
lu
r
,
as
w
e
ll
as
th
e
r
o
ad
s
u
r
f
ac
e
t
h
at
b
ec
o
m
e
s
m
o
r
e
s
lip
p
er
y
.
T
h
e
u
n
clea
r
i
m
ag
e
o
f
t
h
e
lan
e
m
ar
k
er
s
te
n
d
to
ca
u
s
e
in
co
r
r
ec
t
d
ec
is
io
n
s
b
y
th
e
d
r
iv
er
s
,
as
w
el
l
as th
e
a
u
to
m
ated
d
r
iv
i
n
g
ass
i
s
t
an
ce
s
y
s
te
m
,
to
ch
a
n
g
e
d
ir
ec
ti
o
n
o
r
o
v
er
tak
e
th
e
f
r
o
n
t v
e
h
icl
es.
T
h
e
in
v
e
s
ti
g
ated
lan
e
m
ar
k
er
class
i
f
icatio
n
m
ec
h
a
n
i
s
m
w
il
l
also
i
m
p
r
o
v
e
th
e
co
n
f
u
s
io
n
m
atr
i
x
i
n
d
etec
tin
g
d
i
f
f
er
e
n
t
lan
e
m
ar
k
er
ty
p
e
s
an
d
r
ed
u
cin
g
r
o
ad
c
r
ash
es
w
h
en
i
m
p
le
m
e
n
ted
in
A
D
A
S.
Dete
cti
n
g
lan
es,
w
h
ic
h
ar
e
d
esi
g
n
ed
to
d
elin
ea
te
a
n
d
r
eg
u
late
t
h
e
tr
a
f
f
i
c
lo
w
o
n
th
e
r
o
ad
s
,
is
cr
u
cial
f
o
r
r
o
ad
s
af
et
y
.
T
h
e
lan
e
m
ar
k
er
s
alo
n
g
t
h
e
r
o
ad
s
m
u
s
t
b
e
p
r
o
p
er
ly
d
etec
ted
an
d
u
n
d
er
s
to
o
d
.
Ho
w
e
v
er
th
e
s
e
l
an
e
m
ar
k
er
s
te
n
d
to
b
ec
o
m
e
u
n
c
lear
to
th
e
r
o
ad
u
s
er
s
w
h
o
ar
e
d
r
iv
i
n
g
v
e
h
icle
s
alo
n
g
t
h
e
r
o
ad
w
h
en
th
e
w
ea
th
er
ch
a
n
g
es
[
1
,
2
]
esp
ec
iall
y
f
o
r
ad
v
an
ce
d
d
r
iv
e
r
ass
is
ta
n
ce
s
y
s
te
m
s
[
3
-
5
]
.
T
h
e
s
tati
s
tics
p
r
ep
ar
ed
b
y
Ma
l
a
y
s
ia
n
I
n
s
tit
u
te
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
4
,
A
u
g
u
s
t
2021
:
3
3
6
5
-
3373
3366
R
o
ad
Saf
et
y
R
e
s
ea
r
ch
(
MI
R
OS)
in
2
0
1
6
h
as
s
h
o
w
n
t
h
at
1
5
6
d
r
iv
er
s
o
u
t
o
f
4
4
6
1
4
d
r
iv
er
s
h
ad
m
ad
e
ille
g
al
o
v
er
tak
i
n
g
alo
n
g
r
o
ad
s
w
i
th
d
o
u
b
le
s
o
lid
r
o
ad
m
ar
k
in
g
[
6
]
.
Fo
g
g
y
,
s
n
o
wy
a
n
d
r
ain
y
w
ea
t
h
er
co
n
d
itio
n
s
ca
u
s
e
d
if
f
ic
u
lt
ies
o
n
t
h
e
r
o
ad
u
s
er
s
to
clea
r
l
y
d
etec
t
th
e
la
n
e
a
n
d
co
r
r
ec
tly
p
er
ce
i
v
e
t
h
e
t
y
p
es
o
f
t
h
e
la
n
e
m
ar
k
er
s
an
d
h
av
e
t
u
r
n
ed
o
u
t to
b
e
am
o
n
g
t
h
e
m
aj
o
r
f
ac
to
r
s
th
at
ca
u
s
e
r
o
ad
c
r
ash
es [
7
]
.
A
la
n
e
d
etec
tio
n
m
ec
h
a
n
i
s
m
b
ased
o
n
lan
e
m
ar
k
er
class
i
f
i
ca
tio
n
f
o
r
f
o
g
g
y
w
ea
t
h
er
co
n
d
itio
n
h
a
s
b
ee
n
p
r
o
p
o
s
ed
in
[
8
]
,
d
ev
elo
p
ed
b
ased
o
n
h
eu
r
is
tic
R
OI
.
T
h
e
f
o
g
g
i
n
g
e
f
f
ec
t o
n
t
h
e
la
n
e
is
r
e
m
o
v
ed
b
y
u
s
i
n
g
a
d
ar
k
ch
an
n
el
p
r
io
r
m
et
h
o
d
in
o
r
d
er
to
allo
w
th
e
p
r
o
p
o
s
ed
m
ec
h
an
i
s
m
to
w
o
r
k
.
T
h
is
m
et
h
o
d
,
alth
o
u
g
h
ap
p
lies
a
u
n
iq
u
e
m
et
h
o
d
to
d
ef
o
g
th
e
r
o
ad
im
ag
e
s
,
is
also
e
m
b
ed
d
ed
w
it
h
th
e
t
y
p
ical
Ho
u
g
h
tr
a
n
s
f
o
r
m
tec
h
n
iq
u
e
to
d
etec
t th
e
lan
e.
An
o
th
er
lan
e
m
ar
k
er
cla
s
s
i
f
ic
atio
n
m
ec
h
an
i
s
m
h
as
b
ee
n
p
r
o
p
o
s
ed
in
[
9
]
.
T
h
is
w
o
r
k
h
as
f
o
cu
s
ed
o
n
th
e
s
tead
y
s
teer
in
g
o
n
t
h
e
h
ig
h
w
a
y
s
i
n
d
i
f
f
er
en
t
w
ea
th
er
co
n
d
itio
n
s
i
n
clu
d
i
n
g
t
h
e
r
ain
y
d
a
y
s
.
Ho
w
e
v
er
,
th
is
ap
p
r
o
ac
h
is
ca
r
r
ied
o
u
t
o
n
l
y
f
o
r
tr
ac
to
r
s
e
m
i
-
tr
ailer
ex
cl
u
d
in
g
o
th
er
v
eh
icle
s
.
A
d
if
f
er
en
t
ap
p
r
o
ac
h
o
f
d
etec
tin
g
la
n
es
at
d
i
f
f
er
en
t
w
ea
t
h
er
co
n
d
itio
n
s
i
s
p
r
o
p
o
s
ed
in
[
1
0
]
b
ased
o
n
t
h
e
e
n
tr
o
p
y
ap
p
r
o
ac
h
.
T
h
e
d
etec
tio
n
ac
cu
r
ac
y
r
ep
o
r
ted
f
o
r
th
is
w
o
r
k
is
g
o
o
d
alth
o
u
g
h
th
is
w
o
r
k
is
i
m
p
le
m
e
n
ted
u
s
i
n
g
t
h
e
s
u
r
v
eilla
n
ce
ca
m
er
a.
T
h
e
p
r
o
p
o
s
ed
m
ec
h
a
n
is
m
i
s
ca
r
r
ied
o
u
t
u
s
in
g
t
h
e
en
tr
o
p
y
-
b
ased
m
et
h
o
d
an
d
is
in
d
ep
en
d
en
t
o
n
th
e
lan
e
m
ar
k
er
s
,
w
h
ic
h
m
a
y
a
f
f
e
ct
th
e
ac
t
u
al
lan
e
d
etec
tio
n
o
n
th
e
p
h
y
s
ical
r
o
ad
s
as
t
h
e
y
ar
e
d
elin
ea
ted
b
y
th
e
lan
e
m
ar
k
er
s
.
L
a
n
e
m
ar
k
er
cla
s
s
i
f
icatio
n
i
s
an
es
s
en
tial
p
ar
t
o
f
th
e
lan
e
d
etec
tio
n
m
ec
h
a
n
i
s
m
s
[
1
1
,
1
2
]
to
ass
is
t
t
h
e
d
r
iv
er
s
m
a
k
i
n
g
t
h
e
r
i
g
h
t
d
ec
i
s
io
n
s
a
s
w
ell
as
to
e
n
h
a
n
ce
th
e
ad
v
a
n
ce
d
d
r
iv
er
ass
i
s
tan
c
e
s
y
s
te
m
s
.
A
lan
e
m
ar
k
er
c
la
s
s
i
f
icatio
n
m
eth
o
d
u
s
i
n
g
co
n
to
u
r
an
al
y
s
i
s
h
as
b
ee
n
p
r
o
p
o
s
ed
in
[
1
3
,
1
4
]
.
T
h
is
r
esear
ch
p
ap
er
p
r
esen
ts
a
co
m
p
r
eh
e
n
s
i
v
e
r
ev
ie
w
o
n
r
o
ad
m
ar
k
in
g
clas
s
i
f
icatio
n
m
ec
h
an
i
s
m
s
ap
p
lied
in
r
ain
y
a
n
d
f
o
g
g
y
w
ea
t
h
er
co
n
d
itio
n
s
w
i
th
a
n
in
itial
f
r
a
m
e
w
o
r
k
o
f
lan
e
m
ar
k
er
class
i
f
icat
io
n
m
ec
h
a
n
i
s
m
f
o
r
all
-
w
ea
t
h
er
co
n
d
itio
n
s
.
T
h
e
s
y
s
te
m
m
o
d
el
o
f
a
g
en
er
ic
la
n
e
m
ar
k
er
cla
s
s
if
ica
tio
n
m
ec
h
a
n
i
s
m
i
s
p
r
esen
ted
in
s
ec
t
io
n
2
f
o
r
a
g
en
er
al
l
y
g
o
o
d
w
ea
t
h
er
co
n
d
itio
n
.
I
n
all
-
w
ea
t
h
er
co
n
d
itio
n
s
,
t
h
er
e
ar
e
ch
al
len
g
es
th
at
n
ee
d
to
b
e
ad
d
r
es
s
ed
to
p
er
f
o
r
m
la
n
e
m
ar
k
er
class
i
f
icatio
n
as
d
escr
ib
ed
in
s
ec
tio
n
3
.
A
th
o
r
o
u
g
h
r
e
v
ie
w
o
n
la
n
e
m
ar
k
er
class
i
f
icatio
n
an
d
th
e
r
elate
d
lan
e
d
etec
tio
n
m
ec
h
a
n
is
m
s
i
s
p
r
esen
ted
in
s
ec
tio
n
4
alo
n
g
w
it
h
th
e
p
r
o
p
o
s
ed
f
r
a
m
e
w
o
r
k
o
f
th
e
co
n
to
u
r
-
a
n
g
le
m
et
h
o
d
f
o
r
class
if
y
i
n
g
an
d
d
etec
tin
g
th
e
la
n
e
m
ar
k
er
s
in
all
-
w
ea
t
h
er
co
n
d
itio
n
s
.
U
s
ef
u
l r
ec
o
m
m
e
n
d
atio
n
s
an
d
co
n
cl
u
s
io
n
s
f
o
r
th
e
f
u
t
u
r
e
w
o
r
k
ar
e
p
u
t f
o
r
w
ar
d
in
s
ec
tio
n
5
.
2.
L
AN
E
M
ARK
E
R
CL
ASS
I
F
I
CAT
I
O
N
M
E
T
H
O
DS
As
to
co
n
tr
ib
u
te
in
i
n
telli
g
e
n
t
tr
an
s
p
o
r
t
s
y
s
te
m
s
in
cl
u
d
in
g
th
e
a
u
to
-
a
s
s
i
s
t
d
r
iv
i
n
g
s
y
s
te
m
s
(
A
DS)
,
lan
e
m
ar
k
er
clas
s
if
icatio
n
m
o
d
els
m
u
s
t
b
e
d
esi
g
n
ed
as
e
f
f
icien
tl
y
a
n
d
ef
f
ec
ti
v
el
y
a
s
p
o
s
s
ib
le
to
s
u
p
p
l
y
r
eliab
le
in
p
u
t
a
n
d
s
u
p
p
o
r
t
to
th
e
s
e
s
y
s
te
m
s
.
T
h
r
ee
f
u
n
d
a
m
en
tal
s
tep
s
r
eq
u
ir
ed
to
ca
r
r
y
o
u
t
la
n
e
m
ar
k
er
class
i
f
icatio
n
i
n
g
e
n
er
als
ar
e
th
e
r
eg
io
n
o
f
in
ter
est
(
R
O
I
)
s
elec
tio
n
,
i
m
ag
e
p
r
e
-
p
r
o
ce
s
s
i
n
g
a
n
d
f
ea
tu
r
e
ex
tr
ac
tio
n
f
o
r
m
ar
k
er
clas
s
i
f
ic
atio
n
,
all
o
f
w
h
ich
ar
e
f
u
r
t
h
er
d
escr
ib
ed
as f
o
llo
w
s
:
2
.
1
.
RO
I
s
elec
t
io
n
T
h
e
f
ir
s
t
ess
e
n
tial
s
tep
in
i
m
p
l
e
m
en
tin
g
la
n
e
m
ar
k
er
class
i
f
i
ca
tio
n
is
t
h
e
s
elec
tio
n
o
f
th
e
R
OI
,
w
h
ich
co
n
tain
s
t
h
e
n
ec
e
s
s
ar
y
f
ea
tu
r
e
s
o
r
i
n
f
o
r
m
atio
n
o
f
th
e
v
id
eo
f
r
a
m
e
s
f
r
o
m
w
h
ich
t
h
e
la
n
e
m
ar
k
er
clas
s
i
f
icatio
n
is
ca
r
r
ied
o
u
t.
T
h
er
e
ar
e
v
ar
io
u
s
m
et
h
o
d
s
p
r
o
p
o
s
ed
in
liter
a
tu
r
e
to
id
e
n
ti
f
y
t
h
e
R
OI
s
u
c
h
as
v
a
n
i
s
h
in
g
p
o
in
t
m
et
h
o
d
[
1
5
]
as
w
ell
as
a
g
en
t
-
b
ased
d
etec
tio
n
an
d
tr
ac
k
i
n
g
m
et
h
o
d
s
[
1
6
]
.
Ho
w
e
v
er
,
th
e
m
ec
h
a
n
is
m
s
f
o
r
d
etec
tin
g
R
OI
in
all
-
w
ea
t
h
er
co
n
d
itio
n
s
f
o
r
lan
e
m
ar
k
er
cl
ass
i
f
icatio
n
is
s
till
at
in
f
a
n
c
y
s
tag
e.
Mo
s
t
o
f
th
e
p
r
o
p
o
s
ed
R
OI
id
en
tif
icat
io
n
m
ec
h
a
n
i
s
m
s
i
n
all
-
w
ea
th
er
co
n
d
itio
n
s
ar
e
d
ev
elo
p
ed
f
o
r
lan
e
d
etec
tio
n
,
as
g
iv
e
n
in
[
1
7
]
f
o
r
in
s
tan
ce
,
a
n
d
n
o
t d
ed
icate
d
f
o
r
lan
e
m
ar
k
er
clas
s
i
f
icatio
n
.
2
.
2
.
I
m
a
g
e
pre
-
pro
ce
s
s
ing
Af
ter
id
e
n
ti
f
y
i
n
g
t
h
e
R
OI
,
t
h
e
n
e
x
t
i
m
p
o
r
tan
t
s
tep
is
t
h
e
i
m
ag
e
p
r
e
-
p
r
o
ce
s
s
i
n
g
.
T
h
e
s
e
l
ec
ted
R
OI
m
u
s
t b
e
p
r
e
-
p
r
o
ce
s
s
ed
in
o
r
d
er
to
ex
tr
ac
t th
e
i
m
p
o
r
ta
n
t f
ea
t
u
r
es f
r
o
m
t
h
e
i
m
ag
e
o
f
th
e
la
n
e
m
ar
k
er
f
o
r
m
ak
in
g
th
e
cla
s
s
i
f
icatio
n
d
ec
is
io
n
.
G
e
n
er
all
y
t
h
er
e
ar
e
t
w
o
w
id
el
y
a
p
p
lied
i
m
ag
e
p
r
o
ce
s
s
in
g
tec
h
n
iq
u
es
i
n
p
r
o
ce
s
s
i
n
g
th
e
R
OI
,
n
a
m
el
y
th
e
f
ilter
i
n
g
an
d
co
lo
u
r
ad
j
u
s
t
m
e
n
t
o
r
th
r
e
s
h
o
ld
in
g
.
T
h
e
f
i
lter
in
g
o
p
er
atio
n
s
ar
e
ca
r
r
ied
o
u
t
to
i
m
p
r
o
v
e
o
r
en
h
a
n
ce
t
h
e
q
u
a
lit
y
o
f
th
e
R
OI
.
T
h
is
ca
n
b
e
r
e
alize
d
b
y
v
ar
io
u
s
p
r
e
-
p
r
o
ce
s
s
i
n
g
tec
h
n
iq
u
e
s
s
u
ch
as
r
e
m
o
v
in
g
t
h
e
n
o
i
s
e
th
a
t
e
x
is
t
s
i
n
t
h
e
i
m
ag
e,
ad
j
u
s
ti
n
g
th
e
ill
u
m
i
n
atio
n
t
h
at
ap
p
ea
r
s
in
t
h
e
p
ix
el
s
o
f
t
h
e
i
m
a
g
e,
b
lu
r
r
y
i
m
ag
e
co
r
r
ec
tio
n
,
ed
g
e
en
h
an
ce
m
e
n
t,
s
ta
tis
tic
al
an
d
m
at
h
p
r
o
ce
s
s
in
g
a
n
d
m
o
r
p
h
o
lo
g
y
[
1
8
]
.
Of
t
h
e
d
i
f
f
er
en
t
i
m
a
g
e
p
r
e
-
p
r
o
ce
s
s
in
g
m
et
h
o
d
s
i
n
li
ter
atu
r
e
,
t
w
o
o
f
t
h
e
f
r
eq
u
e
n
tl
y
u
s
ed
o
n
es
ar
e
th
e
n
o
is
e
f
i
lter
in
g
an
d
ill
u
m
in
a
tio
n
ad
j
u
s
t
m
e
n
t.
No
is
e
f
i
lter
in
g
is
m
ai
n
l
y
i
m
p
le
m
e
n
ted
to
r
e
m
o
v
e
t
h
e
n
o
is
e
t
h
at
ex
is
t
s
i
n
th
e
i
m
ag
e
s
to
b
e
p
r
o
ce
s
s
ed
.
T
h
e
n
o
i
s
e
m
a
y
co
m
e
f
r
o
m
t
h
e
d
ef
ec
t
s
o
f
t
h
e
r
o
ad
,
t
h
e
u
n
w
a
n
ted
o
b
j
ec
ts
th
at
p
r
ese
n
t
o
n
t
h
e
r
o
ad
an
d
t
h
e
p
r
esen
ce
o
f
o
th
er
ele
m
e
n
ts
s
u
c
h
as
s
n
o
w
an
d
r
ai
n
d
r
o
p
lets
w
h
ich
f
al
l
alo
n
g
th
e
lan
e
s
o
r
co
v
er
th
e
w
i
n
d
s
cr
ee
n
o
f
t
h
e
v
e
h
icle
as
w
el
l a
s
t
h
e
ca
m
er
a
u
s
ed
to
ca
p
tu
r
e
th
e
i
m
ag
e
o
f
th
e
la
n
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
A
d
va
n
ce
s
in
la
n
e
ma
r
kin
g
d
etec
tio
n
a
lg
o
r
ith
ms fo
r
a
ll
-
w
ea
th
er
co
n
d
itio
n
s
(
Ha
d
h
r
a
mi
A
b
.
Gh
a
n
i
)
3367
On
e
o
f
t
h
e
g
e
n
er
all
y
ad
o
p
ted
n
o
is
e
f
ilter
i
n
g
m
eth
o
d
s
is
Ga
u
s
s
ia
n
f
ilter
i
n
g
[
1
9
]
Gau
s
s
ian
f
ilter
in
g
i
s
ap
p
lied
to
i
m
p
r
o
v
e
t
h
e
q
u
ali
t
y
o
f
t
h
e
R
OI
b
y
r
e
m
o
v
i
n
g
th
e
n
o
is
e.
I
t
is
a
li
n
ea
r
f
ilte
r
,
h
en
ce
r
eq
u
ir
in
g
r
elativ
el
y
s
h
o
r
ter
co
m
p
u
ta
tio
n
al
ti
m
e
a
s
co
m
p
ar
ed
to
o
th
er
n
o
n
-
lin
ea
r
n
o
is
e
f
il
ter
s
.
A
s
f
o
r
th
e
co
lo
r
ad
j
u
s
t
m
e
n
t
an
d
th
r
es
h
o
ld
in
g
,
o
n
e
o
f
th
e
w
id
el
y
ap
p
lied
s
ch
e
m
es
i
s
Ots
u
th
r
es
h
o
ld
in
g
[
2
0
-
2
5
]
w
h
ich
i
s
u
s
ed
f
o
r
co
n
v
er
ti
n
g
t
h
e
i
m
a
g
e
o
r
R
OI
to
b
lack
an
d
w
h
ite.
T
h
is
th
r
es
h
o
ld
in
g
ap
p
r
o
ac
h
w
o
r
k
s
b
y
id
e
n
ti
f
y
i
n
g
th
e
th
r
es
h
o
ld
v
alu
e
o
f
th
e
v
ar
ian
c
e
b
et
w
ee
n
t
h
e
b
ac
k
g
r
o
u
n
d
an
d
th
e
f
o
r
eg
r
o
u
n
d
co
lo
u
r
o
f
th
e
R
OI
.
2
.
3
.
F
ea
t
ure
ex
t
ra
ct
io
n f
o
r
m
a
r
ker
cla
s
s
if
ica
t
io
n
W
h
en
th
e
R
OI
is
f
ilter
ed
a
n
d
co
n
v
er
ted
to
b
lack
an
d
w
h
ite
,
th
e
n
e
x
t
m
aj
o
r
s
tep
i
s
t
h
e
la
n
e
m
ar
k
er
class
i
f
icatio
n
.
I
n
m
o
s
t
o
f
th
e
r
o
ad
s
av
ailab
le
t
h
r
o
u
g
h
o
u
t
t
h
e
w
o
r
ld
n
o
w
ad
a
y
s
,
th
er
e
ar
e
f
i
v
e
co
m
m
o
n
l
y
u
s
ed
lan
e
m
ar
k
er
t
y
p
e
s
as
s
h
o
w
n
in
Fig
u
r
e
1
.
T
h
ey
ar
e
s
i
n
g
le
d
as
h
ed
(
D)
,
s
in
g
le
s
o
lid
(
S),
d
ash
ed
s
o
lid
(
DS)
,
s
o
lid
d
ash
ed
(
SD)
an
d
d
o
u
b
le
s
o
lid
(
SS
)
.
(
a)
(
b
)
(
c)
(
d
)
(
e)
Fig
u
r
e
1
.
Fiv
e
co
m
m
o
n
t
y
p
es
o
f
lan
e
m
ar
k
er
[
1
3
]
,
(
a)
Sin
g
le
s
o
lid
(
S),
(
b
)
Dash
ed
(
D)
,
(
c)
Dash
ed
s
o
lid
(D
S
)
,
(
d
)
So
lid
d
ash
ed
(S
D
)
,
(
e
)
Do
u
b
le
s
o
lid
(
SS
)
I
n
o
r
d
er
to
class
if
y
t
h
e
la
n
e
m
ar
k
er
s
,
t
h
e
f
ea
tu
r
e
s
w
h
ic
h
a
r
e
u
s
ed
in
t
h
e
d
ec
is
io
n
r
u
le
n
ee
d
to
b
e
d
eter
m
in
ed
.
I
n
liter
at
u
r
e,
s
o
m
e
o
f
t
h
e
f
ea
t
u
r
es
t
h
at
h
a
v
e
b
e
en
u
s
ed
i
n
cl
u
d
e
ed
g
es
[
2
6
]
,
h
is
to
g
r
a
m
s
[
2
7
]
an
d
o
th
er
g
eo
m
etr
ical
f
ea
t
u
r
es
i
n
clu
d
in
g
l
in
e
s
,
cu
r
v
es
an
d
co
n
to
u
r
s
[
1
3
,
1
4
]
.
T
h
e
s
elec
tio
n
o
f
th
e
s
e
f
ea
t
u
r
es
d
ep
en
d
s
o
n
t
h
e
n
u
m
b
er
o
f
d
i
f
f
er
en
t
t
y
p
es
o
f
lan
e
m
ar
k
er
s
t
ar
g
eted
to
b
e
class
if
ied
,
b
esid
es
th
e
te
m
p
o
r
al
a
n
d
co
m
p
u
tatio
n
al
lo
ad
s
t
h
at
n
ee
d
to
b
e
m
i
n
i
m
ized
a
n
d
co
n
tr
o
ll
ed
.
C
h
o
o
s
i
n
g
th
e
s
i
m
p
le
t
y
p
es
o
f
f
ea
t
u
r
es
s
u
c
h
a
s
lin
es
a
n
d
cu
r
v
es
m
a
y
r
eq
u
ir
e
l
o
w
er
co
m
p
u
tatio
n
al
ti
m
e
to
cl
ass
i
f
y
ce
r
tai
n
t
y
p
es
o
f
lan
e
m
ar
k
er
s
.
Ho
w
e
v
er
,
i
f
th
e
n
u
m
b
er
o
f
lan
e
m
ar
k
er
t
y
p
e
s
i
n
cr
ea
s
es,
m
o
r
e
t
y
p
e
s
o
f
f
ea
t
u
r
es
w
il
l
b
e
r
eq
u
ir
ed
t
o
class
i
f
y
t
h
e
la
n
e
m
ar
k
er
s
r
e
n
d
er
in
g
lo
n
g
er
co
m
p
u
tatio
n
al
ti
m
e
a
n
d
lo
ad
.
R
o
ad
m
ak
er
s
w
h
ich
ap
p
ea
r
to
b
e
clo
s
e
to
ea
ch
o
th
er
s
u
c
h
a
s
th
e
s
o
lid
d
ash
ed
(
SD)
a
n
d
t
h
e
d
as
h
ed
s
o
lid
(
DS)
ten
d
to
r
eq
u
ir
e
s
lig
h
tl
y
m
o
r
e
co
m
p
licated
ap
p
r
o
a
ch
to
b
e
d
if
f
er
en
tia
ted
an
d
class
i
f
ied
.
W
ith
o
u
t
a
p
r
o
p
er
class
if
icatio
n
m
e
t
h
o
d
,
th
e
ac
cu
r
ac
y
o
f
t
h
e
lan
e
m
a
r
k
er
d
etec
tio
n
w
ill
b
e
r
ed
u
ce
d
,
b
esid
es
r
es
u
lti
n
g
h
ig
h
er
co
m
p
u
tatio
n
al
co
m
p
le
x
it
y
a
n
d
lo
n
g
er
ex
ec
u
tio
n
ti
m
e.
I
n
th
e
n
e
x
t
s
ec
tio
n
,
a
s
p
ec
if
ic
d
is
cu
s
s
io
n
o
n
t
h
e
ch
alle
n
g
e
s
ca
u
s
ed
b
y
al
l
-
w
ea
t
h
er
co
n
d
itio
n
s
i
n
la
n
e
m
ar
k
er
class
i
f
icatio
n
.
3.
AL
L
-
W
E
A
T
H
E
R
CH
A
L
L
E
NG
E
S
W
h
en
i
m
p
le
m
e
n
ti
n
g
t
h
e
lan
e
m
ar
k
er
clas
s
if
icatio
n
i
n
all
-
wea
th
er
co
n
d
itio
n
s
,
th
e
c
h
alle
n
g
es
s
tar
t
in
th
e
R
OI
s
elec
tio
n
as
th
e
d
esir
ed
f
ea
tu
r
es
o
f
t
h
e
i
m
a
g
e
ar
e
ex
p
ec
ted
to
b
e
af
f
ec
ted
b
y
th
e
w
ea
t
h
er
co
n
d
itio
n
s
,
s
u
c
h
as
r
ain
y
a
n
d
f
o
g
g
y
d
a
y
s
.
T
h
e
s
elec
ted
R
OI
is
th
e
b
est
w
h
e
n
th
e
a
m
o
u
n
t
o
f
r
eq
u
ir
ed
in
f
o
r
m
atio
n
o
r
f
ea
t
u
r
es
o
f
t
h
e
v
id
eo
f
r
a
m
e
i
s
m
ax
i
m
ized
an
d
th
e
a
m
o
u
n
t
o
f
n
o
i
s
e
d
u
e
to
th
e
w
ea
th
er
c
h
an
g
e
i
s
m
in
i
m
ized
.
T
h
is
tr
ad
e
-
o
f
f
i
s
a
r
ea
l c
h
alle
n
g
e
to
b
e
ad
d
r
ess
ed
in
clas
s
i
f
y
i
n
g
la
n
e
m
ar
k
er
s
i
n
all
-
w
ea
t
h
er
co
n
d
itio
n
s
.
A
s
t
h
e
q
u
al
it
y
o
f
th
e
r
esu
lted
f
ilter
ed
R
OI
i
s
ex
p
ec
ted
to
b
e
l
o
w
er
t
h
an
th
e
q
u
ali
t
y
o
f
t
h
e
f
il
ter
ed
R
O
I
in
a
g
o
o
d
w
ea
t
h
er
co
n
d
itio
n
,
th
e
class
if
ica
tio
n
m
ec
h
a
n
i
s
m
m
u
s
t
b
e
d
esig
n
ed
to
p
er
f
o
r
m
b
etter
an
d
m
o
r
e
r
eliab
le
in
all
-
w
ea
th
er
co
n
d
iti
o
n
s
.
T
h
e
p
r
o
b
ab
ilit
y
t
h
at
t
h
e
d
esire
d
f
ea
tu
r
es
o
f
t
h
e
lan
e
m
ar
k
er
s
u
ch
a
s
th
e
ed
g
es
a
n
d
t
h
e
o
t
h
er
g
eo
m
etr
ical
f
ea
tu
r
e
s
ar
e
a
f
f
ec
ted
b
y
th
e
w
ea
th
er
co
n
d
itio
n
s
i
s
h
i
g
h
er
i
n
al
l
-
w
ea
th
er
co
n
d
itio
n
s
d
u
e
to
th
e
r
ain
d
r
o
p
s
,
lack
in
g
v
i
s
ib
ilit
y
a
n
d
s
o
f
o
r
th
.
T
h
ese
r
ain
an
d
w
a
ter
d
r
o
p
lets
ar
e
n
o
t
o
n
ly
p
o
u
r
in
g
o
n
t
h
e
r
o
ad
s
u
r
f
ac
e,
b
u
t
t
h
e
y
ar
e
al
s
o
f
o
u
n
d
o
n
t
h
e
s
cr
ee
n
o
f
t
h
e
ca
m
er
as,
i
f
t
h
e
ca
m
er
a
is
m
o
u
n
ted
o
u
ts
id
e
th
e
ca
r
,
o
r
at
least th
e
w
i
n
d
s
cr
ee
n
s
o
f
t
h
e
v
e
h
icle
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
4
,
A
u
g
u
s
t
2021
:
3
3
6
5
-
3373
3368
4.
L
AN
E
M
ARK
E
R
CL
ASS
I
F
I
CAT
I
O
N
M
O
DE
L
S IN A
L
L
-
WE
A
T
H
E
R
CO
NDI
T
I
O
NS
T
h
er
ef
o
r
e,
in
th
e
n
ex
t
s
ec
tio
n
,
a
th
o
r
o
u
g
h
r
ev
ie
w
o
n
lan
e
m
ar
k
er
clas
s
i
f
icatio
n
al
g
o
r
ith
m
s
s
tu
d
ied
an
d
p
r
o
p
o
s
ed
in
th
e
r
esear
ch
c
o
m
m
u
n
it
y
to
f
ac
e
s
o
m
e
o
f
th
e
s
e
ch
alle
n
g
es
w
ill
b
e
p
r
esen
te
d
.
T
h
e
lan
e
m
ar
k
er
class
i
f
icatio
n
m
o
d
els
w
h
ich
a
r
e
d
esig
n
ed
to
w
o
r
k
in
all
-
w
e
ath
er
co
n
d
itio
n
s
d
ep
en
d
o
n
th
e
ty
p
es
o
f
d
if
f
er
e
n
t
w
ea
t
h
er
co
n
d
itio
n
s
co
n
s
id
er
ed
.
T
h
er
ef
o
r
e,
in
th
is
s
ec
tio
n
t
h
e
lan
e
m
ar
k
er
class
i
f
icatio
n
m
o
d
el
s
p
r
o
p
o
s
ed
in
liter
atu
r
e
w
il
l
b
e
th
o
r
o
u
g
h
l
y
s
tu
d
ied
ac
co
r
d
in
g
to
t
h
e
t
y
p
es
o
f
w
ea
t
h
er
s
co
n
s
id
er
ed
,
as
d
escr
ib
ed
in
t
h
e
f
o
llo
w
in
g
s
u
b
s
ec
tio
n
s
.
4
.
1
.
Ra
iny
w
ea
t
her
A
b
if
u
r
ca
tio
n
m
e
th
o
d
i
s
p
r
o
p
o
s
ed
[
9
]
f
o
r
lan
e
c
h
an
g
e
a
n
d
co
n
tr
o
l
i
n
r
ai
n
y
w
ea
t
h
er
.
T
h
e
o
b
j
ec
tiv
e
o
f
th
e
r
esear
ch
is
s
i
g
n
i
f
ican
t
es
p
ec
iall
y
in
i
n
cr
ea
s
i
n
g
t
h
e
s
af
et
y
lev
el
o
f
h
u
g
e
v
e
h
icle
s
lik
e
tr
u
ck
s
a
n
d
lo
r
r
ies
w
h
e
n
d
r
iv
i
n
g
o
n
a
r
ain
y
d
a
y
.
T
h
is
is
b
a
s
ed
o
n
t
h
e
Ho
p
f
b
if
u
r
ca
tio
n
t
h
eo
r
y
,
w
h
ic
h
i
s
r
e
lated
to
th
e
cr
it
ical
p
o
in
t
th
at
d
eter
m
in
e
s
th
e
s
tab
i
lit
y
.
I
n
t
h
i
s
r
esear
ch
s
u
b
j
ec
t,
s
tab
ilit
y
r
e
f
er
s
to
th
at
o
f
th
e
v
e
h
icle
w
h
en
m
o
v
in
g
o
n
a
r
ain
y
d
a
y
.
A
lt
h
o
u
g
h
th
e
m
o
d
el
co
n
s
id
er
s
t
h
e
r
ai
n
y
w
e
ath
er
as
t
h
e
co
n
s
tr
ain
t,
t
h
e
i
s
s
u
e
o
f
d
etec
ti
n
g
an
d
class
i
f
y
in
g
th
e
lan
e
m
ar
k
er
s
i
s
n
o
t
ad
d
r
ess
ed
i
n
t
h
is
p
ap
er
.
T
h
e
la
n
e
m
ar
k
er
s
,
w
h
ich
d
iv
id
e
th
e
r
o
ad
b
et
w
ee
n
lan
es
esp
ec
i
all
y
o
f
d
if
f
er
en
t
d
ir
ec
tio
n
s
,
ar
e
ess
en
t
ial
to
b
e
d
etec
ted
esp
ec
iall
y
in
A
D
S
in
o
r
d
er
to
en
s
u
r
e
th
e
co
r
r
ec
t d
ec
is
io
n
is
tak
e
n
w
h
i
le
th
e
v
e
h
icle
i
s
m
o
v
in
g
a
n
d
m
a
n
eu
v
er
ed
.
L
a
n
e
d
etec
tio
n
m
ec
h
an
i
s
m
o
n
r
ain
y
d
a
y
s
h
a
s
b
ee
n
p
r
o
p
o
s
ed
in
[
2
8
,
2
9
]
f
o
r
d
etec
tin
g
th
e
l
an
es
b
ased
o
n
t
h
e
v
id
eo
ca
p
tu
r
ed
f
r
o
m
t
h
e
v
eh
icle.
T
h
is
la
n
e
d
etec
ti
o
n
s
c
h
e
m
e
ap
p
lies
t
h
e
C
an
n
y
d
etec
to
r
an
d
ed
g
e
d
etec
tio
n
s
c
h
e
m
e
to
d
etec
t t
h
e
lan
e
o
n
r
ain
y
d
a
y
s
.
A
lt
h
o
u
g
h
th
e
la
n
es a
r
e
s
u
cc
e
s
s
f
u
ll
y
d
ete
cted
u
s
i
n
g
t
h
is
la
n
e
d
etec
tio
n
m
o
d
el,
th
e
t
y
p
es
o
f
lan
e
m
ar
k
er
s
w
h
ic
h
d
iv
id
e
th
e
lan
es
ar
e
n
o
t
class
if
ied
an
d
d
etec
ted
.
A
n
o
t
h
er
m
et
h
o
d
is
p
r
o
p
o
s
ed
to
p
er
f
o
r
m
f
ast
lear
n
i
n
g
tech
n
iq
u
e
b
as
ed
o
n
co
n
v
o
l
u
tio
n
a
l
n
e
u
tr
al
n
et
w
o
r
k
ap
p
r
o
ac
h
in
d
etec
tin
g
t
h
e
la
n
es
[
1
9
]
.
I
t
is
clai
m
ed
in
t
h
is
p
ap
er
th
at
t
h
e
lan
es
ca
n
b
e
s
u
cc
es
s
f
u
ll
y
d
etec
ted
in
ex
tr
e
m
e
co
n
d
itio
n
s
in
c
lu
d
i
n
g
r
ain
y
wea
th
er
.
Ho
w
ev
er
f
u
r
th
er
w
o
r
k
i
s
n
ee
d
ed
if
lan
e
m
ar
k
er
cl
ass
i
f
icatio
n
is
to
b
e
i
m
p
le
m
en
ted
as
th
i
s
ap
p
r
o
ac
h
o
n
l
y
w
o
r
k
s
f
o
r
d
etec
tin
g
lan
es,
n
o
t
t
h
e
lan
e
m
ar
k
er
s
w
h
ic
h
d
iv
id
e
t
h
e
la
n
es.
T
h
e
p
r
e
-
p
r
o
ce
s
s
in
g
tec
h
n
iq
u
e
s
i
m
p
le
m
e
n
ted
in
th
e
a
f
o
r
em
en
tio
n
ed
la
n
e
c
h
a
n
g
in
g
,
co
n
tr
o
l
an
d
d
etec
tio
n
s
ch
e
m
es
in
th
is
s
ec
tio
n
ar
e
w
o
r
th
to
b
e
co
n
s
id
er
ed
w
h
en
a
n
e
w
lan
e
m
ar
k
er
clas
s
if
ica
tio
n
m
o
d
el
i
s
to
b
e
d
ev
elo
p
ed
.
4
.
2
.
F
o
g
g
y
w
e
a
t
her
An
o
th
er
ch
alle
n
g
i
n
g
t
y
p
e
o
f
w
ea
t
h
er
e
s
p
ec
iall
y
i
n
t
h
e
h
ill
y
an
d
co
ld
ar
ea
s
is
f
o
g
g
y
w
ea
t
h
er
.
A
la
n
e
d
etec
tio
n
m
ec
h
a
n
i
s
m
i
s
p
r
o
p
o
s
ed
in
[
8
]
,
to
d
etec
t
t
h
e
la
n
e
s
i
n
f
o
g
g
y
w
ea
t
h
er
.
A
h
e
u
r
is
tic
a
p
p
r
o
ac
h
is
ap
p
lied
to
id
en
ti
f
y
th
e
R
OI
a
n
d
t
h
e
i
m
p
r
o
v
ed
d
ar
k
ch
a
n
n
el
i
s
al
s
o
i
m
p
le
m
e
n
ted
to
d
etec
t
t
h
e
la
n
es.
Alth
o
u
g
h
i
t
i
s
clai
m
ed
t
h
at
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
is
ab
le
to
r
ea
ch
9
6
%
d
etec
tio
n
ac
cu
r
ac
y
,
th
e
la
n
e
d
ete
ctio
n
s
til
l
ex
cl
u
d
es
th
e
lan
e
o
r
lan
e
m
ar
k
er
t
y
p
e
cl
ass
i
f
icatio
n
.
A
n
i
m
a
g
e
p
r
e
-
p
r
o
ce
s
s
i
n
g
tec
h
n
iq
u
e
i
n
f
o
g
g
y
w
ea
t
h
er
i
s
p
r
esen
ted
i
n
[
3
0
]
b
ased
o
n
s
e
g
m
en
tatio
n
.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
is
d
ev
elo
p
ed
b
ased
o
n
th
e
i
m
p
r
o
v
ed
d
ar
k
ch
an
n
el
ap
p
r
o
ac
h
to
b
etter
d
ef
o
g
g
ed
t
h
e
i
m
a
g
e
ca
p
tu
r
ed
in
f
o
g
g
y
w
ea
t
h
er
.
A
l
th
o
u
g
h
th
e
ap
p
r
o
ac
h
is
n
o
t
ad
d
r
ess
ed
f
o
r
lan
e
m
ar
k
e
r
class
if
icatio
n
,
it
i
s
a
u
s
ef
u
l
tec
h
n
iq
u
e
to
p
r
e
-
p
r
o
ce
s
s
th
e
f
o
g
g
ed
i
m
ag
e
s
o
r
v
id
eo
f
r
am
e
s
.
A
la
n
e
m
ar
k
er
d
etec
tio
n
tech
n
iq
u
e
i
s
p
r
esen
ted
in
[
3
1
,
3
2
]
,
w
h
ic
h
d
etec
ts
t
h
e
d
ash
ed
a
n
d
d
o
u
b
le
s
o
lid
lan
e
m
ar
k
er
s
.
A
lt
h
o
u
g
h
th
e
p
r
o
p
o
s
ed
m
e
th
o
d
h
as
b
ee
n
d
e
m
o
n
s
tr
ated
to
d
etec
t
th
e
t
w
o
-
la
n
e
m
ar
k
er
s
i
n
th
e
d
ar
k
,
th
is
d
etec
tio
n
tec
h
n
iq
u
e
h
as
n
o
t
b
ee
n
s
u
cc
e
s
s
f
u
ll
y
d
e
m
o
n
s
tr
ated
to
b
e
u
s
ed
in
f
o
g
g
y
w
ea
t
h
er
.
I
n
g
en
er
al,
m
o
s
t
o
f
t
h
e
lan
e
d
etec
tio
n
s
c
h
e
m
e
s
[
3
3
,
3
4
]
,
w
h
ich
ar
e
v
is
io
n
-
b
ased
an
d
u
s
e
ca
m
er
a
to
ca
p
tu
r
e
th
e
i
m
a
g
es
o
f
t
h
e
lan
e
an
d
lan
e
m
ar
k
er
s
,
ar
e
ap
p
lied
to
m
er
el
y
d
etec
t
th
e
lan
e
s
,
w
it
h
o
u
t
clas
s
i
f
y
in
g
th
e
t
y
p
e
s
o
f
t
h
e
la
n
e
m
ar
k
er
s
d
iv
id
in
g
t
h
e
lan
e
s
.
A
s
s
ee
n
i
n
Fi
g
u
r
e
2
,
t
w
o
m
a
in
s
tep
s
i
n
lan
e
d
etec
tio
n
ar
e
R
OI
id
en
ti
f
icatio
n
w
i
th
f
ilter
i
n
g
a
n
d
th
r
es
h
o
ld
in
g
an
d
s
ec
o
n
d
l
y
f
ea
tu
r
e
ex
tr
ac
tio
n
,
as
s
h
o
w
n
in
Fi
g
u
r
e
2
(
a)
an
d
Fig
u
r
e
2
(
b
)
r
esp
ec
tiv
el
y
.
(
a)
(
b
)
Fig
u
r
e
2
.
T
w
o
m
ai
n
s
tep
s
in
la
n
e
d
etec
tio
n
[
3
3
]
,
(
a)
R
OI
id
e
n
ti
f
icatio
n
an
d
f
i
lter
in
g
w
it
h
t
h
r
es
h
o
ld
in
g
,
(
b
)
Featu
r
e
ex
tr
ac
tio
n
f
o
r
lan
e
d
etec
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
A
d
va
n
ce
s
in
la
n
e
ma
r
kin
g
d
etec
tio
n
a
lg
o
r
ith
ms fo
r
a
ll
-
w
ea
th
er
co
n
d
itio
n
s
(
Ha
d
h
r
a
mi
A
b
.
Gh
a
n
i
)
3369
An
o
th
er
r
ec
en
t
tr
e
n
d
in
la
n
e
d
etec
tio
n
is
b
y
e
m
p
lo
y
i
n
g
ar
o
u
n
d
v
ie
w
m
o
n
ito
r
i
n
g
(
A
VM
)
,
w
h
ic
h
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
in
[
3
5
,
3
6
]
,
to
n
a
m
e
a
f
e
w
.
T
h
is
tec
h
n
iq
u
e
u
s
es
ca
m
er
as
at
d
if
f
er
en
t
an
g
le
s
o
f
t
h
e
v
e
h
icle
to
ca
p
tu
r
e
a
b
etter
all
-
r
o
u
n
d
v
ie
w
o
f
t
h
e
la
n
e.
Fis
h
e
y
e
ca
m
er
a
s
ar
e
t
y
p
icall
y
u
s
ed
i
n
th
i
s
ap
p
r
o
ac
h
to
ca
p
tu
r
e
th
e
lan
e
i
m
a
g
es.
A
lt
h
o
u
g
h
t
h
is
d
etec
tio
n
ap
p
r
o
ac
h
h
as
b
ee
n
d
e
m
o
n
s
tr
ated
at
d
i
f
f
er
en
t
w
ea
t
h
er
co
n
d
itio
n
s
,
th
e
f
o
cu
s
is
m
ain
l
y
to
d
etec
t th
e
la
n
es,
n
o
t t
h
e
m
ar
k
er
s
.
T
h
e
A
VM
-
b
ased
lan
e
d
etec
tio
n
p
r
esen
ted
in
[
3
3
]
f
o
r
ex
a
m
p
l
e,
h
as
b
ee
n
i
m
p
le
m
e
n
ted
to
d
etec
t
lan
e
s
w
it
h
s
i
n
g
le
s
o
lid
an
d
s
i
n
g
le
d
ash
ed
la
n
e
m
ar
k
er
s
.
T
h
e
la
n
e
d
etec
tio
n
ap
p
r
o
ac
h
i
n
[
3
7
]
h
as
b
ee
n
d
ev
e
lo
p
ed
to
w
o
r
k
i
n
co
m
p
lex
r
o
ad
co
n
d
itio
n
s
an
d
d
y
n
a
m
ic
e
n
v
ir
o
n
m
e
n
t.
T
h
e
co
r
r
ec
tio
n
m
ec
h
an
i
s
m
h
as
b
ee
n
i
m
p
le
m
en
ted
to
i
m
p
r
o
v
e
t
h
e
q
u
alit
y
o
f
th
e
r
o
ad
i
m
a
g
es
b
e
f
o
r
e
d
etec
tin
g
a
n
u
m
b
er
d
o
u
b
le
s
o
lid
an
d
d
ash
ed
lan
es.
4
.
3
.
O
t
her
w
ea
t
her
co
nd
it
io
ns
T
h
e
r
ec
e
n
t
tr
en
d
s
an
d
d
ev
elo
p
m
e
n
ts
i
n
th
i
s
r
esear
ch
ar
ea
h
av
e
s
h
o
w
n
e
n
co
u
r
a
g
i
n
g
g
r
o
w
t
h
in
d
etec
tin
g
th
e
lan
e
s
an
d
la
n
e
m
ar
k
er
s
in
a
ll
-
w
ea
th
er
co
n
d
it
io
n
s
[
3
8
]
.
Oth
er
th
a
n
f
o
g
a
n
d
r
ain
,
o
th
er
t
y
p
e
s
o
f
ch
alle
n
g
i
n
g
w
ea
th
er
co
n
d
itio
n
s
ar
e
s
n
o
w
an
d
h
az
e
m
o
d
el
[
7
,
3
9
-
41]
.
I
n
[
4
1
]
,
a
lan
e
d
etec
tio
n
m
ec
h
an
i
s
m
i
s
p
r
o
p
o
s
ed
to
w
o
r
k
i
n
ad
v
er
s
e
w
ea
t
h
er
co
n
d
itio
n
s
e
s
p
ec
iall
y
in
s
n
o
w
w
h
e
n
th
e
lan
e
m
ar
k
er
s
ar
e
co
v
er
ed
b
y
t
h
e
s
n
o
w
.
T
h
e
p
r
o
p
o
s
ed
s
ch
e
m
e
o
p
er
ates
b
ased
o
n
v
e
h
icle
-
to
-
i
n
f
r
a
s
tr
u
ct
u
r
e
(
V2
I
)
,
w
h
ic
h
is
ap
p
lied
to
s
to
r
e
th
e
r
ef
er
en
ce
i
m
a
g
e
o
f
t
h
e
r
o
ad
in
th
e
clo
u
d
b
ef
o
r
e
it is
co
m
p
ar
ed
w
it
h
t
h
e
d
is
to
r
ted
o
n
e
i
n
t
h
e
ad
v
er
s
e
w
ea
t
h
er
to
d
etec
t
th
e
la
n
e.
A
p
ar
t
f
r
o
m
r
eq
u
ir
in
g
a
lo
t
o
f
co
n
f
ig
u
r
atio
n
s
tep
s
to
en
s
u
r
e
a
r
eliab
le
V2
I
co
m
m
u
n
icatio
n
,
th
i
s
s
ch
e
m
e
h
as
n
o
t
b
ee
n
d
e
m
o
n
s
t
r
ated
to
b
e
ab
le
to
d
etec
t
th
e
l
an
e
m
ar
k
er
s
.
A
s
f
o
r
th
e
h
az
y
w
ea
t
h
er
co
n
d
itio
n
,
a
n
u
m
b
er
o
f
la
n
e
d
etec
tio
n
s
c
h
e
m
e
s
h
a
v
e
al
s
o
b
ee
n
p
r
o
p
o
s
ed
as
s
ee
n
i
n
[
4
0
,
4
1
]
.
T
h
e
d
eh
az
i
n
g
m
e
th
o
d
p
r
o
p
o
s
ed
in
[
4
2
]
ap
p
lies
a
w
ei
g
h
t
in
g
f
ac
to
r
to
s
h
ar
p
en
t
h
e
e
d
g
e
o
f
th
e
r
o
ad
i
m
a
g
e,
h
e
n
ce
d
eh
az
in
g
it.
O
n
e
o
f
th
e
in
ter
n
al
o
p
er
atio
n
s
h
as a
l
s
o
b
ee
n
r
em
o
v
ed
to
s
p
ee
d
u
p
th
e
d
eh
az
in
g
p
r
o
ce
s
s
.
A
p
ar
t
f
r
o
m
s
n
o
w
a
n
d
h
az
e,
th
er
e
ar
e
o
th
er
t
y
p
es
o
f
w
ea
th
er
co
n
d
itio
n
s
w
h
ic
h
h
a
v
e
also
b
ee
n
ad
d
r
ess
ed
an
d
co
n
s
id
er
ed
in
liter
atu
r
e.
T
h
ese
w
ea
t
h
er
co
n
d
itio
n
s
i
n
cl
u
d
e
d
u
s
k
an
d
clo
u
d
y
,
as
p
r
esen
ted
i
n
[
3
7
,
4
0
]
,
f
o
r
in
s
tan
ce
.
Ho
w
ev
e
r
,
th
ese
t
y
p
e
s
o
f
w
ea
t
h
er
co
n
d
itio
n
s
ar
e
n
o
t
d
is
c
u
s
s
ed
in
d
et
ails
i
n
th
i
s
p
ap
er
as
th
e
y
ar
e
m
ai
n
l
y
r
elate
d
to
th
e
ch
an
g
e
i
n
co
lo
r
an
d
illu
m
in
a
ti
o
n
o
f
th
e
la
n
e
i
m
ag
e
s
.
T
ab
le
1
s
u
m
m
ar
ize
s
t
h
e
r
ev
ie
w
s
t
u
d
ied
in
th
i
s
s
ec
tio
n
o
n
th
e
lan
e
d
etec
t
io
n
a
n
d
la
n
e
m
ar
k
er
class
i
f
icatio
n
m
ec
h
a
n
i
s
m
s
p
r
o
p
o
s
ed
in
liter
at
u
r
e
f
o
r
d
if
f
er
e
n
t
w
ea
t
h
er
co
n
d
itio
n
s
.
I
t
is
cl
ea
r
f
r
o
m
th
i
s
tab
le
th
at
m
o
s
t
o
f
th
e
p
r
o
p
o
s
ed
s
ch
e
m
es
f
o
cu
s
o
n
la
n
e
d
etec
tio
n
an
d
o
n
l
y
a
n
u
m
b
er
o
f
s
c
h
e
m
e
s
h
av
e
ad
d
r
ess
ed
th
e
is
s
u
e
o
f
la
n
e
m
ar
k
er
cla
s
s
i
f
ica
tio
n
i
n
d
i
f
f
er
en
t
w
ea
th
er
s
.
F
u
r
th
er
m
o
r
e,
o
n
l
y
o
n
e
o
r
t
w
o
t
y
p
es
o
f
lan
e
m
ar
k
er
s
ar
e
class
i
f
ied
in
t
h
ese
s
ch
e
m
es.
Mo
s
t
o
f
t
h
e
lan
e
m
ar
k
er
class
i
f
icatio
n
m
e
th
o
d
s
p
r
o
p
o
s
ed
in
liter
atu
r
e
ar
e
d
ev
elo
p
ed
f
o
r
n
o
r
m
al
w
ea
th
er
co
n
d
itio
n
s
,
as
s
u
m
m
ar
ized
in
T
ab
le
2
.
T
ab
le
1
.
Su
m
m
ar
y
o
f
la
n
e
d
et
ec
tio
n
an
d
lan
e
m
ar
k
er
class
i
f
i
ca
tio
n
in
d
i
f
f
er
e
n
t
w
ea
t
h
er
co
n
d
itio
n
s
R
e
f
Pre
-
p
r
o
c
e
ssi
n
g
L
a
n
e
D
e
t
e
c
t
i
o
n
L
a
n
e
M
a
r
k
e
r
C
l
a
ssi
f
i
c
a
t
i
o
n
W
e
a
t
h
e
r
[
3
5
]
F
.
H
.
D
.
a
l
g
o
r
i
t
h
m
√
R
a
i
n
,
F
o
g
[
1
9
]
G
a
u
ssi
a
n
smo
o
t
h
i
n
g
√
R
a
i
n
,
D
u
s
k
[
8
]
D
a
r
k
c
h
a
n
n
e
l
p
r
i
o
r
√
F
o
g
[
3
0
]
D
a
r
k
c
h
a
n
n
e
l
p
r
i
o
r
√
F
o
g
,
H
a
z
e
[
3
2
]
B
i
n
a
r
i
z
a
t
i
o
n
√
D
,
S
R
a
i
n
,
F
o
g
[
3
7
]
M
a
s
k
O
p
e
r
a
t
i
o
n
√
S
S
,
D
C
l
o
u
d
y
[
4
0
]
O
t
su
t
h
r
e
sh
o
l
d
i
n
g
√
S
S
,
D
D
u
sk
[
4
1
]
V
2
I
√
S
n
o
w
[
4
2
]
W
e
i
g
h
t
i
n
g
f
a
c
t
o
r
√
H
a
z
e
I
t
ca
n
b
e
s
ee
n
f
r
o
m
T
ab
le
2
th
at
m
o
s
t
o
f
t
h
e
lan
e
m
ar
k
er
class
i
f
icatio
n
m
et
h
o
d
s
in
li
t
er
atu
r
e
ar
e
d
esig
n
ed
to
d
etec
t
u
p
to
th
r
ee
t
y
p
es
o
f
r
o
ad
m
ar
k
er
s
,
w
h
ic
h
ar
e
S,
D
a
n
d
SS
.
T
h
ese
ar
e
th
e
m
o
s
t
co
m
m
o
n
t
y
p
es
o
f
lan
e
m
ar
k
er
s
an
d
t
h
e
ea
s
iest
o
n
es
to
b
e
clas
s
i
f
ied
.
A
f
e
w
la
n
e
m
ar
k
er
cla
s
s
i
f
ica
tio
n
s
c
h
e
m
es
h
av
e
b
ee
n
p
r
o
p
o
s
ed
in
liter
atu
r
e
to
d
etec
t
th
e
m
o
r
e
ch
allen
g
i
n
g
ty
p
e
s
o
f
lan
e
m
ar
k
er
s
,
w
h
ich
a
r
e
DS
an
d
SD
s
u
c
h
as
in
[
1
2
,
4
7
,
5
0
]
.
Du
e
to
th
e
d
if
f
ic
u
lt
y
i
n
d
etec
tin
g
a
n
d
d
if
f
er
en
tiati
n
g
D
S
an
d
S
D
,
th
e
ac
cu
r
ac
y
o
f
class
i
f
y
in
g
th
e
s
e
lan
e
m
ar
k
er
s
is
t
y
p
icall
y
lo
w
er
th
a
n
9
0
%.
Ou
r
p
r
ev
io
u
s
m
et
h
o
d
in
d
etec
tin
g
f
i
v
e
lan
e
m
ar
k
er
s
i
n
cl
u
d
in
g
DS
a
n
d
SD
h
av
e
b
ee
n
d
e
m
o
n
s
tr
ated
to
in
cr
ea
s
e
th
e
ac
cu
r
ac
y
b
et
w
ee
n
9
4
%
to
1
0
0
%
[
1
2
]
,
[
1
4
]
in
n
o
r
m
al
w
ea
th
er
.
W
ith
ef
f
ec
tiv
e
p
r
e
-
p
r
o
ce
s
s
in
g
tec
h
n
iq
u
es,
th
is
m
eth
o
d
ca
n
b
e
f
u
r
t
h
er
i
m
p
r
o
v
ed
to
b
e
ap
p
lied
in
f
o
g
g
y
a
n
d
r
ain
y
w
e
ath
er
.
4
.
4
.
Co
nto
ur
-
a
ng
le
m
et
ho
d f
o
r
la
ne
m
a
r
ker
cla
s
s
if
ica
t
io
n
T
h
e
co
n
to
u
r
an
al
y
s
i
s
m
eth
o
d
is
d
ev
elo
p
ed
b
ased
o
n
th
e
s
tu
d
y
o
f
t
h
e
co
n
to
u
r
l
in
e
s
d
etec
t
ed
o
n
th
e
R
OI
o
f
t
h
e
r
o
ad
i
m
a
g
es.
Af
ter
th
e
i
m
a
g
e
p
r
ep
r
o
ce
s
s
in
g
s
ta
g
e
,
w
h
ic
h
i
n
clu
d
e
s
th
e
co
n
v
er
s
io
n
o
f
th
e
i
m
a
g
e
to
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
4
,
A
u
g
u
s
t
2021
:
3
3
6
5
-
3373
3370
b
in
ar
y
b
lack
an
d
w
h
ite
f
o
r
m
at
,
th
e
co
n
to
u
r
lin
es
a
r
e
ca
lcu
lat
ed
in
th
e
v
er
tical
d
ir
ec
tio
n
.
T
h
e
d
esire
d
co
n
to
u
r
lin
e,
w
h
ich
r
ep
r
esen
ts
t
h
e
b
o
u
n
d
ar
y
o
f
th
e
w
h
ite
-
co
lo
u
r
ed
r
o
ad
lan
e
m
ar
k
er
,
is
d
etec
ted
v
ia
th
e
f
o
llo
w
in
g
m
ec
h
a
n
i
s
m
T
h
e
b
in
ar
y
R
OI
is
s
ca
n
n
ed
v
er
ticall
y
in
a
n
u
m
b
er
o
f
lo
ca
tio
n
s
w
h
ich
h
a
v
e
an
eq
u
al
in
t
er
v
al
b
et
w
ee
n
ea
ch
o
th
er
in
-
ax
i
s
.
T
h
e
o
u
tp
u
t
o
f
t
h
is
o
p
er
atio
n
is
a
b
in
ar
y
co
lu
m
n
v
ec
to
r
,
,
w
h
er
e
‘
0
’
r
ep
r
esen
ts
a
b
lack
p
ix
el
an
d
‘
1
’
r
ep
r
esen
t
s
a
w
h
ite
p
ix
el
f
o
r
=
1
,
⋯
,
.
T
h
e
n
ex
t
s
tep
i
s
to
p
er
f
o
r
m
a
b
it
-
w
i
s
e
XO
R
o
p
er
atio
n
b
et
wee
n
c
o
n
s
ec
u
t
iv
e
b
it
s
,
[
]
an
d
[
+
1
]
,
in
ea
ch
co
lu
m
n
v
ec
to
r
f
o
r
=
1
,
⋯
,
−
1
w
h
er
e
is
th
e
le
n
g
th
o
f
t
h
e
co
l
u
m
n
v
ec
to
r
.
T
h
e
o
u
tp
u
t
o
f
th
is
o
p
er
atio
n
is
=
∑
[
]
−
1
=
1
⨁
[
+
1
]
(
1
)
B
ased
o
n
th
e
f
i
v
e
co
m
m
o
n
t
y
p
e
s
o
f
lan
e
m
ar
k
er
s
o
n
t
h
e
r
o
ad
[
1
2
-
1
4
]
,
th
e
n
u
m
b
er
o
f
co
n
to
u
r
li
n
es
r
an
g
e
s
f
r
o
m
ze
r
o
to
f
o
u
r
.
Fo
r
ex
a
m
p
le,
t
h
e
n
u
m
b
er
o
f
co
n
to
u
r
lin
e
s
f
o
r
m
ar
k
er
S is
t
w
o
an
d
D
is
f
o
u
r
.
Fo
r
d
etec
tin
g
SD
an
d
D
S
la
n
e
m
ar
k
er
s
,
t
h
er
e
is
an
ad
d
iti
o
n
al
s
tep
n
ee
d
ed
as
t
h
e
k
n
o
w
led
g
e
o
f
is
u
n
ab
le
to
tell
ap
ar
t
b
et
w
ee
n
t
h
ese
la
n
e
m
ar
k
er
s
.
T
h
e
t
w
o
e
d
g
es
o
f
t
h
e
lo
n
g
la
n
e
m
ar
k
er
ar
e
d
en
o
ted
as
A
an
d
B
r
esp
ec
tiv
el
y
.
As
f
o
r
th
e
s
h
o
r
t
la
n
e
m
ar
k
er
,
o
r
th
e
d
as
h
lin
e,
p
o
i
n
t
C
is
d
en
o
ted
as
th
e
ce
n
tr
o
id
a
t
t
h
e
ce
n
tr
al
o
f
th
e
la
n
e
m
ar
k
er
,
as
s
ee
n
i
n
Fig
u
r
e
3
.
I
n
o
r
d
er
to
d
if
f
er
en
tia
te
b
et
w
ee
n
SD
an
d
D
S
lan
e
m
ar
k
er
s
,
t
w
o
a
n
g
les
ar
e
m
ea
s
u
r
ed
,
wh
ich
ar
e
∡
an
d
∡
,
as
s
ee
n
i
n
F
ig
u
r
e
3
.
I
f
∡
>
∡
,
th
en
it
i
s
class
i
f
ied
as a
n
SD la
n
e
m
ar
k
e
r
.
I
f
∡
<
∡
,
th
en
it is
cla
s
s
i
f
ied
as a
DS la
n
e
m
ar
k
er
.
T
ab
le
2
.
Su
m
m
ar
y
o
f
la
n
e
m
ar
k
er
class
i
f
icat
io
n
al
g
o
r
ith
m
s
M
a
r
k
e
r
Ty
p
e
s
M
e
t
h
o
d
A
c
c
u
r
a
c
y
[
4
3
]
S,
D
T
e
mp
o
r
a
l
i
n
t
e
g
r
a
t
i
o
n
a
n
a
l
y
si
s
N
o
t
p
r
e
se
n
t
e
d
[
4
4
]
S,
D
F
e
a
t
u
r
e
s o
f
l
o
c
a
l
max
i
ma
-
m
i
n
i
ma
o
f
t
h
e
g
r
a
d
i
e
n
t
&
B
a
y
e
si
a
n
c
l
a
ss
i
f
i
e
r
N
o
t
p
r
e
se
n
t
e
d
[
4
5
]
S,
D
F
e
a
t
u
r
e
o
f
g
e
o
m
e
t
r
i
c
a
l
a
n
d
c
o
n
d
i
t
i
o
n
a
l
r
a
n
d
o
m f
i
e
l
d
c
l
a
ssi
f
i
c
a
t
i
o
n
N
o
t
p
r
e
se
n
t
e
d
[
2
6
]
D
D
e
si
g
n
e
d
d
e
scri
p
t
o
r
o
n
s
p
a
c
e
a
n
d
f
r
e
q
u
e
n
c
y
v
a
l
u
e
s
a
n
d
s
u
p
p
o
r
t
v
e
c
t
o
r
m
a
c
h
i
n
e
N
o
t
p
r
e
se
n
t
e
d
[
4
6
]
S,
D,
SS,
Z
i
g
-
z
a
g
T
e
mp
o
r
a
l
i
n
t
e
g
r
a
t
i
o
n
a
n
a
l
y
si
s
9
5
%
[
2
7
]
S,
D,
DS
T
e
mp
o
r
a
l
i
n
t
e
g
r
a
t
i
o
n
a
n
a
l
y
si
s
N
o
t
p
r
e
se
n
t
e
d
[
4
7
]
S,
D,
SS,
D
S
,
S
D
,
F
e
a
t
u
r
e
s o
f
l
o
c
a
l
max
i
ma
-
m
i
n
i
ma
o
f
t
h
e
g
r
a
d
i
e
n
t
&
B
a
y
e
si
a
n
c
l
a
ss
i
f
i
e
r
7
1
%
-
8
5
%
[
4
8
]
S,
z
i
g
-
z
a
g
,
i
n
t
e
r
se
c
t
i
o
n
,
b
o
x
e
d
j
u
n
c
t
i
o
n
&
sp
e
c
i
a
l
l
a
n
e
F
e
a
t
u
r
e
o
f
g
e
o
m
e
t
r
i
c
a
l
a
n
d
C
o
n
d
i
t
i
o
n
a
l
R
a
n
d
o
m
F
i
e
l
d
c
l
a
ssi
f
i
c
a
t
i
o
n
6
9
%
-
9
4
%
[
4
9
]
S,
D,
SS
V
P
G
N
e
t
:
N
e
u
r
a
l
n
e
t
w
o
r
k
P
r
e
se
n
t
e
d
i
n
F
1
sco
r
e
[
1
2
]
S,
D,
SS,
S
D
,
DS
C
o
n
t
o
u
r
a
n
a
l
y
si
s
9
4
%
-
1
0
0
%
[
5
0
]
S,
D,
SS,
S
D
,
D
S
,
DD
S
e
man
t
i
c
d
a
t
a
8
5
%
-
9
0
%
Fig
u
r
e
3
.
T
h
e
an
g
le
m
ea
s
u
r
e
m
en
t f
o
r
d
if
f
er
e
n
tiati
n
g
SD a
n
d
DS la
n
e
m
ar
k
er
s
T
h
e
p
r
o
p
o
s
ed
class
i
f
icatio
n
m
et
h
o
d
is
i
m
p
le
m
e
n
ted
o
n
th
e
r
ec
o
r
d
ed
v
id
eo
clip
s
co
n
tai
n
in
g
i
m
ag
e
f
r
a
m
e
s
o
f
d
i
f
f
er
e
n
t
lan
e
m
ar
k
e
r
s
as
s
tated
in
T
ab
le
3
.
T
h
ese
v
id
eo
clip
s
ar
e
r
ec
o
r
d
ed
at
3
0
f
r
a
m
e
s
p
er
s
ec
o
n
d
.
T
h
e
class
i
f
icatio
n
ac
cu
r
ac
y
i
s
m
ea
s
u
r
ed
b
y
co
m
p
ar
in
g
t
h
e
class
i
f
ied
la
n
e
m
ar
k
er
s
w
it
h
t
h
e
g
r
o
u
n
d
tr
u
t
h
a
n
d
th
e
co
r
r
esp
o
n
d
in
g
r
es
u
lt
s
ar
e
r
ec
o
r
d
e
d
in
T
a
b
le
3
.
A
f
ter
tr
ai
n
in
g
t
h
e
p
r
o
p
o
s
ed
class
if
icati
o
n
m
ec
h
an
i
s
m
w
i
th
th
r
ee
v
id
eo
clip
s
,
it
is
t
h
e
n
t
ested
to
r
u
n
t
h
e
class
if
ica
tio
n
o
p
er
atio
n
o
n
th
r
ee
o
th
er
d
i
f
f
er
en
t
v
id
eo
clip
s
co
n
tain
i
n
g
a
m
ix
tu
r
e
o
f
d
i
f
f
e
r
en
t
lan
e
m
ar
k
er
t
y
p
es.
I
t
ca
n
b
e
o
b
s
er
v
ed
f
r
o
m
t
h
is
tab
le
th
at
t
h
e
r
ec
o
r
d
ed
ac
cu
r
ac
y
v
al
u
es
ar
e
all
ab
o
v
e
9
3
%.
An
ac
c
u
r
ac
y
v
al
u
e
o
f
1
0
0
%
is
r
ec
o
r
d
ed
in
t
w
o
d
if
f
er
en
t
v
id
eo
clip
s
,
w
h
ic
h
ar
e
C
lip
1
an
d
C
lip
2
T
e
s
t.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
h
a
s
also
b
ee
n
test
ed
w
i
th
d
i
f
f
er
en
t
d
atasets
i
n
cl
u
d
in
g
th
o
s
e
ap
p
lied
in
[
1
2
-
14,
4
7
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
A
d
va
n
ce
s
in
la
n
e
ma
r
kin
g
d
etec
tio
n
a
lg
o
r
ith
ms fo
r
a
ll
-
w
ea
th
er
co
n
d
itio
n
s
(
Ha
d
h
r
a
mi
A
b
.
Gh
a
n
i
)
3371
T
ab
le
3.
C
lass
if
icatio
n
a
cc
u
r
ac
y
m
ea
s
u
r
e
m
e
n
t
C
l
i
p
N
a
me
R
e
so
l
u
t
i
o
n
F
P
S
Ty
p
e
F
r
a
me
s
T
r
a
i
n
T
e
st
A
c
c
u
r
a
c
y
1
C
l
i
p
1
1
2
8
0
x
7
2
0
30
SS
-
DS
1
0
9
9
x
1
0
0
%
2
C
l
i
p
2
1
2
8
0
x
7
2
0
30
D
-
SS
-
SD
1
8
4
0
x
9
8
.
0
4
%
3
C
l
i
p
3
1
2
8
0
x
7
2
0
30
SS
-
S
3
5
7
x
9
3
.
8
4
%
4
C
l
i
p
1
T
e
st
1
2
8
0
x
7
2
0
30
SS
-
DS
-
D
2
0
1
9
x
9
7
.
1
7
%
5
C
l
i
p
2
T
e
st
1
2
8
0
x
7
2
0
30
D
-
SD
-
SS
1
2
8
7
x
1
0
0
%
6
C
l
i
p
3
T
e
st
1
2
8
0
x
7
2
0
30
SS
-
D
-
SD
1
5
4
7
x
9
7
.
5
4
%
5.
F
UT
UR
E
WO
RK
Vis
io
n
-
b
ased
la
n
e
m
ar
k
i
n
g
d
e
tectio
n
is
e
x
p
ec
ted
to
b
e
an
i
m
p
o
r
tan
t
f
u
tu
r
e
ar
ea
o
f
r
esear
c
h
in
A
D
AS
an
d
in
telli
g
en
t
tr
an
s
p
o
r
t
s
y
s
te
m
s
[
3
3
,
3
4
]
.
B
ased
o
n
th
e
r
e
v
ie
w
ca
r
r
ied
o
u
t
an
d
p
r
ese
n
ted
in
t
h
i
s
p
ap
er
,
th
er
e
ar
e
a
n
u
m
b
er
o
f
ar
ea
s
i
n
w
h
ic
h
m
o
r
e
w
o
r
k
ca
n
b
e
f
u
r
t
h
er
co
n
ti
n
u
ed
,
i
m
p
r
o
v
ed
an
d
ad
d
r
es
s
ed
in
th
e
f
u
t
u
r
e,
as
s
u
m
m
ar
ized
n
ex
t:
5
.
1
.
L
a
ne
m
a
r
k
er
cla
s
s
if
ica
t
io
n
L
a
n
e
d
etec
tio
n
is
n
o
d
o
u
b
t
a
n
es
s
e
n
tial
is
s
u
e
th
at
m
u
s
t
b
e
ad
d
r
ess
ed
f
o
r
A
D
AS.
I
n
al
l
-
w
ea
t
h
er
co
n
d
itio
n
s
,
th
e
d
i
f
f
icu
lt
y
i
n
d
etec
tin
g
th
e
la
n
es
d
u
e
to
th
e
d
is
to
r
ted
q
u
alit
y
o
f
th
e
r
o
ad
an
d
lan
e
i
m
a
g
es
n
ee
d
s
to
b
e
ad
d
r
ess
ed
,
as
d
is
cu
s
s
ed
an
d
o
b
s
er
v
ed
in
th
e
m
an
y
ci
ted
r
ef
er
en
ce
s
i
n
th
i
s
p
ap
er
.
Ho
w
e
v
er
,
as
f
ar
as
A
D
AS
is
co
n
ce
r
n
ed
,
class
i
f
y
in
g
t
h
e
t
y
p
es
o
f
la
n
es
[
1
2
,
1
4
]
is
p
ar
am
o
u
n
t
to
en
s
u
r
e
th
at
th
e
i
m
p
o
r
tan
t
d
ec
is
io
n
s
s
u
ch
as
la
n
e
c
h
a
n
g
in
g
o
r
d
ir
ec
tio
n
c
h
an
g
i
n
g
ar
e
co
r
r
ec
tly
m
ad
e.
Alth
o
u
g
h
s
o
m
e
la
n
e
d
etec
tio
n
alg
o
r
ith
m
s
i
n
liter
atu
r
e
[
3
2
,
3
7
,
4
0
]
h
av
e
b
ee
n
d
em
o
n
s
tr
ate
d
to
d
etec
t
s
o
m
e
o
f
th
e
lan
e
m
ar
k
er
s
,
t
h
e
n
u
m
b
er
o
f
d
if
f
er
e
n
t
t
y
p
e
s
o
f
la
n
e
m
ar
k
er
s
d
etec
ted
i
s
o
n
l
y
b
et
w
ee
n
o
n
e
to
t
h
r
ee
t
y
p
e
s
f
o
r
all
-
w
ea
t
h
er
co
n
d
itio
n
s
.
T
h
e
DS a
n
d
SD la
n
e
m
ar
k
er
s
f
o
r
in
s
ta
n
ce
,
h
a
v
e
n
o
t b
ee
n
cla
s
s
i
f
i
ed
an
d
ad
d
r
ess
ed
in
th
e
af
o
r
e
m
en
tio
n
ed
s
c
h
e
m
es.
5
.
2
.
Ra
iny
a
nd
clo
ud
y
w
ea
t
her
I
t
i
s
o
b
s
er
v
ed
f
r
o
m
o
u
r
r
e
v
ie
w
th
a
t
t
h
e
r
esear
c
h
i
n
la
n
e
m
ar
k
in
g
d
etec
tio
n
f
o
r
r
ain
y
d
a
y
s
[
1
9
,
2
8
]
r
ec
eiv
es
les
s
atten
t
io
n
th
a
t
th
a
t
o
f
th
e
f
o
g
g
y
d
a
y
s
.
O
n
e
o
f
th
e
ch
allen
g
es
i
n
d
etec
tin
g
t
h
e
lan
es
i
n
r
ain
y
d
a
y
s
i
s
th
e
p
r
esen
ce
o
f
r
ain
d
r
o
p
lets
w
h
ich
r
en
d
er
s
th
e
la
n
e
i
m
a
g
es
n
o
i
s
y
.
T
h
er
ef
o
r
e,
f
ilter
in
g
is
ess
e
n
tial
w
h
e
n
i
m
p
le
m
en
t
in
g
lan
e
m
ar
k
i
n
g
d
etec
tio
n
in
r
ain
y
d
a
y
s
.
T
h
is
is
a
ch
allen
g
e
th
at
s
h
o
u
ld
b
e
ad
d
r
ess
ed
in
th
e
f
u
t
u
r
e.
Fu
r
t
h
er
m
o
r
e,
th
e
la
n
e
m
ar
k
in
g
d
etec
tio
n
in
r
ai
n
y
d
a
y
s
m
u
s
t
also
b
e
i
m
p
le
m
e
n
ted
alo
n
g
w
it
h
th
at
o
f
th
e
clo
u
d
y
w
ea
t
h
er
.
T
h
is
is
b
ec
a
u
s
e
clo
u
d
an
d
r
ain
a
l
w
a
y
s
co
m
e
to
g
e
th
er
.
A
la
n
e
m
ar
k
i
n
g
d
etec
tio
n
alg
o
r
it
h
m
w
h
ic
h
is
ab
le
to
d
etec
t
th
e
la
n
e
an
d
th
e
m
ar
k
er
s
in
clo
u
d
y
d
ay
s
s
h
o
u
ld
also
b
e
ab
le
to
d
etec
t
th
e
m
i
n
r
ain
y
d
ay
s
.
5
.
3
.
L
a
ne
m
a
r
k
er
a
n
d o
bje
ct
ide
ntif
ica
t
io
n
C
las
s
i
f
y
i
n
g
th
e
lan
e
m
ar
k
er
s
is
ess
en
t
ial
f
o
r
r
eg
u
lati
n
g
an
d
m
an
e
u
v
er
in
g
th
e
v
eh
ic
le.
Ho
w
ev
er
,
d
etec
tin
g
t
h
e
lan
e
a
n
d
th
e
m
ar
k
er
s
is
t
y
p
ical
l
y
a
f
f
ec
ted
b
y
t
h
e
o
b
j
ec
ts
f
o
u
n
d
o
n
th
e
r
o
ad
s
.
So
m
e
o
f
th
e
o
b
j
ec
ts
s
p
o
tted
o
n
th
e
r
o
ad
i
m
ag
e
m
i
g
h
t
b
e
in
ter
p
r
eted
as
p
ar
t
o
f
t
h
e
n
o
i
s
e
w
h
ic
h
m
a
y
b
e
n
eg
le
cted
.
B
u
t
m
o
s
t
th
e
o
b
j
ec
ts
f
o
u
n
d
o
n
t
h
e
r
o
ad
m
u
s
t
b
e
p
r
o
p
er
ly
id
en
ti
f
ied
a
s
t
h
e
y
m
a
y
p
o
s
e
a
d
a
n
g
er
to
t
h
e
r
o
ad
u
s
er
s
.
T
h
ese
o
b
j
ec
ts
m
u
s
t
b
e
d
etec
ted
an
d
i
d
en
tifie
d
to
en
s
u
r
e
th
e
p
r
i
m
ar
y
o
b
j
ec
tiv
e
o
f
lan
e
m
ar
k
er
d
et
ec
tio
n
,
w
h
ic
h
is
to
en
s
u
r
e
th
e
s
a
f
et
y
o
f
t
h
e
r
o
ad
u
s
er
s
,
i
s
ac
h
ie
v
ed
.
T
h
is
s
itu
a
t
io
n
b
ec
o
m
e
s
m
o
r
e
o
b
v
io
u
s
an
d
s
ig
n
if
ica
n
t
w
h
e
n
th
e
tr
a
f
f
ic
lev
el
is
i
n
cr
ea
s
ed
wh
er
e
h
i
g
h
er
v
o
l
u
m
e
s
o
f
v
e
h
icl
es
ar
e
f
o
u
n
d
o
n
t
h
e
r
o
ad
.
T
h
er
ef
o
r
e,
lan
e
m
ar
k
er
class
i
f
icatio
n
m
u
s
t
b
e
in
te
g
r
a
ted
w
it
h
o
b
j
ec
t
id
en
tif
icat
io
n
,
w
h
ic
h
is
a
n
i
m
p
o
r
ta
n
t
p
r
o
b
le
m
to
b
e
ad
d
r
ess
ed
an
d
s
o
lv
ed
in
t
h
e
f
u
t
u
r
e.
T
h
e
n
ee
d
f
o
r
a
v
i
g
o
r
o
u
s
w
o
r
k
o
f
r
e
s
ea
r
ch
i
n
lan
e
m
ar
k
i
n
g
clas
s
i
f
icatio
n
is
h
i
g
h
.
So
m
e
o
f
t
h
e
r
esear
ch
g
ap
s
h
ig
h
li
g
h
ted
h
e
r
e
s
h
o
u
ld
b
e
ad
d
r
ess
ed
to
en
h
an
ce
th
e
f
u
tu
r
e
A
D
AS
an
d
i
n
telli
g
e
n
t
tr
an
s
p
o
r
t
s
y
s
te
m
s
s
o
as
to
en
s
u
r
e
th
e
s
af
et
y
an
d
co
n
v
e
n
ie
n
ce
o
f
th
e
r
o
ad
u
s
er
s
an
d
th
e
p
u
b
lic
at
lar
g
e.
T
h
e
f
u
tu
r
e
d
ir
ec
tio
n
o
f
o
u
r
r
esear
ch
w
ill
b
e
in
lan
e
m
ar
k
er
clas
s
i
f
icatio
n
in
all
-
w
ea
t
h
er
co
n
d
itio
n
s
.
6.
CO
NCLU
SI
O
N
T
h
e
r
esear
ch
in
lan
e
m
ar
k
i
n
g
class
if
icatio
n
h
as
b
ee
n
ca
r
r
ied
o
u
t
f
o
r
class
if
y
in
g
d
if
f
er
en
t
class
es
o
f
lan
e
m
ar
k
er
s
i
n
cl
u
d
in
g
t
h
e
s
o
lid
,
d
ash
ed
an
d
th
e
co
m
b
i
n
atio
n
o
f
s
o
lid
a
n
d
d
ash
ed
la
n
e
m
ar
k
er
s
.
Var
io
u
s
class
i
f
icatio
n
m
et
h
o
d
s
h
a
v
e
b
ee
n
d
ev
elo
p
ed
w
h
ic
h
i
n
cl
u
d
e,
b
u
t
n
o
t
li
m
ited
to
,
d
ee
p
lear
n
in
g
,
t
e
m
p
o
r
al
i
n
te
g
r
atio
n
a
n
al
y
s
is
,
g
r
ad
ien
t
&
B
a
y
es
ian
c
la
s
s
i
f
ier
an
d
s
o
f
o
r
th
.
T
h
e
ac
h
iev
ab
le
ac
cu
r
ac
y
v
alu
e
s
o
f
d
etec
ti
n
g
th
e
lan
e
m
ar
k
er
s
ar
e
s
till
co
n
s
id
er
ab
l
y
lo
w
an
d
s
h
o
u
ld
b
e
f
u
r
t
h
er
i
m
p
r
o
v
ed
.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
u
s
i
n
g
g
eo
m
etr
ical
m
et
h
o
d
s
an
d
co
n
t
o
u
r
an
al
y
s
i
s
h
a
s
b
ee
n
d
e
m
o
n
s
t
r
ated
to
ac
h
iev
e
t
h
e
ac
cu
r
ac
y
v
alu
e
o
v
er
9
0
%
f
o
r
m
o
s
t
o
f
th
e
la
n
e
m
ar
k
er
t
y
p
e
s
d
etec
ted
.
T
h
is
m
et
h
o
d
is
a
g
o
o
d
ca
n
d
id
ate
to
b
e
f
u
r
th
e
r
i
m
p
le
m
e
n
ted
an
d
f
u
r
t
h
er
i
m
p
r
o
v
ed
f
o
r
d
etec
tin
g
th
e
lan
e
m
ar
k
er
s
at
d
if
f
er
en
t
w
ea
t
h
er
co
n
d
itio
n
s
an
d
s
ce
n
ar
io
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
4
,
A
u
g
u
s
t
2021
:
3
3
6
5
-
3373
3372
ACK
NO
WL
E
D
G
E
M
E
NT
T
h
e
au
th
o
r
s
o
f
th
is
p
ap
er
ac
k
n
o
w
led
g
e
an
d
th
a
n
k
th
e
g
o
v
e
r
n
m
e
n
t
o
f
Ma
la
y
s
ia
f
o
r
s
p
o
n
s
o
r
in
g
th
i
s
p
r
o
j
ec
t
as
w
ell
as
e
v
er
y
o
n
e
wh
o
h
as
h
elp
ed
th
e
au
th
o
r
s
to
ca
r
r
y
o
u
t
t
h
e
w
o
r
k
r
eq
u
ir
ed
to
co
m
p
lete
t
h
is
p
ap
er
,
d
ir
ec
tl
y
o
r
in
d
ir
ec
tl
y
.
T
h
is
p
ap
er
is
f
u
n
d
ed
u
n
d
er
a
M
ala
y
s
ia
n
Mi
n
i
s
tr
y
o
f
Hi
g
h
er
E
d
u
ca
tio
n
Gr
a
n
t
(
FR
GS/1
/2
0
1
9
/T
K0
4
/MM
U/0
2
/2
)
.
RE
F
E
R
E
NC
E
S
[1
]
Y.
Da
rm
a
,
M
.
R.
Ka
rim
,
a
n
d
S
.
A
b
d
u
ll
a
h
,
“
A
n
a
n
a
ly
si
s
o
f
M
a
la
y
sia
ro
a
d
traf
f
ic
d
e
a
th
d
ist
rib
u
ti
o
n
b
y
ro
a
d
e
n
v
iro
n
m
e
n
t,
”
S
ā
d
h
a
n
ā
,
v
o
l.
4
2
,
n
o
.
9
,
p
p
.
1
6
0
5
–
1
6
1
5
,
S
e
p
.
2
0
1
7
,
d
o
i:
1
0
.
1
0
0
7
/s1
2
0
4
6
-
0
1
7
-
0
6
9
4
-
9.
[2
]
A
.
G
a
m
b
i,
M.
M
u
e
ll
e
r,
a
n
d
G
.
F
r
a
se
r,
“
A
g
e
n
e
ti
c
p
ro
g
ra
m
m
in
g
a
p
p
ro
a
c
h
t
o
e
x
p
lo
re
th
e
c
ra
sh
se
v
e
rit
y
o
n
m
u
lt
i
-
lan
e
ro
a
d
s,”
IS
S
T
A
2
0
1
9
-
Pro
c
e
e
d
i
n
g
s
o
f
t
h
e
2
8
th
ACM
S
IGS
OFT
In
ter
n
a
ti
o
n
a
l
S
y
mp
o
si
u
m
o
n
S
o
ft
w
a
re
T
e
stin
g
a
n
d
An
a
lys
is
,
2
0
1
9
,
p
p
.
2
7
3
–
2
8
3
,
d
o
i
:
1
0
.
1
1
4
5
/3
2
9
3
8
8
2
.
3
3
3
0
5
6
6
[3
]
A
.
M
.
Ku
m
a
r
a
n
d
P
.
S
im
o
n
,
“
Re
v
ie
w
o
f
L
a
n
e
De
te
c
ti
o
n
a
n
d
T
ra
c
k
in
g
A
l
g
o
rit
h
m
s
in
A
d
v
a
n
c
e
d
Driv
e
r
A
ss
it
a
n
c
e
S
y
st
e
m
,
”
In
t.
J
.
Co
m
p
u
t.
S
c
i.
In
f.
T
e
c
h
n
o
l
.
IJ
CS
IT
,
v
o
l.
7
,
n
o
.
4
,
p
p
.
6
5
–
7
8
,
A
u
g
.
2
0
1
5
.
[4
]
H.
Zh
u
,
K.
-
V
.
Y
u
e
n
,
L
.
M
ih
a
y
lo
v
a
,
a
n
d
H.
Leu
n
g
,
“
Ov
e
rv
ie
w
o
f
E
n
v
iro
n
m
e
n
t
P
e
rc
e
p
ti
o
n
f
o
r
In
telli
g
e
n
t
V
e
h
icle
s,”
IEE
E
T
ra
n
s.
I
n
tell.
T
ra
n
sp
.
S
y
st.
,
v
o
l.
1
8
,
n
o
.
1
0
,
p
p
.
2
5
8
4
–
2
6
0
1
,
Oc
t.
2
0
1
7
,
d
o
i:
1
0
.
1
1
0
9
/T
IT
S
.
2
0
1
7
.
2
6
5
8
6
6
2
.
[5
]
J.
Ju
n
g
a
n
d
S
.
H
.
Ba
e
,
“
Re
a
l
-
T
i
m
e
Ro
a
d
L
a
n
e
De
t
e
c
t
io
n
in
Urb
a
n
A
re
a
s
Us
in
g
L
iD
A
R
Da
ta,”
El
e
c
tro
n
ics
,
v
o
l.
7
,
n
o
.
1
1
,
No
v
.
2
0
1
8
,
A
rt
.
n
o
.
2
7
6
,
d
o
i:
1
0
.
3
3
9
0
/ele
c
tro
n
ics
7
1
1
0
2
7
6
.
[6
]
“
L
a
p
o
ra
n
T
a
h
u
n
a
n
2
0
1
8
,
”
M
a
l
a
y
sia
n
In
stit
u
te
o
f
Ro
a
d
S
a
f
e
ty
R
e
se
a
r
c
h
,
h
tt
p
s:
//
ww
w
.
m
iro
s.g
o
v
.
m
y
,
2
0
1
8
.
[
On
li
n
e
]
.
A
v
a
il
a
b
le:
h
tt
p
s://
w
ww
.
m
ir
o
s.g
o
v
.
m
y
/x
s/p
e
n
e
rb
it
a
n
.
p
h
p
?
p
a
g
e
t
y
p
e
=
1
7
.
[7
]
Z.
Ch
e
n
,
X
.
Qin
,
a
n
d
M
.
R.
R.
S
h
a
o
n
,
“
M
o
d
e
l
in
g
lan
e
-
c
h
a
n
g
e
-
re
late
d
c
ra
sh
e
s
w
it
h
lan
e
-
sp
e
c
i
f
ic
re
a
l
-
ti
m
e
tra
ff
ic
a
n
d
w
e
a
th
e
r
d
a
ta,”
J
o
u
rn
a
l
o
f
In
telli
g
e
n
t
T
r
a
n
sp
o
rta
ti
o
n
S
y
st
e
ms
,
v
o
l.
2
2
,
n
o
.
4
,
p
p
.
2
9
1
–
3
0
0
,
Ju
l
.
2
0
1
8
,
d
o
i:
1
0
.
1
0
8
0
/1
5
4
7
2
4
5
0
.
2
0
1
7
.
1
3
0
9
5
2
9
.
[8
]
L
.
W
e
i
a
n
d
J.
Zh
o
u
,
“
S
tu
d
y
o
n
lan
e
d
e
tec
ti
o
n
in
f
o
g
we
a
th
e
r
b
a
se
d
o
n
h
e
u
risti
c
re
g
io
n
o
f
in
tere
st an
d
im
p
ro
v
e
d
d
a
rk
c
h
a
n
n
e
l
p
rio
r
,
”
[
O
n
li
n
e
]
.
A
v
a
il
a
b
le:
h
tt
p
s:/
/www
.
re
se
a
rc
h
g
a
te.n
e
t/
p
u
b
li
c
a
ti
o
n
/
3
1
1
4
3
4
9
9
6
_
S
tu
d
y
_
o
n
_
la
n
e
_
d
e
tec
ti
o
n
_
i
n
_
f
o
g
_
w
e
a
th
e
r_
b
a
se
d
_
o
n
_
h
e
u
risti
c
_
re
g
io
n
_
o
f
_
in
tere
st_
a
n
d
_
im
p
ro
v
e
d
_
d
a
rk
_
c
h
a
n
n
e
l_
p
rio
r
.
[9
]
T
.
P
e
n
g
,
Z.
G
u
a
n
,
R.
Zh
a
n
g
,
J.
D
o
n
g
,
K.
L
i,
a
n
d
H.
Xu
,
“
Bif
u
rc
a
ti
o
n
o
f
L
a
n
e
Ch
a
n
g
e
a
n
d
Co
n
tro
l
o
n
Hig
h
w
a
y
f
o
r
T
ra
c
to
r
-
S
e
m
it
ra
il
e
r
u
n
d
e
r
Ra
in
y
W
e
a
th
e
r,
”
J
o
u
rn
a
l
o
f
a
d
v
a
n
c
e
d
tr
a
n
sp
o
rta
t
io
n
,
v
o
l.
2
0
1
7
,
p
p
.
1
–
2
0
,
2
0
1
7
.
[1
0
]
F
.
He
rm
o
sill
o
-
Re
y
n
o
so
,
D.
T
o
rre
s
-
Ro
m
a
n
,
J.
S
a
n
ti
a
g
o
-
P
a
z
,
a
n
d
J
.
Ra
m
ire
z
-
P
a
c
h
e
c
o
,
“
A
No
v
e
l
A
l
g
o
rit
h
m
Ba
se
d
o
n
t
h
e
P
ix
e
l
-
En
tr
o
p
y
f
o
r
A
u
to
m
a
ti
c
De
tec
ti
o
n
o
f
Nu
m
b
e
r
o
f
L
a
n
e
s,
Lan
e
Ce
n
ters
,
a
n
d
L
a
n
e
Div
isio
n
L
in
e
s
F
o
rm
a
ti
o
n
,
”
En
tro
p
y
,
v
o
l.
2
0
,
n
o
.
1
0
,
Oc
t.
2
0
1
8
,
A
rt
.
n
o
.
7
2
5
,
d
o
i:
1
0
.
3
3
9
0
/e2
0
1
0
0
7
2
5
.
[1
1
]
P
.
Ro
c
h
a
,
A
.
S
id
d
i
q
u
i,
a
n
d
M
.
S
tad
ler,
“
Im
p
ro
v
in
g
e
n
e
rg
y
e
ff
i
c
ien
c
y
v
i
a
s
m
a
rt
b
u
il
d
i
n
g
e
n
e
rg
y
m
a
n
a
g
e
m
e
n
t
s
y
ste
m
s:
A
c
o
m
p
a
riso
n
w
it
h
p
o
li
c
y
m
e
a
su
re
s,”
En
e
rg
y
Bu
il
d
.
,
v
o
l.
8
8
,
p
p
.
2
0
3
–
2
1
3
,
F
e
b
.
2
0
1
5
,
d
o
i:
1
0
.
1
0
1
6
/j
.
e
n
b
u
il
d
.
2
0
1
4
.
1
1
.
0
7
7
.
[1
2
]
Z.
M
.
S
a
n
i
,
H.
A
.
G
h
a
n
i,
R.
Be
sa
r,
A
.
A
z
i
z
a
n
,
a
n
d
H.
A
b
a
s,
“
Re
a
l
-
T
i
m
e
V
id
e
o
P
r
o
c
e
ss
in
g
u
sin
g
C
o
n
to
u
r
Nu
m
b
e
rs
a
n
d
A
n
g
les
f
o
r
No
n
-
u
r
b
a
n
R
o
a
d
M
a
rk
e
r
Clas
sif
ic
a
ti
o
n
,
”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
8
,
n
o
.
4
,
p
p
.
2
5
4
0
–
2
5
4
8
,
A
u
g
.
2
0
1
8
,
d
o
i
:
1
0
.
1
1
5
9
1
/i
jec
e
.
v
8
i4
.
p
p
2
5
4
0
-
2
5
4
8
.
[1
3
]
Z.
M
.
S
a
n
i
,
L
.
W
.
S
e
n
,
H.
A
.
Gh
a
n
i,
a
n
d
R.
Be
sa
r,
“
Re
a
l
-
ti
m
e
d
a
y
ti
m
e
ro
a
d
m
a
rk
e
r
re
c
o
g
n
it
io
n
u
sin
g
f
e
a
tu
re
s
v
e
c
to
rs an
d
n
e
u
ra
l
n
e
tw
o
rk
,
”
in
2
0
1
5
IE
EE
Co
n
fer
e
n
c
e
o
n
S
u
sta
i
n
a
b
le Uti
li
za
t
io
n
a
n
d
De
v
e
lo
p
me
n
t
In
En
g
i
n
e
e
rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
(
CS
UD
ET
)
,
2
0
1
5
.
[1
4
]
Z.
M
.
S
a
n
i,
H.
G
h
a
n
i,
R.
Be
sa
r,
a
n
d
W
.
L
o
i,
“
Da
y
ti
m
e
ro
a
d
m
a
rk
e
r
re
c
o
g
n
it
io
n
u
si
n
g
g
ra
y
s
c
a
le h
isto
g
ra
m
a
n
d
p
ix
e
l
v
a
lu
e
s,”
In
ter
n
e
two
rk
in
g
In
d
o
n
e
s.
J
.
,
v
o
l.
8
,
n
o
.
1
,
p
p
.
1
1
–
1
6
,
Ja
n
.
2
0
1
6
.
[1
5
]
H.
Zh
o
u
a
n
d
H
.
W
a
n
g
,
“
V
isi
o
n
-
b
a
se
d
lan
e
d
e
tec
ti
o
n
a
n
d
trac
k
in
g
f
o
r
d
riv
e
r
a
ss
istan
c
e
s
y
st
e
m
s:
A
su
rv
e
y
,
”
2
0
1
7
IEE
E
In
ter
n
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Cy
b
e
rn
e
ti
c
s
a
n
d
In
telli
g
e
n
t
S
y
ste
ms
,
CIS
2
0
1
7
a
n
d
IEE
E
Co
n
fer
e
n
c
e
o
n
Ro
b
o
ti
c
s,
Au
to
ma
ti
o
n
a
n
d
M
e
c
h
a
tr
o
n
ics
,
RA
M
2
0
1
7
–
Pro
c
e
e
d
i
n
g
s
,
2
0
1
7
,
p
p
.
6
6
0
–
6
6
5
,
d
o
i:
1
0
.
1
1
0
9
/ICCIS
.
2
0
1
7
.
8
2
7
4
8
5
6
.
[1
6
]
T
.
Ra
tek
e
,
K.
A
.
Ju
ste
n
,
V.
F
.
C
h
i
a
re
ll
a
,
A
.
C.
S
o
b
iera
n
sk
i,
E.
Co
m
u
n
e
ll
o
,
a
n
d
A
.
V
.
W
a
n
g
e
n
h
e
im
,
“
P
a
ss
iv
e
V
isi
o
n
Re
g
io
n
-
Ba
se
d
Ro
a
d
De
tec
ti
o
n
:
A
L
it
e
ra
tu
re
Re
v
ie
w
,
”
ACM
Co
mp
u
t.
S
u
rv
.
C
S
UR
,
v
o
l
.
5
2
,
n
o
.
2
,
p
p
.
1
–
3
4
,
2
0
1
9
.
[1
7
]
N.
M
a
,
G
.
P
a
n
g
,
X
.
S
h
i,
a
n
d
Y.
Zh
a
i,
“
A
n
A
ll
-
w
e
a
th
e
r
Lan
e
De
tec
ti
o
n
S
y
ste
m
Ba
se
d
o
n
S
im
u
la
ti
o
n
I
n
tera
c
ti
o
n
P
latf
o
rm
,
”
IEE
E
Acc
e
ss
,
p
p
.
1
–
1
,
De
c
.
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/A
CCES
S
.
2
0
1
8
.
2
8
8
5
5
6
8
.
[1
8
]
S
.
Krig
,
“
Im
a
g
e
P
re
-
P
ro
c
e
ss
in
g
,
”
in
Co
m
p
u
ter
Vi
si
o
n
M
e
trics
:
S
u
rv
e
y
,
T
a
x
o
n
o
my
,
a
n
d
A
n
a
lys
is
,
Be
rk
e
le
y
,
C
A:
A
p
re
ss
,
p
p
.
3
9
–
83
,
2
0
1
4
.
[1
9
]
J.
Kim
,
J.
Ki
m
,
G
.
J.
Ja
n
g
,
a
n
d
M
.
L
e
e
,
“
F
a
st
le
a
rn
in
g
m
e
th
o
d
f
o
r
c
o
n
v
o
l
u
ti
o
n
a
l
n
e
u
ra
l
n
e
tw
o
rk
s
u
sin
g
e
x
tre
m
e
lea
rn
in
g
m
a
c
h
in
e
a
n
d
it
s
a
p
p
li
c
a
t
io
n
to
lan
e
d
e
tec
ti
o
n
,
”
Ne
u
ra
l
Ne
tw.
,
v
o
l.
8
7
,
p
p
.
1
0
9
–
1
2
1
,
M
a
r.
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/j
.
n
e
u
n
e
t.
2
0
1
6
.
1
2
.
0
0
2
.
[2
0
]
N.
Otsu
,
“
A
T
h
re
sh
o
ld
S
e
lec
ti
o
n
M
e
th
o
d
f
ro
m
G
r
a
y
-
L
e
v
e
l
Histo
g
ra
m
s,”
IEE
E
T
ra
n
s.
S
y
st.
M
a
n
Cy
b
e
rn
.
,
v
o
l.
9
,
n
o
.
1
,
p
p
.
6
2
–
6
6
,
Ja
n
.
1
9
7
9
,
d
o
i:
1
0
.
1
1
0
9
/T
S
M
C.
1
9
7
9
.
4
3
1
0
0
7
6
.
[2
1
]
V
.
Ng
u
y
e
n
,
H.
Ki
m
,
S
.
Ju
n
,
a
n
d
K.
Bo
o
,
“
A
S
tu
d
y
o
n
Re
a
l
-
T
i
m
e
De
tec
ti
o
n
M
e
th
o
d
o
f
Lan
e
a
n
d
V
e
h
icle
f
o
r
L
a
n
e
Ch
a
n
g
e
A
s
sista
n
t
S
y
st
e
m
Us
in
g
V
isio
n
S
y
ste
m
o
n
Hig
h
w
a
y
,
”
En
g
.
S
c
i.
T
e
c
h
n
o
l
.
In
t
.
J
.
,
v
o
l.
2
1
,
n
o
.
5
,
p
p
.
8
2
2
–
8
3
3
,
Oc
t.
2
0
1
8
,
d
o
i:
1
0
.
1
0
1
6
/j
.
jes
tch
.
2
0
1
8
.
0
6
.
0
0
6
.
[2
2
]
Y.
S
.
T
a
n
g
,
D.
H.
X
ia,
G
.
Y.
Zh
a
n
g
,
L
.
N.
G
e
,
a
n
d
X
.
Y.
Ya
n
,
“
T
h
e
De
tec
ti
o
n
M
e
th
o
d
o
f
L
a
n
e
L
in
e
Ba
se
d
o
n
th
e
Im
p
ro
v
e
d
Otsu
T
h
re
sh
o
ld
S
e
g
m
e
n
tatio
n
,
”
A
p
p
l.
M
e
c
h
.
M
a
ter
.
,
v
o
l
.
7
4
1
,
p
p
.
3
5
4
–
3
5
8
,
2
0
1
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
A
d
va
n
ce
s
in
la
n
e
ma
r
kin
g
d
etec
tio
n
a
lg
o
r
ith
ms fo
r
a
ll
-
w
ea
th
er
co
n
d
itio
n
s
(
Ha
d
h
r
a
mi
A
b
.
Gh
a
n
i
)
3373
[2
3
]
J.
L
ian
g
,
N.
Ho
m
a
y
o
u
n
f
a
r,
W.
C.
M
a
,
S.
W
a
n
g
,
a
n
d
R.
Urta
s
u
n
,
“
Co
n
v
o
lu
ti
o
n
a
l
re
c
u
rre
n
t
n
e
tw
o
rk
f
o
r
ro
a
d
b
o
u
n
d
a
ry
e
x
trac
ti
o
n
,
”
Pro
c
e
e
d
in
g
s
o
f
th
e
IEE
E
Co
mp
u
ter
S
o
c
iety
Co
n
fer
e
n
c
e
o
n
Co
mp
u
ter
Vi
sio
n
a
n
d
Pa
t
ter
n
Rec
o
g
n
it
io
n
,
2
0
1
9
,
p
p
.
9
5
0
4
–
9
5
1
3
,
d
o
i:
1
0
.
1
1
0
9
/C
VP
R.
2
0
1
9
.
0
0
9
7
4
.
[2
4
]
Y.
X
i
n
,
M
.
J
F
,
E.
M
a
rti
n
a
,
a
n
d
L
.
S
.
L
o
u
r
d
e
s,
“
A
n
I
m
p
ro
v
e
d
Otsu
T
h
re
sh
o
ld
S
e
g
m
e
n
tatio
n
M
e
th
o
d
f
o
r
Un
d
e
rw
a
te
r
S
im
u
lt
a
n
e
o
u
s L
o
c
a
li
z
a
ti
o
n
a
n
d
M
a
p
p
in
g
-
Ba
se
d
Na
v
ig
a
ti
o
n
,
”
S
e
n
s
o
rs
,
v
o
l.
1
6
,
n
o
.
1
7
,
2
0
1
6
,
A
rt
.
n
o
.
1
1
4
8
.
[2
5
]
He
lo
d
e
,
P
riy
a
n
k
a
S
.
,
K.
H.
Walse
,
Ka
ra
n
d
e
M
.
U,
“
Otsu
T
h
re
sh
o
ld
in
g
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
In
n
o
v
a
t
ive
Res
e
a
rc
h
in
C
o
mp
u
ter
a
n
d
C
o
mm
u
n
ica
ti
o
n
E
n
g
i
n
e
e
rin
g
,
v
o
l.
5
,
v
o
l
.
4
,
p
p
.
8
1
9
8
-
8
2
0
5
,
2
0
1
7
.
[2
6
]
C.
L
in
,
L
.
L
i,
Z.
Ca
i,
K.
C.
P
.
W
a
n
g
,
D.
X
iao
,
W.
L
u
o
,
a
n
d
J.
G
u
o
,
“
De
e
p
L
e
a
rn
in
g
-
Ba
se
d
L
a
n
e
M
a
r
k
in
g
De
tec
ti
o
n
u
sin
g
A
2
-
L
M
De
t,
”
T
ra
n
sp
o
rta
ti
o
n
Res
e
a
rc
h
Rec
o
r
d
:
J
.
o
f
th
e
T
r
a
n
sp
o
rta
t
io
n
Res
e
a
rc
h
Bo
a
r
d
,
2
0
2
0
,
d
o
i:
1
0
.
1
1
7
7
/0
3
6
1
1
9
8
1
2
0
9
4
8
5
0
8
.
[2
7
]
K.
Ch
o
i,
J.
K.
S
u
h
r,
a
n
d
H.
G
.
Ju
n
g
,
“
In
-
L
a
n
e
L
o
c
a
li
z
a
ti
o
n
a
n
d
Eg
o
-
L
a
n
e
Id
e
n
ti
f
ica
ti
o
n
M
e
th
o
d
Ba
se
d
o
n
Hig
h
w
a
y
L
a
n
e
En
d
p
o
in
ts,”
J
o
u
rn
a
l
o
f
A
d
v
a
n
c
e
d
T
r
a
n
s
p
o
rt
a
ti
o
n
.
,
v
o
l.
2
0
2
0
,
2
0
2
0
,
d
o
i:
1
0
.
1
1
0
9
/T
IT
S
.
2
0
1
2
.
2
2
2
8
1
9
1
.
[2
8
]
O.
K
h
a
li
f
a
,
M
.
Isla
m
,
A
.
A
s
sid
i,
A
.
H.
A
b
d
u
ll
a
h
,
a
n
d
S
.
K
h
a
n
,
“
V
isio
n
Ba
se
d
Ro
a
d
L
a
n
e
De
tec
ti
o
n
S
y
ste
m
f
o
r
V
e
h
icle
s G
u
id
a
n
c
e
,
”
Au
st
.
J
.
B
a
si
c
Ap
p
l
.
S
c
i.
,
v
o
l.
5
,
p
p
.
7
2
8
–
7
3
8
,
M
a
y
2
0
1
1
.
[2
9
]
M
.
S
u
s
h
it
h
a
n
d
S
.
S
u
d
h
ir,
“
Ex
tra
c
ti
o
n
o
f
ro
a
d
u
si
n
g
so
f
t
c
o
m
p
u
ti
n
g
tec
h
n
iq
u
e
s,”
S
o
ft
C
o
mp
u
t.
,
v
o
l.
2
3
,
A
p
r.
2
0
1
9
,
d
o
i:
1
0
.
1
1
5
5
/2
0
2
0
/
8
6
8
4
9
1
2
.
[3
0
]
A.
S
a
b
ir,
K.
Kh
u
rsh
id
,
a
n
d
A
.
S
a
lm
a
n
,
“
S
e
g
m
e
n
tatio
n
-
b
a
se
d
im
a
g
e
d
e
f
o
g
g
in
g
u
sin
g
m
o
d
if
ied
d
a
rk
c
h
a
n
n
e
l
p
ri
o
r,
”
EURA
S
IP
J
.
Ima
g
e
Vi
d
e
o
Pro
c
e
ss
.
,
v
o
l.
2
0
2
0
,
n
o
.
1
,
F
e
b
.
2
0
2
0
,
A
rt
.
n
o
.
6
,
d
o
i:
1
0
.
1
1
8
6
/s1
3
6
4
0
-
0
2
0
-
0
4
9
3
-
9.
[3
1
]
M
.
S
h
a
f
iq
u
e
,
M
.
F
a
h
im
,
a
n
d
P
.
P
y
d
ip
o
g
u
,
“
Ro
b
u
st
lan
e
d
e
tec
ti
o
n
a
n
d
o
b
jec
t
trac
k
in
g
,
”
M
a
ste
r’s
Th
e
sis,
Blek
in
g
e
In
stit
u
te
o
f
T
e
c
h
n
o
lo
g
y
,
S
we
d
e
n
,
2
0
1
3
.
[3
2
]
P
.
Be
re
sn
e
v
,
A
.
T
u
m
a
so
v
,
D.
Ty
u
g
in
,
D.
Zez
iu
li
n
,
V.
F
il
a
to
v
,
a
n
d
D.
P
o
r
u
b
o
v
,
“
A
u
to
m
a
ted
Driv
in
g
S
y
ste
m
b
a
se
d
o
n
Ro
a
d
w
a
y
a
n
d
T
ra
ff
ic
Co
n
d
i
ti
o
n
s
M
o
n
it
o
rin
g
,
”
in
Pro
c
e
e
.
o
f
th
e
4
th
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Veh
icle
T
e
c
h
n
o
l
o
g
y
a
n
d
I
n
telli
g
e
n
t
T
r
a
n
s
p
o
rt
S
y
ste
ms
,
2
0
1
8
,
p
p
.
3
6
3
–
3
7
0
,
d
o
i:
1
0
.
5
2
2
0
/0
0
0
6
7
0
0
3
0
3
6
3
0
3
7
0
.
[3
3
]
Y.
X
in
g
e
t
a
l.
,
“
A
d
v
a
n
c
e
s
in
V
isio
n
-
Ba
se
d
L
a
n
e
De
te
c
ti
o
n
:
A
lg
o
rit
h
m
s,
In
teg
ra
ti
o
n
,
A
s
se
ss
m
e
n
t,
a
n
d
P
e
rsp
e
c
ti
v
e
s
o
n
A
CP
-
Ba
se
d
P
a
ra
ll
e
l
V
isi
o
n
,
”
IEE
ECA
A
J
.
Au
to
m.
S
in
.
,
v
o
l.
5
,
n
o
.
3
,
p
p
.
6
4
5
–
6
6
1
,
M
a
y
2
0
1
8
,
d
o
i:
1
0
.
1
1
0
9
/JA
S
.
2
0
1
8
.
7
5
1
1
0
6
3
.
[3
4
]
P
.
P
riy
a
d
h
a
rsh
in
i,
P
.
Nik
e
th
a
,
K.
S
a
a
n
th
a
L
a
k
sh
m
i,
S
.
S
h
a
r
m
il
a
,
a
n
d
R.
Div
y
a
,
“
A
d
v
a
n
c
e
s
in
V
isi
o
n
b
a
se
d
L
a
n
e
De
tec
ti
o
n
A
lg
o
rit
h
m
Ba
s
e
d
o
n
Re
li
a
b
le
L
a
n
e
M
a
rk
in
g
s,”
in
2
0
1
9
5
th
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
A
d
v
a
n
c
e
d
Co
mp
u
t
in
g
&
Co
mm
u
n
ica
ti
o
n
S
y
ste
ms
(
ICACCS
)
,
2
0
1
9
,
p
p
.
8
8
0
–
8
8
5
.
[3
5
]
C.
H.
Ku
m
,
D.
C.
Ch
o
,
M
.
S
.
Ra
,
a
n
d
W
.
Y.
Ki
m
,
“
L
a
n
e
d
e
t
e
c
ti
o
n
sy
ste
m
w
it
h
a
ro
u
n
d
v
iew
m
o
n
it
o
ri
n
g
f
o
r
in
telli
g
e
n
t
v
e
h
icle
,
”
in
2
0
1
3
I
n
ter
n
a
ti
o
n
a
l
S
o
C
De
sig
n
Co
n
f
e
re
n
c
e
(
IS
OCC)
,
No
v
.
2
0
1
3
,
p
p
.
2
1
5
–
2
1
8
,
d
o
i:
1
0
.
1
1
0
9
/IS
OCC.
2
0
1
3
.
6
8
6
4
0
1
1
.
[3
6
]
S
.
He
c
k
e
r,
D.
D
a
i,
a
n
d
L
.
V
a
n
G
o
o
l,
“
En
d
-
to
-
e
n
d
lea
rn
in
g
o
f
d
riv
in
g
m
o
d
e
ls
w
it
h
su
rro
u
n
d
-
v
iew
c
a
m
e
ra
s
a
n
d
ro
u
te
p
lan
n
e
rs,” i
n
Pr
o
c
e
e
d
in
g
s
o
f
t
h
e
E
u
ro
p
e
a
n
c
o
n
fer
e
n
c
e
o
n
c
o
mp
u
te
r v
isio
n
(
e
c
c
v
)
,
2
0
1
8
,
p
p
.
4
3
5
–
4
5
3
.
[3
7
]
J.
Ca
o
,
C.
S
o
n
g
,
S
.
S
o
n
g
,
F
.
X
ia
o
,
a
n
d
S
.
P
e
n
g
,
“
L
a
n
e
d
e
tec
ti
o
n
a
lg
o
rit
h
m
f
o
r
in
telli
g
e
n
t
v
e
h
icle
s
in
c
o
m
p
lex
ro
a
d
c
o
n
d
i
ti
o
n
s a
n
d
d
y
n
a
m
i
c
e
n
v
iro
n
m
e
n
ts,”
S
e
n
so
rs
,
v
o
l.
1
9
,
n
o
.
1
4
,
2
0
1
9
,
A
rt
.
n
o
.
3
1
6
6
.
[3
8
]
S.
Ju
n
g
,
J.
Yo
u
n
,
a
n
d
S.
S
u
ll
,
“
Eff
icie
n
t
Lan
e
De
tec
ti
o
n
Ba
se
d
o
n
S
p
a
ti
o
tem
p
o
ra
l
Im
a
g
e
s,”
IEE
E
T
ra
n
s.
I
n
tell.
T
ra
n
sp
.
S
y
st.
,
v
o
l.
1
7
,
n
o
.
1
,
p
p
.
2
8
9
–
2
9
5
,
2
0
1
5
.
[3
9
]
A
.
G
e
rn
,
R.
M
o
e
b
u
s,
a
n
d
U.
F
ra
n
k
e
,
“
V
isio
n
-
b
a
se
d
la
n
e
re
c
o
g
n
it
i
o
n
u
n
d
e
r
a
d
v
e
rse
we
a
th
e
r
c
o
n
d
it
io
n
s
u
si
n
g
o
p
ti
c
a
l
f
lo
w
,
”
IEE
E
In
tell
.
Veh
.
S
y
mp
.
,
v
o
l.
2
,
p
p
.
6
5
2
–
6
5
7
,
2
0
0
2
.
[4
0
]
N.
Y.
Ers
h
a
d
i,
J.
M
.
M
e
n
é
n
d
e
z
,
a
n
d
D.
Jim
é
n
e
z
,
“
Ro
b
u
st
v
e
h
icle
d
e
tec
ti
o
n
i
n
d
if
fe
re
n
t
w
e
a
th
e
r
c
o
n
d
it
i
o
n
s:
Us
in
g
M
I
P
M
,
”
P
L
OS
ONE
,
v
o
l.
1
3
,
n
o
.
3
,
M
a
r.
2
0
1
8
,
A
rt
.
n
o
.
e
0
1
9
1
3
5
5
,
d
o
i:
1
0
.
1
3
7
1
/j
o
u
r
n
a
l.
p
o
n
e
.
0
1
9
1
3
5
5
.
[4
1
]
M
.
Ho
ra
n
i
a
n
d
O.
Ra
w
a
sh
d
e
h
,
“
A
F
ra
m
e
w
o
rk
f
o
r
V
isio
n
-
Ba
se
d
L
a
n
e
L
in
e
De
tec
ti
o
n
in
A
d
v
e
rse
W
e
a
th
e
r
Co
n
d
it
io
n
s
Us
in
g
V
e
h
icle
-
to
-
In
f
ra
stru
c
tu
re
(
V
2
I)
Co
m
m
u
n
ica
ti
o
n
,
”
S
A
E
In
tern
a
t
io
n
a
l,
W
a
rre
n
d
a
le,
P
A
,
S
A
E
T
e
c
h
n
ica
l
P
a
p
e
r
2
0
1
9
-
01
–
0
6
8
4
,
A
p
r.
2
0
1
9
.
d
o
i
:
1
0
.
4
2
7
1
/
2
0
1
9
-
01
-
0
6
8
4
.
[4
2
]
J.
A
h
n
,
J
.
Ki
m
,
a
n
d
Y.
L
e
e
,
“
S
h
a
rp
n
e
ss
-
a
w
a
re
re
a
l
-
ti
m
e
h
a
z
e
re
m
o
v
a
l
f
o
r
a
d
v
a
n
c
e
d
d
riv
e
r
a
ss
ist
a
n
c
e
s
y
ste
m
s,”
p
re
se
n
ted
a
t
th
e
2
0
1
6
I
n
ter
n
a
ti
o
n
a
l
S
o
C
De
sig
n
Co
n
fer
e
n
c
e
(
IS
OCC)
,
2
0
1
6
,
p
p
.
4
7
–
48
.
[4
3
]
J.
M
.
Co
ll
a
d
o
,
C.
Hilario
,
A
.
d
e
l
a
Esc
a
ler
a
,
a
n
d
J.
M
.
A
r
m
in
g
o
l,
“
De
tec
ti
o
n
a
n
d
c
las
sif
ic
a
ti
o
n
o
f
ro
a
d
lan
e
s
w
it
h
a
f
re
q
u
e
n
c
y
a
n
a
l
y
sis,”
IEE
E
Pro
c
.
I
n
tell.
Ve
h
.
S
y
mp
.
2
0
0
5
,
2
0
0
5
,
d
o
i:
1
0
.
1
1
0
9
/
IVS.
2
0
0
5
.
1
5
0
5
0
8
1
.
[4
4
]
S.
M
a
n
o
h
a
ra
n
,
“
A
n
i
m
p
ro
v
e
d
sa
f
e
t
y
a
l
g
o
rit
h
m
f
o
r
a
rti
f
icia
l
in
telli
g
e
n
c
e
e
n
a
b
led
p
ro
c
e
ss
o
rs
in
se
l
f
d
riv
in
g
c
a
rs,
”
J
o
u
rn
a
l
o
f
Arti
fi
c
i
a
l
I
n
telli
g
e
n
c
e
,
v
o
l.
1
,
n
o
.
2
,
p
p
.
9
5
–
1
0
4
,
2
0
1
9
,
d
o
i:
1
0
.
3
6
5
4
8
/j
a
icn
.
2
0
1
9
.
2
.
0
0
5
.
[4
5
]
R.
S
c
h
u
b
e
rt,
K.
S
c
h
u
lze
,
a
n
d
G
.
W
a
n
ielik
,
“
S
it
u
a
ti
o
n
A
ss
e
ss
m
e
n
t
f
o
r
A
u
to
m
a
ti
c
L
a
n
e
-
Ch
a
n
g
e
M
a
n
e
u
v
e
rs,”
IEE
E
T
ra
n
s.
I
n
tell.
T
ra
n
sp
.
S
y
st.
,
v
o
l.
1
1
,
n
o
.
3
,
p
p
.
6
0
7
–
6
1
6
,
S
e
p
.
2
0
1
0
,
d
o
i:
1
0
.
1
1
0
9
/T
IT
S
.
2
0
1
0
.
2
0
4
9
3
5
3
.
[4
6
]
R.
K.
S
a
tz
o
d
a
,
S
.
S
u
c
h
it
ra
,
a
n
d
T
.
S
rik
a
n
th
a
n
,
“
Ro
b
u
st
e
x
trac
ti
o
n
o
f
l
a
n
e
m
a
rk
in
g
s
u
sin
g
g
ra
d
ien
t
a
n
g
le
h
isto
g
ra
m
s
a
n
d
d
irec
ti
o
n
a
l
sig
n
e
d
e
d
g
e
s,”
p
re
se
n
ted
a
t
t
h
e
In
telli
g
e
n
t
Veh
icle
s
S
y
mp
o
si
u
m
,
A
lca
lá
d
e
He
n
a
re
s
,
S
p
a
in
,
Ju
n
.
2
0
1
2
,
p
p
.
7
5
4
–
7
5
9
.
[
O
n
li
n
e
].
Av
a
il
a
b
le:
h
tt
p
s://
w
ww
.
s
e
m
a
n
ti
c
sc
h
o
lar.
o
rg
/p
a
p
e
r/Ro
b
u
st
-
e
x
tr
a
c
ti
o
n
-
of
-
l
a
n
e
-
m
a
rk
in
g
s
-
u
sin
g
-
g
ra
d
ien
t
-
S
a
tzo
d
a
-
S
a
th
y
a
n
a
ra
y
a
n
a
/2
a
1
b
a
d
0
d
f
a
4
a
5
3
c
7
0
4
8
1
7
8
2
2
9
3
f
8
c
1
e
f
e
a
4
8
b
9
a
a
.
[4
7
]
M
.
B.
d
e
P
a
u
la
a
n
d
C.
R
.
Ju
n
g
,
“
A
u
to
m
a
ti
c
De
te
c
ti
o
n
a
n
d
Clas
si
f
ica
ti
o
n
o
f
Ro
a
d
L
a
n
e
M
a
rk
in
g
s
Us
in
g
On
b
o
a
rd
V
e
h
icu
lar Cam
e
r
a
s,”
IEE
E
T
ra
n
s.
In
tell.
T
ra
n
sp
.
S
y
st.
, v
o
l
.
1
6
,
n
o
.
6
,
p
p
.
3
1
6
0
–
3
1
6
9
,
2
0
1
5
.
[4
8
]
B.
M
a
th
i
b
e
la,
P
.
Ne
wm
a
n
,
a
n
d
I.
P
o
s
n
e
r,
“
Re
a
d
in
g
th
e
R
o
a
d
:
R
o
a
d
M
a
rk
in
g
Clas
sif
ica
ti
o
n
a
n
d
In
terp
re
tatio
n
,
”
IEE
E
T
ra
n
s.
I
n
tell.
T
ra
n
sp
.
S
y
st.
,
v
o
l.
1
6
,
n
o
.
4
,
p
p
.
2
0
7
2
–
2
0
8
1
,
A
u
g
.
2
0
1
5
,
d
o
i:
1
0
.
1
1
0
9
/T
IT
S
.
2
0
1
5
.
2
3
9
3
7
1
5
.
[4
9
]
O.
Ba
il
o
,
S
.
L
e
e
,
F
.
Ra
m
e
a
u
,
J.
S
.
Yo
o
n
,
a
n
d
I.
S
.
Kw
e
o
n
,
“
Ro
b
u
st
Ro
a
d
M
a
rk
in
g
De
tec
ti
o
n
a
n
d
Re
c
o
g
n
it
io
n
Us
in
g
De
n
sity
-
B
a
se
d
G
ro
u
p
i
n
g
a
n
d
M
a
c
h
in
e
L
e
a
rn
in
g
T
e
c
h
n
iq
u
e
s,”
in
2
0
1
7
IEE
E
W
i
n
ter
Co
n
fer
e
n
c
e
o
n
Ap
p
li
c
a
ti
o
n
s
o
f
Co
mp
u
ter
V
isio
n
(
W
ACV
)
,
M
a
r.
2
0
1
7
,
p
p
.
7
6
0
–
7
6
8
,
d
o
i:
1
0
.
1
1
0
9
/
WA
CV
.
2
0
1
7
.
9
0
.
[5
0
]
M.
L
i,
Y.
L
i,
a
n
d
M.
Jia
n
g
,
“
L
a
n
e
De
tec
ti
o
n
Ba
se
d
o
n
C
o
n
n
e
c
ti
o
n
o
f
V
a
rio
u
s
F
e
a
tu
re
Ex
trac
ti
o
n
M
e
th
o
d
s,”
Ad
v
.
M
u
lt
ime
d
.
,
v
o
l
.
2
0
1
8
,
2
0
1
8
.
[
On
l
i
n
e
]
.
A
v
a
il
a
b
le:
h
tt
p
s://
w
ww
.
h
in
d
a
w
i.
c
o
m
/j
o
u
rn
a
ls/am
/2
0
1
8
/
8
3
2
0
2
0
7
/
.
Evaluation Warning : The document was created with Spire.PDF for Python.