I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
25
,
p
p
.
3238
~
3
2
4
5
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
15
i
3
.
pp
3
2
3
8
-
3
2
4
5
3238
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
M
o
nkey
det
ec
tion usin
g
deep
learni
ng
f
o
r mo
nkey
-
re
pellent
Nur
L
a
t
if
Azy
ze
M
o
hd
Sh
a
a
ri
Azy
ze
1
,
T
eo
w
K
him
i Q
ua
n
1
,
I
da
Sy
a
f
iza
M
d Is
a
2
,
M
uh
a
m
m
a
d Af
if
H
us
m
a
n
3
1
D
e
p
a
r
t
me
n
t
o
f
M
e
c
h
a
t
r
o
n
i
c
s E
n
g
i
n
e
e
r
i
n
g
,
F
a
k
u
l
t
i
Te
k
n
o
l
o
g
i
D
a
n
K
e
j
u
r
u
t
e
r
a
a
n
E
l
e
k
t
r
i
k
,
U
n
i
v
e
r
si
t
i
Te
k
n
i
k
a
l
M
a
l
a
y
s
i
a
M
e
l
a
k
a
,
M
e
l
a
k
a
,
M
a
l
a
y
s
i
a
2
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
o
n
i
c
a
n
d
C
o
m
p
u
t
e
r
,
F
a
k
u
l
t
i
Te
k
n
o
l
o
g
i
D
a
n
K
e
j
u
r
u
t
e
r
a
a
n
E
l
e
k
t
r
o
n
i
k
D
a
n
K
o
mp
u
t
e
r
,
U
n
i
v
e
r
si
t
i
Te
k
n
i
k
a
l
M
a
l
a
y
s
i
a
M
e
l
a
k
a
,
M
e
l
a
k
a
,
M
a
l
a
y
s
i
a
3
D
e
p
a
r
t
me
n
t
o
f
M
e
c
h
a
t
r
o
n
i
c
s,
K
u
l
l
i
y
y
a
h
o
f
E
n
g
i
n
e
e
r
i
n
g
,
I
n
t
e
r
n
a
t
i
o
n
a
l
I
sl
a
mi
c
U
n
i
v
e
r
s
i
t
y
M
a
l
a
y
si
a
,
K
u
a
l
a
L
u
m
p
u
r
,
M
a
l
a
y
si
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
7
,
2
0
2
4
R
ev
is
ed
No
v
2
8
,
2
0
2
4
Acc
ep
ted
J
an
1
4
,
2
0
2
5
An
ima
l
in
tru
si
o
n
h
a
s
c
a
u
se
d
m
a
n
y
issu
e
s
fo
r
h
u
m
a
n
b
e
i
n
g
s,
e
sp
e
c
ially
m
o
n
k
e
y
s.
M
o
n
k
e
y
s
h
a
v
e
c
a
u
se
d
m
a
n
y
p
ro
b
lem
s
su
c
h
a
s
y
ield
c
ro
p
d
a
m
a
g
e
,
d
a
m
a
g
e
to
h
u
m
a
n
fa
c
il
it
ies
a
n
d
a
ss
e
ts
a
n
d
ste
a
li
n
g
f
o
o
d
.
T
h
is
st
u
d
y
a
ims
to
in
v
e
stig
a
te
t
h
e
u
se
o
f
d
e
e
p
lea
rn
in
g
t
o
d
e
tec
t
m
o
n
k
e
y
p
re
se
n
c
e
a
c
c
u
ra
tely
a
n
d
u
se
a
p
r
o
p
e
r
re
p
e
ll
e
n
t
sy
st
e
m
to
sc
a
re
th
e
m
a
wa
y
.
A
d
e
e
p
lea
rn
i
n
g
a
lg
o
rit
h
m
is
c
o
n
stru
c
ted
wit
h
su
p
e
rv
ise
d
lea
rn
i
n
g
,
wh
ich
i
n
c
lu
d
e
s
th
e
m
o
n
k
e
y
d
a
tas
e
t
with
a
p
p
ro
p
riat
e
lab
e
ll
in
g
o
f
th
e
o
b
jec
t
o
f
in
t
e
re
st.
Th
e
d
e
tec
ti
o
n
o
f
th
e
m
o
n
k
e
y
c
o
m
e
s
with
a
b
o
u
n
d
in
g
b
o
x
wit
h
r
e
sp
e
c
ti
v
e
c
o
n
fid
e
n
c
e
o
f
d
e
tec
ti
o
n
.
T
h
e
re
su
lt
is
th
e
n
u
se
d
to
e
v
a
lu
a
te
th
e
a
c
c
u
ra
c
y
o
f
m
o
n
k
e
y
d
e
tec
ti
o
n
.
T
h
e
a
c
c
u
ra
c
y
o
f
t
h
e
train
e
d
m
o
d
e
l
is
a
ss
e
ss
e
d
b
y
e
v
a
lu
a
ti
n
g
i
ts
p
e
rfo
rm
a
n
c
e
u
n
d
e
r
v
a
ry
in
g
c
o
n
d
it
io
n
s
o
f
c
a
m
e
ra
q
u
a
li
ty
a
n
d
d
istan
c
e
s.
Th
e
stu
d
y
f
o
c
u
se
s
o
n
p
ro
v
in
g
th
e
re
li
a
b
i
li
ty
o
f
d
e
e
p
lea
rn
in
g
t
o
d
e
tec
t
m
o
n
k
e
y
s
a
n
d
a
u
t
o
m
a
ti
c
a
ll
y
p
e
rf
o
rm
c
o
rre
sp
o
n
d
in
g
a
c
t
io
n
s
l
ik
e
re
p
e
ll
in
g
m
o
n
k
e
y
s.
He
n
c
e
th
is
m
a
y
re
d
u
c
e
t
h
e
re
li
a
n
c
e
o
f
m
a
n
p
o
we
r
t
o
se
c
u
re
a
larg
e
sp
a
c
e
a
n
d
imp
ro
v
e
sa
fe
ty
issu
e
s.
K
ey
w
o
r
d
s
:
C
o
n
v
o
lu
tio
n
n
eu
r
al
n
etwo
r
k
Dee
p
lear
n
in
g
R
ep
ellen
t
Su
p
er
v
is
ed
lear
n
in
g
YOL
O
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
:
Nu
r
L
atif
Azy
ze
Mo
h
d
Sh
aa
r
i
Azy
ze
Fak
u
lti T
ek
n
o
lo
g
i D
an
Keju
r
u
ter
aa
n
E
lek
tr
ik
,
U
n
iv
er
s
iti T
ek
n
ik
al
Ma
lay
s
ia
Me
lak
a
Han
g
T
u
ah
J
ay
a,
7
6
1
0
0
Du
r
ia
n
T
u
n
g
g
al,
Me
lak
a,
Ma
lay
s
ia
E
m
ail: la
tifa
zy
ze
@
u
tem
.
ed
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
Hu
m
an
-
m
o
n
k
ey
c
o
n
f
lict
h
as
b
ee
n
a
s
er
io
u
s
is
s
u
e
o
v
er
th
e
y
ea
r
s
f
o
r
ex
am
p
le
d
am
ag
e
to
y
ield
cr
o
p
s
,
in
tr
u
s
io
n
in
to
h
u
m
an
f
ac
ilit
ies
an
d
s
tealin
g
h
u
m
an
f
o
o
d
.
T
h
e
wild
life
d
ep
ar
tm
en
t
h
as
co
n
tin
u
ed
to
cu
ll
lo
n
g
-
tailed
m
ac
aq
u
es,
with
u
p
to
7
0
,
0
0
0
an
im
als
b
ein
g
k
illed
a
n
n
u
ally
b
etwe
en
2
0
1
3
a
n
d
2
0
1
6
d
u
e
to
th
e
a
v
er
ag
e
o
f
3
,
8
0
0
co
m
p
lain
ts
f
r
o
m
th
e
p
u
b
lic
m
ad
e
n
atio
n
ally
ea
ch
y
ea
r
.
Ho
wev
er
,
ac
co
r
d
i
n
g
to
[
1
]
th
e
cu
llin
g
o
f
th
ese
m
o
n
k
ey
s
h
as
d
ec
r
ea
s
ed
in
r
ec
en
t
y
ea
r
s
,
with
o
f
f
icials
n
o
w
f
o
cu
s
in
g
m
o
r
e
o
n
tr
an
s
f
er
r
in
g
r
ath
er
th
a
n
k
illi
n
g
th
em
.
M
o
v
i
n
g
t
h
e
m
o
n
k
e
y
wi
l
l
n
o
t
s
o
l
v
e
t
h
e
p
r
o
b
l
e
m
i
n
t
h
e
l
o
n
g
t
er
m
.
B
u
t
i
t
c
a
n
n
o
t
b
e
s
u
r
e
t
h
a
t
o
t
h
e
r
g
r
o
u
p
s
o
f
m
o
n
k
e
y
s
w
i
ll
n
o
t
c
o
m
e
a
g
a
in
i
n
t
h
e
f
u
t
u
r
e
.
T
h
is
i
s
w
h
e
r
e
an
i
m
a
l
c
l
ass
i
f
i
ca
t
i
o
n
c
o
m
es
i
n
p
a
r
t
i
c
u
l
a
r
l
y
h
a
n
d
y
t
o
s
o
l
v
e
h
u
m
a
n
-
m
o
n
k
e
y
c
o
n
f
l
i
cts
i
n
t
h
e
l
o
n
g
t
e
r
m
[
2
]
.
I
n
t
h
es
e
ci
r
c
u
m
s
ta
n
c
e
s
,
a
n
i
m
a
g
e
cla
s
s
i
f
i
e
r
/i
d
e
n
t
i
f
i
e
r
is
u
s
e
f
u
l
s
i
n
c
e
it
a
u
t
o
m
a
te
s
t
h
e
p
r
o
c
e
s
s
o
f
cl
a
s
s
i
f
y
i
n
g
a
n
d
i
d
en
t
i
f
y
i
n
g
t
h
e
m
o
n
k
e
y
[
3
]
.
U
t
il
iz
i
n
g
d
e
e
p
l
e
a
r
n
i
n
g
i
n
t
e
g
r
a
t
e
d
w
it
h
a
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
(
C
N
N
)
a
p
p
r
o
a
c
h
f
a
c
i
l
i
ta
t
es
t
h
e
s
t
r
a
i
g
h
t
f
o
r
w
a
r
d
d
e
t
e
c
t
i
o
n
o
f
m
o
n
k
e
y
s
v
i
a
c
a
m
e
r
a
[
4
]
.
T
h
is
m
e
t
h
o
d
i
s
al
s
o
p
r
es
e
n
t
e
d
i
n
o
th
e
r
a
p
p
l
i
c
a
t
i
o
n
s
w
h
i
c
h
r
es
u
l
ted
i
n
t
h
e
o
b
j
e
c
t
b
ei
n
g
d
e
t
e
c
t
e
d
s
u
c
c
es
s
f
u
ll
y
[
5
]
–
[
7
]
.
T
h
i
s
s
t
u
d
y
r
e
q
u
i
r
es
a
c
o
m
p
u
t
er
t
h
a
t
ca
n
r
u
n
P
y
t
h
o
n
c
o
d
e
f
o
r
t
r
a
i
n
i
n
g
t
h
e
m
o
d
e
l
a
l
g
o
r
i
t
h
m
,
t
h
e
n
i
t
c
a
n
i
n
t
e
r
a
c
t
w
i
t
h
a
2
4
/
7
c
a
m
e
r
a
f
o
r
m
o
n
i
to
r
i
n
g
t
h
e
p
r
e
s
e
n
c
e
o
f
a
m
o
n
k
e
y
a
n
d
a
s
p
e
a
k
e
r
t
o
p
r
o
d
u
c
e
a
p
p
r
o
p
r
i
a
t
e
r
e
p
e
l
l
e
n
t
s
o
u
n
d
t
o
s
c
a
r
e
t
h
e
m
o
n
k
e
y
a
w
ay
[
8
]
,
[
9
]
.
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
-
8
7
0
8
Mo
n
ke
y
d
etec
tio
n
u
s
in
g
d
ee
p
lea
r
n
in
g
fo
r
mo
n
ke
y
-
r
ep
ellen
t
(
N
u
r
La
tif A
z
yze
Mo
h
d
S
h
a
a
r
i A
z
yze
)
3239
An
im
al
in
tr
u
s
io
n
h
as
b
ee
n
a
p
r
o
b
lem
f
o
r
h
u
m
a
n
b
ein
g
s
f
o
r
a
lo
n
g
tim
e.
Pre
v
io
u
s
r
esear
ch
er
s
h
as
b
ee
n
tak
in
g
p
ar
t
i
n
s
af
eg
u
a
r
d
in
g
th
e
ass
ets
an
d
life
o
f
h
u
m
an
b
ei
n
g
s
wh
ich
in
cl
u
d
e
t
h
e
r
esid
en
tial
ar
ea
,
wo
r
k
in
g
p
lace
,
in
s
titu
tio
n
,
a
n
d
ag
r
icu
ltu
r
al
c
r
o
p
a
r
ea
[
1
0
]
–
[
1
3
]
.
T
h
e
m
o
s
t
co
m
m
o
n
a
n
im
al
r
elate
d
to
th
is
k
in
d
o
f
in
tr
u
s
io
n
is
th
e
m
o
n
k
ey
.
C
o
n
v
en
tio
n
al
an
im
al
d
etec
ti
o
n
with
p
ass
iv
e
in
f
r
ar
ed
(
PIR
)
s
en
s
o
r
s
lack
s
ac
cu
r
ac
y
an
d
v
is
u
al
ev
id
en
ce
,
p
o
ten
tially
co
m
p
r
o
m
is
in
g
r
eliab
ilit
y
[
1
4
]
.
T
h
ese
s
en
s
o
r
s
d
etec
t
p
r
esen
ce
b
u
t
n
o
t
lo
ca
tio
n
p
r
ec
is
ely
,
s
en
s
itiv
e
to
f
ac
to
r
s
lik
e
o
b
je
ct
s
ize
an
d
d
is
tan
ce
.
T
h
is
ca
n
af
f
ec
t
tr
an
s
p
a
r
en
cy
a
n
d
r
el
iab
ilit
y
,
cr
u
cial
f
o
r
r
ep
ellin
g
m
o
n
k
e
y
s
wh
ile
ex
clu
d
in
g
o
th
er
m
o
v
in
g
o
b
ject
s
.
Ad
d
itio
n
ally
,
th
e
m
ac
h
i
n
e
lear
n
in
g
m
o
d
el'
s
ef
f
ec
tiv
en
ess
in
id
en
tify
in
g
m
o
n
k
ey
s
m
a
y
b
e
h
i
n
d
er
ed
b
y
i
n
ad
eq
u
ate
tr
ain
in
g
d
ata,
lea
d
in
g
to
b
iased
o
r
p
o
o
r
p
er
f
o
r
m
an
ce
[
1
5
]
–
[
1
9
]
.
T
h
e
m
o
n
k
e
y
d
etec
tio
n
m
eth
o
d
r
ef
er
s
to
th
e
v
ar
io
u
s
tech
n
iq
u
es
an
d
ap
p
r
o
ac
h
es
u
s
ed
to
id
en
tif
y
a
n
d
l
o
ca
te
m
o
n
k
e
y
s
in
ea
ch
en
v
ir
o
n
m
en
t
[
2
0
]
,
[
2
1
]
.
T
h
e
lin
e
m
o
d
el
ap
p
r
o
ac
h
co
n
s
is
ts
o
f
s
ev
er
al
p
r
o
ce
s
s
es
to
d
etec
t
m
o
n
k
ey
s
,
th
e
p
r
o
ce
s
s
is
b
ac
k
g
r
o
u
n
d
ex
tr
ac
tio
n
,
s
tar
s
k
eleto
n
izatio
n
,
an
d
lin
e
m
o
d
el
m
atch
in
g
p
r
o
ce
s
s
[
2
2
]
,
[
2
3
]
.
A
s
tr
aig
h
tf
o
r
war
d
b
ac
k
g
r
o
u
n
d
s
u
b
tr
ac
tio
n
tech
n
iq
u
e
is
u
s
ed
to
r
em
o
v
e
th
e
p
r
im
ar
y
c
o
n
s
titu
en
t
p
o
r
tio
n
f
r
o
m
th
e
b
ac
k
g
r
o
u
n
d
.
Su
p
er
v
is
ed
lear
n
in
g
alg
o
r
ith
m
s
ar
e
tr
a
in
ed
with
lab
elled
ex
am
p
les,
s
u
ch
as
a
n
in
p
u
t
w
ith
a
k
n
o
wn
d
esire
d
o
u
t
p
u
t
[
2
4
]
.
W
h
en
p
r
esen
ted
with
n
ew
d
ata,
th
e
m
o
d
el
is
tr
ain
ed
to
r
ec
o
g
n
ize
th
e
u
n
d
e
r
ly
in
g
p
atter
n
s
an
d
c
o
r
r
elatio
n
s
b
etwe
en
th
e
in
p
u
t
d
ata
a
n
d
th
e
o
u
tp
u
t
lab
els,
allo
win
g
it to
p
r
o
d
u
ce
ac
cu
r
at
e
lab
ellin
g
r
esu
lts
.
Ho
wev
er
,
th
e
ac
cu
r
ac
y
o
f
m
o
n
k
e
y
d
etec
ti
o
n
h
as n
o
t y
et
b
ee
n
s
tu
d
ied
ac
r
o
s
s
v
id
eo
q
u
alities
an
d
at
v
ar
y
in
g
d
is
tan
ce
s
.
T
h
is
is
b
ec
au
s
e
th
e
ac
cu
r
ac
y
o
f
th
e
m
o
d
el
is
s
u
b
ject
to
th
e
in
p
u
t
im
ag
e
q
u
ality
[
2
5
]
.
T
h
e
m
ain
o
b
jectiv
e
o
f
th
is
p
r
o
ject
is
to
d
ev
elo
p
a
n
im
ag
e
c
lass
if
icatio
n
m
eth
o
d
u
s
in
g
YOL
Ov
7
alg
o
r
ith
m
tailo
r
ed
f
o
r
ac
cu
r
ately
d
etec
tin
g
m
o
n
k
e
y
s
.
T
h
e
ac
cu
r
ac
y
o
f
th
i
s
d
etec
tio
n
will
b
e
ass
es
s
ed
ac
r
o
s
s
v
ar
io
u
s
lev
el
s
o
f
im
ag
e
q
u
ality
an
d
d
is
tan
ce
s
,
wh
ich
ar
e
cr
itical
f
ac
t
o
r
s
in
f
lu
en
cin
g
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
m
o
d
el
in
p
r
ac
tical
s
ce
n
ar
io
s
.
2.
M
E
T
H
O
D
An
in
teg
r
ated
p
i
p
elin
e
f
o
r
m
o
n
k
ey
d
etec
tio
n
u
s
in
g
d
ee
p
lea
r
n
in
g
is
illu
s
tr
ated
in
Fig
u
r
e
1
.
First,
th
e
co
llectio
n
o
f
th
e
d
ataset
is
o
b
tain
ed
b
y
ca
p
tu
r
in
g
a
n
im
ag
e
a
t
d
if
f
er
en
t
ar
ea
s
with
d
if
f
er
en
t
an
g
les.
Nex
t,
ea
c
h
im
ag
e
is
lab
elled
to
cr
ea
te
a
c
o
m
p
r
eh
e
n
s
iv
e
m
o
n
k
ey
clu
s
ter
f
o
r
tr
ain
in
g
p
u
r
p
o
s
es.
T
h
e
d
at
a
was tr
ain
ed
b
y
th
e
YOL
Ov
7
alg
o
r
ith
m
to
e
d
u
ca
t
e
th
e
m
ac
h
i
n
e
lear
n
in
g
o
n
r
ec
o
g
n
izin
g
m
o
n
k
ey
s
.
Fo
llo
win
g
th
e
tr
ain
in
g
p
h
ase,
th
e
ac
cu
r
ac
y
an
d
p
e
r
f
o
r
m
an
c
e
o
f
t
h
e
tr
ain
e
d
m
o
d
el
ar
e
ev
alu
ated
.
I
f
th
e
r
esu
lts
d
o
n
o
t
m
ee
t
ex
p
ec
tatio
n
s
,
h
y
p
er
p
ar
am
eter
tu
n
in
g
is
co
n
d
u
cted
,
ad
ju
s
tin
g
p
ar
am
eter
s
s
u
ch
as
lear
n
in
g
r
ate,
o
p
tim
izer
weig
h
t
d
ec
ay
,
an
d
m
o
m
en
tu
m
am
o
n
g
o
th
er
s
.
I
n
th
is
s
tu
d
y
,
3
6
3
m
o
n
k
e
y
im
a
g
es
ar
e
u
s
ed
with
7
0
,
2
0
an
d
1
0
%
f
o
r
tr
ain
in
g
,
v
alid
atio
n
an
d
test
r
esp
ec
tiv
ely
.
Fo
llo
ws
ar
e
d
etails
ex
p
lan
atio
n
s
o
f
d
ataset
co
llectio
n
,
d
ataset
lab
ellin
g
an
d
tr
ain
in
g
m
o
d
el.
2
.
1
.
Da
t
a
s
et
co
llect
i
o
n
I
n
th
e
co
n
tex
t
o
f
m
o
n
k
ey
v
id
eo
an
aly
s
is
,
th
e
p
r
o
ce
s
s
in
v
o
l
v
es
r
ec
o
r
d
in
g
a
m
o
n
k
ey
v
id
e
o
u
s
in
g
a
s
m
ar
tp
h
o
n
e
o
r
ca
m
er
a
an
d
t
h
en
co
n
v
er
tin
g
it
in
to
a
s
et
o
f
s
ep
ar
ate
p
h
o
t
o
s
.
T
h
e
d
ata
s
et
co
llectio
n
an
d
p
r
ep
ar
atio
n
p
r
o
ce
s
s
as
f
o
llo
w
s
:
i
)
b
eg
in
b
y
r
ec
o
r
d
in
g
a
v
i
d
eo
o
f
m
o
n
k
e
y
s
u
s
in
g
a
s
m
a
r
tp
h
o
n
e
o
r
ca
m
er
a,
en
s
u
r
in
g
t
h
e
v
i
d
eo
q
u
ality
is
s
u
f
f
icien
t
to
ca
p
tu
r
e
th
eir
b
eh
a
v
io
r
a
n
d
m
o
v
em
e
n
t
ef
f
ec
tiv
ely
wh
ile
co
n
s
id
er
in
g
f
ac
to
r
s
s
u
ch
as
lig
h
tin
g
co
n
d
itio
n
s
,
ca
m
er
a
s
tab
ilit
y
,
an
d
s
u
b
ject
clar
ity
;
ii
)
af
ter
r
ec
o
r
d
in
g
th
e
v
id
eo
,
p
r
o
ce
ed
to
co
n
v
er
t
it
in
to
a
s
eq
u
en
ce
o
f
in
d
iv
id
u
al
im
ag
es
u
s
in
g
an
MP4
to
J
P
G
co
n
v
er
ter
av
ailab
le
o
n
lin
e.
T
h
is
to
o
l
ex
tr
ac
ts
ea
ch
f
r
a
m
e
f
r
o
m
t
h
e
v
id
eo
an
d
s
av
es
it
as
a
s
ep
a
r
ate
J
PEG
im
ag
e
f
ile.
I
t
i
s
im
p
o
r
tan
t
t
o
s
elec
t
a
d
ep
en
d
a
b
le
co
n
v
e
r
ter
th
at
s
u
p
p
o
r
ts
y
o
u
r
s
p
ec
if
ic
v
id
eo
f
o
r
m
at
an
d
o
f
f
er
s
ess
en
tial
cu
s
t
o
m
izatio
n
f
ea
tu
r
es
;
iii
)
af
ter
co
n
v
er
tin
g
th
e
v
id
e
o
in
to
an
im
ag
e
s
eq
u
en
ce
,
th
er
e
will
b
e
a
s
er
ies
o
f
f
r
am
es
r
e
p
r
esen
tin
g
d
if
f
er
en
t
m
o
m
en
ts
f
r
o
m
th
e
r
ec
o
r
d
ed
v
id
eo
.
No
t
all
f
r
am
es
m
ay
b
e
s
u
itab
le
f
o
r
t
h
e
d
ataset,
esp
ec
ially
th
o
s
e
lack
in
g
s
ig
n
if
ican
t
m
o
v
e
m
en
t.
I
t
i
s
im
p
o
r
tan
t
t
o
m
a
n
u
ally
r
ev
iew
an
d
s
elec
t
f
r
am
es
th
at
s
h
o
w
n
o
ticea
b
le
c
h
an
g
es
f
r
o
m
o
n
e
f
r
am
e
to
an
o
th
er
.
T
h
is
en
s
u
r
es
th
at
th
e
d
ataset
in
clu
d
es
d
i
v
er
s
e
in
s
tan
ce
s
o
f
m
o
n
k
ey
b
eh
a
v
io
r
,
f
ac
ilit
atin
g
ef
f
ec
tiv
e
lear
n
in
g
b
y
th
e
m
o
d
el
;
an
d
iv
)
e
v
alu
ate
th
e
q
u
ality
o
f
th
e
s
elec
ted
f
r
a
m
es to
co
n
f
ir
m
th
e
y
m
ee
t
th
e
d
ataset
cr
iter
ia.
Fa
cto
r
s
to
co
n
s
id
er
in
clu
d
e
clar
ity
,
f
o
cu
s
,
lig
h
tin
g
,
a
n
d
o
v
e
r
all
im
ag
e
q
u
ality
.
E
x
clu
d
e
f
r
am
es
th
at
ar
e
b
lu
r
r
y
,
d
is
to
r
ted
,
o
r
in
a
d
eq
u
ately
l
it
to
m
ain
tain
a
d
ataset
co
m
p
r
is
in
g
h
ig
h
-
q
u
ality
im
ag
es.
T
h
is
p
r
o
ce
s
s
aim
s
to
r
ed
u
ce
n
o
is
e
a
n
d
u
n
wan
ted
v
ar
iatio
n
s
th
at
c
o
u
ld
h
in
d
e
r
t
h
e
m
o
d
el'
s
tr
ain
in
g
ef
f
ec
tiv
en
ess
.
2
.
2
.
Da
t
a
s
et
la
belin
g
Data
s
et
lab
ellin
g
is
d
o
n
e
u
s
in
g
th
e
ap
p
licatio
n
“
la
b
elI
m
g
”,
La
b
elI
mg
is
a
p
o
p
u
lar
g
r
a
p
h
i
ca
l
im
ag
e
an
n
o
tatio
n
t
o
o
l
u
s
ed
f
o
r
lab
elin
g
o
b
jects
in
im
ag
es.
I
t
p
r
o
v
id
es
a
u
s
er
-
f
r
ien
d
ly
in
te
r
f
ac
e
f
o
r
m
an
u
ally
an
n
o
tatin
g
o
b
jects o
f
in
ter
est.
“
Op
en
Dir
”
is
wh
er
e
to
p
lace
o
u
r
d
ataset
wh
ich
is
r
ea
d
y
f
o
r
lab
el,
an
d
“
C
h
a
n
g
e
S
a
ve
Dir
”
is
th
e
d
ir
ec
to
r
y
wh
e
r
e
we
s
av
e
th
e
an
n
o
tatio
n
f
ile.
“
C
r
ea
te
R
ec
tBo
x
”
is
u
s
ed
to
c
r
ea
te
a
r
ec
tan
g
u
lar
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
25
:
3
2
3
8
-
3
2
4
5
3240
b
o
u
n
d
in
g
b
o
x
,
o
n
ce
we
d
r
aw
th
e
b
o
u
n
d
in
g
b
o
x
,
a
p
o
p
-
o
u
t
win
d
o
w
will
p
r
o
m
p
t
u
s
to
in
p
u
t
th
e
lab
el
f
o
r
th
e
im
ag
e,
in
th
is
ca
s
e
“
mo
n
ke
y
”.
Fig
u
r
e
1
.
I
n
teg
r
ated
p
ip
elin
e
f
o
r
m
o
n
k
ey
d
etec
tio
n
u
s
in
g
d
ee
p
lear
n
in
g
2
.
3
.
T
ra
ini
ng
m
o
del
Utilizin
g
Go
o
g
le
C
o
lab
f
o
r
tr
ain
in
g
a
YOL
O
m
o
d
el
o
f
f
er
s
a
co
n
v
en
ien
t
an
d
ef
f
icien
t
ap
p
r
o
ac
h
to
h
ar
n
ess
th
e
ca
p
ab
ilit
ies
o
f
clo
u
d
c
o
m
p
u
ti
n
g
f
o
r
d
ee
p
lear
n
in
g
task
s
.
Go
o
g
le
C
o
lab
s
ea
m
le
s
s
ly
in
teg
r
ates
with
Go
o
g
le
Dr
iv
e,
en
ab
lin
g
d
ir
ec
t
ac
ce
s
s
to
f
iles
s
to
r
ed
in
y
o
u
r
Go
o
g
le
Dr
iv
e
ac
co
u
n
t
f
r
o
m
with
in
th
e
C
o
lab
n
o
teb
o
o
k
en
v
ir
o
n
m
en
t.
T
o
i
n
itiate
th
e
tr
ain
in
g
p
r
o
ce
s
s
,
b
eg
in
b
y
clo
n
in
g
t
h
e
YO
L
O
v
7
r
ep
o
s
ito
r
ies.
Fo
llo
win
g
th
is
,
d
o
wn
lo
ad
th
e
p
r
e
-
tr
ai
n
ed
m
o
d
el
p
r
o
v
id
ed
b
y
an
o
p
en
-
s
o
u
r
ce
p
latf
o
r
m
o
n
GitHu
b
.
Fin
ally
,
ex
ec
u
te
th
e
co
m
m
an
d
d
is
p
lay
ed
b
el
o
w
to
co
m
m
en
ce
th
e
tr
a
in
in
g
p
r
o
ce
d
u
r
e.
I
t
is
im
p
o
r
ta
n
t
to
en
s
u
r
e
th
at
t
h
e
d
ataset
f
ile
is
r
ea
d
ily
av
ailab
le
with
in
y
o
u
r
Go
o
g
le
Dr
iv
e
ac
c
o
u
n
t.
! python train.py
--
device 0
--
workers 4
--
batch
-
size 4
--
epochs 100
--
img 640
640
--
data data/custom_data.yaml
--
hyp data/hyp.scratch.custom.yaml
--
cfg
cfg/training/yolov7
-
custom.yaml
--
weight yolov7.pt
--
name yolov7
-
custom
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
ch
ap
ter
ex
am
in
es
two
c
ases
to
ev
alu
ate
d
etec
tio
n
ac
cu
r
ac
y
.
First,
it
an
aly
ze
s
th
e
im
p
ac
t
o
f
ca
m
er
a
r
eso
lu
tio
n
b
y
co
m
p
a
r
i
n
g
th
r
ee
d
if
f
er
en
t
r
eso
lu
tio
n
s
:
2
.
1
,
0
.
9
,
an
d
0
.
2
MP.
Seco
n
d
,
it
ev
alu
ates
th
e
s
y
s
tem
'
s
ac
cu
r
ac
y
at
v
ar
y
in
g
d
is
tan
ce
s
b
etwe
en
th
e
o
b
ject
an
d
th
e
ca
m
er
a
b
etwe
en
4
0
,
8
0
,
an
d
1
2
0
cm
.
3
.
1
.
CASE
1
:
t
he
ef
f
ec
t
o
f
ca
m
er
a
re
s
o
lutio
n o
n det
ec
t
io
n
a
cc
ura
cy
Fig
u
r
e
2
s
h
o
ws
th
e
r
esu
lts
o
n
th
e
m
o
n
k
ey
d
etec
tio
n
ac
cu
r
a
cy
with
3
d
if
f
er
e
n
t
ca
m
er
a
p
i
x
els
wh
ich
ar
e
Fig
u
r
es 2
(
a)
2
.
1
MP,
2
(
b
)
0
.
9
MP
an
d
2
(
c
)
0
.
2
MP.
T
h
e
s
am
e
im
ag
e
is
u
s
ed
a
n
d
th
e
ca
m
er
a
d
is
tan
ce
is
s
et
at
4
0
cm
f
r
o
m
th
e
im
ag
e.
B
ased
o
n
t
h
e
r
esu
lt
as
s
h
o
w
n
in
Fig
u
r
e
2
,
h
ig
h
er
ca
m
er
a
r
eso
lu
tio
n
d
o
es
n
o
t
n
ec
ess
ar
ily
g
u
ar
an
tee
im
p
r
o
v
ed
d
etec
tio
n
ac
c
u
r
ac
y
co
m
p
a
r
ed
to
a
lo
wer
r
eso
lu
tio
n
ca
m
er
a.
W
h
ile
it
m
ay
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
-
8
7
0
8
Mo
n
ke
y
d
etec
tio
n
u
s
in
g
d
ee
p
lea
r
n
in
g
fo
r
mo
n
ke
y
-
r
ep
ellen
t
(
N
u
r
La
tif A
z
yze
Mo
h
d
S
h
a
a
r
i A
z
yze
)
3241
s
ee
m
lo
g
ical
th
at
m
o
r
e
p
ix
els wo
u
ld
p
r
o
v
id
e
clea
r
er
a
n
d
m
o
r
e
d
etailed
im
ag
es f
o
r
t
h
e
m
o
d
el
to
an
aly
ze
,
o
th
er
f
ac
to
r
s
co
m
e
in
t
o
p
lay
.
On
e
k
ey
f
ac
to
r
is
th
e
q
u
ality
a
n
d
d
i
v
er
s
ity
o
f
th
e
tr
ai
n
in
g
d
ata
u
s
ed
to
tr
ain
th
e
m
o
d
el.
T
h
e
m
o
d
el
n
ee
d
s
to
b
e
e
x
p
o
s
e
d
to
a
wid
e
r
an
g
e
o
f
s
ce
n
ar
io
s
an
d
v
ar
iatio
n
s
to
g
en
er
alize
well
an
d
ac
cu
r
ately
d
etec
t
o
b
jects.
An
o
th
er
co
n
s
id
er
atio
n
is
th
e
co
m
p
u
tatio
n
al
co
m
p
lex
ity
ass
o
ciate
d
with
h
ig
h
er
-
r
eso
lu
tio
n
im
ag
es.
Hig
h
er
-
r
eso
lu
tio
n
i
m
ag
es
r
eq
u
ir
e
m
o
r
e
p
r
o
ce
s
s
in
g
p
o
wer
an
d
m
em
o
r
y
,
wh
ich
ca
n
im
p
ac
t
th
e
ef
f
icien
cy
a
n
d
s
p
ee
d
o
f
t
h
e
d
e
tectio
n
p
r
o
ce
s
s
.
T
h
is
ca
n
b
ec
o
m
e
a
c
h
allen
g
e,
esp
ec
ially
wh
en
r
ea
l
-
tim
e
o
r
n
ea
r
r
ea
l
-
tim
e
d
etec
tio
n
is
r
eq
u
ir
e
d
.
No
is
e
is
an
o
th
er
f
ac
to
r
t
o
co
n
s
id
er
.
Hig
h
e
r
-
r
eso
lu
tio
n
im
ag
es
m
ay
also
ca
p
tu
r
e
m
o
r
e
n
o
is
e
o
r
u
n
wan
ted
ar
tef
ac
ts
,
wh
ich
ca
n
in
ter
f
er
e
with
th
e
d
etec
tio
n
p
r
o
ce
s
s
.
No
is
e
r
ed
u
ctio
n
tech
n
iq
u
es
ca
n
h
elp
m
itig
ate
th
is
is
s
u
e,
b
u
t
it
ad
d
s
an
ex
tr
a
lay
er
o
f
co
m
p
lex
ity
to
th
e
p
ip
elin
e.
L
astl
y
,
it
i
s
im
p
o
r
tan
t
to
a
d
d
r
ess
th
e
is
s
u
e
o
f
g
e
n
er
aliza
tio
n
.
A
m
o
d
el
tr
ain
ed
o
n
l
o
w
-
r
eso
lu
tio
n
im
ag
es
m
ay
s
tr
u
g
g
le
to
ac
cu
r
ately
d
etec
t
o
b
jects
in
h
ig
h
er
-
r
eso
lu
tio
n
im
a
g
es.
T
h
is
lack
o
f
g
en
e
r
aliza
tio
n
ca
n
r
esu
lt
in
r
ed
u
ce
d
d
etec
tio
n
ac
cu
r
ac
y
wh
en
u
s
in
g
h
ig
h
er
-
r
eso
lu
tio
n
ca
m
er
as
to
ca
p
tu
r
e
v
i
d
eo
,
b
u
t
th
e
m
o
d
el
is
f
ed
b
y
a
lo
wer
r
eso
lu
tio
n
o
r
q
u
ality
d
ataset
f
o
r
tr
ain
in
g
.
(
a)
(
b
)
(
c)
Fig
u
r
e
2
.
Mo
n
k
ey
d
etec
tio
n
ac
cu
r
ac
y
with
d
i
f
f
er
en
t c
a
m
er
a
p
ix
el
q
u
ality
(
a)
2
.
1
MP,
(
b
)
0
.
9
MP
an
d
(
c)
0
.
2
MP
I
n
co
n
clu
s
io
n
,
wh
ile
h
ig
h
e
r
c
am
er
a
r
eso
lu
tio
n
ca
n
o
f
f
e
r
b
e
n
ef
its
in
ce
r
tain
s
ce
n
ar
io
s
,
it
is
n
o
t
th
e
s
o
le
d
eter
m
in
an
t
o
f
d
etec
tio
n
ac
cu
r
ac
y
.
T
h
e
q
u
ality
an
d
d
iv
er
s
ity
o
f
tr
ain
in
g
d
ata,
co
m
p
u
t
atio
n
al
co
m
p
lex
ity
,
n
o
is
e,
an
d
g
e
n
er
aliza
tio
n
is
s
u
e
s
all
in
f
lu
en
ce
th
e
o
v
er
all
ac
cu
r
ac
y
o
f
o
b
ject
d
etec
tio
n
s
y
s
tem
s
.
Op
tim
izin
g
th
e
en
tire
d
etec
tio
n
p
ip
elin
e
,
co
n
s
id
er
in
g
th
ese
v
ar
io
u
s
f
ac
to
r
s
is
cr
u
cial
f
o
r
ac
h
iev
in
g
ac
cu
r
ate
an
d
r
eliab
le
r
esu
lts
.
3
.
2
.
CASE
2
:
t
he
ef
f
ec
t
o
f
ca
m
er
a
dis
t
a
nce
o
n det
ec
t
io
n a
cc
ura
cy
T
h
e
ex
p
e
r
im
en
t'
s
f
in
d
in
g
s
r
ev
ea
l
an
in
ter
esti
n
g
tr
e
n
d
:
as
th
e
ca
m
er
a
d
is
tan
ce
in
c
r
ea
s
es,
we
o
b
s
er
v
e
a
n
o
ticea
b
le
d
ec
r
ea
s
e
in
th
e
ac
c
u
r
ac
y
o
f
o
b
ject
d
etec
tio
n
as sh
o
wn
in
T
ab
le
1
.
T
h
is
m
ea
n
s
th
at
wh
en
th
e
ca
m
er
a
is
p
lace
d
f
u
r
th
er
awa
y
f
r
o
m
th
e
tar
g
et
o
b
ject,
th
e
a
b
ilit
y
o
f
t
h
e
d
etec
tio
n
alg
o
r
ith
m
to
ac
cu
r
ately
id
en
tify
a
n
d
class
if
y
o
b
jects
d
im
in
is
h
es.
On
e
p
o
s
s
ib
le
ex
p
lan
atio
n
f
o
r
th
i
s
p
h
en
o
m
e
n
o
n
is
th
e
d
im
i
n
is
h
in
g
v
is
ib
ilit
y
o
f
th
e
tar
g
et
o
b
ject.
As
th
e
ca
m
er
a
m
o
v
es
f
u
r
th
er
awa
y
f
r
o
m
th
e
o
b
ject,
it
ap
p
ea
r
s
s
m
aller
in
th
e
ca
p
tu
r
ed
im
ag
e,
o
cc
u
p
y
i
n
g
f
ewe
r
p
ix
els.
T
h
is
r
ed
u
ctio
n
in
o
b
ject
v
is
ib
ilit
y
p
o
s
es
a
ch
allen
g
e
f
o
r
th
e
d
etec
tio
n
alg
o
r
ith
m
,
m
ak
in
g
it m
o
r
e
d
if
f
icu
lt to
p
r
e
cisely
d
etec
t a
n
d
lo
ca
te
th
e
o
b
ject
with
in
th
e
f
r
am
e.
Fu
r
th
er
m
o
r
e
,
th
e
lo
s
s
o
f
f
in
e
d
etails
co
n
tr
ib
u
tes
to
t
h
e
d
ec
lin
e
in
d
etec
tio
n
ac
cu
r
ac
y
.
W
ith
a
n
in
cr
ea
s
ed
ca
m
er
a
d
is
tan
ce
,
th
e
im
ag
e
m
ay
lack
th
e
in
tr
icate
t
ex
tu
r
es,
p
atter
n
s
,
an
d
s
m
aller
f
ea
tu
r
es
th
at
ass
is
t
in
ac
cu
r
ate
o
b
ject
d
etec
tio
n
.
T
h
ese
f
in
er
d
etails
b
ec
o
m
e
less
d
is
tin
g
u
is
h
ab
le,
lead
in
g
to
m
i
s
class
if
icatio
n
s
,
o
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
25
:
3
2
3
8
-
3
2
4
5
3242
ev
en
m
is
s
ed
d
etec
tio
n
s
b
y
th
e
alg
o
r
ith
m
.
Ad
d
itio
n
ally
,
t
h
e
in
cr
ea
s
e
in
ca
m
er
a
d
is
tan
ce
i
n
tr
o
d
u
ce
s
p
o
ten
tial
is
s
u
es
s
u
ch
as
n
o
is
e,
b
lu
r
r
in
g
,
an
d
d
is
to
r
tio
n
.
Facto
r
s
lik
e
atm
o
s
p
h
er
ic
co
n
d
itio
n
s
,
lim
itatio
n
s
o
f
th
e
ca
m
er
a
len
s
,
o
r
im
ag
e
c
o
m
p
r
ess
io
n
c
an
co
n
tr
ib
u
te
to
th
ese
p
r
o
b
le
m
s
.
T
h
e
p
r
esen
ce
o
f
n
o
is
e
an
d
d
is
to
r
tio
n
h
a
m
p
er
s
th
e
d
etec
tio
n
alg
o
r
ith
m
'
s
ab
ilit
y
to
c
o
r
r
ec
tly
id
en
tif
y
a
n
d
class
if
y
o
b
jects,
f
u
r
th
er
d
im
i
n
is
h
in
g
th
e
o
v
er
all
ac
cu
r
ac
y
.
I
n
ess
en
ce
,
t
h
ese
r
esu
lts
h
ig
h
lig
h
t
th
e
cr
itical
im
p
o
r
tan
ce
o
f
f
in
d
i
n
g
th
e
o
p
tim
al
c
am
er
a
d
is
tan
ce
f
o
r
ac
h
iev
in
g
th
e
h
ig
h
est
ac
cu
r
ac
y
in
o
b
ject
d
etec
tio
n
.
I
t
b
ec
o
m
es
cr
u
cial
to
s
tr
ik
e
a
b
alan
ce
wh
er
e
th
e
tar
g
et
o
b
ject
is
v
is
ib
le
en
o
u
g
h
,
th
e
ess
en
tial
d
etails
ar
e
p
r
eser
v
ed
,
an
d
n
o
is
e
lev
els
ar
e
m
in
im
i
ze
d
.
Un
d
e
r
s
tan
d
in
g
th
is
tr
ad
e
-
o
f
f
is
v
ital
wh
en
c
o
n
s
id
er
in
g
th
e
p
lace
m
en
t
o
f
ca
m
er
as
in
r
ea
l
-
wo
r
ld
ap
p
lic
atio
n
s
th
at
r
ely
o
n
ac
cu
r
ate
o
b
ject
d
etec
tio
n
.
T
ab
le
1
.
Per
ce
n
ta
g
e
o
f
d
etec
tio
n
with
d
if
f
er
en
t c
am
e
r
a
d
is
tan
ce
s
f
r
o
m
th
e
d
etec
tio
n
tar
g
et
C
a
mer
a
d
i
s
t
a
n
c
e
f
r
o
m
t
h
e
d
e
t
e
c
t
i
o
n
t
a
r
g
e
t
O
b
serv
a
t
i
o
n
o
n
d
e
t
e
c
t
i
o
n
P
e
r
c
e
n
t
a
g
e
o
f
d
e
t
e
c
t
i
o
n
40
cm
80
cm
1
2
0
cm
I
n
s
u
m
m
a
r
y
,
o
u
r
e
x
p
er
im
e
n
t
estab
lis
h
es
a
d
ef
in
itiv
e
c
o
r
r
el
atio
n
b
etwe
en
ca
m
er
a
p
r
o
x
im
ity
an
d
th
e
p
r
ec
is
io
n
o
f
o
b
ject
d
etec
tio
n
.
T
h
ese
r
esu
lts
u
n
d
er
s
co
r
e
t
h
e
im
p
o
r
ta
n
ce
o
f
ca
r
ef
u
l
ca
m
er
a
p
o
s
itio
n
in
g
to
ac
h
iev
e
m
ax
im
al
p
er
f
o
r
m
an
ce
.
B
y
ac
k
n
o
wled
g
in
g
th
e
im
p
a
ct
o
f
ca
m
er
a
d
is
tan
ce
o
n
d
ete
ctio
n
ac
cu
r
ac
y
,
we
ca
n
m
ak
e
m
o
r
e
in
f
o
r
m
e
d
d
ec
i
s
io
n
s
in
p
r
ac
tical
s
ce
n
ar
io
s
wh
er
e
p
r
ec
is
e
o
b
ject
d
etec
tio
n
is
cr
u
cial.
4.
CO
NCLU
SI
O
N
I
n
co
n
clu
s
io
n
,
th
e
tr
ain
ed
m
o
d
el
h
as
d
em
o
n
s
tr
ated
its
ca
p
ab
ilit
y
to
d
etec
t
m
o
n
k
ey
s
ef
f
ec
tiv
ely
.
A
cr
u
cial
f
ac
to
r
co
n
tr
i
b
u
tin
g
to
th
e
s
u
cc
ess
o
f
d
ee
p
lear
n
in
g
a
p
p
r
o
ac
h
es
in
m
o
n
k
e
y
d
etec
tio
n
is
th
e
av
ailab
ilit
y
o
f
lar
g
e,
an
n
o
tated
d
atasets
co
n
tain
in
g
d
iv
er
s
e
m
o
n
k
e
y
im
ag
es
an
d
v
id
eo
s
.
T
h
ese
d
atasets
h
av
e
p
lay
ed
a
p
i
v
o
tal
r
o
le
in
im
p
r
o
v
in
g
th
e
m
o
d
el'
s
p
er
f
o
r
m
a
n
c
e
an
d
g
e
n
er
aliza
tio
n
ab
ilit
ies.
Desp
ite
u
tili
zin
g
a
r
elativ
ely
s
m
all
d
ataset
o
f
o
n
ly
3
6
3
im
ag
es,
th
e
t
r
ain
ed
m
o
d
el
ex
h
ib
ited
co
m
m
en
d
a
b
le
p
er
f
o
r
m
an
ce
i
n
d
etec
tin
g
m
o
n
k
ey
s
.
Ho
wev
er
,
it
is
im
p
o
r
ta
n
t
to
ac
k
n
o
wled
g
e
th
e
lim
itatio
n
s
o
f
wo
r
k
in
g
with
a
s
m
all
d
at
aset.
Su
ch
lim
itatio
n
s
ca
n
h
in
d
e
r
th
e
m
o
d
el'
s
ab
ilit
y
to
g
en
er
alize
ef
f
ec
tiv
ely
to
u
n
s
ee
n
d
ata.
C
o
n
s
eq
u
en
tly
,
t
h
is
m
ay
lead
to
s
u
b
o
p
tim
al
p
er
f
o
r
m
a
n
ce
o
n
test
o
r
v
alid
atio
n
d
at
asets
,
as
well
as
in
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
s
.
W
h
en
tr
ain
in
g
a
m
ac
h
in
e
lear
n
in
g
m
o
d
el,
it
is
ess
en
tial
to
e
m
p
lo
y
a
d
iv
er
s
e
a
n
d
r
e
p
r
es
en
tativ
e
d
ataset
th
at
en
co
m
p
ass
es
th
e
f
u
ll
s
p
ec
tr
u
m
o
f
p
o
s
s
ib
le
in
p
u
ts
an
d
o
u
t
p
u
ts
th
e
m
o
d
el
is
ex
p
ec
te
d
to
en
co
u
n
ter
.
A
s
m
all
d
ataset
m
ay
lac
k
s
u
f
f
icien
t
e
x
am
p
les
o
f
th
ese
v
ar
ied
co
m
b
in
atio
n
s
,
th
u
s
r
esu
ltin
g
in
a
m
o
d
el
th
at
is
ill
-
eq
u
ip
p
e
d
f
o
r
th
e
task
at
h
an
d
.
C
o
n
s
eq
u
en
tly
,
th
e
tr
ai
n
ed
m
o
d
el
m
ay
o
cc
asio
n
ally
e
x
h
ib
it
f
alse
p
o
s
itiv
e
d
etec
tio
n
s
an
d
s
tr
u
g
g
le
to
d
ete
ct
u
n
s
ee
n
d
ata
ef
f
ec
tiv
ely
.
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
-
8
7
0
8
Mo
n
ke
y
d
etec
tio
n
u
s
in
g
d
ee
p
lea
r
n
in
g
fo
r
mo
n
ke
y
-
r
ep
ellen
t
(
N
u
r
La
tif A
z
yze
Mo
h
d
S
h
a
a
r
i A
z
yze
)
3243
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
is
s
u
p
p
o
r
ted
b
y
Un
iv
er
s
iti
T
ek
n
ik
al
Ma
lay
s
ia
Me
lak
a
an
d
Fak
u
lti
T
ek
n
o
lo
g
i
d
a
n
Keju
r
u
ter
aa
n
E
lek
tr
ik
.
Als
o
i
n
p
ar
t
o
f
Fu
n
d
am
en
tal
R
esear
ch
Gr
a
n
t
Sch
em
e
(
FR
GS)
u
n
d
er
g
r
an
t
n
u
m
b
er
,
FR
GS
/1
/2
0
2
3
/I
C
T
0
8
/UT
E
M/
0
3
/0
1
f
r
o
m
Mi
n
is
tr
y
o
f
Hig
h
er
E
d
u
ca
tio
n
Ma
la
y
s
ia
(
MO
HE
)
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Nu
r
L
atif
Azy
ze
Mo
h
d
Sh
aa
r
i A
zy
ze
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
T
eo
w
Kh
im
i Q
u
an
✓
✓
✓
✓
✓
✓
✓
✓
I
d
a
Sy
af
iza
Md
I
s
a
✓
✓
✓
✓
✓
✓
Mu
h
am
m
ad
Af
i
f
Hu
s
m
an
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
All a
u
th
o
r
s
d
ec
lar
e
th
at
th
ey
h
av
e
n
o
c
o
n
f
lict o
f
in
ter
est.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
t
th
e
f
in
d
in
g
s
o
f
th
is
s
tu
d
y
ar
e
av
ailab
le
f
r
o
m
th
e
co
r
r
esp
o
n
d
in
g
au
t
h
o
r
,
NL
A,
u
p
o
n
r
ea
s
o
n
ab
le
r
eq
u
est.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
F
.
S
h
a
h
,
“
P
e
r
h
i
l
i
t
a
n
p
r
e
f
e
r
s
t
o
t
r
a
p
a
n
d
r
e
l
o
c
a
t
e
m
o
n
k
e
y
s,
”
T
h
e
S
t
a
r
,
2
0
2
2
.
h
t
t
p
s
:
/
/
w
w
w
.
t
h
e
s
t
a
r
.
c
o
m
.
my
/
m
e
t
r
o
/
m
e
t
r
o
-
n
e
w
s/
2
0
2
2
/
0
8
/
2
5
/
p
e
r
h
i
l
i
t
a
n
-
p
r
e
f
e
r
s
-
to
-
t
r
a
p
-
a
n
d
-
r
e
l
o
c
a
t
e
-
mo
n
k
e
y
s (a
c
c
e
ss
e
d
M
a
y
2
1
,
2
0
2
4
)
.
[
2
]
V
.
Ja
n
a
n
i
a
n
d
C
.
S
h
a
n
t
h
i
,
“
H
u
ma
n
-
a
n
i
ma
l
c
o
n
f
l
i
c
t
a
n
a
l
y
s
i
s
a
n
d
m
a
n
a
g
e
me
n
t
-
a
c
r
i
t
i
c
a
l
s
u
r
v
e
y
,
”
i
n
Pr
o
c
e
e
d
i
n
g
s
o
f
t
h
e
2
0
2
2
1
1
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
S
y
s
t
e
m
Mo
d
e
l
i
n
g
a
n
d
A
d
v
a
n
c
e
m
e
n
t
i
n
Re
s
e
a
rch
T
r
e
n
d
s
,
S
MA
RT
2
0
2
2
,
D
e
c
.
2
0
2
2
,
p
p
.
1
0
0
3
–
1
0
0
7
,
d
o
i
:
1
0
.
1
1
0
9
/
S
M
A
R
T5
5
8
2
9
.
2
0
2
2
.
1
0
0
4
7
4
8
7
.
[
3
]
M
.
V
a
msh
i
,
N
.
M
a
r
u
p
a
k
a
,
S
.
C
.
N
a
l
l
a
mo
t
h
u
,
a
n
d
A
.
N
a
u
r
e
e
n
,
“
A
d
e
e
p
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
-
m
o
n
k
e
y
d
e
t
e
c
t
i
o
n
u
si
n
g
Y
O
LO
v
7
,
”
i
n
2
0
2
3
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
E
v
o
l
u
t
i
o
n
a
ry
A
l
g
o
ri
t
h
m
s
a
n
d
S
o
f
t
C
o
m
p
u
t
i
n
g
T
e
c
h
n
i
q
u
e
s,
EA
S
C
T
2
0
2
3
,
O
c
t
.
2
0
2
3
,
p
p
.
1
–
7
,
d
o
i
:
1
0
.
1
1
0
9
/
EA
S
C
T
5
9
4
7
5
.
2
0
2
3
.
1
0
3
9
3
3
2
6
.
[
4
]
R
.
P
i
l
l
a
i
,
R
.
G
u
p
t
a
,
N
.
S
h
a
r
m
a
,
a
n
d
R
.
K
.
B
a
n
s
a
l
,
“
A
d
e
e
p
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
f
o
r
d
e
t
e
c
t
i
o
n
a
n
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
t
e
n
sp
e
c
i
e
s
o
f
mo
n
k
e
y
s
,
”
i
n
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
S
m
a
r
t
S
y
s
t
e
m
s
f
o
r
Ap
p
l
i
c
a
t
i
o
n
s
i
n
El
e
c
t
ri
c
a
l
S
c
i
e
n
c
e
s,
I
C
S
S
ES
2
0
2
3
,
Ju
l
.
2
0
2
3
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
S
S
ES5
8
2
9
9
.
2
0
2
3
.
1
0
1
9
9
7
6
2
.
[
5
]
I
.
S
.
B
.
M
d
I
sa,
C
.
Ja
Y
e
o
n
g
,
a
n
d
N
.
L.
A
.
b
i
n
M
o
h
d
S
h
a
a
r
i
A
z
y
z
e
,
“
R
e
a
l
-
t
i
me
t
r
a
f
f
i
c
s
i
g
n
d
e
t
e
c
t
i
o
n
a
n
d
r
e
c
o
g
n
i
t
i
o
n
u
s
i
n
g
R
a
s
p
b
e
r
r
y
P
i
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
t
r
i
c
a
l
a
n
d
C
o
m
p
u
t
e
r
En
g
i
n
e
e
ri
n
g
(
I
J
EC
E)
,
v
o
l
.
1
2
,
n
o
.
1
,
p
p
.
3
3
1
–
3
3
8
,
F
e
b
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
e
c
e
.
v
1
2
i
1
.
p
p
3
3
1
-
3
3
8
.
[
6
]
P
.
M
a
l
h
o
t
r
a
a
n
d
E.
G
a
r
g
,
“
O
b
j
e
c
t
d
e
t
e
c
t
i
o
n
t
e
c
h
n
i
q
u
e
s:
a
c
o
m
p
a
r
i
so
n
,
”
i
n
2
0
2
0
7
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
S
m
a
rt
S
t
r
u
c
t
u
re
s
a
n
d
S
y
s
t
e
m
s
,
I
C
S
S
S
2
0
2
0
,
J
u
l
.
2
0
2
0
,
p
p
.
1
–
4
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
S
S
S
4
9
6
2
1
.
2
0
2
0
.
9
2
0
2
2
5
4
.
[
7
]
S
.
S
r
i
su
k
,
C
.
S
u
w
a
n
n
a
p
o
n
g
,
S
.
K
i
t
i
s
r
i
w
o
r
a
p
a
n
,
A
.
K
a
e
w
s
o
n
g
,
a
n
d
S
.
O
n
g
k
i
t
t
i
k
u
l
,
“
P
e
r
f
o
r
m
a
n
c
e
e
v
a
l
u
a
t
i
o
n
o
f
r
e
a
l
-
t
i
me
o
b
j
e
c
t
d
e
t
e
c
t
i
o
n
a
l
g
o
r
i
t
h
ms,
”
i
n
i
E
EC
O
N
2
0
1
9
-
7
t
h
I
n
t
e
rn
a
t
i
o
n
a
l
E
l
e
c
t
ri
c
a
l
En
g
i
n
e
e
ri
n
g
C
o
n
g
ress
,
Pr
o
c
e
e
d
i
n
g
s
,
M
a
r
.
2
0
1
9
,
p
p
.
1
–
4
,
d
o
i
:
1
0
.
1
1
0
9
/
i
EE
C
O
N
4
5
3
0
4
.
2
0
1
9
.
8
9
3
8
6
8
7
.
[
8
]
R
.
S
a
r
a
sw
a
t
h
i
,
G
.
S
h
o
b
a
r
a
n
i
,
A
.
S
u
b
r
a
man
i
,
a
n
d
D
.
Ta
m
i
l
a
r
a
sa
n
,
“
A
p
p
l
y
i
n
g
d
e
e
p
l
e
a
r
n
i
n
g
a
n
d
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
f
o
r
i
mp
r
o
v
i
n
g
a
n
i
m
a
l
i
n
t
r
u
si
o
n
d
e
t
e
c
t
i
o
n
i
n
a
g
r
i
c
u
l
t
u
r
e
f
a
r
ms
,
”
i
n
2
0
2
3
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
D
a
t
a
S
c
i
e
n
c
e
,
A
g
e
n
t
s
a
n
d
Art
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
,
I
C
D
S
AAI
2
0
2
3
,
D
e
c
.
2
0
2
3
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
D
S
A
A
I
5
9
3
1
3
.
2
0
2
3
.
1
0
4
5
2
4
4
2
.
[
9
]
K
.
K
h
a
t
r
i
,
C
.
C
.
A
s
h
a
,
a
n
d
J.
M
.
D
’
S
o
u
z
a
,
“
D
e
t
e
c
t
i
o
n
o
f
a
n
i
ma
l
s
i
n
t
h
e
r
mal
i
ma
g
e
r
y
f
o
r
s
u
r
v
e
i
l
l
a
n
c
e
u
si
n
g
G
A
N
a
n
d
o
b
j
e
c
t
d
e
t
e
c
t
i
o
n
f
r
a
m
e
w
o
r
k
,
”
i
n
2
0
2
2
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
f
o
r
A
d
v
a
n
c
e
m
e
n
t
i
n
T
e
c
h
n
o
l
o
g
y
,
I
C
O
N
AT
2
0
2
2
,
Ja
n
.
2
0
2
2
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
O
N
A
T5
3
4
2
3
.
2
0
2
2
.
9
7
2
5
8
8
3
.
[
1
0
]
N
.
K
.
B
h
a
n
u
a
n
d
K
.
S
a
h
a
n
a
,
“
F
a
r
m
v
i
g
i
l
a
n
c
e
:
s
m
a
r
t
I
o
T
s
y
st
e
m
f
o
r
f
a
r
m
l
a
n
d
m
o
n
i
t
o
r
i
n
g
a
n
d
a
n
i
ma
l
i
n
t
r
u
si
o
n
d
e
t
e
c
t
i
o
n
u
si
n
g
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
i
n
2
0
2
1
Asi
a
n
C
o
n
f
e
r
e
n
c
e
o
n
I
n
n
o
v
a
t
i
o
n
i
n
T
e
c
h
n
o
l
o
g
y
,
A
S
I
AN
C
O
N
2
0
2
1
,
A
u
g
.
2
0
2
1
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
A
S
I
A
N
C
O
N
5
1
3
4
6
.
2
0
2
1
.
9
5
4
4
9
2
6
.
[
1
1
]
R
.
S
u
m
a
t
h
i
,
P
.
R
a
v
e
e
n
a
,
P
.
R
a
k
s
h
a
n
a
,
P
.
N
i
g
i
l
a
,
a
n
d
P
.
M
a
h
a
l
a
k
s
h
mi
,
“
R
e
a
l
-
t
i
m
e
p
r
o
t
e
c
t
i
o
n
o
f
f
a
r
ml
a
n
d
s
f
r
o
m
a
n
i
m
a
l
i
n
t
r
u
s
i
o
n
,
”
i
n
Pr
o
c
e
e
d
i
n
g
s
-
2
0
2
2
I
EE
E
Wo
r
l
d
C
o
n
f
e
r
e
n
c
e
o
n
Ap
p
l
i
e
d
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
C
o
m
p
u
t
i
n
g
,
AI
C
2
0
2
2
,
J
u
n
.
2
0
2
2
,
p
p
.
8
5
9
–
8
6
3
,
d
o
i
:
1
0
.
1
1
0
9
/
A
I
C
5
5
0
3
6
.
2
0
2
2
.
9
8
4
8
8
0
8
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
25
:
3
2
3
8
-
3
2
4
5
3244
[
1
2
]
C
.
A
sh
o
k
k
u
mar
,
M
.
A
.
K
u
mar,
R
.
S
.
K
r
i
s
h
n
a
n
,
S
.
M
.
P
r
i
y
a
,
K
.
L.
N
a
r
a
y
a
n
a
n
,
a
n
d
E.
G
.
J
u
l
i
e
,
“
A
n
o
v
e
l
C
N
N
-
b
a
se
d
I
o
T
sy
st
e
m
a
r
c
h
i
t
e
c
t
u
r
e
f
o
r
r
e
a
l
-
t
i
m
e
d
e
t
e
c
t
i
o
n
a
n
d
p
r
e
v
e
n
t
i
o
n
o
f
a
n
i
mal
i
n
t
r
u
si
o
n
i
n
f
a
r
ml
a
n
d
,
”
i
n
Pro
c
e
e
d
i
n
g
s
o
f
t
h
e
4
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
S
m
a
rt
E
l
e
c
t
r
o
n
i
c
s
a
n
d
C
o
m
m
u
n
i
c
a
t
i
o
n
,
I
C
O
S
EC
2
0
2
3
,
S
e
p
.
2
0
2
3
,
p
p
.
1
3
5
5
–
1
3
6
1
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
O
S
EC
5
8
1
4
7
.
2
0
2
3
.
1
0
2
7
6
3
0
0
.
[
1
3
]
B
.
M
e
e
n
a
k
sh
i
,
S
.
R
.
Y
.
A
o
u
t
h
i
t
h
i
y
e
B
a
r
a
t
h
w
a
j
,
N
.
C
.
H
a
a
r
i
h
a
r
a
n
,
L.
K
r
i
s
h
n
a
k
a
n
t
h
,
a
n
d
J.
A
b
i
sh
e
k
,
“
A
n
i
ma
l
i
n
t
r
u
si
o
n
d
e
t
e
c
t
i
o
n
a
n
d
r
a
n
g
i
n
g
s
y
st
e
m
u
s
i
n
g
Y
O
LO
v
4
a
n
d
Lo
R
a
,
”
i
n
3
r
d
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
Po
w
e
r
,
E
n
e
r
g
y
,
C
o
n
t
ro
l
a
n
d
T
r
a
n
sm
i
ssi
o
n
S
y
s
t
e
m
s,
I
C
PE
C
T
S
2
0
2
2
-
Pr
o
c
e
e
d
i
n
g
s
,
D
e
c
.
2
0
2
2
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
P
EC
TS
5
6
0
8
9
.
2
0
2
2
.
1
0
0
4
7
7
2
9
.
[
1
4
]
M
.
S
h
u
k
r
i
a
n
d
M
.
S
.
Za
i
n
a
l
,
“
S
m
a
r
t
p
e
t
s
mo
n
i
t
o
r
i
n
g
sy
s
t
e
m
u
s
i
n
g
mo
t
i
o
n
s
e
n
s
o
r
b
a
s
e
d
o
n
I
o
T,
”
Pr
o
g
ress
i
n
E
n
g
i
n
e
e
r
i
n
g
Ap
p
l
i
c
a
t
i
o
n
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
2
,
n
o
.
2
,
p
p
.
6
5
8
–
6
6
9
,
2
0
2
1
,
d
o
i
:
1
0
.
3
0
8
8
0
/
p
e
a
t
.
2
0
2
1
.
0
2
.
0
2
.
0
6
3
.
[
1
5
]
E.
C
h
a
n
d
r
a
l
e
k
h
a
,
A
.
M
u
z
a
mm
i
l
A
l
i
,
V
.
R
i
t
e
s
h
,
a
n
d
M
.
K
.
S
r
i
n
i
v
a
sa
n
,
“
A
n
i
mal
i
n
t
r
u
si
o
n
d
e
t
e
c
t
i
o
n
s
y
st
e
m u
s
i
n
g
S
I
F
T
f
e
a
t
u
r
e
s a
n
d
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
w
i
t
h
M
o
b
i
l
e
N
e
t
V
2
,
”
i
n
3
r
d
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
I
n
n
o
v
a
t
i
v
e
Me
c
h
a
n
i
sm
s
f
o
r
I
n
d
u
s
t
ry
A
p
p
l
i
c
a
t
i
o
n
s,
I
C
I
MIA
2
0
2
3
-
Pr
o
c
e
e
d
i
n
g
s
,
D
e
c
.
2
0
2
3
,
p
p
.
1
3
3
9
–
1
3
4
4
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
I
M
I
A
6
0
3
7
7
.
2
0
2
3
.
1
0
4
2
6
5
8
4
.
[
1
6
]
N
.
M
a
ma
t
,
M
.
F
.
O
t
h
m
a
n
,
a
n
d
F
.
Y
a
k
u
b
,
“
A
n
i
ma
l
i
n
t
r
u
s
i
o
n
d
e
t
e
c
t
i
o
n
i
n
f
a
r
mi
n
g
a
r
e
a
u
si
n
g
Y
O
LO
v
5
a
p
p
r
o
a
c
h
,
”
i
n
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
C
o
n
t
r
o
l
,
A
u
t
o
m
a
t
i
o
n
a
n
d
S
y
st
e
m
s
,
2
0
2
2
,
v
o
l
.
2
0
2
2
-
N
o
v
e
m
,
p
p
.
1
–
5
,
d
o
i
:
1
0
.
2
3
9
1
9
/
I
C
C
A
S
5
5
6
6
2
.
2
0
2
2
.
1
0
0
0
3
7
8
0
.
[
1
7
]
E.
C
h
a
n
d
r
a
l
e
k
h
a
,
S
.
R
a
v
i
k
u
m
a
r
,
K
.
V
i
j
a
y
,
a
n
d
P
.
T
h
i
r
u
se
l
v
a
n
,
“
A
n
i
ma
l
i
n
t
r
u
si
o
n
d
e
t
e
c
t
i
o
n
sy
s
t
e
m
:
p
r
o
t
e
c
t
e
d
c
r
o
p
s
a
n
d
p
r
o
m
o
t
e
d
safet
y
u
si
n
g
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
,
”
i
n
2
0
2
3
I
EEE
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
Re
s
e
a
r
c
h
M
e
t
h
o
d
o
l
o
g
i
e
s
i
n
K
n
o
w
l
e
d
g
e
M
a
n
a
g
e
m
e
n
t
,
Art
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
T
e
l
e
c
o
m
m
u
n
i
c
a
t
i
o
n
En
g
i
n
e
e
r
i
n
g
,
R
M
K
MA
T
E
2
0
2
3
,
N
o
v
.
2
0
2
3
,
p
p
.
1
–
5
,
d
o
i
:
1
0
.
1
1
0
9
/
R
M
K
M
A
TE
5
9
2
4
3
.
2
0
2
3
.
1
0
3
6
8
9
4
3
.
[
1
8
]
P
.
E.
A
n
u
v
i
n
d
,
C
.
K
.
A
b
h
i
s
h
e
k
,
M
.
S
h
i
b
i
l
i
,
C
.
K
.
R
a
h
i
l
a
,
a
n
d
K
.
N
e
e
t
h
u
,
“
D
e
v
e
l
o
p
me
n
t
a
n
d
i
m
p
l
e
me
n
t
a
t
i
o
n
o
f
a
n
a
n
i
m
a
l
i
n
t
r
u
s
i
o
n
d
e
t
e
c
t
i
o
n
sy
s
t
e
m
u
si
n
g
i
mag
e
a
n
d
a
u
d
i
o
p
r
o
c
e
ssi
n
g
,
”
i
n
2
0
2
3
1
4
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
C
o
m
p
u
t
i
n
g
C
o
m
m
u
n
i
c
a
t
i
o
n
a
n
d
N
e
t
w
o
r
k
i
n
g
T
e
c
h
n
o
l
o
g
i
e
s,
I
C
C
C
N
T
2
0
2
3
,
J
u
l
.
2
0
2
3
,
p
p
.
1
–
7
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
C
C
N
T
5
6
9
9
8
.
2
0
2
3
.
1
0
3
0
7
5
7
4
.
[
1
9
]
G
.
V
e
n
k
a
t
e
s
h
,
G
.
S
a
i
S
u
m
a
n
,
V
.
S
a
i
N
i
k
h
i
l
e
s
h
,
a
n
d
S
.
K
.
A
h
m
e
d
,
“
P
r
e
v
e
n
t
i
o
n
o
f
a
n
i
m
a
l
a
t
t
a
c
k
s
o
n
f
a
r
ms
w
i
t
h
I
o
T
sy
s
t
e
m,”
i
n
Pro
c
e
e
d
i
n
g
s
-
2
n
d
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
S
m
a
rt
El
e
c
t
ro
n
i
c
s
a
n
d
C
o
m
m
u
n
i
c
a
t
i
o
n
,
I
C
O
S
EC
2
0
2
1
,
O
c
t
.
2
0
2
1
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
O
S
EC
5
1
8
6
5
.
2
0
2
1
.
9
5
9
1
6
9
5
.
[
2
0
]
P
.
R
.
R
e
d
d
y
,
M
.
V
.
K
u
m
a
r
,
K
.
H
.
V
.
K
u
mari
,
T
.
P
r
a
t
h
i
ma
,
a
n
d
S
.
K
a
t
t
a
,
“
P
r
e
v
e
n
t
i
n
g
mo
n
k
e
y
m
e
n
a
c
e
u
si
n
g
Y
O
LO
-
b
a
se
d
o
b
j
e
c
t
d
e
t
e
c
t
i
o
n
m
o
d
e
l
,
”
i
n
2
0
2
3
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
N
e
t
w
o
rk
,
Mu
l
t
i
m
e
d
i
a
a
n
d
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
,
N
MIT
C
O
N
2
0
2
3
,
S
e
p
.
2
0
2
3
,
p
p
.
1
–
7
,
d
o
i
:
1
0
.
1
1
0
9
/
N
M
I
TC
O
N
5
8
1
9
6
.
2
0
2
3
.
1
0
2
7
6
3
0
6
.
[
2
1
]
R
.
R
.
P
i
n
e
d
a
,
T.
K
u
b
o
,
M
.
S
h
i
ma
d
a
,
a
n
d
K
.
I
k
e
d
a
,
“
E
v
a
l
u
a
t
i
o
n
o
f
t
h
e
e
f
f
e
c
t
o
f
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
t
o
m
u
l
t
i
-
i
n
s
t
a
n
c
e
d
e
t
e
c
t
i
o
n
o
f
mo
n
k
e
y
s
,
”
2
0
2
1
As
i
a
-
P
a
c
i
f
i
c
S
i
g
n
a
l
a
n
d
I
n
f
o
rm
a
t
i
o
n
Pr
o
c
e
ss
i
n
g
Asso
c
i
a
t
i
o
n
An
n
u
a
l
S
u
m
m
i
t
a
n
d
C
o
n
f
e
re
n
c
e
,
A
PS
I
PA
AS
C
2
0
2
1
-
Pro
c
e
e
d
i
n
g
s
.
p
p
.
1
3
5
7
–
1
3
6
2
,
2
0
2
1
.
[
2
2
]
S
.
M
.
M
.
R
o
o
mi
,
P
.
R
a
j
e
sh
,
R
.
J.
P
r
i
y
a
,
a
n
d
M
.
S
e
n
t
h
i
l
a
r
a
s
i
,
“
A
l
i
n
e
m
o
d
e
l
b
a
se
d
a
p
p
r
o
a
c
h
f
o
r
m
o
n
k
e
y
i
n
t
r
u
si
o
n
d
e
t
e
c
t
i
o
n
,
”
i
n
2
0
1
0
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
S
i
g
n
a
l
Pr
o
c
e
ssi
n
g
a
n
d
C
o
m
m
u
n
i
c
a
t
i
o
n
s
(
S
P
C
O
M)
,
Ju
l
.
2
0
1
0
,
p
p
.
1
–
5
,
d
o
i
:
1
0
.
1
1
0
9
/
S
P
C
O
M
.
2
0
1
0
.
5
5
6
0
5
0
7
.
[
2
3
]
W
.
A
.
K
u
s
u
m
a
a
n
d
L
.
H
u
s
n
i
a
h
,
“
S
k
e
l
e
t
o
n
i
z
a
t
i
o
n
u
s
i
n
g
t
h
i
n
n
i
n
g
m
e
t
h
o
d
f
o
r
h
u
m
a
n
m
o
t
i
o
n
s
y
s
t
e
m
,
”
i
n
2
0
1
5
I
n
t
e
r
n
a
t
i
o
n
a
l
S
e
m
i
n
a
r
o
n
I
n
t
e
l
l
i
g
e
n
t
T
e
c
h
n
o
l
o
g
y
a
n
d
I
t
s
A
p
p
l
i
c
a
t
i
o
n
s
,
I
S
I
T
I
A
2
0
1
5
-
P
r
o
c
e
e
d
i
n
g
,
M
a
y
2
0
1
5
,
p
p
.
1
0
3
–
1
0
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
S
I
TI
A
.
2
0
1
5
.
7
2
1
9
9
6
2
.
[
2
4
]
M
.
H
a
r
sh
i
n
i
,
M
.
M
a
n
a
sw
i
n
i
,
N
.
S
.
Y
o
,
K
.
M
a
n
i
d
e
e
p
,
G
.
R
o
s
y
,
a
n
d
K
.
S
a
h
i
t
h
i
,
“
A
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
b
a
s
e
d
s
c
a
r
e
c
r
o
w
p
r
e
v
e
n
t
i
o
n
f
r
o
m
c
r
o
p
d
e
s
t
r
u
c
t
i
o
n
,
”
i
n
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
S
u
s
t
a
i
n
a
b
l
e
C
o
m
p
u
t
i
n
g
a
n
d
S
m
a
rt
S
y
st
e
m
s,
I
C
S
C
S
S
2
0
2
3
-
Pro
c
e
e
d
i
n
g
s
,
Ju
n
.
2
0
2
3
,
p
p
.
6
2
8
–
6
3
4
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
S
C
S
S
5
7
6
5
0
.
2
0
2
3
.
1
0
1
6
9
3
6
0
.
[
2
5
]
R
.
R
a
n
j
a
n
,
K
.
S
h
a
r
r
e
r
,
S
.
T
su
k
u
d
a
,
a
n
d
C
.
G
o
o
d
,
“
Ef
f
e
c
t
s
o
f
i
ma
g
e
d
a
t
a
q
u
a
l
i
t
y
o
n
a
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
t
r
a
i
n
e
d
i
n
-
t
a
n
k
f
i
sh
d
e
t
e
c
t
i
o
n
m
o
d
e
l
f
o
r
r
e
c
i
r
c
u
l
a
t
i
n
g
a
q
u
a
c
u
l
t
u
r
e
sy
s
t
e
ms
,
”
C
o
m
p
u
t
e
rs
a
n
d
E
l
e
c
t
r
o
n
i
c
s
i
n
A
g
ri
c
u
l
t
u
re
,
v
o
l
.
2
0
5
,
p
.
1
0
7
6
4
4
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
mp
a
g
.
2
0
2
3
.
1
0
7
6
4
4
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Nur
La
tif
Az
y
z
e
Mo
h
d
S
h
a
a
r
i
Az
y
z
e
is
a
lec
tu
re
r
in
th
e
De
p
a
rtme
n
t
o
f
M
e
c
h
a
tro
n
ic,
U
n
iv
e
rsiti
Tek
n
ik
a
l
M
a
lay
sia
M
e
lak
a
(UTe
M
),
M
e
l
a
k
a
,
M
a
lay
sia
.
He
h
o
ld
s
a
n
M
.
En
g
.
d
e
g
re
e
in
e
lec
tri
c
a
l
m
e
c
h
a
tro
n
ics
a
n
d
c
o
n
tro
l
e
n
g
i
n
e
e
rin
g
a
n
d
B
.
En
g
.
in
e
lec
tri
c
a
l
m
e
c
h
a
tro
n
ic
fro
m
Un
iv
e
rsiti
Te
k
n
o
lo
g
i
M
a
lay
sia
.
His
re
se
a
rc
h
a
re
a
is
e
m
b
e
d
d
e
d
sy
ste
m
s,
ro
b
o
ti
c
s,
re
h
a
b
il
it
a
ti
o
n
e
n
g
in
e
e
ri
n
g
,
a
n
d
i
n
tern
e
t
-
of
-
th
in
g
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
latifaz
y
z
e
@u
tem
.
e
d
u
.
m
y
.
Te
o
w
K
h
im
i
Q
u
a
n
o
b
tain
e
d
b
a
c
h
e
lo
r
d
e
g
re
e
fro
m
Un
iv
e
rsiti
Tek
n
i
k
a
l
M
a
lay
sia
M
e
lak
a
in
e
lec
tri
c
a
l
m
e
c
h
a
tro
n
ic
e
n
g
in
e
e
rin
g
in
2
0
2
3
.
Cu
rre
n
t
ly
,
wo
rk
a
s
h
a
rd
wa
re
d
e
sig
n
e
n
g
in
e
e
r
a
t
E
x
is T
e
c
h
S
d
n
Bh
d
.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
:
b
0
1
2
0
1
0
2
2
4
@s
tu
d
e
n
t.
u
tem
.
e
d
u
.
m
y
.
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
-
8
7
0
8
Mo
n
ke
y
d
etec
tio
n
u
s
in
g
d
ee
p
lea
r
n
in
g
fo
r
mo
n
ke
y
-
r
ep
ellen
t
(
N
u
r
La
tif A
z
yze
Mo
h
d
S
h
a
a
r
i A
z
yze
)
3245
Ida
S
y
a
fiza
b
in
ti
Md
Is
a
re
c
e
iv
e
d
a
P
h
.
D.
d
e
g
re
e
fro
m
th
e
U
n
iv
e
rsit
y
o
f
Lee
d
s,
U.K.,
i
n
2
0
2
0
,
wo
r
k
e
d
o
n
e
n
e
rg
y
e
ffi
c
ien
t
a
c
c
e
ss
n
e
two
r
k
s
d
e
sig
n
fo
r
h
e
a
lt
h
c
a
re
a
p
p
l
ica
ti
o
n
s.
S
h
e
is
c
u
rre
n
tl
y
a
lec
tu
re
r
wit
h
U
n
iv
e
rsiti
Tek
n
ik
a
l
M
a
lay
sia
M
e
la
k
a
(UTe
M
),
M
a
lay
sia
.
S
h
e
h
a
s
p
u
b
li
sh
e
d
se
v
e
ra
l
a
rti
c
les
in
t
h
is
a
re
a
.
He
r
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
n
e
two
r
k
a
rc
h
it
e
c
tu
re
d
e
sig
n
,
e
n
e
rg
y
e
ffi
c
ien
c
y
,
n
e
two
r
k
o
p
ti
m
iza
ti
o
n
,
m
ix
e
d
-
in
teg
e
r
li
n
e
a
r
p
ro
g
ra
m
m
in
g
,
a
n
d
Io
T
h
e
a
lt
h
c
a
re
sy
ste
m
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
id
a
sy
a
fiza
@u
te
m
.
e
d
u
.
m
y
.
Mu
h
a
m
m
a
d
Afif
H
u
sm
a
n
h
i
s
B.
En
g
(M
e
c
h
a
tr
o
n
ics
)
fr
o
m
In
t
e
rn
a
ti
o
n
a
l
Isla
m
i
c
Un
iv
e
rsity
M
a
lay
sia
(IIUM
)
i
n
2
0
1
1
,
M
E
n
g
.
(Bio
m
e
d
ica
l)
fro
m
Un
iv
e
rsity
o
f
M
a
lay
a
i
n
2
0
1
3
,
a
n
d
P
h
.
D
.
in
m
e
c
h
a
n
ica
l
e
n
g
i
n
e
e
rin
g
fro
m
Lee
d
s
Un
iv
e
rsit
y
U
K
in
2
0
1
8
.
He
is
c
u
rre
n
tl
y
se
rv
in
g
a
s
a
n
a
ss
istan
t
p
r
o
fe
ss
o
r
i
n
th
e
De
p
a
rtme
n
t
o
f
M
e
c
h
a
tro
n
ics
,
Ku
ll
iy
y
a
h
o
f
En
g
i
n
e
e
rin
g
IIUM
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
se
n
so
r
in
terfa
c
in
g
,
so
ftwa
re
d
e
v
e
lo
p
m
e
n
t
a
n
d
tele
o
p
e
ra
ted
ro
b
o
t
c
o
m
m
u
n
ica
ti
o
n
.
He
is
c
u
rre
n
tl
y
a
tt
a
c
h
e
d
wit
h
th
e
Ce
n
ter
fo
r
U
n
m
a
n
n
e
d
Tec
h
n
o
l
o
g
y
(CUTe
)
IIUM
,
with
m
a
in
fo
c
u
s
a
s
t
h
e
lea
d
d
e
v
e
lo
p
e
r
fo
r
IIUM
M
e
d
ib
o
t,
a
sp
e
c
ial
m
e
d
ica
l
ro
b
o
t
c
a
p
a
b
le
o
f
tele
p
r
e
se
n
c
e
a
n
d
in
telli
g
e
n
t
n
a
v
ig
a
ti
o
n
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
fifh
u
sm
a
n
@iiu
m
.
e
d
u
.
m
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.