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C
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y
m
o
n
i
to
r
i
n
g
[
1
2
]
–
[
1
6
]
,
s
y
m
b
o
l
r
ec
o
g
n
iti
o
n
[
1
7
]
–
[
1
8
]
,
ag
r
i
cu
lt
u
r
al
s
e
ct
o
r
[
1
9
]
–
[
2
1
]
,
ele
ct
r
ic
al
cir
c
u
it
an
al
y
s
is
[
2
2
]
–
[
2
4
]
,
a
n
d
s
ig
n
la
n
g
u
ag
e
[
2
5
]
.
H
o
w
ev
e
r
,
th
e
a
p
p
li
ca
ti
o
n
o
f
YO
L
O
v
5
i
n
tr
a
n
s
l
ati
n
g
lo
g
i
c
g
ate
s
y
m
b
o
ls
in
t
o
B
o
o
l
ea
n
f
u
n
cti
o
n
s
r
em
ai
n
s
u
n
d
e
r
e
x
p
l
o
r
e
d
.
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
th
e
d
ev
e
lo
p
m
en
t
o
f
a
s
y
s
tem
ca
p
ab
le
o
f
d
etec
tin
g
an
d
t
r
an
s
latin
g
lo
g
ic
g
ate
s
y
m
b
o
l
im
ag
es
in
to
B
o
o
lean
f
u
n
ctio
n
s
u
s
in
g
YOL
Ov
5
.
A
cu
s
to
m
d
ataset
o
f
8
0
0
im
ag
es
was
co
llected
u
n
d
er
v
ar
ied
lig
h
tin
g
co
n
d
itio
n
s
an
d
b
ac
k
g
r
o
u
n
d
s
to
e
n
h
an
ce
g
en
er
aliza
b
ilit
y
.
T
h
e
d
ataset
co
n
s
is
ts
o
f
s
tan
d
ar
d
ized
lo
g
ic
g
ate
s
y
m
b
o
ls
g
e
n
er
ated
with
Dr
aw.
io
,
p
r
o
v
id
in
g
r
o
b
u
s
tn
ess
u
n
d
er
d
iv
er
s
e
ca
p
t
u
r
e
co
n
d
itio
n
s
.
I
m
a
g
es
wer
e
an
n
o
tated
an
d
p
r
o
ce
s
s
ed
u
s
in
g
R
o
b
o
f
lo
w
t
o
g
en
er
ate
tr
ain
in
g
,
v
alid
atio
n
,
a
n
d
test
in
g
s
u
b
s
ets.
T
h
e
tr
ain
ed
YOL
Ov
5
m
o
d
el
was
th
en
in
teg
r
ate
d
in
to
a
tr
a
n
s
latio
n
s
y
s
tem
ca
p
ab
le
o
f
r
e
co
g
n
izin
g
lo
g
ic
g
ate
co
m
p
o
n
en
ts
(
in
p
u
ts
,
o
p
er
ato
r
s
)
an
d
c
o
n
v
e
r
tin
g
th
em
in
to
co
r
r
esp
o
n
d
in
g
B
o
o
lean
e
x
p
r
ess
io
n
s
.
T
h
e
m
ain
co
n
tr
ib
u
tio
n
s
o
f
th
i
s
wo
r
k
ar
e:
i)
t
h
e
cr
ea
tio
n
o
f
a
d
iv
er
s
e
d
ataset
o
f
p
r
in
ted
lo
g
ic
g
ate
s
y
m
b
o
ls
;
ii)
t
h
e
im
p
lem
e
n
tatio
n
o
f
YOL
Ov
5
f
o
r
ac
cu
r
ate
d
etec
tio
n
o
f
lo
g
ic
g
ate
co
m
p
o
n
en
ts
;
iii)
t
h
e
d
esig
n
o
f
a
tr
an
s
latio
n
s
y
s
tem
th
at
m
ap
s
d
etec
ted
g
ates
in
to
B
o
o
lean
f
u
n
ctio
n
s
;
an
d
iv
)
v
ali
d
atio
n
o
f
th
e
s
y
s
tem
p
er
f
o
r
m
an
ce
th
r
o
u
g
h
q
u
a
n
titativ
e
m
etr
ics
(
p
r
ec
is
io
n
,
r
ec
all,
an
d
m
ea
n
av
er
a
g
e
p
r
ec
i
s
io
n
o
r
m
AP)
an
d
q
u
alitativ
e
ev
alu
atio
n
o
f
tr
an
s
lated
ex
p
r
ess
io
n
s
.
T
h
is
r
esear
ch
b
r
id
g
es
th
e
g
ap
b
etwe
en
c
o
m
p
u
ter
v
is
io
n
an
d
d
ig
ital
lo
g
ic
d
esig
n
,
o
f
f
er
in
g
a
to
o
l
th
at
s
im
p
lifie
s
th
e
p
r
o
ce
s
s
o
f
B
o
o
lean
f
u
n
ctio
n
d
er
i
v
atio
n
f
r
o
m
v
is
u
al
cir
cu
it
r
ep
r
esen
tatio
n
s
.
T
h
e
f
i
n
d
in
g
s
c
o
n
tr
ib
u
te
to
b
o
th
t
h
e
ac
ad
em
ic
f
ield
o
f
AI
in
elec
tr
o
n
ics
an
d
p
r
ac
tical
ap
p
licatio
n
s
in
ed
u
ca
ti
o
n
an
d
d
ig
ital sy
s
tem
d
esig
n
.
2.
M
E
T
H
O
D
2
.1
.
Da
t
a
s
et
a
cquis
it
io
n
T
h
e
d
ataset
u
s
ed
in
th
is
s
tu
d
y
co
n
s
is
ted
o
f
8
0
0
im
a
g
es
o
f
d
ig
ital
lo
g
ic
g
ate
s
y
m
b
o
ls
.
T
h
e
s
y
m
b
o
ls
wer
e
f
ir
s
t
cr
ea
ted
d
ig
itally
u
s
i
n
g
th
e
Dr
aw.
io
ap
p
licatio
n
an
d
th
en
p
r
in
ted
with
an
E
PS
ON
L
1
5
1
5
0
p
r
in
ter
t
o
en
s
u
r
e
s
tan
d
ar
d
ized
a
n
d
s
tr
u
ctu
r
ed
s
h
ap
es.
T
h
ese
p
r
in
ted
s
h
ee
ts
wer
e
s
u
b
s
eq
u
en
tly
p
h
o
to
g
r
a
p
h
ed
u
s
in
g
a
POC
O
M5
s
m
ar
tp
h
o
n
e
ca
m
er
a
u
n
d
er
v
a
r
ied
lig
h
tin
g
c
o
n
d
itio
n
s
,
an
g
les,
a
n
d
b
ac
k
g
r
o
u
n
d
s
to
i
n
tr
o
d
u
ce
n
atu
r
al
d
iv
e
r
s
ity
an
d
s
im
u
late
r
ea
l
-
wo
r
ld
v
ar
iab
ilit
y
.
T
h
is
s
tr
ateg
y
en
s
u
r
ed
th
at,
wh
ile
th
e
s
y
m
b
o
ls
r
em
ain
e
d
co
n
s
is
ten
t
in
d
esig
n
,
th
e
ca
p
tu
r
ed
im
ag
es
r
ef
lecte
d
p
r
ac
tic
al
v
ar
iatio
n
s
en
co
u
n
ter
e
d
in
r
ea
l
en
v
ir
o
n
m
en
ts
,
m
ak
in
g
th
e
d
ataset
m
o
r
e
r
o
b
u
s
t
f
o
r
tr
ain
in
g
t
h
e
d
etec
tio
n
m
o
d
el.
T
h
e
d
ataset
co
v
e
r
ed
s
ev
en
ty
p
es
o
f
l
o
g
ic
g
ates
(
AND,
OR
,
XOR,
NAND
,
NOR,
XNOR,
NOT
)
alo
n
g
with
two
c
o
n
s
is
ten
t
in
p
u
t
s
y
m
b
o
ls
(
I
n
p
u
t
1
an
d
I
n
p
u
t
2
)
.
E
ac
h
im
a
g
e
co
n
tain
ed
at
least
o
n
e
lo
g
ic
g
ate
a
n
d
two
i
n
p
u
t
elem
e
n
ts
,
r
ef
le
ctin
g
ty
p
ical
cir
c
u
it
r
ep
r
esen
tatio
n
s
u
s
ed
in
d
ig
it
al
elec
tr
o
n
ics.
2
.2
.
Da
t
a
p
re
pro
ce
s
s
ing
B
ef
o
r
e
tr
ain
in
g
,
s
ev
er
al
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
wer
e
ap
p
lied
to
p
r
ep
ar
e
th
e
d
ataset
f
o
r
YOL
Ov
5
tr
ain
in
g
:
−
R
esizin
g
:
all
im
ag
es
wer
e
r
esized
to
2
5
6
×2
5
6
p
ix
els.
T
h
is
s
ize
was
ch
o
s
en
as
a
tr
ad
e
-
o
f
f
b
etwe
en
p
r
eser
v
in
g
s
u
f
f
icien
t
d
etail
o
f
lo
g
ic
g
ate
s
y
m
b
o
ls
wh
ile
k
e
ep
in
g
th
e
co
m
p
u
tatio
n
al
lo
ad
m
an
ag
ea
b
le
o
n
th
e
av
ailab
le
h
ar
d
war
e.
−
No
r
m
aliza
tio
n
:
p
ix
el
v
alu
es we
r
e
n
o
r
m
alize
d
to
a
[
0
,
1
]
s
ca
le,
im
p
r
o
v
in
g
c
o
n
v
e
r
g
en
ce
d
u
r
i
n
g
tr
ain
in
g
.
−
A
n
n
o
t
a
t
i
o
n
:
e
a
c
h
o
b
j
e
c
t
(
l
o
g
i
c
g
a
t
e
s
a
n
d
i
n
p
u
t
s
)
w
a
s
a
n
n
o
t
a
te
d
u
s
i
n
g
b
o
u
n
d
i
n
g
b
o
x
e
s
f
o
l
l
o
w
i
n
g
t
h
e
Y
O
L
O
1
.
1
f
o
r
m
a
t
.
A
n
n
o
t
a
t
i
o
n
s
we
r
e
p
e
r
f
o
r
m
e
d
u
s
i
n
g
R
o
b
o
f
l
o
w
,
w
h
ic
h
a
l
s
o
e
n
s
u
r
e
d
c
o
n
s
is
t
e
n
t
l
a
b
el
f
o
r
m
a
t
t
i
n
g
.
−
D
a
t
a
a
u
g
m
e
n
t
at
i
o
n
:
s
e
v
e
r
a
l
au
g
m
e
n
t
a
t
i
o
n
t
e
c
h
n
i
q
u
es
s
u
c
h
a
s
r
a
n
d
o
m
r
o
t
a
ti
o
n
s
,
f
l
i
p
p
i
n
g
,
s
c
a
li
n
g
,
a
n
d
b
r
i
g
h
t
n
e
s
s
a
d
j
u
s
t
m
e
n
ts
w
e
r
e
a
p
p
l
i
e
d
t
o
i
n
c
r
e
a
s
e
d
a
ta
s
et
v
a
r
i
a
b
i
l
it
y
.
A
u
g
m
e
n
t
a
t
i
o
n
h
e
l
p
e
d
i
m
p
r
o
v
e
t
h
e
r
o
b
u
s
t
n
e
s
s
o
f
t
h
e
m
o
d
e
l
b
y
s
i
m
u
l
a
t
i
n
g
r
e
a
l
-
w
o
r
l
d
v
a
r
i
a
t
i
o
n
s
s
u
c
h
a
s
t
il
t
e
d
d
r
a
wi
n
g
s
o
r
u
n
e
v
e
n
i
l
l
u
m
i
n
a
ti
o
n
.
Af
ter
p
r
ep
r
o
ce
s
s
in
g
,
th
e
d
at
aset
was
s
p
lit
in
to
:
i)
7
0
%
f
o
r
tr
ain
in
g
(
5
6
0
im
ag
es)
;
ii)
2
0
%
f
o
r
v
alid
atio
n
(
1
6
0
im
ag
es)
;
an
d
iii)
1
0
%
f
o
r
test
in
g
(
8
0
im
ag
es)
.
T
h
is
s
p
lit
e
n
s
u
r
ed
b
al
an
ce
d
tr
ai
n
in
g
an
d
r
eliab
le
ev
alu
atio
n
o
f
m
o
d
el
p
er
f
o
r
m
a
n
ce
.
2
.3
.
H
a
rdwa
re
a
nd
s
o
f
t
wa
re
re
qu
irem
ent
s
T
h
e
tr
ain
in
g
an
d
im
p
lem
en
ta
tio
n
wer
e
co
n
d
u
cted
o
n
a
la
p
to
p
with
th
e
f
o
llo
win
g
s
p
ec
if
icatio
n
s
:
i)
p
r
o
ce
s
s
o
r
:
AM
D
R
y
ze
n
5
7
5
3
5
HS
with
R
ad
eo
n
Gr
ap
h
ics
(
3
.
3
0
GHz
)
;
ii)
m
em
o
r
y
:
1
6
GB
R
AM
,
1
T
B
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
3
2
2
1
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
,
Vo
l.
7
,
No
.
3
,
No
v
em
b
er
20
26
:
3
4
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35
2
348
SSD
;
iii)
GP
U:
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DI
A
Ge
Fo
r
ce
R
T
X
2
0
5
0
,
4
GB
V
R
AM
;
iv
)
o
p
er
atin
g
s
y
s
tem
:
W
in
d
o
ws
1
1
6
4
-
b
it
(
v
er
s
io
n
2
4
H2
)
;
an
d
v
)
s
o
f
twa
r
e
an
d
t
o
o
ls
:
−
An
ac
o
n
d
a
Nav
i
g
ato
r
(
v
2
.
3
.
1
)
f
o
r
en
v
ir
o
n
m
en
t m
an
a
g
em
en
t
,
−
J
u
p
y
ter
No
teb
o
o
k
(
v
6
.
4
.
1
2
)
as
th
e
d
ev
elo
p
m
en
t in
te
r
f
ac
e
,
−
Py
th
o
n
(
v
3
.
1
2
.
8
)
as th
e
p
r
o
g
r
a
m
m
in
g
en
v
ir
o
n
m
en
t
,
−
Vis
u
al
Stu
d
io
C
o
d
e
(
v
1
.
1
0
0
.
2
)
f
o
r
ad
d
itio
n
al
co
d
in
g
task
s
,
−
Go
o
g
le
C
h
r
o
m
e
(
v
1
3
6
.
0
.
7
1
0
3
.
1
1
4
)
f
o
r
we
b
-
b
ased
to
o
ls
,
−
R
o
b
o
f
lo
w
f
o
r
d
ataset
p
r
ep
r
o
ce
s
s
in
g
an
d
an
n
o
tatio
n
,
−
Dr
aw.
io
an
d
iLo
v
eI
MG
f
o
r
cr
ea
ti
n
g
lo
g
ic
g
ate
s
y
m
b
o
ls
an
d
im
ag
e
ad
ju
s
tm
en
ts
,
−
GitH
u
b
(
Ultr
aly
tics
r
ep
o
s
ito
r
y
)
f
o
r
YOL
Ov
5
m
o
d
el
s
o
u
r
ce
c
o
d
e
,
−
NVI
DI
A
C
UDA
T
o
o
lk
it a
n
d
c
u
DNN
lib
r
ar
ies to
en
ab
le
GP
U
ac
ce
ler
atio
n
,
an
d
−
Py
T
o
r
ch
as th
e
d
ee
p
lear
n
in
g
f
r
am
ewo
r
k
b
ac
k
en
d
.
T
h
e
ch
o
ice
o
f
t
h
ese
h
ar
d
wa
r
e
an
d
s
o
f
twar
e
to
o
ls
e
n
s
u
r
ed
ef
f
icien
t
tr
ai
n
in
g
with
in
th
e
co
n
s
tr
ain
ts
o
f
a
m
id
-
r
an
g
e
GPU
s
y
s
tem
.
2
.4
.
M
o
del
t
ra
ini
ng
T
h
e
YOL
Ov
5
s
m
o
d
el
was c
h
o
s
en
d
u
e
to
its
b
alan
ce
b
etwe
en
ac
cu
r
ac
y
an
d
co
m
p
u
tatio
n
al
e
f
f
icien
cy
,
m
ak
in
g
it
s
u
i
tab
le
f
o
r
s
m
all
o
b
ject
d
etec
tio
n
task
s
s
u
ch
as
lo
g
ic
g
ate
r
ec
o
g
n
itio
n
.
T
h
e
tr
ain
in
g
s
etu
p
was
d
ef
in
ed
as:
−
I
n
p
u
t
r
eso
lu
tio
n
: 2
5
6
×2
5
6
p
i
x
els.
−
B
atch
s
ize
an
d
ep
o
ch
s
:
a
b
atch
s
ize
o
f
3
2
was
u
s
ed
,
with
tr
ain
in
g
co
n
d
u
cted
f
o
r
1
0
0
ep
o
ch
s
.
E
ac
h
ep
o
ch
r
ep
r
esen
ted
a
c
o
m
p
lete
p
ass
t
h
r
o
u
g
h
th
e
t
r
ain
in
g
s
et,
with
i
m
ag
es
s
h
u
f
f
led
at
e
v
er
y
iter
atio
n
to
im
p
r
o
v
e
g
en
er
aliza
tio
n
.
−
Pre
-
tr
ain
ed
weig
h
ts
:
th
e
m
o
d
el
u
s
ed
y
o
lo
v
5
s
.
p
t
p
r
e
-
tr
ain
ed
weig
h
ts
as
in
itializatio
n
,
lev
er
ag
in
g
tr
an
s
f
er
lear
n
in
g
to
ac
ce
le
r
ate
co
n
v
er
g
en
ce
an
d
im
p
r
o
v
e
ac
cu
r
ac
y
wi
th
lim
ited
tr
ain
in
g
d
ata.
−
L
o
s
s
f
u
n
cti
o
n
s
:
YO
L
O
v
5
ap
p
l
ies th
r
ee
m
ai
n
l
o
s
s
es:
b
o
x
l
o
s
s
(
f
o
r
b
o
u
n
d
in
g
b
o
x
r
e
g
r
ess
i
o
n
)
,
o
b
jec
tn
ess
l
o
s
s
(
f
o
r
d
e
te
cti
n
g
t
h
e
p
r
ese
n
ce
o
f
o
b
je
cts
)
,
a
n
d
class
if
i
ca
ti
o
n
l
o
s
s
(
f
o
r
d
is
ti
n
g
u
is
h
i
n
g
b
et
we
en
l
o
g
ic
g
at
e
ty
p
es)
.
−
Op
tim
izer
an
d
lear
n
in
g
r
ate:
th
e
d
ef
au
lt
SGD
o
p
tim
izer
with
m
o
m
en
tu
m
was
u
s
ed
,
with
an
ad
ap
tiv
e
lear
n
in
g
r
ate
s
ch
e
d
u
le
as d
ef
in
ed
in
th
e
YOL
Ov
5
co
n
f
i
g
u
r
ati
o
n
.
−
A
u
to
-
an
ch
o
r
s
:
a
u
to
m
atic
r
ec
alcu
latio
n
o
f
an
ch
o
r
b
o
x
es
was
en
ab
led
to
o
p
tim
ize
d
etec
tio
n
o
f
s
m
all,
s
tr
u
ctu
r
ed
o
b
jects su
ch
as lo
g
i
c
g
ate
s
y
m
b
o
ls
.
2
.5
.
E
v
a
lua
t
i
o
n
m
et
rics
T
o
ev
alu
ate
m
o
d
el
p
e
r
f
o
r
m
an
ce
,
th
e
f
o
llo
win
g
m
etr
ics we
r
e
u
s
ed
:
−
P
r
ec
is
io
n
(
P):
th
e
p
r
o
p
o
r
tio
n
o
f
co
r
r
ec
tly
p
r
ed
icted
p
o
s
itiv
e
d
etec
tio
n
s
am
o
n
g
all
d
etec
tio
n
s
.
−
R
ec
all
(
R
)
: th
e
p
r
o
p
o
r
tio
n
o
f
c
o
r
r
ec
tly
d
etec
ted
o
b
jects a
m
o
n
g
all
g
r
o
u
n
d
-
tr
u
th
o
b
jects
.
−
m
AP@
0
.
5
:
m
AP
at
I
o
U
th
r
esh
o
ld
0
.
5
,
th
e
s
tan
d
ar
d
m
etr
ic
f
o
r
o
b
ject
d
etec
tio
n
p
e
r
f
o
r
m
an
ce
.
−
m
AP@
0
.
5
:0
.
9
5
:
m
AP
ac
r
o
s
s
m
u
ltip
le
I
o
U
th
r
esh
o
ld
s
(
0
.
5
t
o
0
.
9
5
)
,
p
r
o
v
id
in
g
a
m
o
r
e
s
tr
i
n
g
en
t
ev
alu
atio
n
.
T
h
ese
m
etr
ics we
r
e
ca
lcu
lated
s
ep
ar
ately
f
o
r
tr
ain
in
g
,
v
alid
a
tio
n
,
an
d
test
in
g
d
atasets
.
2
.6
.
Sy
s
t
e
m
i
m
plem
ent
a
t
io
n
On
ce
th
e
m
o
d
el
was
tr
ain
ed
,
it
was
in
teg
r
ated
in
to
a
tr
an
s
latio
n
s
y
s
tem
ca
p
ab
le
o
f
d
etec
tin
g
lo
g
ic
g
ates
an
d
in
p
u
t
s
y
m
b
o
ls
f
r
o
m
im
ag
es
an
d
co
n
v
er
tin
g
th
e
m
in
to
B
o
o
lea
n
f
u
n
ctio
n
s
.
T
h
e
s
y
s
tem
wo
r
k
f
lo
w
in
clu
d
ed
:
−
I
m
ag
e
in
p
u
t:
u
s
er
s
p
r
o
v
id
e
im
ag
es
v
ia
a
g
r
ap
h
ical
in
ter
f
ac
e.
T
o
en
s
u
r
e
co
n
s
is
ten
t
p
r
o
ce
s
s
in
g
,
th
e
s
y
s
tem
cu
r
r
en
tly
ac
ce
p
ts
a
m
ax
im
u
m
o
f
th
r
ee
in
p
u
t
im
a
g
es.
T
h
is
c
o
n
s
tr
ain
t
allo
ws
th
e
f
i
r
s
t
an
d
s
ec
o
n
d
im
ag
es
to
r
ep
r
esen
t
p
ar
allel
in
p
u
ts
,
wh
il
e
th
e
th
ir
d
im
ag
e
s
er
v
es
as
th
e
o
p
er
ato
r
,
en
a
b
lin
g
t
h
e
co
n
s
t
r
u
ctio
n
o
f
v
alid
b
in
a
r
y
lo
g
ic
ex
p
r
ess
io
n
s
.
−
Ob
ject
d
etec
tio
n
: th
e
tr
ain
e
d
YOL
Ov
5
m
o
d
el
id
e
n
tifie
s
lo
g
ic
g
ates a
n
d
in
p
u
t sy
m
b
o
ls
with
in
th
e
im
ag
e
.
−
Sy
m
b
o
l
m
ap
p
in
g
:
d
etec
ted
o
b
jects
ar
e
m
ap
p
ed
t
o
th
eir
c
o
r
r
esp
o
n
d
in
g
B
o
o
lean
s
y
m
b
o
ls
(
e.
g
.
,
AND→“
·
”,
OR
→“+
”,
NOT
→“¬
”)
.
−
E
x
p
r
ess
io
n
co
n
s
tr
u
ctio
n
:
th
e
s
y
s
tem
p
ar
s
es
th
e
d
etec
ted
co
m
p
o
n
en
ts
to
co
n
s
tr
u
ct
a
v
alid
B
o
o
lean
ex
p
r
ess
io
n
.
T
h
is
s
y
s
tem
b
r
id
g
es
th
e
g
a
p
b
etwe
en
v
is
u
al
r
ep
r
esen
tatio
n
s
o
f
cir
cu
its
an
d
th
eir
s
y
m
b
o
lic
B
o
o
lean
eq
u
iv
alen
ts
,
d
em
o
n
s
tr
atin
g
p
r
ac
tical
ap
p
licab
ilit
y
o
f
AI
-
b
ased
d
e
tectio
n
in
d
ig
ital e
lectr
o
n
ics.
Evaluation Warning : The document was created with Spire.PDF for Python.
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
I
SS
N:
2722
-
3
2
2
1
Dete
ctio
n
a
n
d
tr
a
n
s
la
tio
n
o
f l
o
g
ic
g
a
te
ima
g
es in
to
B
o
o
lea
n
fu
n
ctio
n
s
(
Mu
h
a
mma
d
S
h
id
q
i
i Ta
q
iyyu
d
d
in
)
349
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
T
ra
ini
ng
pro
ce
s
s
o
v
er
v
i
ew
T
h
e
t
r
ai
n
i
n
g
p
r
o
c
ess
o
f
t
h
e
Y
OL
O
v
5
s
m
o
d
el
is
r
e
co
r
d
e
d
i
n
t
h
e
g
r
a
p
h
s
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
B
ase
d
o
n
Fig
u
r
e
1
,
t
h
e
r
es
u
lts
i
n
d
ic
ate
t
h
at
th
e
YOL
Ov
5
s
m
o
d
e
l d
i
d
n
o
t
e
x
p
e
r
i
en
ce
o
v
e
r
f
i
tti
n
g
d
u
r
i
n
g
t
h
e
t
r
ai
n
i
n
g
p
h
ase
.
T
h
e
m
o
d
el
b
e
g
a
n
to
p
la
tea
u
o
r
r
ed
u
ce
its
le
ar
n
i
n
g
r
ate
a
f
t
er
a
p
p
r
o
x
i
m
at
el
y
3
0
ep
o
ch
s
u
n
til
t
h
e
e
n
d
o
f
tr
ai
n
i
n
g
.
Fig
u
r
e
1
.
T
r
ain
in
g
r
esu
lts
o
f
th
e
YOL
Ov
5
s
m
o
d
el
3
.
2
.
Va
lid
a
t
i
o
n pro
ce
s
s
o
v
er
v
iew
T
h
e
v
alid
atio
n
r
esu
lts
o
f
th
e
YOL
Ov
5
s
tr
ain
in
g
p
r
o
c
ess
ar
e
p
r
esen
ted
in
T
a
b
le
1
.
Acc
o
r
d
in
g
to
T
ab
le
1
,
th
e
YOL
Ov
5
s
m
o
d
el
ac
h
iev
ed
h
i
g
h
p
r
e
cisi
o
n
an
d
r
ec
all
v
alu
es,
wh
ich
in
d
icate
s
th
at
th
e
m
o
d
el
was
ab
le
to
co
r
r
ec
tly
d
etec
t
a
n
d
p
r
ed
ict
all
lo
g
ic
g
ate
o
b
jec
ts
an
d
th
eir
in
p
u
ts
with
in
th
e
im
a
g
es.
Fo
r
th
e
m
AP
at
I
o
U
th
r
esh
o
ld
0
.
5
(
m
AP@
5
0
)
,
th
e
m
o
d
el
ac
h
iev
ed
v
alu
es
ab
o
v
e
0
.
9
ac
r
o
s
s
all
class
e
s
,
d
em
o
n
s
tr
atin
g
its
ab
ilit
y
to
ac
cu
r
ately
d
etec
t
o
b
jects
with
I
o
U
≥
0
.
5
in
all
im
ag
es.
Me
an
wh
ile,
f
o
r
m
AP
at
I
o
U
th
r
esh
o
l
d
s
r
an
g
in
g
f
r
o
m
0
.
5
to
0
.
9
5
(
m
AP@
5
0
–
9
5
)
,
th
e
m
o
d
el
o
b
tai
n
ed
v
al
u
es
ab
o
v
e
0
.
9
f
o
r
m
o
s
t
class
es,
ex
ce
p
t
f
o
r
AND,
I
n
p
u
t
1
,
I
n
p
u
t
2
,
an
d
XOR.
T
h
ese
lo
wer
m
AP@
5
0
–
9
5
r
esu
lts
s
u
g
g
est
th
at
th
e
m
o
d
el
s
till
s
tr
u
g
g
les
with
p
r
ec
is
e
b
o
u
n
d
in
g
b
o
x
p
lace
m
en
t
f
o
r
ce
r
tain
c
lass
es,
alth
o
u
g
h
its
p
er
f
o
r
m
an
ce
is
s
t
ill
co
n
s
id
er
ed
s
atis
f
ac
to
r
y
.
I
n
ad
d
itio
n
,
th
e
v
alid
atio
n
r
es
u
lts
ar
e
also
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C
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[
1]
R
.
S
z
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l
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k
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C
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p
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id
.
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