US 2026/0085982 A1 · METHOD FOR COLORIMETRIC TEMPERATURE MEASUREMENT IN ULTRA-HIGH TEMPERATURE ENVIRONMENT BASED ON MULTISOURCE ERROR SELF-CORRECTION

Abstract

The present invention provides a method for colorimetric temperature measurement in ultra-high temperature environment based on multisource error self-correction, which firstly establishes a colorimetric temperature measurement formula, then determines a low-error characteristic curve based on green channel features of N pluralities of calibration images, thus creating a probability model that combines prior knowledge of calibration data with actual observation data in actual measurement, and lastly, performs a iterative optimization based on an established objective function to obtain a corrected temperature, thus the multisource errors are uniformly corrected and the accuracy of colorimetric temperature measurement in ultra-high temperature environment is significantly improved.

A method for colorimetric temperature measurement in ultra-high temperature environment based on multisource error self-correction, comprising:

(1). establishing a colorimetric temperature measurement formula:
{circumflex over (T)}=F(l), where {circumflex over (T)} is a measured temperature, l is a colorimetric value, F(l) is a temperature function of colorimetric value l;
(2). determining a low-error characteristic curve based on green channel features of N pluralities of calibration images
2.1). using the colorimetric temperature measurement formula to perform self-tests on the N pluralities of calibration images to obtain temperature self-test results {circumflex over (T)}(i, j)=F(li,j), i=1, 2, . . . , N, j=1, 2 . . . , Mi, where Mi is a number of ith plurality of calibration images acquired at ith temperature Ti, lij is a colorimetric value and:
l
i
,
j
=
ln
⁢
(
R
¯
i
,
j
/
G
¯
i
,
j
)
where Ri, j is a red channel mean value and Gi, j is a green channel mean value of a jth calibration image of the ith plurality of calibration images;
2.2). calculating a self-test error Ei,j of each calibration image:
E
i
,
j
=
❘
"\[LeftBracketingBar]"
T
ˆ
(
i
,
j
)
-
T
i
❘
"\[RightBracketingBar]"
2.3). choosing a green channel mean value of a calibration image which has minimum self-test error Ei,j among the ith plurality of calibration images and denoting the chosen green channel mean value by
G
Ti
*
;
2.4). determining the low-error characteristic curve
G
ˆ
T
*
by performing a non-linear function fitting based on green channel mean values
G
Ti
*
and temperature Ti, i=1, 2, . . . , N:
G
ˆ
T
*
=
β
g
(
T
)
where βg is a green channel mean value function of temperature T;
(3). calculating a fitting error σg between green channel mean values Gi, j and low-error characteristic curve predications
G
ˆ
T
i
*
,
i
=
1
,
2
,
…
,
N
,
j
=
1
,
2
,
…
,
M
i
:
σ
g
=
1
n
-
1
⁢
∑
i
=
1
N
∑
j
=
1
M
i
(
G
¯
i
,
j
-
G
ˆ
Ti
*
)
2
,
n
=
∑
n
=
1
N
M
i
where n is a total number of the N pluralities of calibration images, and
G
ˆ
Ti
*
=
=
β
g
(
T
i
)
;
(4). calculating an error σi between temperature self-test results {circumflex over (T)}(i, j) and true temperatures, namely temperatures Ti, i=1, 2, . . . , N, j=1, 2, . . . . Mi:
σ
t
=
1
n
-
1
⁢
∑
i
=
1
N
∑
j
=
1
M
E
i
,
j
2
(5). acquiring a RGB image, namely a test image Ptest from a test workpiece in ultra-high temperature environment by using an industrial digital camera with charge-coupled device image sensors, denoting red channel value and green channel value of pixel (u,v) of test image Ptest by R(u,v) and G(u,v) respectively, and calculating colorimetric value l(u,v):
l
(
u
,
v
)
=
ln
⁢
(
R
(
u
,
v
)
/
G
(
u
,
v
)
)
(6). establishing an objective function L(G,T,l) based on Bayesian inference:
L
⁡
(
G
,
T
,
l
)
=
(
G
-
β
g
(
T
)
)
2
2
⁢
σ
g
2
+
(
F
⁡
(
l
)
-
T
)
2
2
⁢
σ
t
2
where G is a green channel value;
(7). iteratively calculating a corrected temperature
7.1). calculating an initial temperature
T
0
t
⁢
e
⁢
s
⁢
t
based on the colorimetric temperature measurement formula
T
0
t
⁢
e
⁢
s
⁢
t
=
F
⁡
(
l
(
u
,
ν
)
)
,
initializing iteration number k=1, green channel initial value
G
0
t
⁢
e
⁢
s
⁢
t
=
G
(
u
,
v
)
,
colorimetric initial value
l
0
t
⁢
e
⁢
s
⁢
t
=
l
(
u
,
v
)
⁢
;
7.2). iteratively updating corrected temperature
7.2.1). calculating first momentum mk and second momentum vk:
m
k
=
β
1
⁢
m
k
-
1
+
(
1
-
β
1
)
⁢
∂
L
⁡
(
G
,
T
,
l
)
∂
T
❘
T
=
T
k
-
1
test
G
=
G
k
-
1
test
l
=
l
k
-
1
test
v
k
=
β
2
⁢
v
k
-
1
+
(
1
-
β
2
)
⁢
∂
2
L
⁡
(
G
,
T
,
l
)
∂
T
2
❘
T
=
T
k
-
1
test
G
=
G
k
-
1
test
l
=
l
k
-
1
test
where β1 and β2 are momentum coefficients, the initial values of first momentum mk and second momentum vk are 0, namely m0=0, v0=0;
7.2.2). calculating bias corrections:
m
ˆ
k
=
m
k
1
-
β
1
k
v
ˆ
k
=
v
k
1
-
β
2
k
where {circumflex over (m)}k is a bias correction of first momentum mk, {circumflex over (v)}k is a bias correction of second momentum vk;
7.2.3). performing a temperature update:
T
k
t
⁢
e
⁢
s
⁢
t
=
T
k
-
1
t
⁢
e
⁢
s
⁢
t
-
α
1
⁢
m
ˆ
k
ε
+
v
ˆ
k
where
T
k
t
⁢
e
⁢
s
⁢
t
is kth iteration's corrected temperature, α1 is a learning rate of temperature update, ε is a constant which is used to prevent denominator from being zero;
7.3). iteratively correcting colorimetric value
7.3.1) calculating square sum
v
k
′
of historical gradients through exponentially weighted moving average:
moving average:
v
k
′
=
β
⁢
v
k
-
1
′
+
(
1
-
β
)
⁢
(
∂
L
⁡
(
G
,
T
,
l
)
∂
l
|
T
=
T
k
-
1
test
G
=
G
k
-
1
test
l
=
l
k
-
1
test
)
2
where β is a weight smoothing coefficient, square sum's initial value
v
0
′
=
0
;
7.3.2). correcting colorimetric value
l
k
t
⁢
e
⁢
s
⁢
t
=
l
k
-
1
t
⁢
e
⁢
s
⁢
t
-
α
2
⁢
1
ε
+
v
k
-
1
′
⁢
∂
L
⁡
(
G
,
T
,
l
)
∂
l
|
T
=
T
k
-
1
test
G
=
G
k
-
1
test
l
=
l
k
-
1
test
where α2 is a learning rate of colorimetric value correction;
7.4). iteratively updating green channel value:
G
k
t
⁢
e
⁢
s
⁢
t
=
G
k
-
1
t
⁢
e
⁢
s
⁢
t
+
α
3
(
β
g
(
T
k
-
1
t
⁢
e
⁢
s
⁢
t
)
-
G
k
-
1
t
⁢
e
⁢
s
⁢
t
)
where α3 is a learning rate of update;
7.5). k=k+1, repeating steps 7.2˜7.4, until variation of the objective function L(G,T,l) before and after the kth iteration is less that a set threshold θ:
L
⁡
(
G
k
t
⁢
e
⁢
s
⁢
t
,
T
k
t
⁢
e
⁢
s
⁢
t
,
l
k
t
⁢
e
⁢
s
⁢
t
)
-
L
⁡
(
G
k
-
1
t
⁢
e
⁢
s
⁢
t
,
T
k
-
1
t
⁢
e
⁢
s
⁢
t
,
l
k
-
1
t
⁢
e
⁢
s
⁢
t
)
<
θ
or iteration number k reaches a maximum iteration number, then taking the kth iteration's corrected temperature
T
k
t
⁢
e
⁢
s
⁢
t
as a colorimetric temperature measurement's output, namely a measured temperature.

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Method for colorimetric temperature measurement in ultra-high temperature environment based on multisource error self-correction

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Method for colorimetric temperature measurement in ultra-high temperature environment based on multisource error self-correction

Life of this patent

Abstract

Claims3