CN112067132A - Flame high-temperature measurement calibration method based on random consistency sampling technology - Google Patents
Flame high-temperature measurement calibration method based on random consistency sampling technology Download PDFInfo
- Publication number
- CN112067132A CN112067132A CN202010667314.2A CN202010667314A CN112067132A CN 112067132 A CN112067132 A CN 112067132A CN 202010667314 A CN202010667314 A CN 202010667314A CN 112067132 A CN112067132 A CN 112067132A
- Authority
- CN
- China
- Prior art keywords
- temperature
- flame
- image
- thermometer
- measurement
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000005516 engineering process Methods 0.000 title claims abstract description 26
- 238000005070 sampling Methods 0.000 title claims abstract description 15
- 238000009529 body temperature measurement Methods 0.000 title claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 27
- 239000010425 asbestos Substances 0.000 claims abstract description 18
- 229910052895 riebeckite Inorganic materials 0.000 claims abstract description 18
- 238000005259 measurement Methods 0.000 claims abstract description 16
- 238000001228 spectrum Methods 0.000 claims abstract description 11
- 230000002159 abnormal effect Effects 0.000 claims abstract description 9
- 230000005540 biological transmission Effects 0.000 claims abstract description 6
- 230000003595 spectral effect Effects 0.000 claims abstract description 5
- 230000005855 radiation Effects 0.000 claims abstract description 4
- 238000004861 thermometry Methods 0.000 claims description 4
- 238000004616 Pyrometry Methods 0.000 claims description 2
- 238000011160 research Methods 0.000 abstract description 6
- 238000011161 development Methods 0.000 description 5
- 238000000691 measurement method Methods 0.000 description 2
- 238000010187 selection method Methods 0.000 description 2
- 230000005457 Black-body radiation Effects 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000004377 microelectronic Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/0014—Radiation pyrometry, e.g. infrared or optical thermometry for sensing the radiation from gases, flames
- G01J5/0018—Flames, plasma or welding
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/48—Thermography; Techniques using wholly visual means
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/80—Calibration
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J2005/0077—Imaging
Abstract
A flame high temperature measurement calibration method based on random consistency sampling technology belongs to the field of digital image processing and parameter measurement application research. The method mainly comprises an image acquisition module and a data processing module, and is characterized in that: the image acquisition module of the method comprises a rectangular asbestos plate, a high-definition camera, a spectral thermometer and a USB transmission line; the data processing module comprises a computer, data processing and interface display software. According to the invention, parameter calibration between temperature and image gray scale is carried out by adopting a random consistency sampling technology according to the temperature value measured by a spectrum thermometer on the round hole on the rectangular asbestos plate in the flame image and the round hole image gray scale value obtained by utilizing a digital image processing technology. The method provided by the invention can reflect the real temperature in the high-temperature process, can process abnormal measurement data points, has high measurement precision, can provide accurate data for flame temperature measurement, and has wide application in the fields of digital image processing, radiation temperature measurement and the like.
Description
Technical Field
The invention relates to a method in the field of digital image processing and parameter measurement application research, in particular to a flame high-temperature measurement calibration method based on a random consistency sampling technology.
Background
With the rapid development of CCD and CMOS image sensor technologies, computer hardware technologies, and digital image processing technologies, high-speed cameras with shutter speeds exceeding 1000 frames/second are increasingly being applied to various non-contact measurement fields, such as displacement, vibration, precision part dimension measurement, temperature measurement of instantaneous high-temperature flames, and the like.
The temperature measurement based on the digital image processing technology is a non-contact measurement method which is developed rapidly, deep research is conducted at home and abroad, and the advantages of real-time performance, flexibility, non-contact and the like avoid the defects of the traditional detection method. Among countries in which research on image measurement is performed, industrial countries such as germany, the united states, japan, and the like are in the front and many principles and methods for image measurement are proposed, and research on image measurement is relatively late in our countries, and research on image measurement technology is started from the middle of the 80 s, and at that time, the main application is to measure the length by using a linear Coupled Device (CCD) and measure the diameter of a steel wire, but the accuracy is not high. In addition, CCD cameras are expensive, so image-based measurement methods are not widespread.
In recent years, with the development of semiconductors, microelectronics, computer technologies, and digital image processing technologies, the development of image measurement technologies is rapid both domestically and abroad. Due to rapid development of a CCD manufacturing process and an Integrated Circuit (IC) technology, the cost of the CCD image measurement system is reduced while the performance of the CCD image measurement system is improved, and the development of the temperature measurement technology based on the CCD image can be promoted with low cost and high performance.
At present, in published documents, a high-temperature field measurement calibration method based on a CCD image sensor mainly uses a high-temperature black body furnace to calibrate the relationship between image brightness and corresponding temperature. However, this calibration method has the following problems: (1) all radiation generated by the high temperature process is regarded as black body radiation, so that the real temperature of the high temperature process cannot be reflected; (2) the least square method commonly used in the calibration process is difficult to solve the problem of abnormal data points.
In order to obtain an accurate relationship between the brightness of the calibration image and its corresponding temperature, it is used to measure the combustion flame temperature. The invention utilizes high temperature resistant asbestos material to prepare a rectangular plate, and round holes are distributed on the surface of the rectangular plate; after a CCD image is obtained for the rectangular plate placed in the flame, a function relation curve between the initial temperature and the brightness value is calibrated according to the actual temperature value measured by each round hole and the brightness value estimated by the corresponding image. The invention establishes a high-precision multi-image temperature and brightness calibration method, and the method has important practical significance for measuring the temperature of the high-temperature flame.
Disclosure of Invention
In view of the above problems, the present invention has an object to: a flame high temperature measurement calibration method based on a random consistency sampling processing technology is provided.
The technical scheme adopted by the invention is as shown in figure 1, the method mainly comprises an image acquisition module 1 and a data processing module 2, and is characterized in that: the image temperature acquisition module 1 of the method mainly realizes that a plurality of images are obtained from different visual angles of flame and an asbestos rectangular plate in the flame through a camera, and a spectrum thermometer measures the real temperature of the flame of each round hole on the asbestos rectangular plate; the data processing system 2 realizes a data processing function and establishes a functional relationship between the actual temperature value and the corresponding image brightness value.
According to the invention, the accurate calibration of the high-temperature flame temperature measurement system can be realized, and the large error caused by the fact that the real temperature in the high-temperature process cannot be reflected by adopting the high-temperature black body furnace calibration is avoided.
The flame high-temperature measurement calibration method based on the random consistency sampling technology is characterized by comprising the following steps:
step 1): and reading temperature data measured by the spectral thermometer on the rectangular round hole point of the asbestos plate in the flame image, and recording the temperature data in the computer.
Step 2): the flame and asbestos board images were imaged at multiple angles using a camera and recorded in a computer.
Step 3): according to the data recorded in the computer in the previous two steps, a random consistency sampling processing technology is adopted for data processing, and a functional relation between the flame image radiant energy and the recording temperature of the spectrum thermometer is calibrated, and the specific process is as follows:
firstly, the relationship between the gray value of the flame CCD image and the corresponding temperature is deduced by the principle of colorimetric thermometry. The formula of the colorimetric thermometry is as follows (1):
wherein R and B are average gray values of an R channel and a B channel of the flame image; lambda [ alpha ]r，λgAnd λbWavelengths of red, green and blue light; t is a flame temperature value measured by a spectrum thermometer; c is a constant in planck's radiation law, with a value of 0.0144 mK; k is an unknown constant and needs to be calibrated. If it is
b＝-(ln K+5ln(λr/λb) The unknown constant K to be calibrated is contained in the parameter b, and a and b are the parameters to be calibrated. Then, equation (1) is transformed into:
ln(R/B)＝a·T-1+b (2)
and secondly, randomly selecting two groups of sample values and estimating the initial model of the formula (2) by adopting a random consistency sampling technology according to the average gray scale ratio R/B of the R channel and the B channel estimated from the position of the asbestos plate circular hole in the flame CCD image and the temperature value T measured by the spectrum thermometer.
Substituting all recorded R/B and T into the estimated model to calculate all errorsi(i denotes the number of measurement points)
i＝||ln(R/B)-(a·T-1+b)|| (3)
Setting an error threshold value tau, wherein the threshold value is usually determined by using a manual experience selection method. And (4) taking the data points with the error larger than tau as abnormal data points, counting the number of the data points meeting the threshold value smaller than tau in the model established by two groups of samples randomly, and finding a group of data points with the least abnormal data points to fit the final model.
Step 4): and 3) measuring the temperature value of any point in the flame CCD image according to the model established in the step 3).
Drawings
FIG. 1 is a block diagram of the system of the present invention;
in the figure: 1 is an image acquisition module, comprising: a1, rectangular asbestos board, A2, camera, A3, USB transmission line and spectrum thermometer, 2 is data processing module, mainly including computer and data processing and display software.
FIG. 2 is a graph plotting temperature values and gray scale values
Detailed Description
The specific structure of this embodiment, referring to fig. 1, this precaution mainly includes image acquisition module 1, data processing module 2, its characterized in that: the image acquisition module 1 for precaution comprises a rectangular asbestos plate A1, a high-definition camera A2 and a USB transmission line A3; the data processing and display interface system 2 comprises a computer, data processing and interface display software; wherein, the high-definition camera and the spectrum thermometer are connected with the computer through a USB transmission line A3.
The contents of the flame pyrometry calibration method based on the random consistency sampling technology of the present invention are further described in detail with reference to the following specific examples:
step 1): images including flames and a rectangular asbestos board were captured by a high-definition camera.
Step 2): processing the flame image to obtain a temperature value and a gray value corresponding to the round hole in the required rectangular asbestos plate;
measuring the temperature of each round hole on the rectangular asbestos plate by using a spectral temperature measuring instrument, and recording the temperature value; then, the average gray values of the red channel R and the blue channel B of the corresponding round hole images in the flame images are read by utilizing an image processing technology. The temperature values, the average gray values of the red channel R and the blue channel B are shown in the following table.
| Temperature value (Unit K) | R | B |
| 1273 | 17.40 | 12.18 |
| 1330 | 26.16 | 14.42 |
| 1385 | 33.23 | 15.46 |
| 1415 | 46.28 | 16.90 |
| 1477 | 72.08 | 20.36 |
| 1525 | 108.25 | 28.43 |
| 1573 | 150.76 | 35.34 |
| 1626 | 206.87 | 44.25 |
Step 3): abnormal data elimination and parameter calibration are carried out on the temperature value and the gray value by utilizing a random consistency sampling technology;
according to the temperature value and the gray value obtained by measuring the round hole on the rectangular asbestos plate, randomly selecting two groups of samples and estimating parameters a and b according to the formula (2); then, setting an error threshold tau to be 0.1, wherein the threshold is determined by a manual experience selection method, and counting the number of data point pairs (R/B, T) smaller than the error threshold; and taking a group of data with the largest number for parameter calibration, taking a data point smaller than the error threshold value for re-calibrating the parameters, and drawing a curve as shown in FIG. 2. In addition, the method can also remove abnormal data points, such as the abnormal points are not involved in calibration after being removed in figure 2.
And 4) calculating a temperature value according to the calibrated parameters by using the values of any point R and B in the flame CCD image according to the calibrated parameters.
Claims (2)
1. A flame high temperature measurement calibration method based on random consistency sampling technology is characterized in that: the method comprises image acquisition (1) and a data processing module (2), wherein the image acquisition module (1) of the method comprises a rectangular asbestos plate (A1), a high-definition camera (A2), a spectral thermometer and a USB transmission line (A3); the data processing module (2) comprises a computer and related software; the high-definition camera (A2) and the spectrum thermometer are connected with a computer through a USB transmission line (A3) to acquire images and measure temperature.
2. The flame pyrometry calibration method applied to the random consistency sampling technique of claim 1, characterized by the steps of:
step 1): and reading temperature data measured by the spectral thermometer on the rectangular round hole point of the asbestos plate in the flame image, and recording the temperature data in the computer.
Step 2): the flame and asbestos board images were imaged at multiple angles using a high speed camera and recorded in a computer.
Step 3): according to the data recorded in the computer in the previous two steps, a random consistency sampling processing technology is adopted for data processing, and a functional relation between the flame image radiant energy and the recording temperature of the spectrum thermometer is calibrated, and the specific process is as follows:
firstly, the relationship between the gray value of the flame CCD image and the corresponding temperature is deduced by the principle of colorimetric thermometry.
The formula of the colorimetric thermometry is as follows (1):
wherein R and B are average gray values of an R channel and a B channel of the flame image; lambda [ alpha ]r，λgAnd λbWavelengths of red, green and blue light; t is a flame temperature value measured by a spectrum thermometer; c is a constant in planck's radiation law, with a value of 0.0144 mK; k is an unknown constant and needs to be calibrated. The unknown constant K to be calibrated is contained in the parameter b, and a and b are parameters to be calibrated. Then, equation (1) is transformed into:
ln(R/B)＝a·T-1+b (2)
and secondly, randomly selecting two groups of sample values and estimating the initial model of the formula (2) by adopting a random consistency sampling technology according to the average gray scale ratio R/B of the R channel and the B channel estimated from the position of the asbestos plate circular hole in the flame CCD image and the temperature value T measured by the spectrum thermometer.
Substituting all recorded R/B and T into the estimated model to calculate all errorsi(i denotes the number of measurement points)
i＝||ln(R/B)-(a·T-1+b)|| (3)
Setting an error threshold tau, making the data points with the error larger than tau be abnormal data points, counting the number of the data points meeting the threshold smaller than tau in the model established by randomly adopting two groups of samples, and finding a group of data points with the least abnormal data points to fit the final model; τ is 0.1;
step 4): and measuring the temperature value of any point in the flame CCD image according to the model established in the step 3).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010667314.2A CN112067132A (en) | 2020-07-13 | 2020-07-13 | Flame high-temperature measurement calibration method based on random consistency sampling technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010667314.2A CN112067132A (en) | 2020-07-13 | 2020-07-13 | Flame high-temperature measurement calibration method based on random consistency sampling technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112067132A true CN112067132A (en) | 2020-12-11 |
Family
ID=73657308
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010667314.2A Pending CN112067132A (en) | 2020-07-13 | 2020-07-13 | Flame high-temperature measurement calibration method based on random consistency sampling technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112067132A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114509166A (en) * | 2022-01-27 | 2022-05-17 | 重庆大学 | High transient high temperature plasma temperature measurement system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105354859A (en) * | 2015-12-09 | 2016-02-24 | 华中科技大学 | Flame visible radiation calibration method |
| CN105606222A (en) * | 2015-09-06 | 2016-05-25 | 东南大学 | Flame three-dimensional temperature field measurement imaging device, measuring device and measuring method |
| CN108389169A (en) * | 2018-03-07 | 2018-08-10 | 哈尔滨工业大学 | A kind of temperature rebuilding method applied to the imaging of flame light field refocusing |
| WO2019048102A1 (en) * | 2017-09-06 | 2019-03-14 | Brainlab Ag | Determining the relative position between a thermal camera and a 3d camera using a hybrid phantom and hybrid phantom |
| CN109556736A (en) * | 2018-12-04 | 2019-04-02 | 南京森林警察学院 | A kind of fire scene temperature measuring device and method based near infrared band colorimetric method |
-
2020
- 2020-07-13 CN CN202010667314.2A patent/CN112067132A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105606222A (en) * | 2015-09-06 | 2016-05-25 | 东南大学 | Flame three-dimensional temperature field measurement imaging device, measuring device and measuring method |
| CN105354859A (en) * | 2015-12-09 | 2016-02-24 | 华中科技大学 | Flame visible radiation calibration method |
| WO2019048102A1 (en) * | 2017-09-06 | 2019-03-14 | Brainlab Ag | Determining the relative position between a thermal camera and a 3d camera using a hybrid phantom and hybrid phantom |
| CN108389169A (en) * | 2018-03-07 | 2018-08-10 | 哈尔滨工业大学 | A kind of temperature rebuilding method applied to the imaging of flame light field refocusing |
| CN109556736A (en) * | 2018-12-04 | 2019-04-02 | 南京森林警察学院 | A kind of fire scene temperature measuring device and method based near infrared band colorimetric method |
Non-Patent Citations (1)
| Title |
|---|
| 王立新;钱扬义;范婉贞;许雯辉;唐文秀;: "利用手持技术探究加热过程中石棉网上表面的温度", 化学教育(中英文), no. 21, pages 1 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114509166A (en) * | 2022-01-27 | 2022-05-17 | 重庆大学 | High transient high temperature plasma temperature measurement system |
| CN114509166B (en) * | 2022-01-27 | 2024-02-23 | 重庆大学 | High-transient high-temperature plasma temperature measurement system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109839072B (en) | DIC-based temperature field and deformation field synchronous measurement method and device | |
| CN110057399B (en) | Temperature field and displacement field synchronous measurement system and measurement method based on 3D-DIC | |
| CN112634373B (en) | Visual measurement system dynamic correction method based on zero-expansion ceramic calibration plate | |
| CN111429540A (en) | Temperature field and deformation field synchronous measurement device and method | |
| CN110160661B (en) | Object surface temperature measuring method and device based on visible light photo | |
| CN112067132A (en) | Flame high-temperature measurement calibration method based on random consistency sampling technology | |
| CN101915618A (en) | Device and method for calibrating emissivity of high-temperature fuel gas | |
| CN112504463A (en) | Temperature measurement system and temperature measurement method thereof | |
| CN111198041A (en) | Device and method for multi-dimensional non-contact high-precision detection of target temperature | |
| CN110310245B (en) | Correction method and correction device for image illumination distribution and terminal | |
| CN1112574C (en) | High-temperature luminous body temperature and temperature distribution measuring method based on three primary colors | |
| CN111289148B (en) | Transient fireball parameter acquisition method based on field calibration | |
| TWI465702B (en) | Non-contact temperature measurung method | |
| CN113758683B (en) | Camera system post-irradiation spectrum degradation evaluation method based on average color saturation | |
| CN109444150A (en) | Contactless crackle measurement method and its device | |
| CN111595458B (en) | Thermal infrared imager radiometric calibration method and device | |
| CN108645522A (en) | Temperature field of molten pool detecting system based on colored CCD under CMT welding procedures | |
| CN1693859A (en) | Contactless high-temp measuring method based on data mixed | |
| CN201731941U (en) | Calibration device for high-temperature gas emission rate | |
| CN117288335A (en) | Online calibration method of thermometer | |
| CN104267002A (en) | Transmission type visibility meter for improving measurement accuracy and measurement method of visibility meter | |
| CN113847992B (en) | Method, device, equipment and storage medium for improving temperature measurement precision of far small target | |
| CN112729565B (en) | Temperature measurement method for increasing distance through infrared lens | |
| CN218157642U (en) | System for measuring surface emissivity and temperature field of graphene PTC heat dissipation device | |
| TWI682151B (en) | High-precision non-contact method for measuring temperature of metal hot-pressing mold |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |