Temperature measurement method based on infrared camera

Abstract

The invention discloses a temperature measurement method based on an infrared camera, and the method comprises the following steps: 1, collecting truth value data, and constructing a truth value table of temperature and raw value; 2, carrying out pixel point level temperature measurement, and checking a truth table according to the raw value of each measured pixel point; determining a temperature value obtained by performing linear interpolation on two adjacent points of the raw value of each pixel point in the truth table; 3, raw data of the infrared camera in the pixel point level temperature measurement process are obtained; and calibrating raw data of existing different batches of infrared cameras to the interval of the truth table through linear mapping. According to the method, the truth value data is obtained by using the black body to establish the truth table, the raw value and temperature conversion is performed by using the linear interpolation lookup table method and the Hermite interpolation, and the data of different batches of infrared cameras are calibrated through linear mapping, so that the temperature measurement precision is improved.

Description

Technical Field

[0001] This invention relates to the field of temperature measurement technology, and in particular to a temperature measurement method based on an infrared camera. Background Technology

[0002] Infrared cameras are widely used in industry, scientific research, and medicine because they can measure the surface temperature of objects without contact. Traditional infrared thermometry relies on a pre-established correlation between temperature and infrared radiation intensity. In current technology, the images acquired by infrared cameras are usually raw infrared data, which requires complex algorithmic processing to convert into temperature information. Furthermore, due to differences in manufacturing processes between different batches of infrared cameras, there are discrepancies between the raw data and the actual temperature, necessitating effective calibration methods to ensure the accuracy of temperature measurements.

[0003] Therefore, how to perform temperature conversion more accurately to improve the accuracy of temperature measurement has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of the above-mentioned deficiencies of the prior art, the present invention provides a temperature measurement method based on an infrared camera, the purpose of which is to improve the accuracy of temperature measurement by performing more precise temperature conversion.

[0005] To achieve the above objectives, this invention discloses a temperature measurement method based on an infrared camera, comprising the following steps:

[0006] Step 1: Acquire true data and construct a truth table of temperature and raw values;

[0007] Step 2: Perform pixel-level temperature measurement and look up the truth table based on the raw value of each pixel.

[0008] The raw value of each pixel is determined to be the temperature value obtained by linear interpolation between two adjacent points in the truth table;

[0009] Step 3: Obtain the raw data of the infrared camera during the pixel-level temperature measurement process; calibrate the raw data of different batches of infrared cameras to the range of the truth table through linear mapping.

[0010] Preferably, step 1 is as follows:

[0011] Step 1.1: Adjust the temperature of the blackbody radiation source so that the radiation range of the blackbody radiation source covers multiple temperature points within the temperature measurement range;

[0012] Step 1.2: Adjust the infrared camera at different distances;

[0013] Step 1.3: For each temperature point and each distance point, collect multiple frames of raw data;

[0014] Step 1.4: Average the data collected from the multiple frames of raw data at each temperature point and distance point to construct a truth table of temperature and raw values.

[0015] Preferably, in step 2, if the raw value of any pixel is not in the truth table, Hermite interpolation is used for extrapolation.

[0016] Preferably, Hermite interpolation constructs a cubic polynomial between adjacent known data points, such that the polynomial not only has the same value at the known data points, but also has the same derivative at these points;

[0017] When a set of known data points (x) i ,y i ) and its derivative (x) i ,y i ′), where i = 0, 1, ..., n;

[0018] Then Hermite interpolation constructs a set of cubic polynomials P i (x) such that it satisfies the following three conditions:

[0019] The first condition is in each interval [x] i ,x i+1 On the above, a cubic polynomial P is used. i (x);

[0020] The second condition, P i (x i )=y i And P i (x i+1 )=y i+1 ;

[0021] The third condition, P i ′(x i )=y i And P i ′(x i+1 )=y i ′ +1 ;

[0022] Therefore, the cubic polynomial P on each interval i (x) can be represented as:

[0023] P i (x)=h 00 (t)y i +h 10(t)(x i+1 -x i )y i ′+h 01 (t)y i+1 +h 11 (t)(x i+1 -x i )y i ′ +1

[0024] Wherein, the basis function h 00 (t),h 10 (t),h 01 (t),h 11 (t) is defined as follows:

[0025] h 00 (t)=2t 3 -3t 2 +1;

[0026] h 10 (t)=t 3 -2t 2 +t;

[0027] h 01 (t)=-2t 3 +3t 2 ;

[0028] h 11 (t)=t 3 -t 2 .

[0029] Where t = x-x i The parameter is for normalization, and the value of t ranges from [0,1].

[0030] x i+1 -x i

[0031] Determining the temperature value obtained by linear interpolation between two adjacent points in the truth table for each pixel's raw value refers to:

[0032] Based on the data points of the raw value of each pixel in the truth table, a linear interpolation method is used to find the corresponding temperature value in the truth table, specifically:

[0033] Between two known data points, the value of an unknown data point can be calculated through a linear relationship, that is, when the data change between two known data points is continuous and can be approximated by a straight line;

[0034] When two known data points are (x1, y1) and (x2, y2), and x1 < x2, the linear interpolation method estimates any x between x1 and x2 by constructing a straight line. n Corresponding y n The formula is expressed by a linear equation in the form of slope-intercept:

[0035] y n =y1+((y2-y1) / (x2-x1))*(x n -x1);

[0036] Among them, y n x is the value corresponding to the unknown data point to be calculated; n The value for the unknown data point.

[0037] Preferably, the linear mapping calibration involves obtaining the raw values ​​at the highest and lowest temperatures of the infrared cameras in the current batch, and calculating the scaling factor and offset, using the following specific formulas:

[0038]

[0039] offset=raw_value_A_min-scale*raw_value_B_min;

[0040] Here, scale is the scaling factor; offset is the offset, used to linearly scale and offset the original data, with the aim of mapping a new original data range to the existing data range.

[0041] ·raw_value_A_max is the raw value representing the highest temperature of the original infrared camera's true value.

[0042] ·raw_value_A_min is the raw value representing the lowest temperature of the original infrared camera.

[0043] ·raw_value_B_max is the raw value representing the highest temperature of the new infrared camera's true value.

[0044] ·raw_value_B_min is the raw value representing the lowest temperature of the new infrared camera's true value.

[0045] More preferably, the deviation is calibrated to the truth table range through the linear mapping to achieve consistency of temperature measurement data from different batches of infrared cameras. The calibration formula is as follows:

[0046] raw calibrated =scale*raw original +offset;

[0047] This is used to perform linear transformations on the original data, specifically scaling and offsetting. It maps the original data to a new range:

[0048] ·raw calibrated This represents the result after scaling and offsetting.

[0049] •scale is the scaling factor, and offset is the offset.

[0050] ·raw original This represents the original data value before any transformation.

[0051] The beneficial effects of this invention are:

[0052] This invention establishes a truth table by acquiring true data using a blackbody, converts raw values ​​to temperature using a linear interpolation lookup table method and Hermite interpolation, and improves temperature measurement accuracy by calibrating data from different batches of infrared cameras through linear mapping.

[0053] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0054] Figure 1 A flowchart of an embodiment of the present invention is shown. Detailed Implementation

[0055] Example

[0056] like Figure 1 As shown, the temperature measurement method based on an infrared camera includes the following steps:

[0057] Step 1: Acquire true data and construct a truth table of temperature and raw values;

[0058] Step 2: Perform pixel-level temperature measurement and look up the truth table based on the raw value of each pixel.

[0059] The raw value of each pixel is determined to be the temperature value obtained by linear interpolation between two adjacent points in the truth table;

[0060] Step 3: Obtain the raw data of the infrared camera during the pixel-level temperature measurement process; calibrate the raw data of different batches of infrared cameras to the range of the truth table through linear mapping.

[0061] This invention constructs a lookup table between raw values ​​and temperatures, and uses an interpolation algorithm to achieve high-precision conversion from raw values ​​to temperatures. In particular, for the data within the lookup table, linear interpolation or other interpolation algorithms are employed to improve the efficiency and accuracy of temperature conversion.

[0062] For data outside the lookup table, this invention uses Hermite interpolation to extrapolate, ensuring the continuity and accuracy of temperature conversion and overcoming the shortcomings of conventional interpolation methods in handling boundary values.

[0063] To address the data discrepancies between different batches of infrared cameras, this invention uses linear mapping to calibrate raw data to the truth table range, thereby achieving consistency and comparability of temperature measurement data from different batches of infrared cameras.

[0064] In some embodiments, step 1 is specifically as follows:

[0065] Step 1.1: Adjust the temperature of the blackbody radiation source so that its radiation range covers multiple temperature points within the temperature measurement range.

[0066] Step 1.2: Adjust the infrared camera at different distances;

[0067] Step 1.3: For each temperature point and each distance point, collect multiple frames of raw data;

[0068] Step 1.4: Average the multiple frames of raw data collected at each temperature and distance point to construct a truth table of temperature and raw values.

[0069] In practical applications, collecting multiple frames of raw data for each temperature point and each distance point can reduce noise and improve measurement accuracy.

[0070] In some embodiments, in step 2, if the raw value of any pixel is not in the truth table, Hermite interpolation is used for extrapolation.

[0071] In practical applications, using Hermite interpolation for extrapolation can provide smoother and more accurate interpolation results.

[0072] In some embodiments, Hermite interpolation constructs a cubic polynomial between adjacent known data points such that the polynomial not only has the same value at the known data points, but also has the same derivative at those points.

[0073] When a set of known data points (x) i ,y i ) and its derivative (x) i ,y i ′), where i = 0, 1, ..., n;

[0074] Then Hermite interpolation constructs a set of cubic polynomials P i (x) such that it satisfies the following three conditions:

[0075] The first condition is in each interval [x]i ,x i+1 On the above, a cubic polynomial P is used. i (x);

[0076] The second condition, P i (x i )=y i And P i (x i+1 )=y i+1 ;

[0077] The third condition, P i ′(x i )=y i And P i ′(x i+1 )=y i ′ +1 ;

[0078] Therefore, the cubic polynomial P on each interval i (x) can be represented as:

[0079] P i (x)=h 00 (t)y i +h 10 (t)(x i+1 -x i )y i ′+h 01 (t)y i+1 +h 11 (t)(x i+1 -x i )y i ′ +1

[0080] Wherein, the basis function h 00 (t),h 10 (t),h 01 (t),h 11 (t) is defined as follows:

[0081] h 00 (t)=2t 3 -3t 2 +1;

[0082] h 10 (t)=t 3 -2t 2 +t;

[0083] h 01 (t)=-2t 3 +3t 2 ;

[0084] h11 (t)=t 3 -t 2 .

[0085] in, The parameter is for normalization, and the value of t ranges from [0,1].

[0086] In practical applications, Hermite interpolation can construct a smooth and continuous series of cubic polynomials, taking into account the location and slope of data points, thus providing more accurate interpolation results.

[0087] In some embodiments, determining the temperature value obtained by linear interpolation between two adjacent points in the truth table for each pixel's raw value means:

[0088] Based on the data points of the raw value of each pixel in the truth table, a linear interpolation method is used to look up the corresponding temperature value in the truth table, specifically:

[0089] Between two known data points, the value of an unknown data point can be calculated through a linear relationship, that is, when the data change between two known data points is continuous and can be approximated by a straight line;

[0090] When two known data points are (x1, y1) and (x2, y2), and x1 < x2, the linear interpolation method estimates any x between x1 and x2 by constructing a straight line. n Corresponding y n The formula is expressed by a linear equation in the form of slope-intercept:

[0091] y n =y1+((y2-y1) / (x2-x1))*(x n -x1);

[0092] Among them, y n x is the value corresponding to the unknown data point to be calculated; n The value for the unknown data point.

[0093] In some embodiments, linear mapping calibration involves obtaining the raw values ​​at the highest and lowest temperatures of the current batch of infrared cameras, calculating the scaling factor and offset, using the following formulas:

[0094]

[0095] offset=raw_value_A_min-scale*raw_value_B_min;

[0096] Here, scale is the scaling factor; offset is the offset, used to linearly scale and offset the original data, with the aim of mapping a new original data range to the existing data range.

[0097] ·raw_value_A_max is the raw value representing the highest temperature of the original infrared camera's true value.

[0098] ·raw_value_A_min is the raw value representing the lowest temperature of the original infrared camera.

[0099] ·raw_value_B_max is the raw value representing the highest temperature of the new infrared camera's true value.

[0100] ·raw_value_B_min is the raw value representing the lowest temperature of the new infrared camera's true value.

[0101] In some embodiments, the deviation is calibrated to the truth table range through linear mapping to achieve consistency of temperature measurement data from different batches of infrared cameras. The calibration formula is as follows:

[0102] raw calibrated =scale*raw original +offset

[0103] This is used to perform linear transformations on the original data, specifically scaling and offsetting. It maps the original data to a new range:

[0104] ·raw calibrated This represents the result after scaling and offsetting.

[0105] •scale is the scaling factor, and offset is the offset.

[0106] ·raw original This represents the original data value before any transformation.

[0107] This invention obtains true data through a blackbody radiation source and uses linear interpolation and Hermite interpolation for temperature conversion, achieving high-precision temperature measurement, which is suitable for applications requiring precise temperature control.

[0108] This invention combines linear interpolation and Hermite interpolation, which ensures high accuracy while having high computational efficiency, making it suitable for real-time temperature measurement needs.

[0109] This invention calibrates infrared camera data from different batches using linear mapping, enabling the system and method to adapt to different models and batches of infrared cameras, thus improving the system's applicability and versatility.

[0110] The linear mapping calibration method in this invention effectively solves the temperature measurement error problem caused by differences in manufacturing processes between different batches of infrared cameras, ensuring the reliability and data consistency of the system during long-term use.

[0111] The system and method of this invention have a simple structure and are easy to implement. They can be upgraded and modified based on existing infrared cameras, have high practical value and promotion prospects, and are more convenient for widespread application.

[0112] This invention obtains true data by using a blackbody, converts raw values ​​to temperature using linear interpolation and Hermite interpolation, and calibrates data from different batches of infrared cameras through linear mapping, thereby improving temperature measurement accuracy. It is applicable to multiple fields such as industry, medical care, and security.

[0113] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A temperature measurement method based on an infrared camera; characterized in that, Includes the following steps: Step 1: Acquire true data and construct a truth table of temperature and raw values; Step 2: Perform pixel-level temperature measurement and look up the truth table based on the raw value of each pixel. The raw value of each pixel is determined to be the temperature value obtained by linear interpolation between two adjacent points in the truth table; Step 3: Obtain the raw data of the infrared camera during the pixel-level temperature measurement process; calibrate the raw data of different batches of infrared cameras to the range of the truth table through linear mapping.

2. The temperature measurement method based on an infrared camera according to claim 1, characterized in that, Step 1 is as follows: Step 1.1: Adjust the temperature of the blackbody radiation source so that the radiation range of the blackbody radiation source covers multiple temperature points within the temperature measurement range; Step 1.2: Adjust the infrared camera at different distances; Step 1.3: For each temperature point and each distance point, collect multiple frames of raw data; Step 1.4: Average the data collected from the multiple frames of raw data at each temperature point and distance point to construct a truth table of temperature and raw values.

3. The temperature measurement method based on an infrared camera according to claim 1, characterized in that, In step 2, if the raw value of any pixel is not in the truth table, Hermite interpolation is used for extrapolation.

4. The temperature measurement method based on an infrared camera according to claim 3, characterized in that, Hermite interpolation constructs a cubic polynomial between adjacent known data points, such that the polynomial not only has the same value at the known data points, but also has the same derivative at these points. When a set of known data points (x) i ,y i ) and its derivative (x) i ,y i ′), where i = 0, 1, ..., n; Then Hermite interpolation constructs a set of cubic polynomials P i (x) such that it satisfies the following three conditions: The first condition is in each interval [x] i ,x i+1 On the above, a cubic polynomial P is used. i (x); The second condition, P i (x i )=y i And P i (x i+1 )=y i+1 ; The third condition, P i ′(x i )=y i And P i ′(x i+1 )=y i ′ +1 ; Therefore, the cubic polynomial P on each interval i (x) can be represented as: P i (x)=h 00 (t)y i +h 10 (t)(x i+1 -x i )y i ′+h 01 (t)y i+1 +h 11 (t)(x i+1 -x i )y i ′ +1 ; Wherein, the basis function h 00 (t),h 10 (t),h 01 (t),h 11 (t) is defined as follows: h 00 (t)=2t 3 -3t 2 +1; h 10 (t)=t 3 -2t 2 +t; h 01 (t)=-2t 3 +3t 2 ; h 11 (t)=t 3 -t 2 ; in, The parameter is for normalization, and the value of t ranges from [0,1].

5. The temperature measurement method based on an infrared camera according to claim 1, characterized in that, Determining the temperature value obtained by linear interpolation between two adjacent points in the truth table for each pixel's raw value refers to: Based on the data points of the raw value of each pixel in the truth table, a linear interpolation method is used to find the corresponding temperature value in the truth table, specifically: Between two known data points, the value of an unknown data point can be calculated through a linear relationship, that is, when the data change between two known data points is continuous and can be approximated by a straight line; When two known data points are (x1, y1) and (x2, y2), and x1 < x2, the linear interpolation method estimates any x between x1 and x2 by constructing a straight line. n Corresponding y n The formula is expressed by a linear equation in the form of slope-intercept: y n =y1+((y2-y1) / (x2-x1))*(x n -x1); Among them, y n x is the value corresponding to the unknown data point to be calculated; n The value of the unknown data point.

6. The temperature measurement method based on an infrared camera according to claim 1, characterized in that, The linear mapping calibration involves obtaining the raw values ​​of the infrared cameras in the current batch at the highest and lowest temperatures, and calculating the scaling factor and offset. The specific formulas are as follows: offset=raw_value_A_min-scale*raw_value_B_min; Where scale is the scaling factor; offset is the offset, which is used to linearly scale and offset the original data, with the purpose of mapping a new original data range to the existing data range. raw_value_A_max is the raw value representing the highest temperature in the original infrared camera's true value; raw_value_A_min is the raw value representing the first temperature of the original infrared camera's true value; raw_value_B_max is the raw value representing the highest temperature of the new infrared camera's true value; raw_value_B_min is the raw value representing the lowest temperature of the new infrared camera's true value.

7. The temperature measurement method based on an infrared camera according to claim 6, characterized in that, The deviation is calibrated to the truth table range through the linear mapping, thus achieving consistency in temperature measurement data from different batches of infrared cameras. The calibration formula is as follows: raw calibrated =scale*raw original +offset; This is used to perform linear transformations on the original data, specifically scaling and offsetting. It maps the original data to a new range: raw calibrated This represents the result after scaling and offsetting; scale is the scaling factor, and offset is the offset. raw original This represents the original data value before any transformation.

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