Method and system for infrared image temperature calibration

Abstract

The application relates to a method and system for infrared image temperature calibration. The error amount caused by the measurement distance between an infrared radiation source and an infrared imager is determined through atmospheric transmittance. The surface emissivity error amount of the infrared radiation source in the actual measurement process is reduced by calling the prior information of the surface emissivity of the object. Based on the atmospheric transmittance, the infrared radiation model and the forward model are established to form a joint estimation model of temperature and hyperparameters. The surface emissivity of the object is derived by using the Bayesian inference method, the prior information of different measurement materials is considered, and the model parameters and temperature distribution are reconstructed by using the improved model and the joint maximum posterior estimation method. The improved infrared thermal radiation model is suitable for scenes with different measurement distances, humidity factors and emissivity. Through multiple experiments of a black body and a high-speed direct-drive fan, it is verified that the method can significantly reduce the measurement error and improve the accuracy of temperature measurement.

Description

Technical Field

[0001] This application relates to the field of infrared image technology, and in particular to a method and system for infrared image temperature calibration. Background Technology

[0002] Infrared thermometry can quickly obtain the surface temperature of an object through non-contact measurement, making it widely used in medical, industrial, transportation, construction, and security fields. However, in practical applications, the accuracy of infrared thermometry is often affected by various factors such as ambient temperature, humidity, atmospheric attenuation, the emissivity of the object's material, measurement distance, and sensor performance, resulting in measured temperatures often being lower than the actual temperatures. Therefore, it is necessary to address the low accuracy of traditional infrared image temperature measurement by proposing a method and system for infrared image temperature calibration. Summary of the Invention

[0003] Therefore, it is necessary to propose a method and system for infrared image temperature calibration to address the shortcomings of low accuracy in traditional infrared image temperature measurement.

[0004] This application provides a method for infrared image temperature calibration, including:

[0005] Determine atmospheric transmittance;

[0006] An infrared radiation model was established based on atmospheric transmittance.

[0007] Call upon the prior art emitted from the object's surface;

[0008] Construct the forward model;

[0009] Based on the infrared radiation model, prior information on the emission from the object surface, and the forward model, a joint estimation model for temperature and hyperparameters is formed.

[0010] An alternating iterative optimization algorithm is used in the joint estimation model of temperature and hyperparameters to obtain the final temperature result.

[0011] Furthermore, data on relative humidity, measurement distance, and absorption characteristics in the infrared band are obtained;

[0012] The relative humidity, measurement distance, and absorption characteristics in the infrared band are incorporated into the parameter formula of Beer-Lambert's law.

[0013] Based on the parameter formula of Beer-Lambert's law, the attenuation data of infrared radiation in the atmosphere are calculated;

[0014] Using data on the attenuation of infrared radiation in the atmosphere, an expression for atmospheric transmittance is determined.

[0015] Furthermore, the expression for atmospheric transmittance is:

[0016]

[0017] Where τ is the atmospheric transmittance, k(h) w ) for water vapor content h w The relevant attenuation coefficients are given, where d is the propagation distance of infrared radiation and e is the natural constant.

[0018] Furthermore, the relationship between the attenuation coefficient and temperature and humidity is as follows:

[0019]

[0020] in, This represents the amount of water vapor condensed at temperature T1. This represents the amount of water vapor condensed at temperature T2. and Relative humidity at different temperatures Let T be the attenuation coefficient at temperature T1. is the attenuation coefficient at temperature T2.

[0021] Furthermore, the emission rate is defined as a parameter to be assigned a value;

[0022] Invoke the initial infrared radiation model;

[0023] Emissivity is incorporated into the initial infrared radiation model to form an infrared radiation model.

[0024] Furthermore, the expression for the temperature measured by the infrared imager in the infrared radiation model is:

[0025]

[0026] Among them, T m T is the temperature measured by an infrared imager. r T represents the actual temperature of the target object, ε represents the emissivity of the object's surface, and T represents the actual temperature of the target object. u For background temperature, T a Let be the atmospheric temperature, and n be a constant related to the infrared sensor material.

[0027] Furthermore, the surface emissivity of the object is defined as a parameter to be assigned a value;

[0028] Experimental measurement data of the emissivity of an object's surface;

[0029] Statistical analysis of experimental measurement data;

[0030] Using the Gaussian distribution, the mean and variance of the surface emissivity of the object in the experimental measurement data are determined.

[0031] Furthermore, the forward model of the temperature measured by the infrared imager is expressed as follows:

[0032] T m =H(T) r ,ε)+ζ

[0033] Where ζ represents the measured swing amount;

[0034] Based on the inverse gamma distribution, the variance of the measured swing is pre-determined;

[0035] The variance of the actual temperature distribution of the target object is defined as an inverse gamma distribution.

[0036] Furthermore, based on the actual temperature of the target object, the surface emissivity of the object, the first hidden variable about the actual temperature of the target object, and the second hidden variable about the surface emissivity of the object, a joint estimation model of temperature and hyperparameters is established.

[0037] The joint estimation model of temperature and hyperparameters is optimized using the JMAP estimation method;

[0038] Generate the cost function for the joint estimation model of temperature and hyperparameters.

[0039] This application provides a system for infrared image temperature calibration, comprising:

[0040] The processing end is used to execute the infrared image temperature calibration method described above;

[0041] An external device is communicatively connected to the processing terminal, and the external device includes one or more of an infrared camera and a parameter acquisition device.

[0042] This application relates to a method and system for infrared image temperature calibration. It determines the error caused by the measurement distance between the infrared radiation source and the infrared imager by utilizing atmospheric transmittance. Prior information on the surface emissivity of the object reduces the error in the surface emissivity of the infrared radiation source during actual measurement. Based on atmospheric transmittance, an infrared radiation model and a forward model are established to form a joint estimation model for temperature and hyperparameters. The surface emissivity of the object is derived using Bayesian inference methods, while considering prior information from different measurement materials. Combined with the improved model, the model parameters and temperature distribution are reconstructed using a joint maximum a posteriori estimation method. The improved infrared thermal radiation model is applicable to scenarios with different measurement distances, humidity factors, and emissivity. Multiple experiments using a blackbody and a high-speed direct-drive fan verify that this method can significantly reduce measurement errors and improve the accuracy of temperature measurement. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a method for infrared image temperature calibration according to an embodiment of this application.

[0044] Figure 2 This is a structural connection diagram of an infrared image temperature calibration system provided in an embodiment of this application.

[0045] Figure label:

[0046] 100 - Processing unit; 200 - External device; 210 - Infrared camera; 220 - Parameter acquisition unit. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] This application provides a method for infrared image temperature calibration.

[0049] like Figure 1 As shown, in one embodiment of this application, a method for infrared image temperature calibration includes:

[0050] S100 determines atmospheric transmittance.

[0051] S200, based on atmospheric transmittance, establishes an infrared radiation model.

[0052] S300 calls upon the prior information emitted from the object's surface.

[0053] S400, build the forward model.

[0054] S500, based on the infrared radiation model, prior information on the emission from the object surface, and the forward model, forms a joint estimation model for temperature and hyperparameters.

[0055] S600 uses an alternating iterative optimization algorithm in the joint estimation model of temperature and hyperparameters to obtain the final temperature result.

[0056] Specifically, emissivity is treated as a Gaussian distributed variable with known mean and variance, and directly incorporated into the forward model for temperature calibration. The advantage of this approach is that it allows for a more flexible description of emissivity variations under different measurement conditions, improving the model's applicability and the accuracy of temperature calibration. By analyzing and correcting the measurement data using Bayesian inference methods, the new model effectively reduces measurement errors and achieves more accurate temperature measurement results under varying measurement distances, humidity levels, and material properties.

[0057] More specifically, instead of treating emissivity as a fixed value, it is introduced as a random variable with known mean and variance, thus fully considering the variation characteristics of emissivity in the forward model. By combining Bayesian inference to optimize the model parameters, the accuracy and robustness of temperature measurements under various complex environments are ensured. This method overcomes the limitations of traditional methods in handling emissivity, achieving better calibration results in experiments and demonstrating its reliability in the field of heat source measurement.

[0058] This embodiment relates to a method for infrared image temperature calibration, which determines the error caused by the measurement distance between the infrared radiation source and the infrared imager by using atmospheric transmittance. It utilizes prior information about the surface emissivity of the object to reduce the surface emissivity error of the infrared radiation source during actual measurement. Based on atmospheric transmittance, an infrared radiation model and a forward model are established to form a joint estimation model for temperature and hyperparameters. The surface emissivity of the object is derived using Bayesian inference methods, while considering prior information from different measurement materials. Combined with the improved model, the model parameters and temperature distribution are reconstructed using a joint maximum a posteriori estimation method. The improved infrared thermal radiation model is applicable to scenarios with different measurement distances, humidity factors, and emissivity. Multiple experiments using a blackbody and a high-speed direct-drive fan have verified that this method can significantly reduce measurement errors and improve the accuracy of temperature measurement.

[0059] In one embodiment of this application, S100 includes:

[0060] S110 acquires relative humidity, measurement distance, and absorption characteristic data in the infrared band.

[0061] S120 incorporates relative humidity, measurement distance, and infrared absorption characteristics into the parameter formula of Beer-Lambert's law.

[0062] S130, based on the parameter formula of Beer-Lambert's law, calculates the attenuation data of infrared radiation in the atmosphere.

[0063] S140, using data on the attenuation of infrared radiation in the atmosphere, determines the expression for atmospheric transmittance.

[0064] Specifically, atmospheric transmittance is inferred. In infrared thermometry, atmospheric transmittance is a crucial parameter that directly affects the accuracy of temperature calibration. First, relative humidity, measurement distance, and absorption characteristics in the infrared band are obtained from the measurement environment. Then, Beer-Lambert's law is used to calculate the attenuation of infrared radiation in the atmosphere.

[0065] The expression for atmospheric transmittance is:

[0066]

[0067] Where τ is the atmospheric transmittance, k(h) w ) for water vapor content h w The relevant attenuation coefficients are given, where d is the propagation distance of infrared radiation and e is the natural constant.

[0068] Since water vapor affects the attenuation coefficient under different temperature conditions, this embodiment will also consider calculating the relationship between the attenuation coefficient and temperature and humidity:

[0069] The relationship between the attenuation coefficient and temperature and humidity is as follows:

[0070]

[0071] in, This represents the amount of water vapor condensed at temperature T1. This represents the amount of water vapor condensed at temperature T2. and Relative humidity at different temperatures Let T be the attenuation coefficient at temperature T1. is the attenuation coefficient at temperature T2.

[0072] The attenuation coefficient k(h) under the current environment can be determined. w ), and based on this, the atmospheric transmittance τ is obtained.

[0073] In one embodiment of this application, S200 includes:

[0074] S210 defines the emissivity as a parameter to be assigned a value.

[0075] S220, invoke the initial infrared radiation model.

[0076] S230 incorporates emissivity into the initial infrared radiation model to form an infrared radiation model.

[0077] Specifically, an improved infrared radiation model is established. In traditional infrared radiation models, the relationship between measured temperature and actual temperature is usually limited by the assumption that emissivity is constant. This invention, however, treats emissivity as a variable and directly incorporates it into the model. The improved infrared radiation model can more realistically reflect the infrared radiation characteristics under different measurement conditions.

[0078] The expression for the temperature measured by the infrared imager in the infrared radiation model is:

[0079]

[0080] Among them, T m T is the temperature measured by an infrared imager. r T represents the actual temperature of the target object, ε represents the emissivity of the object's surface, and T represents the actual temperature of the target object.u For background temperature, T a Let be the atmospheric temperature, and n be a constant related to the infrared sensor material.

[0081] In one embodiment of this application, S300 includes:

[0082] S310 defines the emissivity of an object's surface as a parameter to be assigned a value.

[0083] S320 receives experimental measurement data of the emissivity of an object's surface.

[0084] S330, Statistical experimental measurement data.

[0085] S340 uses a Gaussian distribution to determine the mean and variance of the surface emissivity of an object in experimental measurement data.

[0086] Specifically, this invention constructs prior information on the emissivity of an object's surface. The invention uses the emissivity of the object's surface as a variable and constructs its prior information through experimental measurement data. First, multiple experiments are conducted using an infrared thermometer on the surface of an object made of the same material under different environmental conditions to obtain a set of representative emissivity measurements. Then, the obtained measurements are statistically analyzed, assuming that the emissivity follows a Gaussian distribution. By fitting the measurement data, the mean emissivity μ is obtained. ε Sum of variance v ε , which serves as the distribution parameter for emissivity. The Gaussian distribution of emissivity is expressed as:

[0087] ε~N(ε|μ ε v ε ) Formula 4

[0088] After establishing prior information on emissivity, the emissivity model is used for subsequent iterative calculations and temperature calibration. This model can dynamically reflect the changing characteristics of emissivity, ensuring the accuracy and robustness of temperature measurements.

[0089] In one embodiment of this application, S400 includes:

[0090] The forward model expression for the temperature measured by the S410 infrared imager is as follows:

[0091] T m =H(T) r Formula 5

[0092] Where ζ represents the measured oscillation.

[0093] S420, based on the inverse gamma distribution, predetermines the variance of the measured oscillation.

[0094] S430 defines the variance of the actual temperature distribution of the target object as an inverse gamma distribution.

[0095] Specifically, ζ represents the measurement uncertainty. In the forward model, emissivity is introduced as a variable. In this way, changes in emissivity can be dynamically reflected in the temperature measurement process, improving the flexibility and accuracy of temperature calibration. The scaling operator H(.) is based on the improved infrared radiation model.

[0096] In the forward model, the noise ζ is assumed to be independent and identically distributed, and follows a Gaussian distribution. However, its variance is actually unknown and needs to be estimated. Therefore, we treat it as an inverse gamma distribution:

[0097]

[0098] Since we assume that the measurement error follows a Gaussian distribution, we have:

[0099] P(T m |T r v r v ζ )=N(T m |T r v ζ ) Formula 7

[0100] And T r It is set to a Gaussian distribution, and its variance is also set to an inverse gamma distribution:

[0101]

[0102] Where T av Based on prior information, it can be calculated using an improved infrared radiation model.

[0103] In one embodiment of this application, S500 includes:

[0104] S510 establishes a joint estimation model for temperature and hyperparameters based on the actual temperature of the target object, the surface emissivity of the object, the first hidden variable about the actual temperature of the target object, and the second hidden variable about the surface emissivity of the object.

[0105] S520 utilizes the JMAP estimation method to optimize the joint estimation model of temperature and hyperparameters.

[0106] S530 generates the cost function for the joint estimation model of temperature and hyperparameters.

[0107] Specifically, the expression for the joint estimation model of temperature and hyperparameters is as follows:

[0108] P(T r ,ε,v r ,v ζ |Tm )=P(T m |,T r ,ε,v ξ )P(T r |,T av v r )

[0109] P(ε|μ ε ,ν ε )P(ν r |a t b t )P(ν ζ |a ζ b ζ ) Formula 9

[0110] JMAP estimation was performed on the joint estimation model of temperature and hyperparameters, yielding the following results:

[0111]

[0112] More specifically, the alternating cost minimization function is:

[0113]

[0114] The cost function for the joint estimation model of temperature and hyperparameters is:

[0115]

[0116] In one embodiment of this application, S600 includes:

[0117] Joint posterior probabilistic inference: T r , ε, v r and v ζ The cost function can be estimated by alternately minimizing it.

[0118]

[0119] Gradient descent can be used to obtain The value of .

[0120]

[0121] Similarly, we can update the variable ε to obtain... Value:

[0122]

[0123] And v r and v ζ By fixing T r and ε, and v r and vζ The derivative is calculated when it equals 0:

[0124]

[0125] Using an alternating iterative optimization algorithm, a predetermined number of iterations is set to obtain the final temperature result.

[0126] This application provides a system for infrared image temperature calibration.

[0127] like Figure 2 As shown in one embodiment of this application, an infrared image temperature calibration system includes a processing terminal 100 and an external device 200.

[0128] The processing terminal 100 is used to perform the infrared image temperature calibration method.

[0129] The external device 200 is communicatively connected to the processing terminal 100, and the external device 200 includes one or more of an infrared camera 210 and a parameter acquisition device 220.

[0130] Specifically, the infrared camera 210 is used to acquire infrared images.

[0131] The parameter acquisition unit 220 includes a temperature sensor, a humidity sensor, a distance measuring device, etc. The parameter acquisition unit 220 is used to perform measurements of atmospheric transmittance and to construct prior art emission information from the surface of an object.

[0132] This embodiment relates to an infrared image temperature calibration system. The processing unit 100 calculates atmospheric transmittance using an external device 200 to determine the error caused by the measurement distance between the infrared radiation source and the infrared imager. The processing unit 100 utilizes prior information on the surface emissivity of the object to reduce the surface emissivity error of the infrared radiation source during actual measurement. Based on atmospheric transmittance, an infrared radiation model and a forward model are established to form a joint estimation model for temperature and hyperparameters. The surface emissivity of the object is derived using Bayesian inference methods, while considering prior information from different measurement materials. Combined with the improved model, the model parameters and temperature distribution are reconstructed using a joint maximum a posteriori estimation method. The improved infrared thermal radiation model is applicable to scenarios with different measurement distances, humidity factors, and emissivity. Multiple experiments using a blackbody and a high-speed direct-drive fan have verified that this method can significantly reduce measurement errors and improve the accuracy of temperature measurement.

[0133] The technical features of the above embodiments can be combined arbitrarily, and the execution order of the method steps is not restricted. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method of infrared image temperature calibration, characterized by, The method comprises the following steps: determining the atmospheric transmittance, which comprises: acquiring the relative humidity, the measurement distance and the absorption characteristic data of the infrared wave band; incorporating the relative humidity, the measurement distance and the absorption characteristic data of the infrared wave band into the parameter formula of Beer-Lambert law; calculating the attenuation data of the infrared radiation in the atmosphere based on the parameter formula of Beer-Lambert law; determining the expression of the atmospheric transmittance by using the attenuation data of the infrared radiation in the atmosphere and combining the relationship formula of the attenuation coefficient changing with the temperature and the humidity; establishing the infrared radiation model based on the atmospheric transmittance; calling the prior information of the surface emissivity of the object; constructing the forward model; forming the joint estimation model of the temperature and the hyperparameter based on the infrared radiation model, the prior information of the surface emissivity of the object and the forward model; obtaining the final temperature result by using the alternating iterative optimization algorithm in the joint estimation model of the temperature and the hyperparameter; wherein the relationship formula of the attenuation coefficient changing with the temperature and the humidity is: wherein is the temperature is the condensation amount of the water vapor, is the temperature is the condensation amount of the water vapor, and is the relative humidity at different temperatures, is the attenuation coefficient at the temperature is the attenuation coefficient at the temperature is the attenuation coefficient at the temperature is the attenuation coefficient at the temperature 2. The method of infrared image temperature calibration of claim 1, wherein, the expression of the atmospheric transmittance is: in, Atmospheric transmittance, To be related to water vapor content The relevant attenuation coefficient, The propagation distance of infrared radiation. It is a natural constant.

3. The method of infrared image temperature calibration of claim 2, wherein, the infrared radiation model is established based on the atmospheric transmittance, which comprises: defining the emissivity as a parameter to be assigned; calling the initial infrared radiation model; incorporating the emissivity into the initial infrared radiation model to form the infrared radiation model.

4. The method of infrared image temperature calibration of claim 3, wherein, the emissivity is incorporated into the initial infrared radiation model to form the infrared radiation model, which comprises: defining the expression of the temperature measured by the infrared imager in the infrared radiation model as: wherein, T is the temperature measured by the infrared imager, T is the actual temperature of the target object, ε is the emissivity of the object surface, T is the background temperature, T is the atmospheric temperature, is a constant related to the material of the infrared sensor.

5. The method of infrared image temperature calibration of claim 4, wherein, the prior information of the surface emissivity of the object is called, which comprises: defining the surface emissivity of the object as a parameter to be assigned; receiving the experimental measurement data of the surface emissivity of the object; statistically analyzing the experimental measurement data; determining the mean value and the variance of the surface emissivity of the object in the experimental measurement data by using the Gaussian distribution.

6. The method of infrared image temperature calibration of claim 5, wherein, the forward model of the temperature measured by the infrared imager is constructed, which comprises: defining the expression of the forward model of the temperature measured by the infrared imager as: wherein to measure the amount of swing; predetermining the variance of the measurement swing based on the inverse gamma distribution; defining the variance of the actual temperature distribution of the target object as the inverse gamma distribution.

7. The method of infrared image temperature calibration of claim 6, wherein, the joint estimation model of the temperature and the hyperparameter is formed based on the infrared radiation model, the prior information of the surface emissivity of the object and the forward model, which comprises: establishing the joint estimation model of the temperature and the hyperparameter based on the actual temperature of the target object, the surface emissivity of the object, the first hidden variable about the actual temperature of the target object and the second hidden variable about the surface emissivity of the object; optimizing the joint estimation model of the temperature and the hyperparameter by using the JMAP estimation method; generating the cost function of the joint estimation model of the temperature and the hyperparameter.

8. A system for infrared image temperature calibration, characterized by The method comprises the following steps: a processing end for executing the infrared image temperature calibration method according to any one of claims 1 to 7; an external device in communication connection with the processing end, wherein the external device comprises one or more of an infrared camera and a parameter collector.

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