Curved surface infrared thermal imaging detection temperature correction device and method based on laser array

Abstract

The invention belongs to the field of infrared thermal imaging temperature correction, and particularly relates to a curved surface infrared thermal imaging detection temperature correction device and method based on a laser array. The correction method comprises the steps of establishing a correction device system, measuring a laser array, calculating an included angle between a normal direction of each measurement point and a radiation source direction, calculating a correction factor, measuring radiation brightness by a radiation emission method, calculating an uncorrected emissivity and a real emissivity, and calculating a real temperature. According to the correction device and method, a set of efficient, accurate and high-adaptability curved surface object temperature measurement system is formed by integrating a laser array scanning technology, radiation reflection method measurement and a computer processing technology, and efficient, accurate and high-adaptability detection of the surface temperature of the curved surface object is achieved; a traditional complex algorithm and a large amount of calculation processes are simplified, the influence of the curved surface shape on temperature measurement is effectively solved through the dynamic correction factor, and the measurement precision and reliability are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of infrared thermal imaging temperature correction, specifically relating to a curved surface infrared thermal imaging detection temperature correction device and method based on a laser array. Background Technology

[0002] With the continuous development of technology, infrared thermal imaging technology has been widely used in many fields such as industrial inspection, medical diagnosis, and security monitoring due to its advantages of non-contact, visualization, and convenience. In practical applications, the geometry of curved surfaces leads to complex reflection and refraction of infrared radiation, making it difficult to accurately determine the relationship between the radiation signal received by the imaging system and the actual temperature, resulting in errors in the temperature measurement of infrared thermal imagers. However, temperature detection of curved objects is extremely common in many fields, such as complex curved surface components in the aerospace industry and curved body parts in automobile manufacturing. Accurate measurement of their surface temperature is crucial for defect detection and quality control.

[0003] Currently, there is a method and system for infrared imaging correction of different emissivity based on image enhancement technology. This method uses multiple target infrared images and establishes a temperature calibration model based on a neural network model to achieve image output of the acceptable temperature. However, establishing the temperature calibration model requires capturing a large number of target infrared images and performing temperature calibration, resulting in a complex algorithm and high computational load. Furthermore, this method divides the target infrared image into target and reference regions for fusion calculation. While it is relatively accurate for temperature correction of planar objects, it fails to meet the high-precision measurement requirements of curved surfaces due to significant variations in surface emissivity, leading to poor adaptability. Summary of the Invention

[0004] To address the aforementioned shortcomings of existing technologies, this invention provides a curved infrared thermal imaging temperature correction device and method based on a laser array, which solves the problems of low measurement accuracy caused by complex algorithms, large computational load, and poor adaptability in existing infrared thermal imaging temperature correction methods.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for temperature correction in curved surface infrared thermal imaging based on a laser array is provided, comprising the following steps: S1. Establish a calibration device system: Place a known emissivity device on one side of the object to be measured. The reference object is set up with an infrared thermal imager, a laser array, a first radiation source and a second radiation source, and the relative positions of the first radiation source, the second radiation source and the infrared thermal imager are fixed and known. S2. Laser Array Measurement: A laser array is used to scan the surfaces of the object under test and the reference object to obtain the shape and spatial coordinate information of the surfaces of the object under test and the reference object. Based on the shape and spatial coordinate information, the measurement points on the surface of the object under test are calculated. unit normal vector ; S3. Calculate the angle between the normal direction at each measurement point and the direction of the radiation source: based on each measurement point unit normal vector and each measurement point Calculate the spatial coordinates of the first and second radiation sources for each measurement point. The angle between the normal direction and the direction pointing to the first and second radiation sources ; S4. Calculate the correction factor: based on the included angle Each measurement point Calculate the distances between the object and the first and second radiation sources, and the measurement points on the surface of the object to be measured. The correction factor k relative to the reference object; S5. Measurement of Radiance by Radiation Emission Method: Using a first radiation source and a second radiation source, the test object and a reference object are subjected to two active radiation events of different intensities, respectively. The radiance of the test object under the first and second radiation events is recorded. Radiance of the reference object , ; S6. Calculate the uncorrected emissivity and true emissivity: based on and the known emissivity of the reference object Calculate the uncorrected emissivity Then, by combining the correction factor k, the true emissivity is obtained. ; S7. Calculate the true temperature: based on the true emissivity. The true temperature of the object's surface is calculated using the radiance L measured by the infrared thermal imager. .

[0006] The beneficial effects of adopting the above technical solution are as follows: This correction method combines laser array scanning and radiation reflection methods, using the geometric information obtained by the laser array to dynamically correct the emissivity, significantly improving the accuracy of temperature calculation, and eliminating the need for complex neural network models or large amounts of calibration data, thus simplifying the measurement and calculation process. S1 involves placing a known emissivity object on one side of the object to be measured. By using a reference object and fixing the relative positions of the infrared imager, laser array, and at least two radiation sources, a stable measurement environment can be provided for subsequent measurements, ensuring the uniformity and repeatability of measurement parameters, reducing errors introduced by equipment or structural changes, and thus ensuring measurement accuracy. S2 scans the surfaces of the object under test and the reference object to obtain their shape and spatial coordinate information, and calculates the spatial coordinates of each measurement point. unit normal vector It can accurately capture the three-dimensional geometric features of a surface without soil, thus providing a basis for subsequent angle measurements. The calculation of the correction factor k provides accurate input data, avoiding inaccurate measurement results caused by the complexity of the surface shape; S3 calculates the angle between the normal direction and the radiation source direction. This accurately reflects the response behavior of curved surfaces to radiation, providing geometric parameters for calculating the correction factor k and ensuring the accuracy of emissivity correction; in S4, based on the included angle... and measurement points The distance between the object and the radiation source is used to calculate the measurement points on the surface of the object. Compared to the correction factor k of the reference object, the correction factor k effectively compensates for the influence of the surface shape on the radiation propagation path, enabling the emissivity calculation to adapt to changes in curvature and improving its adaptability to complex surfaces; S5 acquires radiation data through the radiation reflection method, and performs active radiation of different intensities using two radiation sources, recording the radiance of the measured object and the reference object, providing reliable data support for the calculation of uncorrected emissivity and reducing the impact of environmental interference on the measurement results; S6 is based on the known emissivity of the reference object. Calculate uncorrected emissivity The true emissivity is obtained by combining the correction factor k with dynamic correction. This effectively overcomes the emissivity fluctuations caused by changes in the angle and distance of curved object surfaces, making the temperature calculation closer to the true value and improving measurement accuracy; the S7 calculates the true temperature of the object surface based on the true emissivity and the radiance measured by the infrared thermal imager. Furthermore, by utilizing the Stefan-Boltzmann law to convert radiation data into temperature values, it can accurately reflect the actual thermal state of curved objects.

[0007] Furthermore, in S1, the surface of the reference object is a plane, and the surface of the reference object is parallel to the detection surfaces of the first radiation source and the second radiation source. The operating bands of the first radiation source and the second radiation source are matched with the operating bands of the infrared thermal imager.

[0008] The beneficial effects of adopting the above technical solution are: by using a known emissivity A planar reference object, parallel to the radiation source detection surface, provides a stable and known benchmark for the entire measurement system, simplifying subsequent calculations by minimizing the impact of environmental radiation reflection. Simultaneously, the radiation source's operating wavelength is matched to the thermal imager, ensuring efficient and accurate reception of active radiation energy by the infrared detector, guaranteeing the accuracy of calculations for uncorrected emissivity. The quality of the raw data provides a solid physical and mathematical foundation for achieving high-precision surface temperature correction.

[0009] Furthermore, in S2, the measurement points on the surface of the object to be measured are calculated. unit normal vector Specifically, it includes the following steps: S21. Select measurement points from the acquired shape and position information of the object to be measured. And obtain the m neighboring points around the measurement point to form a set. , where m is a preset positive integer; S22, Calculate the set of neighboring points center coordinates The calculation formula is as follows:

[0010] in, The coordinates of the center point of the neighborhood point set are represented by ; m represents the number of neighborhood points. This represents the coordinate vector of the j-th neighboring point; Represents all points in the neighborhood set. x The average value of the coordinates; Represents all points in the neighborhood set. y The average value of the coordinates; Represents all points in the neighborhood set. z The average value of the coordinates; Each component is determined by the following calculation formula:

[0011]

[0012]

[0013] in, Indicates the first j neighborhood points x Coordinate values; Indicates the first j neighborhood points y Coordinate values; Indicates the first j neighborhood points z Coordinate values; S23, For the set of neighboring points Decentralization, the calculation formula is as follows:

[0014] in, Represents the decentralized first j A vector of coordinates of neighboring points; T represents the transpose operation, converting the row vector into a column vector; Indicates the first j Neighboring points x The offset of the coordinates relative to the corresponding coordinates of the center point; Indicates the first j Neighboring points y The offset of the coordinates relative to the corresponding coordinates of the center point; Indicates the first j Neighboring points z The offset of the coordinates relative to the corresponding coordinates of the center point; S24. Construct the covariance matrix C, calculated using the following formula:

[0015] in,

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] Where C represents the covariance matrix of the neighborhood point set, which is used to describe the distribution characteristics of the neighborhood points in three-dimensional space; Indicates that the neighboring point is in x The variance along the axis reflects x The degree of dispersion of coordinate values; Indicates that the neighboring point is in y The variance along the axis reflects y The degree of dispersion of coordinate values; Indicates that the neighboring point is in z The variance along the axis reflects z The degree of dispersion of coordinate values; Indicates that the neighboring point is in xy The covariance between them reflects x coordinates and y Coordinate correlation; Indicates that the neighboring point is in xz The covariance between them reflects x coordinates and z Coordinate correlation; Indicates that the neighboring point is in yz The covariance between them reflects y coordinates and z Coordinate correlation; S25. Solve for the eigenvalues ​​of the covariance matrix C using the following formula:

[0022] Then we get a result about cubic equation:

[0023] in,

[0024]

[0025]

[0026] Among them, among them, Represents the eigenvalues ​​of the covariance matrix C; Represents the identity matrix; Represents the covariance matrix C traces; Represents the covariance matrix C invariants; Represents the covariance matrix C The determinant value; Describe the trace of matrix C. Represent the determinant of matrix C; S26. Analyze the above cubic equation to obtain three real eigenvalues. , , And satisfy ≤ ≤ , the minimum eigenvalue The corresponding unit eigenvector is used to determine the measurement point. unit normal vector .

[0027] The beneficial effects of adopting the above technical solution are as follows: S21 selects the measurement point from the shape and position information of the object to be measured obtained from the laser array. The selection of the selected neighborhood points ensures that subsequent normal vector calculations are not based on a single isolated point, but rather on the local surface morphology at that point. This more accurately reflects the true geometric characteristics of the surface at that point, avoiding calculation errors caused by noise or bias in a single data point, and providing a reliable data foundation for subsequent normal vector calculations. S22 calculates the selected neighborhood point set... The center coordinates are obtained to obtain the geometric center of the local surface region, which provides a reference point for subsequent decentralization. This allows the subsequent covariance calculation to focus on the distribution pattern and orientation change of neighboring points relative to the center point, thereby more accurately describing the orientation and undulation characteristics of the local surface and preparing for the solution of the normal vector. S23 subtracts the center coordinates from the coordinates of each neighboring point to achieve decentralization, eliminating the influence of the local overall position offset on the shape analysis. This purely reflects the distribution direction and discreteness of neighboring points around the center point, thereby better capturing the normal direction information of the local surface and improving the accuracy of subsequent normal vector calculation. S24 constructs a covariance matrix using the coordinates of decentralized neighboring points, quantitatively describing the variance and covariance relationship of neighboring points in various directions in three-dimensional space. This reflects the spatial distribution characteristics and main extension directions of the local region, providing a data foundation for the next step of solving for the normal vector that best represents the orientation of the local surface through eigenvalue decomposition. S25 obtains three eigenvalues ​​by solving the characteristic equation of the covariance matrix C. The magnitudes of these three eigenvalues ​​correspond to the degree of change of the local surface in different principal directions, providing a quantitative basis for determining the principal and secondary directions of the local surface, and is a key step in solving for the normal vector. S26 calculates the minimum eigenvalue of the covariance matrix. The corresponding unit eigenvector is determined as the unit normal vector of the measurement point. This step transforms the data into a vector that can accurately and quantitatively describe the orientation of the local surface space at that point, which is beneficial for subsequent radiation correction and temperature calculations, and ensures the accuracy of emissivity correction and temperature calculations.

[0028] Furthermore, S3 calculates the values ​​for each measurement point. The angle between the normal direction and the direction pointing to the first and second radiation sources Specifically, it includes the following steps: S31. Based on the known spatial coordinates of the first and second radiation sources and measurement points coordinates Calculate from the measurement point Directional vectors pointing to the first and second radiation sources ; S32. Calculate the measurement points using the dot product formula. normal direction and direction vector Angle between cosine value:

[0029] in, Indicates measurement point The unit normal vector, Indicates from the measurement point Directional vectors pointing to the first and second radiation sources; S33. Obtain the included angle using the inverse cosine function. :

[0030] in, Indicates measurement point The angle between the direction of the normal and the direction pointing to the first or second radiation source.

[0031] The beneficial effects of adopting the above technical solution are: S31 uses the known spatial coordinates of the radiation source and the measurement point Coordinates are calculated from the measurement point. The direction vector pointing to the radiation source establishes the spatial geometric relationship between the measurement point and the radiation source, and quantifies this geometric relationship into a specific vector. This vector serves as an indispensable input for subsequent calculations of the radiation incident angle, laying the foundation for accurately analyzing the reflection behavior of radiation energy on curved surfaces. S32 uses the dot product formula, utilizing the direction vector obtained in the previous step and the known measurement point... Using unit vectors, the cosine of the angle between them can be calculated, transforming spatial directional relationships into cosine values. This avoids geometric derivation, simplifies the calculation process, and facilitates efficient and accurate angle calculation. S33 uses the inverse cosine function to transform the cosine value calculated in the previous step into a specific spatial angle, completing the quantitative determination from vector relationship to specific geometric parameters. This yields a correction that can be directly used for physical model calculations, directly reflecting the relative relationship between the incident direction of the radiation source and the local normal of the surface. This lays the foundation for subsequent calculation of the correction factor k to supplement the influence of the surface geometry on radiation reflection, thereby improving the accuracy of temperature correction.

[0032] Furthermore, in S4, the measurement points on the surface of the object to be measured are calculated. The correction factor k relative to the reference object is calculated using the following formula:

[0033] in, Indicates the measurement points on the surface of the object to be measured. Spatial distance to the first or second radiation source Indicates the measurement points on the surface of the object to be measured. The angle between the radiation source and the first or second radiation source; Indicates the distance between the reference object surface and the measurement point The spatial distance from corresponding points with the same viewing angle to the first or second radiation source. This represents the angle between the direction of the normal to a corresponding point on the surface of the reference object and the direction pointing towards the first or second radiation source.

[0034] The beneficial effects of adopting the above technical solution are as follows: the correction factor k can dynamically quantify the difference in geometric relationship between any measurement point on the surface object to be measured and the reference plane object. By comprehensively considering the spatial distance from the measurement point to the radiation source and the changes in surface detection and reflection characteristics, the uncorrected reflectivity calculated based on the plane reference object can be effectively corrected to the true emissivity that conforms to the actual situation of the surface, thereby significantly improving the accuracy of temperature measurement of the surface object.

[0035] Furthermore, the uncorrected emissivity is calculated in S6. The calculation formula is as follows:

[0036] in, Represents the known emissivity of the reference object; This indicates the radiance of the object being measured during the first radiation exposure. This indicates the radiance of the object being measured during the second radiation. This indicates the radiance of the reference object during the first radiation. This indicates the radiance of the reference object during the second radiation.

[0037] The beneficial effects of adopting the above technical solution are as follows: S6 uses the principle of radiation reflection method, applies active radiation of different intensities through two radiation sources and simultaneously collects the radiance data of the object under test and the reference object, then calculates the difference to separate the emitted radiation component of the object surface, and combines it with the known reflectivity. The reference object can be used to directly calculate the reflectivity of the object under test before considering the influence of its surface geometry, thus providing an accurate and reliable initial data foundation for subsequent accurate correction based on geometric information.

[0038] Furthermore, the actual emission rate in S6 The calculation formula is as follows:

[0039] Where k represents the correction factor calculated in S4; This indicates the uncorrected emissivity.

[0040] The beneficial effects of adopting the above technical solution are as follows: by dynamically applying the correction factor k, which reflects the geometric characteristics of the surface under test, to the uncorrected emissivity, the conversion from the apparent emissivity obtained based on the planar reference datum to the emissivity that conforms to the real surface characteristics of the curved object is completed. This effectively compensates for the influence of the changes in the normal direction and distance of each point on the surface on the radiation reflection capability, overcomes the problem of emissivity fluctuation of the curved object, and provides a real and reliable emissivity parameter for the final high-precision temperature inversion based on the Stefan-Boltzmann law.

[0041] Furthermore, S7 calculates the true temperature of the surface of the object being measured. The calculation formula is as follows:

[0042] in, This represents the radiance of the object under test as measured by an infrared thermal imager in its natural radiation state. This represents the actual emissivity calculated in S6; This represents the Stefan-Boltzmann constant.

[0043] The beneficial effects of adopting the above technical solution are as follows: S7, based on the well-known physical principle of the Stefan-Boltzmann law, compares the radiance data directly measured by the infrared thermal imager with the true emissivity obtained after a series of geometric corrections. By combining these methods, the true temperature of an object's surface was finally determined from the radiation signal. The accurate inversion, that is, the completion of this entire correction process, ensures that the final output temperature result can accurately reflect the actual thermal state of the curved object, thus fundamentally solving the problem of infrared temperature measurement error caused by the geometry of the curved surface.

[0044] Based on the above-mentioned method for temperature correction of curved surface infrared thermal imaging based on laser array, the present invention provides a device for temperature correction of curved surface infrared thermal imaging based on laser array, comprising at least a first radiation source and a second radiation source, the first radiation source and the second radiation source being stacked, the radiation end of the first radiation source and the second radiation source being provided with a test object and a reference object, the test object and the reference object being provided on the same side, the first radiation source and the second radiation source being provided on a support, the support being provided with an infrared thermal imager and a laser array, the infrared thermal imager and the laser array being stacked sequentially on one side of the first radiation source and the second radiation source, and both the infrared thermal imager and the laser array being electrically connected to a computer.

[0045] The beneficial effects of adopting the above technical solution are as follows: This curved surface infrared thermal imaging detection temperature correction device based on laser array integrates an infrared thermal imager, a laser array, and a dual radiation source system, combined with computer processing technology, to achieve high-precision temperature measurement of curved objects of any unknown size. Moreover, the device has a simple structure and low computational load, effectively overcoming the problems of complex algorithms, large computational load, and poor adaptability of existing infrared thermal imaging temperature correction methods, and significantly improving the accuracy and reliability of surface temperature measurement of curved objects. The superimposed arrangement of the first and second radiation sources allows the device to generate radiation of varying intensities at different times, enhancing the flexibility of radiation control and enabling more accurate simulation of various real-world radiation environments. This, in turn, improves the diversity and accuracy of temperature measurements. Furthermore, placing the object under test and the reference object at the same radiation end ensures that both are measured under identical radiation conditions, eliminating errors caused by inconsistent radiation conditions and improving the accuracy of emissivity calculations. The support structure, serving as the overall structural support for the device, not only stably supports the first and second radiation sources, the infrared thermal imager, and the laser array, but also, through its three-dimensional adjustable characteristics, adapts to different ground conditions during the measurement process, ensuring the stability and accuracy of each component. Furthermore, the infrared thermal imager and laser array are sequentially positioned on one side of the radiation source. This layout saves space and ensures their coordinated operation during the measurement process, enabling simultaneous acquisition of the radiance and geometry of the curved object's surface. The infrared thermal imager accurately acquires the radiance of the object in front, covering the curved object and reference object surfaces. Its high resolution (≥640*480) ensures the accuracy of the radiance data, providing a reliable basis for subsequent temperature calculations. The laser array acquires the geometric dimensions and positional information of the object's surface through linear scanning, covering the curved object and reference object surfaces. It provides the necessary geometric parameters for calculating the correction factor at each measurement point, which is key to achieving high-precision temperature measurement.

[0046] Furthermore, the support is a three-dimensional adjustable structure, used to achieve synchronous full-coverage imaging of the object under test and the reference object by the laser array and the infrared thermal imager.

[0047] The beneficial effects of adopting the above technical solution are as follows: The support adopts a three-dimensional adjustable structure, which can flexibly adapt to different detection environments and ground conditions. Through comprehensive adjustment of height, angle and horizontal position, it ensures that the laser array and infrared thermal imager are always in the optimal measurement position, thereby realizing synchronous full-coverage imaging of the object under test and the reference object. This not only eliminates the measurement blind zone caused by changes in object size, shape or detection environment, but also significantly improves the integrity of imaging and the accuracy of data acquisition. It provides comprehensive and reliable basic data for subsequent temperature correction algorithms, thereby improving the accuracy and stability of temperature measurement of curved objects.

[0048] In summary, the beneficial effects of the curved surface infrared thermal imaging detection temperature correction device and method based on laser array provided by this invention are as follows: (1) The laser array-based curved surface infrared thermal imaging temperature correction method improves the temperature measurement accuracy of curved object infrared thermal imaging detection by combining laser array scanning and radiation reflection method. First, the laser array is used to accurately obtain the geometric shape and position information of the object to be measured and the reference object, providing an accurate geometric parameter basis for subsequent temperature correction. Then, the radiation brightness of the object to be measured and the reference object is measured by radiation reflection method. Combined with the known emissivity of the reference object, the uncorrected emissivity is calculated. On this basis, the correction factor of each measurement point is calculated by using the geometric information obtained by the laser array, and the uncorrected emissivity is dynamically corrected to obtain the true emissivity. In this process, a spatial model is first established by geometric measurement, then the initial data is obtained by radiation measurement, and finally the data accuracy is improved by geometric correction, thereby realizing the accurate calculation of the surface temperature of the curved object. This effectively overcomes the problem of low measurement accuracy caused by the complexity of the algorithm and the large amount of calculation in the existing method.

[0049] (2) The curved surface infrared thermal imaging detection temperature correction device based on laser array integrates an infrared thermal imager, a laser array and a dual radiation source system, and combines computer processing technology to achieve high-precision temperature measurement of curved objects of any unknown size. The laser array and infrared thermal imager in the device are both fixed on a three-dimensional adjustable support, which can flexibly adapt to different detection environments and ground conditions, ensuring synchronous full-coverage imaging of curved objects. The dual radiation source system can generate radiation of different intensities at different times, enhancing the flexibility of radiation control and improving the diversity and accuracy of temperature measurement. Moreover, the object under test and the reference object are set at the same radiation end, ensuring that they are measured under the same radiation conditions, eliminating errors caused by inconsistent radiation conditions. In addition, the infrared thermal imager can accurately obtain the radiation brightness of the object in front, while the laser array obtains the geometric dimensions and position information of the object surface through linear scanning. The two work together to provide comprehensive and reliable basic data for subsequent temperature correction algorithms. Furthermore, the device has a simple structure and low algorithm computation, effectively overcoming the problems of complex algorithms, high computational load, and poor adaptability of existing infrared thermal imaging temperature correction methods, and significantly improving the accuracy and reliability of surface temperature measurement of curved objects. Attached Figure Description

[0050] Figure 1 This is a flowchart of the curved surface infrared thermal imaging temperature correction method based on laser array in this invention; Figure 2 This is a schematic diagram of the curved infrared thermal imaging temperature correction device based on a laser array in this invention. Among them, 1. the object to be tested; 2. the reference object; 3. the laser array; 4. the computer; 5. the support; 6. the infrared thermal imager; 7. the first radiation source; and 8. the second radiation source. Detailed Implementation

[0051] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0052] Example 1 like Figure 2 As shown, the curved surface infrared thermal imaging detection temperature correction device based on laser array provided by the present invention includes at least a first radiation source 7 and a second radiation source 8, which are stacked together. The radiation ends of the first radiation source 7 and the second radiation source 8 are provided with a test object 1 and a reference object 2, which are located on the same side. The first radiation source 7 and the second radiation source 8 are mounted on a support 5. An infrared thermal imager 6 and a laser array 3 are mounted on the support 5. The infrared thermal imager 6 and the laser array 3 are sequentially arranged on one side of the first radiation source 7 and the second radiation source 8. Both the infrared thermal imager 6 and the laser array 3 are electrically connected to a computer 4.

[0053] This curved surface infrared thermal imaging temperature correction device based on laser array 3 integrates an infrared thermal imager 6, a laser array 3, and a dual radiation source system, combined with computer processing technology 4, to achieve high-precision temperature measurement of curved objects of arbitrary unknown size. Moreover, the device has a simple structure and low computational load, effectively overcoming the problems of complex algorithms, large computational load, and poor adaptability of existing infrared thermal imaging temperature correction methods, and significantly improving the accuracy and reliability of surface temperature measurement of curved objects. The superimposed arrangement of the first radiation source 7 and the second radiation source 8 allows the device to generate radiation of different intensities at different times, enhancing the flexibility of radiation control and enabling more accurate simulation of various actual radiation environments, thereby improving the diversity and accuracy of temperature measurements. Furthermore, placing the object to be measured 1 and the reference object 2 at the same radiation end ensures that both are measured under the same radiation conditions, eliminating errors caused by inconsistent radiation conditions and improving the accuracy of emissivity calculation. The support structure 5, serving as the overall support for the device, not only stably supports the first radiation source 7, the second radiation source 8, the infrared thermal imager 6, and the laser array 3, but also, through its three-dimensional adjustable characteristics, adapts to different detection environments, ensuring the stability and precision of each component during measurement. Accuracy; In addition, the infrared thermal imager 6 and the laser array 3 are arranged sequentially on one side of the radiation source. This layout saves space and ensures that the two work together during the measurement process, realizing the synchronous acquisition of the radiance and geometry of the curved object surface. The infrared thermal imager 6 can accurately acquire the radiance of the object in front, covering the surface of the curved object and the reference object 2. Its high resolution (≥640*480) ensures the accuracy of the radiance data and provides a reliable basis for subsequent temperature calculation. The laser array 3 acquires the geometric dimensions and position information of the surface of the object in front through linear scanning, covering the surface of the curved object and the reference object 2. It provides the necessary geometric parameters for calculating the correction factor of each measurement point and is the key to realizing high-precision temperature measurement.

[0054] In this embodiment of the invention, the support 5 is a three-dimensional adjustable structure used to achieve synchronous full-coverage imaging of the laser array 3 and the infrared thermal imager 6 of the object under test 1 and the reference object 2. The three-dimensional adjustable support 5 can flexibly adapt to different detection environments and ground conditions. Through comprehensive adjustments of height, angle, and horizontal position, it ensures that the laser array 3 and the infrared thermal imager 6 are always in the optimal measurement position, thereby achieving synchronous full-coverage imaging of the object under test 1 and the reference object 2. This not only eliminates measurement blind spots caused by changes in object size, shape, or detection environment, but also significantly improves the integrity of the imaging and the accuracy of data acquisition. It provides comprehensive and reliable basic data for subsequent temperature correction algorithms, thereby improving the accuracy and stability of temperature measurement of curved objects.

[0055] Example 2 Based on the aforementioned laser array-based curved surface infrared thermal imaging temperature correction device, this invention provides a laser array-based curved surface infrared thermal imaging temperature correction method, such as... Figure 1 As shown, it includes the following steps: S1. Establish a calibration device system: Place a known emissivity device on one side of the object to be measured (1). The reference object 2 is set up with an infrared thermal imager, a laser array 3, a first radiation source 7 and a second radiation source 8. The relative positions of the first radiation source 7, the second radiation source 8 and the infrared thermal imager are fixed and known. S2, Laser Array 3 Measurement: The laser array 3 is used to scan the surfaces of the object under test 1 and the reference object 2 to obtain the shape and position spatial coordinate information of the surfaces of the object under test 1 and the reference object 2. Based on the shape and position spatial coordinate information, the measurement points on the surface of the object under test 1 are calculated. unit normal vector ; S3. Calculate the angle between the normal direction at each measurement point and the direction of the radiation source: based on each measurement point unit normal vector and each measurement point Calculate the spatial coordinates of the first radiation source 7 and the second radiation source 8 for each measurement point. The angle between the normal direction and the direction pointing to the first radiation source 7 and the second radiation source 8 ; S4. Calculate the correction factor: based on the included angle Each measurement point Calculate the distances between the object and the first radiation source 7 and the second radiation source 8, and measure the distances at each measurement point on the surface of the object 1. Correction factor k relative to reference object 2; S5. Measurement of Radiance by Radiation Emission Method: Using a first radiation source 7 and a second radiation source 8, two active radiation events of different intensities are performed on the test object 1 and the reference object 2, respectively. The radiance of the test object 1 under the first and second radiation events is recorded. Radiance of reference object 2 , ; S6. Calculate the uncorrected emissivity and true emissivity: based on and the known emissivity of reference object 2 Calculate the uncorrected emissivity Then, by combining the correction factor k, the true emissivity is obtained. ; S7. Calculate the true temperature: based on the true emissivity. The true temperature of the surface of the object under test (1) is calculated based on the radiance L measured by the infrared thermal imager. .

[0056] This correction method combines laser array 3 scanning with radiation reflection, using the geometric information obtained by laser array 3 to dynamically correct the emissivity, which significantly improves the accuracy of temperature calculation. Moreover, it does not rely on complex neural network models or a large amount of calibration data, simplifying the measurement and calculation process.

[0057] In S1, a known emissivity is placed on one side of the object to be tested 1. The reference object 2, along with the fixed relative positions of the infrared imager, laser array 3, and at least two radiation sources, provides a stable measurement environment for subsequent measurements, ensuring the uniformity and repeatability of measurement parameters and reducing errors introduced by equipment or structural changes, thereby ensuring measurement accuracy. Furthermore, the surface of the reference object 2 is planar and parallel to the detection surfaces of the first radiation source 7 and the second radiation source 8. The operating wavelengths of the first radiation source 7 and the second radiation source 8 are matched with the operating wavelength of the infrared thermal imager. This is achieved by using a known emissivity... The planar reference object 2, parallel to the radiation source detection surface, provides a stable and known benchmark for the entire measurement system, simplifying the impact of environmental radiation reflection in subsequent calculations. Simultaneously, the radiation source's operating wavelength is matched to the thermal imager, ensuring that active radiation energy is efficiently and accurately received by the infrared detector, guaranteeing the accuracy of calculations for uncorrected emissivity. The quality of the raw data provides a solid physical and mathematical foundation for achieving high-precision surface temperature correction.

[0058] S2 calculates each measurement point on the surface of the object to be measured 1. unit normal vector Specifically, it includes the following steps: S21. Select measurement points from the obtained shape and position information of the object to be measured 1. And obtain the m neighboring points around the measurement point to form a set. , where m is a preset positive integer; S22, Calculate the set of neighboring points center coordinates The calculation formula is as follows:

[0059] in, The coordinates of the center point of the neighborhood point set are represented by ; m represents the number of neighborhood points. This represents the coordinate vector of the j-th neighboring point; Represents all points in the neighborhood set. x The average value of the coordinates; Represents all points in the neighborhood set. y The average value of the coordinates; Represents all points in the neighborhood set. z The average value of the coordinates; Each component is determined by the following calculation formula:

[0060]

[0061]

[0062] in, Indicates the first j neighborhood points x Coordinate values; Indicates the first j neighborhood points y Coordinate values; Indicates the first j neighborhood points z Coordinate values; S23, For the set of neighboring points Decentralization, the calculation formula is as follows:

[0063] in, Represents the decentralized first j A vector of coordinates of neighboring points; T represents the transpose operation, converting the row vector into a column vector; Indicates the first j Neighboring points x The offset of the coordinates relative to the corresponding coordinates of the center point; Indicates the first j Neighboring points y The offset of the coordinates relative to the corresponding coordinates of the center point; Indicates the first j Neighboring points z The offset of the coordinates relative to the corresponding coordinates of the center point; S24. Construct the covariance matrix C, calculated using the following formula:

[0064] in,

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] Where C represents the covariance matrix of the neighborhood point set, which is used to describe the distribution characteristics of the neighborhood points in three-dimensional space; Indicates that the neighboring point is in x The variance along the axis reflects x The degree of dispersion of coordinate values; Indicates that the neighboring point is in y The variance along the axis reflects y The degree of dispersion of coordinate values; Indicates that the neighboring point is in z The variance along the axis reflects z The degree of dispersion of coordinate values; Indicates that the neighboring point is in xy The covariance between them reflects x coordinates and y Coordinate correlation; Indicates that the neighboring point is in xz The covariance between them reflects x coordinates and z Coordinate correlation; Indicates that the neighboring point is in yz The covariance between them reflects y coordinates and z Coordinate correlation; S25. Solve for the eigenvalues ​​of the covariance matrix C using the following formula:

[0071] Then we get a result about cubic equation:

[0072] in,

[0073]

[0074]

[0075] Among them, among them, Represents the eigenvalues ​​of the covariance matrix C; Represents the identity matrix; Represents the covariance matrix C traces; Represents the covariance matrix C invariants; Represents the covariance matrix C The determinant value; Describe the trace of matrix C. Represent the determinant of matrix C; S26. Analyze the above cubic equation to obtain three real eigenvalues. , , And satisfy ≤ ≤ , the minimum eigenvalue The corresponding unit eigenvector is used to determine the measurement point. unit normal vector .

[0076] S21 selects a measurement point from the shape and position information of the object to be measured 1 obtained from the laser array 3. The selection of the selected neighborhood points ensures that subsequent normal vector calculations are not based on a single isolated point, but rather on the local surface morphology at that point. This more accurately reflects the true geometric characteristics of the surface at that point, avoiding calculation errors caused by noise or bias in a single data point, and providing a reliable data foundation for subsequent normal vector calculations. S22 calculates the selected neighborhood point set... The center coordinates are obtained to obtain the geometric center of the local surface region, which provides a reference point for subsequent decentralization. This allows the subsequent covariance calculation to focus on the distribution pattern and orientation change of neighboring points relative to the center point, thereby more accurately describing the orientation and undulation characteristics of the local surface and preparing for the solution of the normal vector. S23 subtracts the center coordinates from the coordinates of each neighboring point to achieve decentralization, eliminating the influence of the local overall position offset on the shape analysis. This purely reflects the distribution direction and discreteness of neighboring points around the center point, thereby better capturing the normal direction information of the local surface and improving the accuracy of subsequent normal vector calculation. S24 constructs a covariance matrix using the coordinates of decentralized neighboring points, quantitatively describing the variance and covariance relationship of neighboring points in various directions in three-dimensional space. This reflects the spatial distribution characteristics and main extension directions of the local region, providing a data foundation for the next step of solving for the normal vector that best represents the orientation of the local surface through eigenvalue decomposition. S25 obtains three eigenvalues ​​by solving the characteristic equation of the covariance matrix C. The magnitudes of these three eigenvalues ​​correspond to the degree of change of the local surface in different principal directions, providing a quantitative basis for determining the principal and secondary directions of the local surface, and is a key step in solving for the normal vector. S26 calculates the minimum eigenvalue of the covariance matrix. The corresponding unit eigenvector is determined as the unit normal vector of the measurement point. This step transforms the data into a vector that can accurately and quantitatively describe the orientation of the local surface space at that point, which is beneficial for subsequent radiation correction and temperature calculations, and ensures the accuracy of emissivity correction and temperature calculations.

[0077] Calculate each measurement point in S3 The angle between the normal direction and the direction pointing to the first radiation source 7 and the second radiation source 8 Specifically, it includes the following steps: S31. Based on the known spatial coordinates of the first radiation source 7 and the second radiation source 8 and measurement points coordinates Calculate from the measurement point Directional vectors pointing to the first radiation source 7 and the second radiation source 8 ; S32. Calculate the measurement points using the dot product formula. normal direction and direction vector Angle between cosine value:

[0078] in, Indicates measurement point The unit normal vector, Indicates from the measurement point Directional vectors pointing to the first radiation source 7 and the second radiation source 8; S33. Obtain the included angle using the inverse cosine function. :

[0079] in, Indicates measurement point The angle between the direction of the normal and the direction pointing to the first radiation source 7 or the second radiation source 8.

[0080] S31 uses the known spatial coordinates of the radiation source and measurement points Coordinates are calculated from the measurement point. The direction vector pointing to the radiation source establishes the spatial geometric relationship between the measurement point and the radiation source, and quantifies this geometric relationship into a specific vector. This vector serves as an indispensable input for subsequent calculations of the radiation incident angle, laying the foundation for accurately analyzing the reflection behavior of radiation energy on curved surfaces. S32 uses the dot product formula, utilizing the direction vector obtained in the previous step and the known measurement point... Using unit vectors, the cosine of the angle between them can be calculated, transforming spatial directional relationships into cosine values. This avoids geometric derivation, simplifies the calculation process, and facilitates efficient and accurate angle calculation. S33 uses the inverse cosine function to transform the cosine value calculated in the previous step into a specific spatial angle, completing the quantitative determination from vector relationship to specific geometric parameters. This yields a correction that can be directly used for physical model calculations, directly reflecting the relative relationship between the incident direction of the radiation source and the local normal of the surface. This lays the foundation for subsequent calculation of the correction factor k to supplement the influence of the surface geometry on radiation reflection, thereby improving the accuracy of temperature correction.

[0081] S4 calculates each measurement point on the surface of the object to be measured. The correction factor k relative to reference object 2 is calculated using the following formula:

[0082] in, Indicates the measurement point on the surface of the object to be measured. The spatial distance to the first radiation source 7 or the second radiation source 8 Indicates the measurement point on the surface of the object to be measured. The angle between the radiation source 7 and the first radiation source 8; This indicates the distance between the reference object 2 surface and the measurement point. The spatial distance from corresponding points with the same viewing angle to the first radiation source 7 or the second radiation source 8. This represents the angle between the normal direction of the corresponding point on the surface of reference object 2 and the direction pointing towards the first radiation source 7 or the second radiation source 8.

[0083] Among them, the correction factor k can dynamically quantify the difference in geometric relationship between any measurement point on the surface object under test and the reference plane object. By combining the spatial distance from the measurement point to the radiation source and the changes in surface emission and reflection characteristics, the uncorrected reflectivity calculated based on the plane reference object is effectively corrected to the true reflectivity that conforms to the actual situation of the surface, thereby significantly improving the accuracy of temperature measurement of the surface object.

[0084] S5 acquires radiation data through the radiation reflection method, and actively radiates at different intensities using two radiation sources, recording the radiance of the object under test 1 and the reference object 2. This provides reliable data support for the calculation of uncorrected emissivity and reduces the impact of environmental interference on the measurement results.

[0085] Calculate the uncorrected emissivity in S6 The calculation formula is as follows:

[0086] in, Represents the known emissivity of reference object 2; This represents the radiance of object 1 during the first radiation test; This represents the radiance of object 1 during the second radiation. This represents the radiance of reference object 2 during the first radiation. This indicates the radiance of reference object 2 during the second radiation.

[0087] S6 employs the principle of radiation reflection. It uses two radiation sources to apply active radiation of different intensities while simultaneously acquiring radiance data from the object under test (object 1) and a reference object (object 2). The emitted radiation components from the object's surface are then separated by calculating the difference, and combined with known reflectivity... The reference object 2 can directly calculate the reflectivity of the object under test 1 before considering the influence of its surface geometry, thus providing an accurate and reliable initial data basis for subsequent accurate correction based on geometric information.

[0088] Meanwhile, the actual emission rate in S6 The calculation formula is as follows:

[0089] Where k represents the correction factor calculated in S4; This indicates the uncorrected emissivity.

[0090] By dynamically applying the correction factor k, which reflects the geometric characteristics of the surface under test, to the uncorrected emissivity, the conversion from the apparent emissivity obtained based on the planar reference datum to the emissivity that conforms to the actual surface characteristics of the curved object is completed. This effectively compensates for the influence of the changes in the normal direction and distance of each point on the radiation reflection capability, overcomes the problem of emissivity fluctuation of the curved object, and provides a true and reliable emissivity parameter for the final high-precision temperature inversion based on the Stefan-Boltzmann law.

[0091] Calculate the true temperature of the surface of the object to be measured in S7. The calculation formula is as follows:

[0092] in, This represents the radiance of the object under test as measured by an infrared thermal imager in its natural radiation state. This represents the actual emissivity calculated in S6; This represents the Stefan-Boltzmann constant.

[0093] Based on the well-known physical principle of the Stefan-Boltzmann law, S7 compares the radiance data directly measured by the infrared thermal imager 6 with the true emissivity obtained after a series of geometric corrections. By combining these methods, the true temperature of an object's surface was finally determined from the radiation signal. The accurate inversion, that is, the completion of this entire correction process, ensures that the final output temperature result can accurately reflect the actual thermal state of the curved object, thus fundamentally solving the problem of infrared temperature measurement error caused by the geometry of the curved surface.

[0094] In summary, the laser array-based curved surface infrared thermal imaging temperature correction device and method provided by this invention integrates laser array scanning technology, radiation reflection measurement, and computer processing technology to form a highly efficient, accurate, and adaptable temperature measurement system for curved objects. This system achieves efficient, accurate, and adaptable detection of the surface temperature of curved objects, simplifying the traditional complex algorithms and extensive calculations. Furthermore, the dynamic correction factor effectively addresses the influence of the curved surface shape on temperature measurement, significantly improving measurement accuracy and reliability. This provides a more accurate and practical temperature measurement solution for fields such as industrial inspection and medical diagnosis.

Claims

1. A method for temperature correction in curved surface infrared thermal imaging based on a laser array, characterized in that, Includes the following steps: S1. Establish a calibration device system: Place a known emissivity device on one side of the object to be measured. The reference object is set up with an infrared thermal imager, a laser array, a first radiation source and a second radiation source, and the relative positions of the first radiation source, the second radiation source and the infrared thermal imager are fixed and known. S2. Laser Array Measurement: A laser array is used to scan the surfaces of the object under test and the reference object to obtain the shape and spatial coordinate information of the surfaces of the object under test and the reference object. Based on the shape and spatial coordinate information, the measurement points on the surface of the object under test are calculated. unit normal vector ; S3. Calculate the angle between the normal direction at each measurement point and the direction of the radiation source: based on each measurement point unit normal vector and each measurement point Calculate the spatial coordinates of the first and second radiation sources for each measurement point. The angle between the normal direction and the direction pointing to the first and second radiation sources ; S4. Calculate the correction factor: based on the included angle and each measurement point Calculate the distances between the object and the first and second radiation sources, and the measurement points on the surface of the object to be measured. The correction factor k relative to the reference object; S5. Measurement of Radiance by Radiation Emission Method: Using a first radiation source and a second radiation source, the test object and a reference object are subjected to two active radiation events of different intensities, respectively. The radiance of the test object under the first and second radiation events is recorded. Radiance of the reference object , ; S6. Calculate the uncorrected emissivity and true emissivity: based on and the known emissivity of the reference object Calculate the uncorrected emissivity Then, by combining the correction factor k, the true emissivity is obtained. ; S7. Calculate the true temperature: based on the true emissivity. The true temperature of the object's surface is calculated using the radiance L measured by the infrared thermal imager. .

2. The method for temperature correction of curved surface infrared thermal imaging based on laser array according to claim 1, characterized in that: In S1, the surface of the reference object is a plane, and the surface of the reference object is parallel to the detection surfaces of the first radiation source and the second radiation source. The operating bands of the first radiation source and the second radiation source are matched with the operating bands of the infrared thermal imager.

3. The method for temperature correction of curved surface infrared thermal imaging based on laser array according to claim 1, characterized in that, In step S2, each measurement point on the surface of the object to be measured is calculated. unit normal vector Specifically, it includes the following steps: S21. Select measurement points from the acquired shape and position information of the object to be measured. And obtain the m neighboring points around the measurement point to form a set. , where m is a preset positive integer; S22, Calculate the set of neighboring points center coordinates The calculation formula is as follows: in, The coordinates of the center point of the neighborhood point set are represented by ; m represents the number of neighborhood points. This represents the coordinate vector of the j-th neighboring point; Represents all points in the neighborhood set. x The average value of the coordinates; Represents all points in the neighborhood set. y The average value of the coordinates; Represents all points in the neighborhood set. z The average value of the coordinates; Each component is determined by the following calculation formula: in, Indicates the first j neighborhood points x Coordinate values; Indicates the first j neighborhood points y Coordinate values; Indicates the first j neighborhood points z Coordinate values; S23, For the set of neighboring points Decentralization, the calculation formula is as follows: in, Represents the decentralized first j A vector of coordinates of neighboring points; T represents the transpose operation, converting the row vector into a column vector; Indicates the first j Neighboring points x The offset of the coordinates relative to the corresponding coordinates of the center point; Indicates the first j Neighboring points y The offset of the coordinates relative to the corresponding coordinates of the center point; Indicates the first j Neighboring points z The offset of the coordinates relative to the corresponding coordinates of the center point; S24. Construct the covariance matrix C, calculated using the following formula: in, Where C represents the covariance matrix of the neighborhood point set, which is used to describe the distribution characteristics of the neighborhood points in three-dimensional space; Indicates that the neighboring point is in x The variance along the axis reflects x The degree of dispersion of coordinate values; Indicates that the neighboring point is in y The variance along the axis reflects y The degree of dispersion of coordinate values; Indicates that the neighboring point is in z The variance along the axis reflects z The degree of dispersion of coordinate values; Indicates that the neighboring point is in xy The covariance between them reflects x coordinates and y Coordinate correlation; Indicates that the neighboring point is in xz The covariance between them reflects x coordinates and z Coordinate correlation; Indicates that the neighboring point is in yz The covariance between them reflects y coordinates and z Coordinate correlation; S25. Solve for the eigenvalues ​​of the covariance matrix C using the following formula: Then we get a result about cubic equation: in, in, Represents the eigenvalues ​​of the covariance matrix C; Represents the identity matrix; Represents the covariance matrix C traces; Represents the covariance matrix C invariants; Represents the covariance matrix C The determinant value; Describe the trace of matrix C. Represent the determinant of matrix C; S26. Analyze the above cubic equation to obtain three real eigenvalues. , , And satisfy ≤ ≤ , the minimum eigenvalue The corresponding unit eigenvector is used to determine the measurement point. unit normal vector .

4. The method for temperature correction of curved surface infrared thermal imaging based on laser array according to claim 1, characterized in that, The calculation of each measurement point in S3 The angle between the normal direction and the direction pointing to the first and second radiation sources Specifically, it includes the following steps: S31. Based on the known spatial coordinates of the first and second radiation sources and measurement points coordinates Calculate from the measurement point Directional vectors pointing to the first and second radiation sources ; S32. Calculate the measurement point using the dot product formula. normal direction and direction vector Angle between cosine value: in, Indicates measurement point The unit normal vector, Indicates from the measurement point Directional vectors pointing to the first and second radiation sources; S33. Obtain the included angle using the inverse cosine function. : in, Indicates measurement point The angle between the direction of the normal and the direction pointing to the first or second radiation source.

5. The method for temperature correction of curved surface infrared thermal imaging based on laser array according to claim 1, characterized in that, In step S4, each measurement point on the surface of the object to be measured is calculated. The correction factor k relative to the reference object is calculated using the following formula: in, Indicates the measurement points on the surface of the object to be measured. Spatial distance to the first or second radiation source Indicates the measurement points on the surface of the object to be measured. The angle between the radiation source and the first or second radiation source; Indicates the distance between the reference object surface and the measurement point The spatial distance from corresponding points with the same viewing angle to the first or second radiation source. This represents the angle between the direction of the normal to a corresponding point on the surface of the reference object and the direction pointing towards the first or second radiation source.

6. The method for temperature correction of curved surface infrared thermal imaging based on laser array according to claim 1, characterized in that, The uncorrected emissivity is calculated in step S6. The calculation formula is as follows: in, Represents the known emissivity of the reference object; This indicates the radiance of the object being measured during the first radiation exposure. This indicates the radiance of the object being measured during the second radiation. This indicates the radiance of the reference object during the first radiation. This indicates the radiance of the reference object during the second radiation.

7. The method for temperature correction of curved surface infrared thermal imaging based on laser array according to claim 1, characterized in that, The actual emission rate in S6 The calculation formula is as follows: Where k represents the correction factor calculated in S4; This indicates the uncorrected emissivity.

8. The method for temperature correction of curved surface infrared thermal imaging based on laser array according to claim 1, characterized in that, The S7 step calculates the true temperature of the surface of the object to be measured. The calculation formula is as follows: in, This represents the radiance of the object under test as measured by an infrared thermal imager in its natural radiation state. This represents the actual emissivity calculated in S6; This represents the Stefan-Boltzmann constant.

9. A temperature correction device for curved surface infrared thermal imaging based on a laser array, used to implement the temperature correction method for curved surface infrared thermal imaging based on a laser array as described in any one of claims 1 to 8, characterized in that: It includes at least a first radiation source and a second radiation source, which are stacked together. The radiation ends of the first and second radiation sources are provided with an object to be tested and a reference object, which are located on the same side. The first and second radiation sources are mounted on a support, which is equipped with an infrared thermal imager and a laser array. The infrared thermal imager and the laser array are sequentially arranged on one side of the first and second radiation sources, and both the infrared thermal imager and the laser array are electrically connected to a computer.

10. The curved surface infrared thermal imaging detection temperature correction device based on a laser array according to claim 9, characterized in that: The support is a three-dimensional adjustable structure used to achieve synchronous full-coverage imaging of the object under test and the reference object by the laser array and the infrared thermal imager.

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