Intelligent calibration method and system for infrared thermal imager

Abstract

The invention relates to the technical field of intelligent calibration, and discloses an intelligent calibration method and system for an infrared thermal imager, and the method comprises the steps: obtaining a temperature distribution image and acceleration data; performing attitude angle calculation according to the acceleration data to obtain attitude angle data; performing temperature difference calculation according to the temperature distribution image to obtain an initial temperature difference; performing neighborhood analysis according to the temperature distribution image and the initial temperature difference to obtain an adaptive weight; performing abnormal point detection according to the adaptive weight, the initial temperature difference and a preset standard difference threshold to obtain abnormal point data; and performing calibration optimization according to the abnormal point data and the attitude angle data, and outputting a calibration temperature image. The method has the following effect that the calibration precision of the infrared thermal imager can be improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent calibration technology, and in particular to an intelligent calibration method and system for an infrared thermal imager. Background Art

[0002] With the development of science and technology, infrared thermal imaging technology has been widely used in many fields such as security monitoring, industrial detection and medical diagnosis. For example, in power maintenance, infrared thermal imagers can monitor the temperature changes of equipment in real time and detect hot spots in time to prevent failures. However, due to factors such as changes in ambient temperature, the accuracy drift of the instrument itself, and the aging of the detector, infrared thermal imagers will have measurement errors, affecting their accuracy. Especially in complex working environments, such as high-altitude operations or places with narrow spaces, the impact of these factors is more significant. Therefore, how to intelligently calibrate infrared thermal imagers to ensure their long-term stable operation and provide accurate temperature measurement results has become an important research direction in this field.

[0003] An existing intelligent calibration method for infrared thermal imagers is to use an internal reference source combined with an external standard black body. First, a reference source with a known temperature is set inside the instrument as a fixed reference point during the calibration process. Then, in actual use, the external standard black body is placed in the environment to be tested and its temperature is ensured to remain constant. Next, the infrared thermal imager is used to simultaneously measure the temperature values ​​of the internal reference source and the external black body, and the gain and offset parameters of the instrument are automatically adjusted according to the difference between the two to achieve calibration. This method can compensate for measurement errors caused by changes in ambient temperature by comparing internal and external temperature readings, thereby improving the accuracy of temperature measurement.

[0004] Although the above methods have solved the calibration problem of infrared thermal imagers to a certain extent, there are still some shortcomings. One of the important problems is that the existing methods do not take into account the impact of spatial relationships such as the device attitude angle on the measurement results. When the infrared thermal imager measures at different angles, the data obtained will deviate due to different viewing angles, resulting in low calibration accuracy of the thermal imager. Summary of the invention

[0005] The present invention provides an intelligent calibration method and system for an infrared thermal imager, so as to improve the calibration accuracy of the thermal imager.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides an intelligent calibration method for an infrared thermal imager, comprising: Acquire temperature distribution images and acceleration data; Calculating the attitude angle according to the acceleration data to obtain attitude angle data; Calculate the temperature difference according to the temperature distribution image to obtain an initial temperature difference; Performing neighborhood analysis based on the temperature distribution image and the initial temperature difference to obtain an adaptive weight; Perform outlier detection according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain outlier data; Calibration optimization is performed based on the abnormal point data and the attitude angle data, and a calibration temperature image is output.

[0007] In an optional implementation, the calculating the attitude angle according to the acceleration data to obtain the attitude angle data includes: The pitch angle is calculated using the following formula: The roll angle is calculated using the following formula: in, represents the pitch angle, represents the roll angle, express The acceleration components of the axes, express The acceleration components of the axes, express The acceleration components of the axes; The attitude angle data includes the pitch angle and the roll angle.

[0008] In an optional implementation, the calculating the temperature difference according to the temperature distribution image to obtain the initial temperature difference includes: The horizontal temperature difference is calculated by the following formula: in, Represents pixels The horizontal temperature difference, Represents pixels The temperature value, Represents pixels Temperature value; The vertical temperature difference is calculated by the following formula: in, Represents pixels The vertical temperature difference, Represents pixels The temperature value, Represents pixels Temperature value; The direction of temperature change is calculated by the following formula: in, Represents pixels The direction of temperature change; The initial temperature difference includes the temperature change direction, the horizontal temperature difference and the vertical temperature difference.

[0009] In an optional implementation, performing neighborhood analysis according to the temperature distribution image and the initial temperature difference to obtain an adaptive weight includes: Filling the boundaries of the temperature distribution image to obtain a filled temperature image; Performing variance calculation according to a preset neighborhood rule and the filling temperature image to obtain a neighborhood variance; The gradient is calculated based on the initial temperature difference to obtain the temperature gradient: The weight is calculated using the following formula: in, Represents pixels The adaptive weight of Represents pixels The temperature gradient, represents the neighborhood variance.

[0010] In an optional implementation, performing outlier detection according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain outlier data includes: Performing a temperature difference update according to the adaptive weight and the initial temperature difference to obtain an updated temperature difference; Get the temperature difference magnitude threshold and temperature difference direction threshold; When the updated horizontal temperature difference or the updated vertical temperature difference of the updated temperature difference is greater than the temperature difference threshold, the next step of determination is performed, otherwise an abnormal point detection is performed on the next pixel point; When the updated change direction of the updated temperature difference is greater than the temperature difference direction threshold, the next step of determination is performed, otherwise an abnormal point detection is performed on the next pixel point; The updated standard deviation is calculated by the following formula: in, Represents pixels The updated standard deviation of represents the number of neighborhood pixels, Represents pixels Neighborhood of Represents pixels The updated horizontal temperature difference, Represents pixels Updated vertical temperature difference, represents the average of the updated horizontal temperature differences within the neighborhood, Represents the average of the updated vertical temperature differences within the neighborhood; When the updated standard deviation is greater than the standard deviation threshold, the pixel is determined to be an abnormal point; The abnormal point data includes all the abnormal points.

[0011] In an optional implementation, the performing calibration optimization according to the abnormal point data and the attitude angle data and outputting a calibration temperature image includes: Performing data filling on the abnormal point data to obtain an abnormality corrected image; The calibration temperature of the calibration temperature image is calculated by the following formula: in, Represents pixels The calibration temperature, Indicates the pixels in the abnormal correction image The corrected temperature value, represents the pitch angle, represents the roll angle; A calibration temperature image is generated according to the calibration temperature, and the calibration temperature image is output.

[0012] In an optional implementation, the performing data filling on the outlier point data to obtain an outlier corrected image includes: The temperature value of the abnormal point data is replaced by the average temperature of the neighborhood.

[0013] In a second aspect, the present invention provides an intelligent calibration system for an infrared thermal imager, comprising: A data acquisition module, used to acquire temperature distribution images and acceleration data; A posture analysis module, used to calculate the posture angle according to the acceleration data to obtain posture angle data; A temperature difference calculation module, used to calculate the temperature difference according to the temperature distribution image to obtain an initial temperature difference; A weight calculation module, used for performing neighborhood analysis according to the temperature distribution image and the initial temperature difference to obtain an adaptive weight; An anomaly detection module, used to perform anomaly detection according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain anomaly data; The temperature calibration module is used to perform calibration optimization according to the abnormal point data and the attitude angle data, and output a calibrated temperature image.

[0014] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent calibration method for the infrared thermal imager described in any one of the above is implemented.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned intelligent calibration methods for infrared thermal imagers.

[0016] Compared with the prior art, the present invention has the following beneficial effects: the present invention discloses an intelligent calibration method and system for an infrared thermal imager, the method comprising acquiring a temperature distribution image and acceleration data; performing attitude angle calculation according to the acceleration data to obtain attitude angle data; performing temperature difference calculation according to the temperature distribution image to obtain an initial temperature difference; performing neighborhood analysis according to the temperature distribution image and the initial temperature difference to obtain an adaptive weight; performing abnormal point detection according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain abnormal point data; performing calibration optimization according to the abnormal point data and the attitude angle data, and outputting a calibrated temperature image. The present method has the following effects: the present method can improve the calibration accuracy of the infrared thermal imager.

[0017] Specifically, this method determines the angle of the device relative to the direction of gravity based on the three-dimensional acceleration value output by the accelerometer, which has significant technical advantages. First, this calculation method directly utilizes the data provided by the physical sensor, without the need for additional hardware support, simplifies the system design, and improves the calculation efficiency. Secondly, the use of two-dimensional formulas ensures the accuracy of angle calculation, and can provide reliable posture estimation even in dynamic environments. The application of these formulas enables the infrared thermal imager to automatically adjust its viewing angle and perform image correction, thereby improving image quality and calibration accuracy. Furthermore, the above formula is derived based on the principle of coordinate transformation and the relationship between trigonometric functions, which conforms to the posture description method in rigid body dynamics. Compared with traditional calibration methods, this method not only improves the degree of automation of the calibration process, but also enhances the adaptability to different environmental conditions.

[0018] Furthermore, the method performs neighborhood analysis and calculates the adaptive weights through the temperature distribution image and the initial temperature difference. This process includes filling the boundaries of the temperature distribution image to obtain a filled temperature image, and then performing variance calculation according to the preset neighborhood rules and the filled temperature image to obtain the neighborhood variance. Furthermore, the gradient calculation is performed based on the initial temperature difference to obtain the temperature gradient of each pixel. Finally, the adaptive weight is calculated by the formula.

[0019] This adaptive weight calculation method has many advantages. First, by filling the boundaries of the temperature distribution image, the edge effect can be effectively avoided, the data integrity of the edge area can be ensured, and the overall temperature field analysis can be more accurate. Secondly, the introduction of neighborhood variance takes into account the temperature changes in the local area, which helps to capture subtle temperature differences more accurately and improve the accuracy of calibration. In addition, the calculation of the temperature gradient reflects the rate of change of temperature with space. Combined with the weight calculation formula in the form of an exponential function, it can suppress noise while maintaining important features, thereby enhancing the stability of the image processing results.

[0020] Furthermore, the rationality of this formula is reflected in the fact that it uses an exponential decay form to define the weight, that is, the larger the square of the temperature gradient, the more drastic the temperature change, and the smaller the corresponding weight, and vice versa. This method can naturally emphasize those areas where the temperature changes more slowly, while reducing the impact of areas with rapid changes or noise interference. Compared with traditional methods, this adaptive weighting strategy not only improves the adaptability of infrared thermal imagers to different scenes during the calibration process, but also significantly improves the quality and detection accuracy of the final image.

[0021] Furthermore, the method proposes a step of detecting outliers by adaptive weights, initial temperature differences, and preset standard deviation thresholds, which can effectively improve the image quality and the accuracy of subsequent analysis. This step first updates the temperature difference based on the adaptive weights and the initial temperature difference to obtain an updated temperature difference. This step aims to adjust the temperature difference of each pixel according to the environmental information around it to more accurately reflect the actual situation. First, the temperature difference size and the direction of temperature difference change are screened to reduce the subsequent calculation amount and improve the detection efficiency.

[0022] Furthermore, when the updated standard deviation of a pixel exceeds the preset standard deviation threshold, the pixel is identified as an outlier and included in the outlier data set. This method ensures the accuracy and robustness of outlier detection through a multi-level filtering mechanism (including comparison of temperature difference size, direction and standard deviation). Compared with the traditional single threshold judgment method, it is more adaptable to complex actual scenes, reduces false alarm rate and missed alarm rate, and thus improves the performance and reliability of the entire infrared thermal imaging system.

[0023] Furthermore, the outlier data is processed and combined with the attitude angle data to optimize the calibration process. This method first fills the outlier data, that is, replaces the temperature value of the outlier with the average temperature of the neighborhood, thereby generating an outlier correction image. This method effectively reduces the impact of outliers on the quality of the final temperature image and improves the accuracy and reliability of the temperature distribution. By considering the attitude angle of the device, the formula can compensate for the measurement error caused by the tilt of the device and ensure the accuracy of the temperature measured under different attitudes. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic flow chart of an intelligent calibration method for an infrared thermal imager provided by the first embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of an intelligent calibration system for an infrared thermal imager provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] With the development of science and technology, infrared thermal imaging technology has been widely used in many fields such as security monitoring, industrial detection and medical diagnosis. For example, in power maintenance, infrared thermal imagers can monitor the temperature changes of equipment in real time and detect hot spots in time to prevent failures. However, due to factors such as changes in ambient temperature, the accuracy drift of the instrument itself, and the aging of the detector, infrared thermal imagers will have measurement errors, affecting their accuracy. Especially in complex working environments, such as high-altitude operations or places with narrow spaces, the impact of these factors is more significant. Therefore, how to intelligently calibrate infrared thermal imagers to ensure their long-term stable operation and provide accurate temperature measurement results has become an important research direction in this field.

[0027] An existing intelligent calibration method for infrared thermal imagers is to use an internal reference source combined with an external standard black body. First, a reference source with a known temperature is set inside the instrument as a fixed reference point during the calibration process. Then, in actual use, the external standard black body is placed in the environment to be tested and its temperature is ensured to remain constant. Next, the infrared thermal imager is used to simultaneously measure the temperature values ​​of the internal reference source and the external black body, and the gain and offset parameters of the instrument are automatically adjusted according to the difference between the two to achieve calibration. This method can compensate for measurement errors caused by changes in ambient temperature by comparing internal and external temperature readings, thereby improving the accuracy of temperature measurement.

[0028] Although the above methods have solved the calibration problem of infrared thermal imagers to a certain extent, there are still some shortcomings. One of the important problems is that the existing methods do not take into account the impact of spatial relationships such as the device attitude angle on the measurement results. When the infrared thermal imager measures at different angles, the data obtained will deviate due to different viewing angles, resulting in low calibration accuracy of the thermal imager.

[0029] Reference Figure 1 The first embodiment of the present invention provides an intelligent calibration method for an infrared thermal imager, comprising the following steps: S11, acquiring temperature distribution image and acceleration data; S12, calculating the attitude angle according to the acceleration data to obtain attitude angle data; S13, calculating the temperature difference according to the temperature distribution image to obtain an initial temperature difference; S14, performing neighborhood analysis according to the temperature distribution image and the initial temperature difference to obtain an adaptive weight; S15, performing outlier detection according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain outlier data; S16, performing calibration optimization according to the abnormal point data and the attitude angle data, and outputting a calibration temperature image.

[0030] In step S11 , a temperature distribution image and acceleration data are acquired.

[0031] In one embodiment, the acquisition of temperature distribution images and acceleration data is completed by an infrared thermal imager and a built-in three-axis accelerometer. For the acquisition of temperature distribution images, the basic parameters of the thermal imager need to be set first. For example, the frame rate can be set to 30Hz to ensure that the temperature changes in dynamic scenes can be accurately captured, the resolution is set to 640x480 to ensure image quality while taking into account the speed of data processing, and the temperature range is selected according to the actual application scenario. The appropriate range, such as -20℃ to +500℃. In addition, the focal length needs to be adjusted to make the target area clearly imaged, and the automatic gain control (AGC) function is turned on to optimize the visualization effect in different temperature ranges. For the acquisition of acceleration data, the sampling frequency of the accelerometer needs to be configured to synchronize with the frame rate of the thermal imager, that is, 30Hz, so as to ensure the data matching when calculating the attitude angle; the measurement range is set according to the maximum acceleration experienced by the device, such as ±16g, and the sensitivity needs to be adjusted according to specific needs, which can be set to 1mg / LSB. After the acquisition is completed, in order to ensure the accuracy of subsequent processing, the original temperature distribution image needs to be preprocessed, including noise removal, contrast enhancement and other operations, while the acceleration data needs to be checked for integrity and consistency, and filtered when necessary to reduce the impact of high-frequency noise. The data obtained after such processing is used in the next step of attitude angle calculation and temperature difference analysis process.

[0032] In step S12, the attitude angle is calculated according to the acceleration data to obtain attitude angle data.

[0033] In one implementation, the pitch angle is calculated using the following formula: The roll angle is calculated using the following formula: in, represents the pitch angle, represents the roll angle, express The acceleration components of the axes, express The acceleration components of the axes, express The acceleration components of the axes; The attitude angle data includes the pitch angle and the roll angle.

[0034] It is worth noting that the pitch angle describes the device along the The pitch angle is the angle of inclination of the device along the axis. When the front of the device is pointing upward, the pitch angle is positive; conversely, when the front is pointing downward, the pitch angle is negative. The pitch angle helps determine whether the device is looking up or down. The roll angle describes the device's rotation along the axis. The degree of inclination of the axis, that is, the angle of left-right inclination. When the right side of the device is lifted up, the roll angle is positive; conversely, when the left side is raised up, the roll angle is negative. The roll angle is used to determine whether the device is horizontal and the direction of its roll.

[0035] In step S13, the temperature difference is calculated according to the temperature distribution image to obtain an initial temperature difference.

[0036] In one implementation, the horizontal temperature difference is calculated by the following formula: in, Represents pixels The horizontal temperature difference, Represents pixels The temperature value, Represents pixels Temperature value; The vertical temperature difference is calculated by the following formula: in, Represents pixels The vertical temperature difference, Represents pixels The temperature value, Represents pixels Temperature value; The direction of temperature change is calculated by the following formula: in, Represents pixels The direction of temperature change; The initial temperature difference includes the temperature change direction, the horizontal temperature difference and the vertical temperature difference.

[0037] It is worth noting that the horizontal temperature difference calculates the average temperature difference between each pixel and its left and right adjacent pixels, that is, the temperature gradient in the horizontal direction. The temperature value of the right pixel and the temperature value of the left pixel are divided by 2 to obtain a smooth temperature change rate. The vertical temperature difference calculates the average temperature difference between each pixel and its upper and lower adjacent pixels, that is, the temperature gradient in the vertical direction. The temperature value of the upper pixel and the temperature value of the lower pixel are divided by 2 to obtain a smooth temperature change rate. These two parameters provide information about the rate of temperature change in the local area and are key indicators for measuring the unevenness of the spatial distribution of temperature. This information is very important for identifying areas of sudden temperature changes (abnormal hot spots or cold spots). The direction of temperature change helps to understand the direction of heat flow in the temperature field or the main trend of heat transfer.

[0038] In step S14, a neighborhood analysis is performed based on the temperature distribution image and the initial temperature difference to obtain an adaptive weight.

[0039] In one implementation, the temperature distribution image is border-filled to obtain a filled temperature image; Performing variance calculation according to a preset neighborhood rule and the filling temperature image to obtain a neighborhood variance; The gradient is calculated based on the initial temperature difference to obtain the temperature gradient: The weight is calculated using the following formula: in, Represents pixels The adaptive weight of Represents pixels The temperature gradient, represents the neighborhood variance.

[0040] It is worth noting that the purpose of border filling is to avoid the problem of inaccurate calculation due to lack of neighborhood information of edge pixels in the subsequent processing. A new filled temperature image is created by border filling (Padding) of the original temperature distribution image. The method adopted is to copy the edge pixel values ​​to expand the image boundary to ensure that each pixel has complete neighborhood information for subsequent calculations.

[0041] It is worth noting that the purpose of neighborhood variance calculation is to evaluate the degree of change of temperature values ​​in the local area and provide a basis for the calculation of adaptive weights. Preset neighborhood rule: Define a fixed neighborhood size. This method is set to 3x3 to determine the reference area around each pixel. Variance calculation is based on the filled temperature image, and the variance of the temperature value is calculated in the specified neighborhood of each pixel. The variance reflects the fluctuation of the temperature value in the area. The larger the variance, the more drastic the temperature change, and vice versa, the temperature is relatively uniform and stable.

[0042] It is worth noting that the purpose of temperature gradient calculation is to quantify the speed and direction of temperature change with spatial position and identify areas where the temperature changes sharply. Among them, the neighborhood variance, as a regulating factor, determines the speed of weight decay. The larger the neighborhood variance value, the smoother the weight decline curve, allowing more high-frequency components to pass through; the smaller the neighborhood variance value, the faster the weight will be reduced, suppressing the influence of noise or mutation. This formula adopts the form of exponential decay. When the temperature gradient is large (that is, there is a significant temperature change), the corresponding weight will decrease rapidly, reflecting that the area contains outliers or noise; on the contrary, for areas with relatively gentle temperature changes, the weight is close to 1, emphasizing the importance of these areas. The purpose of this is to effectively suppress noise interference while maintaining important features, thereby improving the accuracy of the final calibration results.

[0043] In step S15, outlier detection is performed according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain outlier data.

[0044] In one implementation, the temperature difference is updated according to the adaptive weight and the initial temperature difference to obtain an updated temperature difference; Get the temperature difference magnitude threshold and temperature difference direction threshold; When the updated horizontal temperature difference or the updated vertical temperature difference of the updated temperature difference is greater than the temperature difference threshold, the next step of determination is performed, otherwise an abnormal point detection is performed on the next pixel point; When the updated change direction of the updated temperature difference is greater than the temperature difference direction threshold, the next step of determination is performed, otherwise an abnormal point detection is performed on the next pixel point; The updated standard deviation is calculated by the following formula: in, Represents pixels The updated standard deviation of represents the number of neighborhood pixels, Represents pixels Neighborhood of Represents pixels The updated horizontal temperature difference, Represents pixels Updated vertical temperature difference, represents the average of the updated horizontal temperature differences within the neighborhood, Represents the average of the updated vertical temperature differences within the neighborhood; When the updated standard deviation is greater than the standard deviation threshold, the pixel is determined to be an abnormal point; The abnormal point data includes all the abnormal points.

[0045] In one embodiment, the temperature difference update involves the horizontal temperature difference and the vertical temperature difference. This process multiplies the original temperature difference by the corresponding weight value. The direction of temperature change remains unchanged.

[0046] It is worth noting that the temperature difference threshold defines a critical value. Only when the updated horizontal temperature difference or the updated vertical temperature difference of a pixel exceeds this threshold will it be further considered as an abnormal point. In addition to considering the size of the temperature difference, it is also checked whether the direction of the temperature change significantly deviates from the normal range, which is also an auxiliary judgment condition. In this method, the temperature difference threshold is set to 2°C; the temperature difference direction threshold is set to 30°.

[0047] It is worth noting that the purpose of the initial screening of abnormal points is to quickly filter out pixels that obviously do not meet the abnormal characteristics based on the two dimensions of temperature difference size and direction. If the updated horizontal temperature difference or updated vertical temperature difference of a pixel is greater than the temperature difference size threshold, it will enter the next detailed evaluation; at the same time, it is also necessary to check whether the updated temperature change direction of the point exceeds the temperature difference direction threshold. If both conditions are met, continue to perform the following steps; otherwise, skip the current pixel and process the next pixel. Used to reduce calculations.

[0048] It is worth noting that the purpose of the updated standard deviation is to quantify the "degree of outlier" of a pixel relative to its surrounding neighbors. A higher standard deviation means that the temperature change pattern of the pixel is significantly different from that of its neighbors, which is caused by the presence of anomalies. The calculated updated standard deviation is compared with the preset standard deviation threshold. If the updated standard deviation of a pixel is greater than the standard deviation threshold, it is considered an outlier and is recorded as part of the outlier data. The standard deviation threshold is set to 2°C. It is worth noting that a smaller standard deviation threshold (such as 1°C) can more sensitively detect outliers in a small range, but it also increases the risk of false alarms; on the contrary, a larger threshold (such as 3°C) reduces the probability of false alarms, but misses some minor anomalies.

[0049] In step S16, calibration optimization is performed based on the abnormal point data and the attitude angle data, and a calibration temperature image is output.

[0050] In one implementation, data filling is performed on the abnormal point data to obtain an abnormality corrected image; The calibration temperature of the calibration temperature image is calculated by the following formula: in, Represents pixels The calibration temperature, Indicates the pixels in the abnormal correction image The corrected temperature value, represents the pitch angle, represents the roll angle; A calibration temperature image is generated according to the calibration temperature, and the calibration temperature image is output.

[0051] In one implementation, the temperature value of the abnormal point data is replaced by the average temperature value of the neighborhood.

[0052] It is worth noting that the purpose of outlier data filling is to repair or fill the data of the detected outliers to ensure that the accuracy of the overall results will not be affected by these outliers during subsequent processing. For all identified outliers, the average temperature value of its neighborhood pixels is used to replace the original abnormal temperature value. For example, if a pixel is marked as an outlier, the average temperature value of all normal pixels in the 3x3 neighborhood around it is used as the new temperature value of the pixel. The purpose of this is to smooth the mutation area in the image and reduce the impact of noise on the calibration process.

[0053] It is worth noting that the purpose of this formula is to compensate for the temperature measurement error caused by the change in the device's posture, that is, the change in the tilt angle. When the device is not in a horizontal state, the temperature distribution diagram obtained by direct measurement will be distorted. By dividing by the cosine value in two directions, taking into account the tilt in the two-dimensional plane, this error caused by different viewing angles can be corrected and the true temperature distribution of the object can be restored.

[0054] In summary, the present invention discloses an intelligent calibration method for an infrared thermal imager, which aims to solve the measurement error problem existing in the prior art. The method first obtains temperature distribution images and acceleration data to lay the foundation for subsequent processing. Based on these data, the attitude angle is further calculated to obtain the attitude angle data of the device. This process specifically considers the data deviation caused by the difference in viewing angle when the device is measured at different angles, thereby improving the calibration accuracy.

[0055] Specifically, in the attitude angle calculation stage, the pitch angle and roll angle are accurately calculated using a specific formula through the three-dimensional acceleration values ​​provided by the three-axis accelerometer. This not only simplifies the system design, but also enhances the accuracy of attitude estimation, allowing the infrared thermal imager to automatically adjust its viewing angle and perform image correction. Next, in the temperature difference calculation link, by quantifying the horizontal and vertical temperature differences between each pixel and its adjacent pixels and determining the direction of temperature change, key information about the rate of temperature change in the local area is provided. This is crucial for identifying areas of sudden temperature changes (abnormal hot spots or cold spots) because this information helps understand the direction of heat flow in the temperature field or the main trend of heat transfer.

[0056] Subsequently, the method combines the temperature distribution image with the initial temperature difference to perform neighborhood analysis to obtain the adaptive weight. This step first fills the boundaries of the temperature distribution image to prevent edge effects from affecting the accuracy of the overall temperature field analysis. Then, the variance calculation is performed according to the preset neighborhood rules to obtain the neighborhood variance. In addition, the temperature gradient of each pixel is calculated based on the initial temperature difference, and the adaptive weight is finally calculated by the formula. This strategy emphasizes the importance of areas with gentle temperature changes, while suppressing the influence of areas with rapid changes or noise interference, thereby improving the overall calibration effect.

[0057] As a key part of the entire calibration process, outlier detection first multiplies the original temperature difference by the corresponding adaptive weight, and then screens out potential outliers based on the set temperature difference size and direction threshold. For each candidate outlier point, the updated standard deviation is calculated and compared with the preset standard deviation threshold to finally determine whether it is an outlier. Once all outliers are identified, the average temperature value of their neighborhood pixels is used to replace the original abnormal temperature value to repair the mutation area in the image. This process effectively reduces the impact of outliers on the quality of the final temperature image and improves the accuracy and reliability of temperature distribution.

[0058] Finally, taking into account the data distortion caused by the tilt of the equipment, the corrected outlier point data is combined with the attitude angle data for calibration optimization to generate a calibrated temperature image that accurately reflects the true temperature distribution of the object. By dividing by the cosine value in two directions and taking into account the tilt in the two-dimensional plane, the error caused by different viewing angles can be corrected to restore the true temperature distribution of the object. This method not only improves the degree of automation of the calibration process, but also enhances the adaptability to different environmental conditions and ensures the accuracy of the temperature measured under different attitudes. In summary, the intelligent calibration method for infrared thermal imagers proposed in the present invention significantly improves the ability of infrared thermal imagers to maintain long-term stability and provide accurate temperature measurement results in complex working environments by improving the accuracy of intelligent calibration.

[0059] Reference Figure 2 The second embodiment of the present invention provides an intelligent calibration system for an infrared thermal imager, comprising: A data acquisition module, used to acquire temperature distribution images and acceleration data; A posture analysis module, used to calculate the posture angle according to the acceleration data to obtain posture angle data; A temperature difference calculation module, used to calculate the temperature difference according to the temperature distribution image to obtain an initial temperature difference; A weight calculation module, used for performing neighborhood analysis according to the temperature distribution image and the initial temperature difference to obtain an adaptive weight; An anomaly detection module, used to perform anomaly detection according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain anomaly data; The temperature calibration module is used to perform calibration optimization according to the abnormal point data and the attitude angle data, and output a calibrated temperature image.

[0060] Preferably, the data acquisition module is used to: Acquire temperature distribution images and acceleration data.

[0061] Preferably, the posture analysis module is used to calculate the posture angle according to the acceleration data to obtain the posture angle data, including: The pitch angle is calculated using the following formula: The roll angle is calculated using the following formula: in, represents the pitch angle, represents the roll angle, express The acceleration components of the axes, express The acceleration components of the axes, express The acceleration components of the axes; The attitude angle data includes the pitch angle and the roll angle.

[0062] Preferably, the temperature difference calculation module is used to: Calculating the temperature difference according to the temperature distribution image to obtain the initial temperature difference includes: The horizontal temperature difference is calculated by the following formula: in, Represents pixels The horizontal temperature difference, Represents pixels The temperature value, Represents pixels Temperature value; The vertical temperature difference is calculated by the following formula: in, Represents pixels The vertical temperature difference, Represents pixels The temperature value, Represents pixels Temperature value; The direction of temperature change is calculated by the following formula: in, Represents pixels The direction of temperature change; The initial temperature difference includes the temperature change direction, the horizontal temperature difference and the vertical temperature difference.

[0063] Preferably, the weight calculation module is used to: Performing neighborhood analysis according to the temperature distribution image and the initial temperature difference to obtain an adaptive weight includes: Filling the boundaries of the temperature distribution image to obtain a filled temperature image; Performing variance calculation according to a preset neighborhood rule and the filling temperature image to obtain a neighborhood variance; The gradient is calculated based on the initial temperature difference to obtain the temperature gradient: The weight is calculated using the following formula: in, Represents pixels The adaptive weight of Represents pixels The temperature gradient, represents the neighborhood variance.

[0064] Preferably, the anomaly detection module is used to: Performing outlier detection according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain outlier data includes: Performing a temperature difference update according to the adaptive weight and the initial temperature difference to obtain an updated temperature difference; Get the temperature difference magnitude threshold and temperature difference direction threshold; When the updated horizontal temperature difference or the updated vertical temperature difference of the updated temperature difference is greater than the temperature difference threshold, the next step of determination is performed, otherwise an abnormal point detection is performed on the next pixel point; When the updated change direction of the updated temperature difference is greater than the temperature difference direction threshold, the next step of determination is performed, otherwise an abnormal point detection is performed on the next pixel point; The updated standard deviation is calculated by the following formula: in, Represents pixels The updated standard deviation of represents the number of neighborhood pixels, Represents pixels Neighborhood of Represents pixels The updated horizontal temperature difference, Represents pixels Updated vertical temperature difference, represents the average of the updated horizontal temperature differences within the neighborhood, Represents the average of the updated vertical temperature differences within the neighborhood; When the updated standard deviation is greater than the standard deviation threshold, the pixel is determined to be an abnormal point; The abnormal point data includes all the abnormal points.

[0065] Preferably, the temperature calibration module is used for: Calibration optimization is performed according to the abnormal point data and the attitude angle data, and a calibration temperature image is output, including: Filling the abnormal point data to obtain an abnormality-corrected image includes: The temperature value of the abnormal point data is replaced by the average temperature of the neighborhood; The calibration temperature of the calibration temperature image is calculated by the following formula: in, Represents pixels The calibration temperature, Indicates the pixels in the abnormal correction image The corrected temperature value, represents the pitch angle, represents the roll angle; A calibration temperature image is generated according to the calibration temperature, and the calibration temperature image is output.

[0066] It should be noted that the intelligent calibration system for an infrared thermal imager provided in an embodiment of the present invention is used to execute all the process steps of the intelligent calibration method for an infrared thermal imager in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0067] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, the steps in the above-mentioned intelligent calibration method for infrared thermal imagers are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0068] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0069] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0070] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0071] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0072] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0073] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0074] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent calibration method for an infrared thermal imager, characterized in that: include: Acquire temperature distribution images and acceleration data; Calculating the attitude angle according to the acceleration data to obtain attitude angle data; Calculate the temperature difference according to the temperature distribution image to obtain an initial temperature difference; Performing neighborhood analysis based on the temperature distribution image and the initial temperature difference to obtain an adaptive weight; Perform outlier detection according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain outlier data; Calibration optimization is performed based on the abnormal point data and the attitude angle data, and a calibration temperature image is output.

2. The intelligent calibration method for infrared thermal imager according to claim 1, characterized in that: The step of calculating the attitude angle according to the acceleration data to obtain the attitude angle data includes: The pitch angle is calculated using the following formula: The roll angle is calculated using the following formula: in, represents the pitch angle, represents the roll angle, express The acceleration components of the axes, express The acceleration components of the axes, express The acceleration components of the axes; The attitude angle data includes the pitch angle and the roll angle.

3. The intelligent calibration method for infrared thermal imager according to claim 1, characterized in that: The step of calculating the temperature difference according to the temperature distribution image to obtain the initial temperature difference comprises: The horizontal temperature difference is calculated by the following formula: in, Represents pixels The horizontal temperature difference, Represents pixels The temperature value, Represents pixels Temperature value; The vertical temperature difference is calculated by the following formula: in, Represents pixels The vertical temperature difference, Represents pixels The temperature value, Represents pixels Temperature value; The direction of temperature change is calculated by the following formula: in, Represents pixels The direction of temperature change; The initial temperature difference includes the temperature change direction, the horizontal temperature difference and the vertical temperature difference.

4. The intelligent calibration method for infrared thermal imager according to claim 1, characterized in that: The performing neighborhood analysis according to the temperature distribution image and the initial temperature difference to obtain an adaptive weight includes: Filling the boundaries of the temperature distribution image to obtain a filled temperature image; Performing variance calculation according to a preset neighborhood rule and the filling temperature image to obtain a neighborhood variance; The gradient is calculated based on the initial temperature difference to obtain the temperature gradient: The weight is calculated using the following formula: in, Represents pixels The adaptive weight of Represents pixels The temperature gradient, represents the neighborhood variance.

5. The intelligent calibration method for infrared thermal imager according to claim 1, characterized in that: The performing of abnormal point detection according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain abnormal point data includes: Performing a temperature difference update according to the adaptive weight and the initial temperature difference to obtain an updated temperature difference; Get the temperature difference magnitude threshold and temperature difference direction threshold; When the updated horizontal temperature difference or the updated vertical temperature difference of the updated temperature difference is greater than the temperature difference threshold, the next step of determination is performed, otherwise an abnormal point detection is performed on the next pixel point; When the updated change direction of the updated temperature difference is greater than the temperature difference direction threshold, the next step of determination is performed, otherwise an abnormal point detection is performed on the next pixel point; The updated standard deviation is calculated by the following formula: in, Represents pixels The updated standard deviation of represents the number of neighborhood pixels, Represents pixels Neighborhood of Represents pixels The updated horizontal temperature difference, Represents pixels Updated vertical temperature difference, represents the average of the updated horizontal temperature differences within the neighborhood, Represents the average of the updated vertical temperature differences within the neighborhood; When the updated standard deviation is greater than the standard deviation threshold, the pixel is determined to be an abnormal point; The abnormal point data includes all the abnormal points.

6. The intelligent calibration method for infrared thermal imager according to claim 1, characterized in that: The step of performing calibration optimization according to the abnormal point data and the attitude angle data and outputting a calibration temperature image comprises: Performing data filling on the abnormal point data to obtain an abnormality corrected image; The calibration temperature of the calibration temperature image is calculated by the following formula: in, Represents pixels The calibration temperature, Indicates the pixels in the abnormal correction image The corrected temperature value, represents the pitch angle, represents the roll angle; A calibration temperature image is generated according to the calibration temperature, and the calibration temperature image is output.

7. The intelligent calibration method for infrared thermal imager according to claim 6, characterized in that: The step of filling the abnormal point data to obtain an abnormality corrected image includes: The temperature value of the abnormal point data is replaced by the average temperature of the neighborhood.

8. An intelligent calibration system for an infrared thermal imager, characterized in that: include: A data acquisition module, used to acquire temperature distribution images and acceleration data; A posture analysis module, used to calculate the posture angle according to the acceleration data to obtain posture angle data; A temperature difference calculation module, used to calculate the temperature difference according to the temperature distribution image to obtain an initial temperature difference; A weight calculation module, used for performing neighborhood analysis according to the temperature distribution image and the initial temperature difference to obtain an adaptive weight; An anomaly detection module, used to perform anomaly detection according to the adaptive weight, the initial temperature difference and a preset standard deviation threshold to obtain anomaly data; The temperature calibration module is used to perform calibration optimization according to the abnormal point data and the attitude angle data, and output a calibrated temperature image.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent calibration method for the infrared thermal imager as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the intelligent calibration method for the infrared thermal imager according to any one of claims 1 to 7.

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