Infrared camera image simulation method and system

Abstract

To provide an image simulation method and system of an infrared camera with which accuracy is secured.SOLUTION: The present invention generates the geometric shape of a target object and then constitutes an image of the target object according to the geometric shape of the target object, constitutes an internal temperature field of the target object image on the basis of the image of the target object, and thereafter constitutes an external temperature field of the target object image by the emission rate feature of a material, and then combines the external temperature field of the target object image and an environmental impact element to simulate the temperature field of a camera lens part, ultimately simulating the image of an infrared camera on the basis of the temperature field of the camera lens part. During simulation of the present application, by introducing an environmental impact element and taking the diffusion of heat radiation into account, the accuracy of the simulation result is heightened, and by acquiring an inverse analysis image of the simulation image and comparing the simulation image with the inverse analysis image to conduct evaluation with respect to the simulation image, higher accuracy of the simulated infrared camera image can be secured.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This application relates to infrared imaging technology, and more particularly to an image simulation method and system for an infrared camera. [Background technology]

[0002] High-precision infrared (IR) thermal imaging is widely considered during industrial non-destructive testing (NDT) and preventive maintenance. In these applications, infrared cameras are used to detect abnormal thermal defects, such as those caused by electrical leakage in motors or overheating in blowers and turbine components. However, the performance of infrared imaging systems is significantly affected by three main factors: their accuracy, precision, and low spatial resolution. Therefore, it is necessary to use simulation techniques to examine the performance of infrared imaging systems.

[0003] Existing simulation methods typically only correct for the non-uniformity of the surface radiation rate or camera pixel, and do not consider the diffusion of thermal radiation, which causes a blurring (convolution) effect and low accuracy of the final simulation results. Summary of the Invention [Problem to be solved by the invention]

[0004] In existing simulation methods, only the emissivity of the surface radiated or the non-uniformity of the camera pixels is usually corrected, and the diffusion of thermal radiation is not taken into account, which causes a blurring (convolution) effect and low accuracy of the final simulation effect. To solve this problem, we provide an infrared camera image simulation method and system. [Means for solving the problem]

[0005] The present invention provides a method for generating a geometric shape of a target object, constructing an image of the target object according to the geometric shape of the target object, and constructing an internal temperature field of a space inside the target object near a surface of the target object based on the image of the target object; Obtaining an external temperature field of a space near a surface of the target object outside the target object according to the emissivity characteristics of the material based on the internal temperature field of the target object image; Simulating the temperature field of the camera lens by combining the external temperature field of the target object image with environmental influence factors; and and simulating an infrared camera image based on the temperature field at the camera lens. A method for simulating an image from an infrared camera is provided.

[0006] The present application further relates to an image generation module and a surface field simulation module used to simulate the external temperature field of the target object; a lens temperature field simulation module used to simulate the temperature diffusion process from the environmental medium of the target object to the camera lens; A camera simulation module is used to fit images taken by the camera with different measurement errors and all modeling errors. An infrared camera image simulation system is provided. [Effects of the Invention]

[0007] This application relates to a method and system for simulating an infrared camera image, which involves generating a geometric shape of a target object, constructing an image of the target object according to the geometric shape of the target object, constructing an internal temperature field of the target object image based on the image of the target object, constructing an external temperature field of the target object image according to the emissivity characteristics of the material, and then combining the external temperature field of the target object image with environmental influence factors to simulate the temperature field of the camera lens, and finally simulating an infrared camera image based on the temperature field of the camera lens. During the simulation of this application, by introducing environmental influence factors and considering the diffusion of thermal radiation, the accuracy of the simulation results can be improved, and by obtaining an inverse analysis image of the simulated image, comparing the simulated image with the inverse analysis image and evaluating the simulated image, further accuracy of the simulated infrared camera image can be ensured. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a method flowchart illustrating an image simulation method for an infrared camera provided by an embodiment of the present application. [Figure 2] 1 is a schematic diagram illustrating the structure of an image simulation system for an infrared camera provided by an embodiment of the present application; [Figure 3] 1 is a schematic diagram of an infrared camera image generated by an infrared camera image simulation method provided by an embodiment of the present application; [Figure 4] 1 is a schematic diagram of a first type image Z and an image according to a z value generated by an image simulation method for an infrared camera provided by an embodiment of the present application; [Figure 5] 1 is a schematic diagram of a first type of image Z, a second type of image T, and an external temperature field image f generated by an image simulation method for an infrared camera provided by an embodiment of the present application; [Figure 6] 1 is a schematic diagram of an external temperature field image f and a camera lens temperature field image g0 generated by an infrared camera image simulation method provided by an embodiment of the present application; [Figure 7] 1 is a schematic diagram of an image g0 of a temperature field at a camera lens and an image g of an infrared camera generated by an infrared camera image simulation method provided by an embodiment of the present application; DETAILED DESCRIPTION OF THE INVENTION

[0009] In order to clarify the purpose, technical means and advantages of the present application, the present application will be described in more detail below in conjunction with the drawings and examples. It should be understood that the specific embodiments described herein are only for the purpose of interpreting the present application, and are not intended to limit the present application.

[0010] The present application provides a method for simulating an image of an infrared camera.

[0011] As shown in FIG. 1 , in an embodiment of the present application, the image simulation method for an infrared camera provided by the present application includes: A step S100 of generating a geometric shape of the target object, constructing an image of the target object according to the geometric shape of the target object, and constructing an internal temperature field of a space inside the target object close to the surface of the target object based on the image of the target object; Step S200: obtaining an external temperature field of a space near the surface of the target object outside the target object according to the emissivity characteristics of the material based on the internal temperature field of the target object image; Step S300: simulating the temperature field of the camera lens by combining the external temperature field of the target object image and environmental influence factors; and and step S400 of simulating an infrared camera image based on the temperature field of the camera lens portion.

[0012] In this embodiment, the geometric shape of the target object is generated, then an image of the target object is constructed according to the geometric shape of the target object, the internal temperature field of the target object is constructed based on the image of the target object, then the external temperature field of the image of the target object is constructed according to the emissivity characteristics of the material, then the external temperature field of the image of the target object is combined with environmental influence factors to simulate the temperature field of the camera lens, and finally the image of the infrared camera is simulated based on the temperature field of the camera lens. During the simulation of this application, by introducing environmental influence factors and considering the diffusion of thermal radiation, the accuracy of the simulation results can be improved, and by obtaining an inverse analysis image of the simulated image, comparing the simulated image with the inverse analysis image and evaluating the simulated image, even higher accuracy of the simulated infrared camera image can be ensured.

[0013] In one embodiment of the present application, step S100 comprises: Step S110 of inputting two-dimensional coordinate information and generating a geometric shape image of a surface view of a target object according to the two-dimensional coordinate information; Step S120 of generating a first class image by filling the surface material type information into the geometric shape of the target object surface view to generate a first class image, denoted as Z, where the first class image Z includes a plurality of first class pixels Z(x,y), where x in Z(x,y) is the horizontal coordinate of the first class pixel and y is the vertical coordinate of the first class pixel; and and generating a second-class image S130 by filling the internal temperature information into the geometric shape of the target object surface view to generate a second-class image, denoted as T, where the second-class image T includes a plurality of second-class pixels T(x,y), where x in T(x,y) is the horizontal coordinate of the first-class pixel and y is the vertical coordinate of the first-class pixel.

[0014] Specifically, the geometric shape includes a circle and a rectangle, and the geometric shape is a shape where the circle and the rectangle are overlapped, and then fill the geometric shape with a material type or temperature value corresponding to the target object.

[0015] In this example, the geometric shape may be filled with a material type corresponding to the target object, and the geometric shape may also be filled with a temperature value corresponding to the target object.

[0016] A synthetic geometry can be generated, and real modeling software can be used to input information to generate the spatial temperature distribution. First, a geometry that simulates the surface view of the object is generated.

[0017] First, a shape consisting of a circle and a rectangle is generated, and the overlapping shape of the circle and the rectangle becomes one geometric shape. Then, the circle or the rectangle is filled with a fixed or slightly variable value, which represents the material type (iron, aluminum) or the temperature value on the surface or inside of the object. If the filled label is the material type, the generated image is the first type image Z, and if the filled label is the temperature value, the generated image is the second type image T.

[0018] Furthermore, the temperature can be assumed to be constant or its mean value can be slowly varied around a certain standard deviation to generate a second type of image T, where the temperature field inside the object is closer to the surface. Alternatively, the temperature can be constant by fixing the image pixel (the T value at position (x,y)). A slow temperature change can be achieved by filling in random values ​​with a certain variance and mean value, or by using a filtered version with a small variation around the mean value.

[0019] In one embodiment of the present application, the step S200 includes: Step S210 of acquiring material emissivity characteristic information of each surface material of the target object; Step S220: forming a coupling relationship between the internal temperature field and the external temperature field according to the material emissivity characteristic information of each surface material of the target object (see Equation 1 for the coupling relationship between the internal temperature field and the external temperature field);

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[0020] Specifically, the firing rate varies depending on the material, and each material corresponds to a single firing rate, e={e1, e2, , e M}, M is the number of types of materials, and the n value is a constant in the physical sense.

[0021] In this embodiment, by constructing an external temperature field image f, the emissivity characteristics e of the target object are correlated with the first type of image Z and the second type of image T, so that the final simulated infrared camera image has higher accuracy.

[0022] In one embodiment of the present application, the step S300 comprises: Step S310: calculating a temperature field of the camera lens of the infrared camera (the temperature field of the camera lens is the temperature field of the space where the infrared camera lens is located) according to Equation 2;

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[0023] Step S320 combines the attenuation effect and the diffusion parameters into the nonlinear calculation of the medium diffusion, and simplifies Equation 2 into Equation 3;

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[0024] Specifically, if h(x,y) = δ(x,y), the convolution effect is not taken into account. The convolution core or point spread function (PSF) can be experimentally obtained or hypothesized, e.g., a Gaussian shape with known or unknown width parameters.

[0025] Among them, δ(x,y) is the Dirac delta function, which has zero values ​​at all points other than zero, but whose integral over all domains is 1.

[0026] Step S330 of obtaining an error, fitting the error to the temperature field of the camera lens of the infrared camera, and obtaining an infrared camera image (see Equation 4 for the expression of the infrared camera image);

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[0027] Specifically, the error ε(x, y) includes measurement error and modeling error.

[0028] In this embodiment, the space in front of the camera lens is simulated by using Equation 2, taking into account the environmental properties (temperature, humidity, etc.) and distance information.

[0029] In some embodiments of this embodiment, step S300 further includes: It also includes step S340, which introduces the attenuation effect and the diffusion medium temperature into φ(f) to obtain equation 5.

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[0030] Substituting Equation 5 into Equation 2 and replacing φ(f) with φ′(f) gives Equation 3.

[0031] In this embodiment, the attenuation effect is a function of the distance d from the infrared camera lens to the target object, and the distance d from the infrared camera lens to the target object is also one of the factors that affect diffusion, so the distance d between the infrared camera lens and the target object is incorporated into the nonlinear calculation of diffusion φ(·) as an influencing factor.

[0032] By introducing distance as an influencing factor, the simulation accuracy of the infrared camera image simulation system can be effectively improved, and the reliability of the simulation results can be further improved.

[0033] Specifically, the attenuation coefficient k is a function of humidity.

[0034] In this example, the accuracy of the simulated image is improved by introducing the attenuation effect and the diffusion medium into φ(·). The accuracy of the simulation must be improved because the influence of errors must be taken into account in the process from simulating the lens temperature field to simulating the camera image.

[0035] In some embodiments of this embodiment, the method for simulating an infrared camera image further includes: Step S410: obtaining the inverse analysis of f(x,y); Specifically, inverse analysis refers to the process of finding a solution in the opposite direction. For example, inverse analysis from g0(x,y) to g(x,y) is the process of finding a solution in the opposite direction from g(x,y) to g0(x,y).

[0036] and step S420 of performing segmentation evaluation on the infrared camera image through formulas 6 and 7.

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[0037] Specifically, Z(x,y) can be substituted into Equation 6 and Equation 7 to obtain the error between Z(x,y) and Z′(x,y), and then Z(x,y) can be compared with Z′(x,y) to evaluate the division effect.

[0038] We can also substitute T(x,y) into Equation 6 and Equation 7 to obtain the error between T(x,y) and T'(x,y), and then compare T(x,y) with T'(x,y) to evaluate the reconstruction effect.

[0039] In this embodiment, first, inverse analysis is performed on f(x, y). The purpose is to perform inverse analysis on each stage of this application.

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[0040] As shown in FIG. 2 , in one embodiment of the present application, an infrared camera image simulation system is provided for use in implementing the infrared camera image simulation method described in any of the embodiments, and the infrared camera image simulation system includes an image generation module 100, a surface field simulation module 200, a lens temperature field simulation module 300, and a camera simulation module 400.

[0041] The surface field simulation module 200 is used to simulate the external temperature field of the target object, the lens temperature field simulation module 300 is used to simulate the temperature field diffusion process of the target object from the environmental medium to the camera lens, and the camera simulation module 400 is used to fit different measurement errors and all modeling errors and simulate the image seen by the camera.

[0042] Specifically, for the sake of conciseness, in the infrared camera image simulation method section, the relevant modules are not numbered, but only the relevant modules in the infrared camera image simulation system are numbered.

[0043] The lens temperature field simulation module 300 simulates the contact surface of an infrared camera lens. There is a certain distance between the target object and the contact surface of the infrared camera lens, and radiation and other influences exist within this distance. Therefore, it is necessary to simulate the temperature diffusion process from the environmental medium to the camera lens to simulate the lens temperature field.

[0044] In this embodiment, the infrared camera image simulation system is used to simulate the entire process from the spatial distribution of the internal temperature of a target object to the measured temperature distribution seen from the infrared camera, and images of the target object at different stages are simulated through multiple modules included in the infrared camera image simulation system.

[0045] FIG. 3 shows, in order, a first type image Z, a second type image T, an external temperature field image f, a camera lens temperature field image g0, and an infrared camera image g.

[0046] For the first type image Z, its ordinate indicates different shape types, for example, when z=0, it is shown in dark blue, and when z=1, it is shown in purple square area.

[0047] Since the target object is often composed of multiple materials, the first type image Z shown in Figure 3 simulates the case where the target object has three materials {m=1,2,3} and the background z=0, and geometric shapes of the same color indicate the same material type.

[0048] For the second type image T, the external temperature field image f, the camera lens temperature field image g0, and the infrared camera image g, the ordinate indicates the temperature, and the higher the temperature, the brighter it is.

[0049] In one embodiment of the present application, the image generation module 100 generates a first class of images Z according to the distribution of material types, and combines the first class of images Z with the initial values ​​of the corresponding materials to generate a second class of images T.

[0050] In this embodiment, the image generation module 100 generates a composite geometric shape to represent the distribution of material types, where the material type values ​​range from 0 to M, and each material type has a specific emission rate e={e1, e2, . . . , e M}, i.e., for a specific material m, the firing rate of material m is e m is.

[0051] As shown in Figure 4, when z=0, the image shows the background area, and z=1, z=2, z=3 show areas of different materials, respectively.

[0052] In one embodiment of the present application, the surface field simulation module 200 combines the first kind of image Z and the second kind of image T with the emissivity of the material and the humidity of the object surface to generate an image f of the temperature distribution on the object surface.

[0053] Specifically, FIG. 5 shows a first type of image Z, a second type of image T and an external temperature field image f in sequence.

[0054] In this embodiment, the surface field simulation module 200 calculates the emissivity of the material e={e1, e2, . . . , e M} and the humidity of the object surface {hu1, hu2, ,hu n} and T and Z obtained from the first module are used to generate an external temperature field image f of the object surface.

[0055] In one embodiment of the present application, the camera simulation module 400 performs evaluation of the simulated image through Equation 6 and Equation 7.

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[0056] Specifically, FIG. 6 shows, in sequence, an external temperature field image f and a camera lens temperature field image g0.

[0057] FIG. 7 shows the camera lens temperature field image g0 and the infrared camera image g, in sequence.

[0058] In this embodiment, since there are many similarity measures between the two images, the relative error is calculated using Equation 6 and Equation 7, and the model error is calculated based on the calculation result. This is the evaluation of the inverse analysis process.

[0059]

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[0060] The above examples only show some embodiments of the present application, and the descriptions are relatively detailed, but this should not be understood as limiting the scope of the claims of the present application. It should be noted that a person skilled in the art can make many modifications and variations without departing from the idea of ​​the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be governed by the "Claims" which are incorporated herein by reference. [Explanation of symbols]

[0061] 100-Image generation module, 200-Surface field simulation module, 300-lens temperature field simulation module, 400-Camera section simulation module.

Claims

1. generating a geometric shape of the target object, constructing an image of the target object according to the geometric shape of the target object, and constructing an internal temperature field of a space inside the target object close to a surface of the target object based on the image of the target object; Obtaining an external temperature field of a space near a surface of the target object outside the target object according to the emissivity characteristics of the material based on the internal temperature field of the target object image; Simulating the temperature field of the camera lens by combining the external temperature field of the target object image with environmental influence factors; and simulating an infrared camera image based on a temperature field at the camera lens; The steps of generating a geometric shape of the object, constructing a target object image of the target object according to the geometric shape of the object, and constructing an internal temperature field of the target object based on the target object image further include: inputting two-dimensional coordinate information and generating a geometric shape image of a target object surface view according to the two-dimensional coordinate information; Filling the surface material type information into the geometric shape of the target object surface view to generate a first class image, denoted as Z, wherein the first class image Z comprises a plurality of first class pixels Z(x, y), where x in Z(x, y) is the abscissa of the first class pixel and y is the ordinate of the first class pixel; and Filling the internal temperature information into the geometric shape of the target object surface view to generate a second class image, denoted as T, wherein the second class image T comprises a plurality of second class pixels T(x, y), where x in T(x, y) is the abscissa of the first class pixel and y is the ordinate of the first class pixel; The step of obtaining an external temperature field of the target object image based on the internal temperature field of the target object image and using the emissivity characteristics of the material further includes: acquiring material emissivity characteristic information of each surface material of the target object; Establishing a coupling relationship between the internal temperature field and the external temperature field according to the material emissivity characteristic information of each surface material of the target object (see Equation 1 for the coupling relationship between the internal temperature field and the external temperature field); [Equation 1] Wherein, f is the external temperature field image, f(x, y) is the pixel value at the (x, y) coordinate in f, T(x, y) is the pixel value at the (x, y) coordinate in the second type image, e is the emission rate of the target object, T u is the ambient temperature of the environment in which the target object is exposed, (x, y) is the position coordinate of the pixel, where x is the horizontal coordinate of the pixel, y is the vertical coordinate of the pixel, and n=4; The step of simulating the temperature field of the camera lens unit by combining the external temperature field of the target object image with environmental influence factors and simulating the infrared camera image based on the temperature field of the camera lens unit further includes: Calculating a temperature field of a camera lens of an infrared camera (the temperature field of the camera lens is the temperature field of a space where the infrared camera lens is located) according to Equation 2; [Equation 2] Wherein, g0 is the temperature field image of the camera lens, g0(x,y) is the pixel value at the (x,y) coordinate in the temperature field image g0 of the camera lens of the infrared camera, h(x,y) is the point diffusion function of the convolution operator, φ(f) is the expression code of the nonlinear operation of medium diffusion, a(d) is the attenuation coefficient according to the distance d from the infrared camera lens to the target object, Combining the attenuation effect and the diffusion parameters and incorporating them into the nonlinear calculation of the medium diffusion, simplifying Equation 2 to Equation 3; [Equation 3] Wherein, g0 is the temperature field image of the camera lens, g0(x,y) is the pixel value at the (x,y) coordinate in the temperature field image g0 of the camera lens of the infrared camera, h(x,y) is the point diffusion function of the convolution operator, φ'(f) is the representation code of the medium diffusion nonlinear operation considering the attenuation effect and diffusion medium parameters, and f in φ'(f) is f(x,y), obtaining an error, and fitting the error to the temperature field of the camera lens of the infrared camera to obtain an infrared camera image (see Equation 4 for the expression of the infrared camera image); [Equation 4] Wherein, g is the infrared camera image, g(x, y) is the pixel value at the (x, y) coordinate in the infrared camera image g, and ε(x, y) is the error at the pixel (x, y). The step of combining the attenuation effect with the diffusion parameters and incorporating it into the nonlinear calculation of medium diffusion to simplify Equation 2 to Equation 3 further includes: introducing the attenuation effect and the diffusion medium temperature into φ(f) to obtain Equation 5; [Equation 5] Wherein, e is the emission rate of the target object, T u is the ambient temperature of the environment in which the target object is located, T a is the air temperature of the environment in which the target object is located, k is the attenuation coefficient, d is the distance from the infrared camera lens to the target object, and φ′(f) is the medium diffusion nonlinear calculation taking into account the attenuation effect and diffusion medium parameters; Substituting Equation 5 into Equation 2 and replacing φ(f) with φ′(f) to obtain Equation 3. A method for simulating an image from an infrared camera.

2. The method for simulating an infrared camera image may further include: obtaining an inverse analysis of f(x,y); and performing segmentation evaluation on the infrared camera image through Equation 6 and Equation 7; [Equation 6] Among them, δf ２ is f(x, y) [Equation 7] f(x, y) is the pixel value at the (x, y) coordinate in the external temperature field image f; [Equation 8] is the inverse analysis for f(x,y), [Equation 9] Among them, δf １ is f(x, y) [Equation 10] f(x, y) is the pixel value at the (x, y) coordinate in the external temperature field image f; [0011] is the inverse analysis for f(x,y), 2. The method for simulating an image of an infrared camera according to claim 1.

3. an image generation module; a surface field simulation module used to simulate the external temperature field of the target object; a lens temperature field simulation module used to simulate the temperature diffusion process from the environmental medium of the target object to the camera lens; a camera simulation module used to fit images taken by the camera with different measurement errors and all modeling errors; The image generation module generates a first type of image Z according to the distribution of material types, and combines the first type of image Z with the temperature values ​​of corresponding materials to generate a second type of image T; The surface field simulation module combines the first type of image Z and the second type of image T with the emissivity characteristic information of the material to generate an external temperature field image f of the target object image; The camera unit simulation module evaluates the simulated image through Equation 6 and Equation 7, [0012] Among them, δf ２ is f(x, y) [0013] f(x, y) is the pixel value at the (x, y) coordinate in the external temperature field image f; [0014] is the inverse analysis for f(x,y), [Equation 15] Among them, δf １ is f(x, y) [0016] f(x, y) is the pixel value at the (x, y) coordinate in the external temperature field image f; [Equation 17] is the inverse analysis for f(x,y), 3. An infrared camera image simulation system used to implement the infrared camera image simulation method according to claim 1 or 2.

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