CN116310203A - Human body target infrared three-dimensional model rapid reconstruction method - Google Patents
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Abstract
The invention discloses a rapid reconstruction method of an infrared three-dimensional model of a human body target, and belongs to the field of model reconstruction algorithms. The method firstly carries out physical modeling on the heat transfer process of the surface of the human body based on infrared physics, and generates human body infrared three-dimensional characteristic images of the target human body under different conditions by utilizing the principle of graphics. The function of the method is 1) based on the measured data, an infrared radiation database simulating the target environment is established. 2) The heat transfer process of the human body surface is physically modeled by using a finite difference method, so that three-dimensional human body infrared model drawing is realized. 3) The infrared characteristic images of human targets with different time, place and climate conditions are generated by using computer iterative calculation. 4) Based on the Planck spectrum energy distribution principle, the human body long-wave infrared simulation image is converted into a medium-wave simulation image. And (3) carrying out similarity and reliability evaluation on the human infrared simulation imaging result through a hash algorithm, wherein the simulation similarity is higher than 70%.
Description
Technical Field
The invention relates to the field of model reconstruction algorithms, in particular to a method for quickly reconstructing an infrared three-dimensional model of a human body target.
Background
Any object with a temperature greater than absolute zero will generate infrared radiation to the outside. Under the conditions that light is darker at night or the target is in a camouflage state, a concealing state and the like, the infrared characteristic image of the object is acquired by utilizing a thermal imaging means, so that some target information which cannot be found or is difficult to find under the condition of visible light can be acquired obviously. Therefore, the infrared identification and detection technology is widely applied in the fields of mechanical and electronic fault diagnosis, national defense and military and the like, and plays an important role.
The target often has the properties of diversity, non-cooperativity and the like, under the condition that the background condition is complex, various infrared characteristic experiments carried out in the existing research have the problems of long research period, high expense consumption, poor accuracy and the like, and the target infrared characteristics under special conditions such as dense fog, rainfall and the like are difficult to obtain. With the development of subjects such as computer graphics and computer vision and the deep basic research, the infrared simulation and target modeling by using computer technology have the advantages of rapidness, repeatability, intuitiveness, visibility and the like, and are widely applied to various fields. Compared with various infrared characteristic experiments carried out in a real environment, the simulation by using a computer technology not only can save the experiment expense, but also can quickly, repeatedly and repeatedly generate various target and background infrared characteristic images under severe natural conditions which are difficult to acquire in a usual way, and has very important application value.
The infrared simulation means that by means of a physical heat transfer model, the temperature field distribution situation of the surface of the object is simulated and calculated by a computer, and the zero line-of-sight infrared radiation value of the surface of the object is solved, so that the infrared characteristic image obtained by shooting of the actual infrared imaging equipment in the real environment is simulated; on the basis, the infrared radiation value of the object under the action of different external influence factors, such as various meteorological conditions, equipment errors and the like, is further calculated, attenuated and reaches the receiving equipment, and the attenuated infrared radiation value is converted into the gray value of each pixel in the simulation image, so that the infrared characteristic gray image of the object and the background is obtained.
The mainstream research method is based on the open source graphic rendering engine such as OpenGL to conduct real-time rendering and generation on infrared characteristic images, and the images generated by the method are good in controllability, long in development period, large in difficulty and poor in universality.
Disclosure of Invention
In order to overcome the technical problems, the invention comprehensively applies basic principles or algorithms in a plurality of fields such as infrared physics, heat transfer science, computer graphics, computer vision and the like, and designs a method for quickly reconstructing a human body target infrared three-dimensional model, which mainly comprises an iterative solution of a human body surface temperature field based on an explicit finite difference method and an infrared band conversion model based on a Planck spectrum energy distribution principle.
The technical scheme adopted by the invention is as follows:
a method for quickly reconstructing an infrared three-dimensional model of a human body target comprises the following steps:
step 1, based on measured data, a target environment database is established, wherein the database comprises spontaneous radiation, sky background radiation and solar radiation data of a target environment;
step 2, solving the distribution of the temperature field on the surface of the human body based on an explicit finite difference method to obtain a relational expression of the temperature change of each node on the surface of the human body along with time;
step 3, obtaining the total radiation variation of the human body surface layer and the temperature variation of each node of the human body surface layer in any time period according to the target environment database data obtained in the step 1 and the physical parameters of each material of the human body surface layer; according to the relational expression of the temperature of each node of the human surface layer, which is obtained in the step (2), along with the time variation, and the human surface temperature field distribution at the beginning of the time period, iteratively solving the human surface temperature field distribution at the end of the time period, and obtaining a human long-wave infrared simulation image;
step 4, repeating the step 3 according to the environmental parameter range in the target environmental database to generate human body long wave infrared simulation images under different time, place and climate conditions;
and step 5, converting the human body long-wave infrared simulation image into a human body medium-wave infrared simulation image based on the Planck spectrum energy distribution principle, and taking the human body long-wave infrared simulation image as a final three-dimensional model reconstruction result.
Further, the relational expression of the temperature change of each node of the human surface layer in the step 2 along with time is as follows:
wherein c is the temperature of a certain node table of the human body at a certain moment; Δx is the mesh distance, i.e., the distance between two nodes; x is x k The position of the kth node is beta is the thermal diffusivity of the position of a certain node of the human body; Δt is the time step, t s Is the current point in time.
Further, in the step 3, the relationship between the total radiation variation and the temperature variation of each node of the surface layer of the human body is:
ΔQ＝CmΔT
wherein DeltaQ is total radiation variation, deltaT is temperature variation of each node of the human body surface layer, m is mass of the human body surface layer, and C is specific heat capacity of the human body surface layer.
Further, the step 5 includes:
5.1 Using Planck spectrum energy distribution principle to calculate maximum brightness L under original long wave infrared band max And minimum brightness L min ：
Wherein T is max And T min Respectively represent the upper and lower temperature limits in the infrared image, epsilon represents emissivity, delta lambda is the long-wave infrared band width, lambda c The center wavelength of the long-wave infrared band; c (C) 1 、C 2 Is constant, C 1 ＝1.191×10 4 Unit W μm 4 /cm 2 sr，C 2 ＝1.428×10 4 Units μmk;
5.2 From gray level G at any point 0 Calculating the brightness L of the point in the original band 0 ：
5.3 Calculating the temperature T of the point from the brightness:
5.4 Calculating the brightness L of the point in the wave band in the target 1 ：
Wherein Deltalambda ′ For the target mid-wave infrared band width lambda c ′ The center wavelength of the target medium wave infrared band;
5.5 A) converting the luminance distribution into a gray distribution:
wherein G is 1 And obtaining the human body medium wave infrared simulation image according to the converted gray level distribution.
The beneficial effects of the invention are as follows:
based on the basic theory of infrared physics and heat transfer science, the invention comprehensively considers the spontaneous radiation, sky radiation, solar radiation and background radiation of the target, carries out physical modeling on the human body heat transfer process, realizes the real-time drawing and simulation of a human body infrared model, and can generate infrared characteristic images of human body targets with different time, place and climate conditions; the invention establishes an infrared band imaging conversion method based on the basic theory of infrared physics and heat transfer science, which can conveniently convert the long-wave infrared model image generated by simulation into a medium-wave infrared model image, thereby further expanding the human infrared characteristic image sample library.
The infrared imaging simulation technology is generated under the requirement of further improving the performance of the infrared imaging seeker, the infrared characteristic image of the target and the background is generated based on the infrared radiation and heat transfer physical model of the target and the background, the infrared characteristic image containing the target and the background is provided for the design of the seeker, and the infrared imaging seeker has very important functions of improving various performance parameters of the seeker and improving the capability of the seeker for finding, identifying and tracking the target under various complex conditions.
In addition, the infrared three-dimensional model rapid reconstruction method provided by the invention has the characteristics of rapidness, simplicity and easiness in use, is suitable for various places, time, atmospheric conditions and target figures, and has strong universality.
Drawings
FIG. 1 is a schematic diagram of a solving process of human body temperature distribution data;
FIG. 2 is a raw three-dimensional model of a human body for use with the present invention;
FIG. 3 is a long wave human infrared simulation effect display;
fig. 4 is a comparison display of a mid-wave simulation image and a long-wave simulation image after band conversion, wherein the left graph is a long-wave infrared simulation result, and the right graph is a mid-wave infrared simulation result.
Detailed Description
The invention will be further described with reference to the drawings and examples. The figures are only schematic illustrations of the invention, some of the block diagrams shown in the figures are functional entities, not necessarily corresponding to physically or logically separate entities, which may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
In this embodiment, based on the principles of infrared physics and heat transfer theory, a method for rapidly reconstructing an infrared three-dimensional model of a human body target is provided, which mainly includes the following steps:
1) Based on the measured data, a spontaneous radiation database, sky background radiation database and solar radiation database of the target environment are established, and the spontaneous radiation database, the sky background radiation database and the solar radiation database are used as simulation parameters to be input into a simulation system.
In this step, the data such as the air temperature, sky background radiation, solar radiation and the like at each moment in a plurality of days of the simulation target site can be obtained through actual measurement, for example, a timer, a wind speed sensor, a temperature and humidity sensor, a sky radiometer, a solar photometer and the like are installed at the target site, so that each parameter is measured every certain period of time, the parameters are stored in a csv format file which is convenient for a computer program to read in a plurality of days, and are used as environmental parameters for subsequent calculation, and table 1 is part of infrared simulation actual measurement data collected in this embodiment.
TABLE 1 partial infrared simulation actual measurement data
2) And solving the distribution of the human body surface temperature field based on an explicit finite difference method.
In this step, consider the heat conduction equation in one dimension:
wherein c is the temperature of a certain node table of the human body at a certain moment, t is the moment, x is the position of the node, and beta represents the thermal diffusivity of the position of the certain node of the human body;
the forward differential form of its first derivative is:
wherein f and f ′ Representing functions of arbitrary form and their derivatives with respect to time, x k Represents a kth node;
assuming that the spatial step size (grid size) is fixed, let the grid scale be Δx=x k+2 -x k+1 ＝x k+1 -x k When the Δx is small enough, the following steps are obtained:
further obtain:
wherein t is s Represents the current point in time, Δt=t s+1 -t s Representing a time step;
for any point in time t s At x k At the node, the following formula exists:
further get t s+1 Time-of-day human body each node table temperature and t s Relational expression of human body node table temperature at moment:
from the above, solve t s+1 When the temperature value of each node on the surface of the human body is measured at any time, the t needs to be solved first s The temperature value of each node on the surface of the human body at any time. Thus, the values c (0, x) of the nodes inside the time t=0 and the values c (t, 0), c (t, 1) of the nodes on the boundary are obtained by iterative solution, and the temperature values of the nodes of the human body at any time can be obtained.
3) By using the radiation data (spontaneous radiation of the target environment, sky background radiation and solar radiation) acquired in the step (1), and combining physical parameters of various materials of the human body surface layer, the total radiation change quantity delta Q of the human body surface layer in any time period can be calculated, and then the relation delta Q=Cm delta T is used (wherein C is the specific heat capacity of the human body surface layer part; m is the mass of the surface layer, and its value is determined by the manually divided surface layer thickness), the temperature change Δt of each node of the surface layer can be obtained. T derived from (2) s+1 Time-of-day human body each node table temperature and t s And (3) carrying out iterative calculation on the relational expression of the temperature of each node table of the human body at any time by using a computer, so as to solve the human body temperature distribution data at the end of the time period. The program implementation process is as shown in fig. 1:
a. inputting weather data and material parameters;
b. initializing the layer number of the surface layer, the internal temperature, the time and the space step length and the total iteration time;
c. judging whether the current moment exceeds the total iteration time or not;
if not, calculating the energy of solar irradiation, sky background radiation, reflected radiation and the like according to the physical property parameters and weather data, and further calculating the surface layer temperature change; and (c) iterating through a finite difference method, calculating the temperature of each layer, and then continuing to return to the step (c).
If yes, iterating again, and obtaining a final result by calculation.
In this step, the input parameters of the target environment database under the condition to be solved, that is, the database parameters established according to the measured data in step 1), comprehensively consider the influence and specific values of solar radiation, sky background radiation, reflected radiation, human body self-radiation and other factors on the energy change of each node on the surface of the human body, and iteratively calculate the surface temperature value of each node by using the deterministic relationship between the energy change and the temperature change. Fig. 2 is a three-dimensional model of the original human body used in the present invention.
4) Based on the target environment database, changing the input parameter range, and generating human body target infrared characteristic images under different time, place and climate conditions, namely human body long wave infrared simulation images, as three-dimensional model reconstruction results.
In the step, the infrared characteristic images of the human body target under various specified conditions can be obtained by intercepting a part of specified places, time periods or atmospheric conditions in an environment database and taking the part as an input parameter of simulation calculation; in this embodiment, fig. 3 shows a long-wave human infrared simulation effect.
5) Based on the Planck spectrum energy distribution principle, the human body long-wave infrared simulation image is converted into a medium-wave simulation image.
When electromagnetic waves pass through the air, the electromagnetic waves are affected by air reflection, absorption and scattering, so that the energy of the electromagnetic waves is attenuated, the attenuation amount is different according to the wavelength of the electromagnetic waves, and the wave bands with higher transmissivity are called an atmosphere window. Infrared light can be divided into three bands according to different wavelengths: the wavelength range is 8-12 mu m, the medium wavelength range is 3-5 mu m, and the short wavelength range is 1.9-2.9 mu m.
Output voltage V of infrared detector det Can be expressed as:
wherein τ amb (lambda) is the atmospheric spectral transmittance at an infrared light wavelength lambda, tau opt (lambda) is the optical spectral transmittance of the detector when the infrared light wavelength is lambda, and S (lambda) is the spectral response of the detector when the infrared light wavelength is lambda. Lambda (lambda) 1 And lambda (lambda) 2 A is the wavelength of the upper and lower limits of the band p Representing the area of the target captured in the infrared detector, Ω representing the solid angle of the detector at the target, L (T) representing the radiance, and T representing the temperature.
Let τ be amb (λ)、τ opt V is present when the three (lambda) and S (lambda) are the steady system det c.L (T). For a two-dimensional infrared detector, the gray scale of the infrared image is also equal to the output voltage V of the infrared detector det The radiation brightness L (T) is proportional to the gray scale.
The target object being within a given wavelength band lambda 1 ～λ 2 Is expressed as:
the following approximation is made to the radiance expression:
the method can obtain:
wherein Δλ=λ 2 -λ 1 For the width of the band of wavelengths,
for the central wavelength, C 1 、C 2 Is constant, C 1 ＝1.191×10 4 [Wμm 4 /cm 2 sr]，C 2 ＝1.428×10 4 [μmK]Epsilon (lambda) represents the spectral emissivity of the object.
Based on the above principle, if the temperature and emissivity of the object corresponding to the gray level of at least two pixels in the image are known, the infrared image of a certain wave band can be converted into any wave band. However, since the wavelength band outside the atmospheric window is greatly affected by air reflection, absorption and scattering, and is rarely used in infrared imaging; compared with other two wave bands, the infrared light in the short wave infrared wave band has irregular transmittance of water molecules and carbon dioxide molecules to infrared light (the transmittance is approximately 1 in the middle wave and the wavelength wave bands), and the error of the conversion result is larger, so the invention only considers the conversion between the middle wave and the wavelength wave bands. The conversion steps are as follows:
5.1 Calculating the maximum brightness L under the original long-wave infrared band (8-12 mu m) by utilizing the Planck spectrum energy distribution principle max And minimum brightness L min ：
Wherein T is max And T min Respectively represent the upper and lower temperature limits in the infrared image, epsilon represents emissivity, delta lambda is the long-wave infrared band width, lambda c The center wavelength of the long-wave infrared band;
5.2 From gray level G at any point 0 Calculating the brightness L of the point in the original band 0 ：
5.3 Calculating the temperature T of the point from the brightness:
5.4 Calculating the brightness L of the point at the target band (3-5 μm) 1 ：
Wherein Deltalambda ′ Represents the target band width (here, 2 μm), λ c ′ The center wavelength of the target mid-wave infrared band (here, 4 μm);
5.5 A) converting the luminance distribution into a gray distribution:
fig. 4 is a comparison display of a mid-wave simulation image and a long-wave simulation image after band conversion, wherein the left graph is a long-wave infrared simulation result, and the right graph is a mid-wave infrared simulation result.
In one embodiment of the invention, a hash algorithm is used to evaluate the credibility of the human long-wave infrared simulation image.
In the embodiment, a difference hash algorithm commonly used in the field of image processing is adopted to compare the similarity between an infrared simulation result and an original infrared camera real shot image, and the implementation steps are as follows:
scaling the resolution of the two pictures subjected to similarity comparison to 8*9;
converting the zoomed picture into a gray image;
calculating the average gray scale of each pixel in the image;
comparing each pixel with the next pixel in the line, if the gray level is greater than the next pixel, marking the pixel as 1, otherwise marking the pixel as 0, and generating a 64-bit hash value containing the image structure information;
and comparing the hash value of the infrared simulation result and the original infrared camera real shooting result, and giving the similarity of the two in a percentage form.
Finally, the simulation similarity of the embodiment under the condition is 71.88 percent after calculation.
The foregoing list is only illustrative of specific embodiments of the invention. Obviously, the invention is not limited to the above embodiments, but many variations are possible. All modifications directly derived or suggested to one skilled in the art from the present disclosure should be considered as being within the scope of the present invention.
Claims (4)
1. The method for quickly reconstructing the infrared three-dimensional model of the human body target is characterized by comprising the following steps of:
step 1, based on measured data, a target environment database is established, wherein the database comprises spontaneous radiation, sky background radiation and solar radiation data of a target environment;
step 2, solving the distribution of the temperature field on the surface of the human body based on an explicit finite difference method to obtain a relational expression of the temperature change of each node on the surface of the human body along with time;
step 3, obtaining the total radiation variation of the human body surface layer and the temperature variation of each node of the human body surface layer in any time period according to the target environment database data obtained in the step 1 and the physical parameters of each material of the human body surface layer; according to the relational expression of the temperature of each node of the human surface layer, which is obtained in the step (2), along with the time variation, and the human surface temperature field distribution at the beginning of the time period, iteratively solving the human surface temperature field distribution at the end of the time period, and obtaining a human long-wave infrared simulation image;
step 4, repeating the step 3 according to the environmental parameter range in the target environmental database to generate human body long wave infrared simulation images under different time, place and climate conditions;
and step 5, converting the human body long-wave infrared simulation image into a human body medium-wave infrared simulation image based on the Planck spectrum energy distribution principle, and taking the human body long-wave infrared simulation image as a final three-dimensional model reconstruction result.
2. The method for quickly reconstructing a three-dimensional model of a human body target according to claim 1, wherein the relational expression of the temperature change of each node of the human body surface layer along with time in the step 2 is as follows:
wherein c is the temperature of a certain node table of the human body at a certain moment; Δx is the mesh distance, i.e., the distance between two nodes; x is x k The position of the kth node is beta is the thermal diffusivity of the position of a certain node of the human body; Δt is the time step, t s Is the current point in time.
3. The method for quickly reconstructing the infrared three-dimensional model of the human body target according to claim 1, wherein in the step 3, the relation between the total radiation variation and the temperature variation of each node of the surface layer of the human body is as follows:
ΔQ＝CmΔT
wherein DeltaQ is total radiation variation, deltaT is temperature variation of each node of the human body surface layer, m is mass of the human body surface layer, and C is specific heat capacity of the human body surface layer.
4. The method for rapidly reconstructing the infrared three-dimensional model of the human target according to claim 1, wherein the step 5 comprises:
5.1 Using Planck spectrum energy distribution principle to calculate maximum brightness L under original long wave infrared band max And minimum brightness L min ：
Wherein T is max And T min Respectively represent the upper and lower temperature limits in the infrared image, epsilon represents emissivity, delta lambda is the long-wave infrared band width, lambda c The center wavelength of the long-wave infrared band; c (C) 1 、C 2 Is constant, C 1 ＝1.191×10 4 Unit W μm 4 /cm 2 sr，C 2 ＝1.428×10 4 Units μmk;
5.2 From gray level G at any point 0 Calculating the brightness L of the point in the original band 0 ：
5.3 Calculating the temperature T of the point from the brightness:
5.4 Calculating the brightness L of the point in the wave band in the target 1 ：
Wherein Deltalambda' is the target mid-wave infrared band width, lambda c ' is the center wavelength of the target mid-wave infrared band;
5.5 A) converting the luminance distribution into a gray distribution:
wherein G is 1 And obtaining the human body medium wave infrared simulation image according to the converted gray level distribution.
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