Complex scene infrared imaging system performance simulation discrimination method based on TTP criterion
Through the infrared imaging system performance simulation and discrimination method based on TTP criteria, the target background temperature model, atmospheric transmission model and sensor model are established, which solves the problem of difficulty in evaluating the performance of infrared imaging systems in complex environments in the existing technology, and realizes the system performance evaluation and accurate judgment of task completion capabilities in different environments.
Patent Information
- Application Number
- CN202411950442.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-27
- Publication Date
- 2025-05-06
AI Technical Summary
The prior art is difficult to comprehensively and accurately evaluate the performance of infrared imaging systems in complex environments, and it is impossible to predict and evaluate the comprehensive performance of infrared imaging systems in designs.
Based on the TTP criterion, by establishing a target background temperature model, atmospheric transmission model and sensor model, inputting each model parameter under different conditions, simulate the imaging recognition probability of the infrared imaging system under different combat environments.
It can determine whether the infrared imaging system can effectively complete tasks in different combat environments, provide important basis for equipment use and tactical decision-making, reduce test costs, and improve the accuracy and applicability of evaluation.
Smart Images
Figure CN119939900A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of simulation identification, in particular to a method for simulating and identifying the performance of a complex scene infrared imaging system based on a TTP criterion. Background Art
[0002] Infrared imaging technology is widely used in industrial inspection and other fields, but the imaging effect of the infrared system will be affected by many factors, such as environmental conditions, detector performance, atmospheric transmission characteristics, target characteristics, etc. Faced with complex application environments and diverse performance requirements, it is necessary to establish a comprehensive and accurate infrared imaging system evaluation method that can predict the operating efficiency of the imaging system under the influence of different factors and ensure that it can meet the mission requirements under various possible conditions.
[0003] Generally speaking, the performance evaluation of infrared imaging systems includes two parts, namely, inherent performance evaluation and on-site performance prediction in complex environments. The main methods for on-site performance prediction are divided into two types, namely, experimental method, semi-physical simulation method and performance evaluation model method. The experimental method is to let the infrared thermal imager measure the performance parameters in the real test environment, and the results are more accurate, but the field measurement point environment is complex and there are many interferences, and it is impossible to fully and accurately evaluate the performance; the semi-physical simulation method uses computers and actual equipment to form a simulation system for measurement, which is costly and difficult to achieve large-scale testing. The response range is limited by the process and has a narrow scope of application. These two methods can only be used for products that have been designed and manufactured, and cannot predict and evaluate the comprehensive performance of the infrared imaging system in the design. Summary of the invention
[0004] The present invention simulates and judges the imaging recognition probability of the infrared imaging system of complex scenes based on the TTP criterion. By establishing a target background temperature model, an atmospheric transmission model and a sensor model, and inputting the model parameters under different conditions, it can determine whether the infrared imaging system can effectively complete the task under different combat environments (such as different weather conditions, target distances, etc.), thereby providing an important basis for the use of equipment and tactical decision-making.
[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:
[0006] A method for simulating and judging the performance of a complex scene infrared imaging system based on a TTP criterion comprises the following steps:
[0007] 1) Based on the type, physical characteristics and working status of the target, a target background temperature model is constructed, and the target background radiation value is obtained through the model;
[0008] 2) Establish an atmospheric loss model, and obtain the target background radiation brightness received by the receiver through the target background radiation value and the atmospheric transmittance;
[0009] 3) Establish an infrared sensor system simulation model, and simulate the target background under different conditions through the target background radiation brightness;
[0010] 4) Calculate the target contrast according to the simulated image;
[0011] 5) Based on the target contrast and other related parameters, establish the human eye contrast threshold function and system contrast threshold function model of the infrared imaging system;
[0012] 6) Through the established function model and TTP model calculation formula, the target TTP value and target task cycle number are calculated, the target detection and identification probability is solved, and the infrared system performance is judged according to the probability value.
[0013] The step 1) is specifically as follows:
[0014] Based on the type, physical characteristics and working status of the target, the heat transfer mathematical model corresponding to the target is established using the heat conduction equation and the energy conservation equation. The mathematical model is solved by numerical calculation method to obtain the temperature field of the target, and the current temperature of the background, that is, the target background radiation value, is obtained through time and space parameters.
[0015] The step 2) is specifically as follows:
[0016] Lrec=Ltransτ(λ,z)
[0017] Where Lrec is the radiance received by the receiver, Ltrans is the radiance of the target background radiation before transmission, and τ(λ,z) is the atmospheric transmittance, which is a function of the wavelength λ and the transmission path height z.
[0018] The step 3) comprises the following steps:
[0019] 3.1) Convert the three-dimensional target background radiation brightness into a two-dimensional energy signal, and use Fourier transform to convert the two-dimensional energy signal in the spatial domain into a frequency domain signal;
[0020] 3.2) Using the principles of geometry and physical optics, computer modeling is performed on the diffraction, aberration, defocus and distortion effects of optical frequency domain signals during transmission in the optical system, and the energy distribution of the output signal is obtained;
[0021] 3.3) The optical signal of energy distribution is converted into an electrical signal through photoelectric conversion simulation. The response characteristics, noise characteristics and filtering characteristics of the optical signal are modeled by simulating the time filtering, spatial filtering and low-pass filtering effects of the detector and signal processing circuit, and the target background signal after photoelectric conversion simulation processing is obtained;
[0022] 3.4) Perform fast Fourier transform on the processed target background signal to obtain the target background simulation image and complete the establishment of the sensor simulation model.
[0023] The step 4) comprises the following steps:
[0024] 4.1) Extract all pixel gray values of the image and select the maximum gray value G max and the minimum gray value G min ;
[0025] 4.2) Calculate the grayscale mean μ of the image, which is the sum of the grayscale values of all pixels Divide by the total number of pixels M×N, where M is the number of image rows and N is the number of image columns;
[0026] 4.3) Subtract the minimum grayscale value from the maximum grayscale value and divide it by the grayscale mean μ to get the target contrast value C tgt :
[0027]
[0028] The step 5) is specifically as follows:
[0029] Based on the bandpass filtering characteristics of the human eye, the human eye contrast threshold function CTF is established through grating target experimental fitting. eye (ξ):
[0030]
[0031] Among them, L 0 is the average brightness of the display, a and b are coefficients;
[0032]
[0033] The simulated image is Fourier transformed and converted into a frequency domain signal. After attenuation through the display and human eye, the contrast threshold function CTF of the target after system processing is obtained. sys (ξ):
[0034]
[0035] Among them, H sys (ξ) is the modulation transfer function of the system display and the human eye.
[0036] The step 6) is specifically as follows:
[0037] Set the target contrast value C tgt Horizontal and vertical spatial frequencies exceeding CTF sys The weighted integration of the parts is performed to obtain the TTP values of the imaging system in the horizontal and vertical directions:
[0038]
[0039] Among them, ξ high is the integration starting frequency; ξ low is the integration cut-off frequency, CTFH sys (ξ) is the horizontal system contrast threshold function; CTFV sys (ξ) is the vertical system contrast threshold function;
[0040] Calculate the target equivalent task cycle number N resolved :
[0041]
[0042] Among them, Range is the observation distance of the target and L tgt is the target critical feature size;
[0043] Using the target task V 50 Criteria, calculate the target recognition probability P:
[0044]
[0045] Among them, V 50 The corresponding target equivalent cycle number when the acquisition probability is 50% obtained by visual experiment measurement, the intermediate variable
[0046] The present invention has the following beneficial effects and advantages:
[0047] 1. Comprehensive conditions: This method takes into account multiple factors such as detector performance, atmospheric transmission characteristics, target characteristics, etc. In the evaluation process, it not only considers the noise of the detector, but also combines external environmental factors such as the absorption and scattering of infrared radiation by water vapor and carbon dioxide in the atmosphere, avoiding the one-sidedness of the evaluation caused by focusing on a single factor, and can more truly reflect the performance of the infrared imaging system in actual use.
[0048] 2. Low cost: Compared with some traditional methods that rely entirely on a large number of field tests or use high-cost actual equipment to build a complex test environment, this simulation judgment method mainly conducts performance evaluation through computer modeling and simulation, avoiding the high costs caused by many links involved in field testing, such as equipment transportation, personnel dispatch, and site rental, thereby greatly reducing the test cost.
[0049] 3. Accurate calculation: Clearly analyze various tasks (such as target detection, recognition, classification, etc.) of infrared imaging systems in practical applications. For different task types, scientifically determine the corresponding performance indicators, such as the detection probability of detection tasks, the recognition accuracy of recognition tasks, and the classification error rate of classification tasks. The precise definition of these indicators ensures that the system's completion of specific tasks can be accurately measured during the calculation process, so that the calculation results can truly reflect the performance of the system in actual task scenarios. BRIEF DESCRIPTION OF THE DRAWINGS
[0050] Figure 1 Target radiation source map;
[0051] Figure 2 Sensor model establishment flow chart;
[0052] Figure 3 Target simulation image generation diagram. DETAILED DESCRIPTION
[0053] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.
[0054] A method for simulating and judging the performance of a complex scene infrared imaging system based on a TTP criterion comprises the following steps:
[0055] Step S1: Establish target background temperature model
[0056] By inputting the target type, various physical properties and working status, the corresponding mathematical model is established based on the heat conduction equation and the energy conservation equation, and the temperature field of the target is solved by the numerical calculation method; by inputting the time and space parameters, the current temperature of the background is obtained.
[0057] Step S2: Establishing atmospheric transport model
[0058] An atmospheric loss model is established, and the target background radiation brightness Q2 received by the receiver is obtained by inputting the target background radiation value Q1 and the atmospheric transmittance τ.
[0059] Step S3: Building a sensor model
[0060] An infrared sensor system simulation model is established, including a focal plane conversion module, an optical system simulation module, and a photoelectric conversion simulation module. The model calculates the simulated image of the target background under different conditions by inputting the radiation energy Q2 in S2.
[0061] Step S4: Target contrast calculation
[0062] Calculate the target background contrast C by using the grayscale value of each pixel of the infrared image calculated in S3 tgt .
[0063] Step S5: Establishment of target contrast threshold function
[0064] The target contrast C in input S4 tgt And other related parameters, the human eye contrast threshold function and system contrast threshold function models of the infrared imaging system are established.
[0065] Step S6: Calculation of target detection probability based on TTP model
[0066] Through the function model and TTP model calculation formula established in S5, the target TTP value and target task cycle number are calculated, the target detection and recognition probability P is solved, and the performance of the infrared system is judged.
[0067] Example
[0068] Step 1: Target background temperature model establishment
[0069] In infrared imaging systems, the establishment of a target-background temperature model is crucial. Because infrared imaging is mainly based on the thermal radiation of objects, the temperature difference between the target and the background is a key factor in the detection and recognition of the target. An accurate temperature model helps to simulate infrared imaging scenes more realistically, and then evaluate the performance of the infrared system, such as target detection probability, recognition accuracy, etc.
[0070] The target can be goods in the industrial field, etc. Different types of targets have different physical properties and working conditions, which directly affect the temperature of the target. The temperature of the target is affected by factors such as its internal heat source, material properties and surface state. The material properties determine the thermal physical properties of the target, such as heat conduction, heat radiation and heat capacity. According to the working conditions and physical properties of different targets, the corresponding mathematical model is established based on the heat conduction equation and the energy conservation equation, and then the temperature field of the target is solved by the numerical calculation method.
[0071] like Figure 1 As shown in the figure, the background temperature is mainly affected by solar radiation, atmospheric environment, geographical conditions and other factors. Solar radiation is an important factor in the change of natural background temperature. During the day, the solar radiation intensity is high, and the temperature of natural backgrounds such as the ground and water surface rises after absorbing the solar radiation heat; at night, there is no solar radiation, and the background temperature will drop. An empirical model of the background changing with time and space is established, and the current temperature of the background is obtained by inputting time and space parameters.
[0072] Step 2: Establishment of atmospheric transport model
[0073] The target background radiation will pass through the atmosphere during the transmission process. In this process, it will be affected by the absorption, scattering and emission effects of the atmosphere, resulting in the attenuation of radiation energy, that is, atmospheric loss. In order to accurately describe this process, it is necessary to establish an atmospheric loss model.
[0074] Atmospheric loss models usually consider the following main factors:
[0075] Atmospheric absorption: Certain components in the atmosphere (such as water vapor, carbon dioxide, ozone, etc.) absorb radiation of specific wavelengths. This absorption will cause the radiation energy to be reduced during the transmission process.
[0076] Atmospheric scattering: Particles in the atmosphere (such as aerosols, dust, water droplets, etc.) will cause radiation to scatter, that is, the radiation energy is dispersed in all directions, resulting in a reduction in the radiation energy in the target direction.
[0077] Atmospheric emission: The atmosphere itself also emits radiation, especially in the infrared band. This emitted radiation will be superimposed on the target background radiation, increasing the radiation energy of the target background.
[0078] To quantify atmospheric losses, the following simplified mathematical model is used:
[0079] Lrec=Ltransτ(λ,z)
[0080] in:
[0081] Lrec is the radiance received by the receiver;
[0082] Ltrans is the radiance of the target background radiation before transmission;
[0083] τ(λ,z) is the atmospheric transmittance, which is a function of the wavelength λ and the transmission path height z. Atmospheric transmittance describes the fraction of radiation that remains after passing through the atmosphere.
[0084] The calculation of atmospheric transmittance is usually based on the radiation transfer equation, which is a complex differential equation that needs to take into account the absorption and scattering characteristics of various components in the atmosphere. In practical applications, empirical formulas, lookup tables or numerical simulation methods can be used to estimate atmospheric transmittance.
[0085] For the infrared band, professional software can be used to calculate the atmospheric transmittance. These software take into account the absorption and scattering effects of various components in the atmosphere and can provide atmospheric transmittance data under different conditions.
[0086] Step 3: Sensor model establishment
[0087] like Figure 2 As shown in the figure, the digital model of the sensor is a key tool for understanding and predicting the output of the sensor. In the infrared imaging system, the output of the infrared detector is crucial for obtaining target information. Establishing an accurate digital model to explore the response characteristics of the sensor to infrared radiation of different intensities and wavelengths with different inputs, as well as the response changes under various environmental conditions and interference factors, is of great significance for accurately evaluating the reliability of the system output.
[0088] The sensor simulation model receives the radiation energy Q2 after passing through the atmospheric transmission model in step S2, first converts the three-dimensional energy signal into a two-dimensional energy signal through the focal plane conversion module in the program, and then uses Fourier transform to convert the spatial domain energy signal into a frequency domain signal X1.
[0089] Subsequently, the frequency domain signal is passed to the optical system simulation module in the sensor digital model. The optical system simulation module uses the principles of geometry and physical optics to perform computer modeling on the diffraction, aberration, defocus and distortion effects of the optical signal during the transmission process of the optical system, and outputs the energy distribution X2 of the signal.
[0090] Afterwards, the signal X2 processed by the optical system is transmitted to the photoelectric conversion simulation module. The photoelectric conversion simulation is used to convert the optical signal into an electrical signal, connect the front-end optical system to realize precise photoelectric signal conversion, simulate the time filtering, spatial filtering and low-pass filtering effects of the detector and signal processing circuit, model the response characteristics, noise characteristics and filtering characteristics of the optical signal, and output the target background signal X3 after photoelectric conversion simulation processing.
[0091] Finally, the processed signal X3 is subjected to inverse fast Fourier transform to output the target background simulation image, as shown in Figure 3 As shown in the figure, the sensor simulation model is established.
[0092] Step 4: Target Contrast Calculation
[0093] Let the gray value of the target image pixel be x ij , the image size is M×N (M is the number of rows, N is the number of columns), the grayscale mean is μ, then the contrast C tgt The calculation formula is:
[0094]
[0095] The specific calculation steps of target contrast are as follows:
[0096] First, extract all pixel gray values of the image and select the maximum gray value G max and the minimum gray value G min ;
[0097] Then calculate the gray mean μ of the image, which is the sum of the gray values of all pixels Divide by the total number of pixels M×N;
[0098] Finally, the target contrast value is obtained by subtracting the minimum grayscale value from the maximum grayscale value and dividing it by the grayscale mean μ.
[0099] Step 5: Establishment of target contrast threshold function
[0100] Based on the bandpass filtering characteristics of the human eye, the human eye contrast threshold function is established through grating target experimental fitting:
[0101]
[0102] Where: L 0 is the average brightness of the display;
[0103]
[0104] The image generated by the sensor model in step 3 is Fourier transformed and converted into a frequency domain signal. After the signal is attenuated through the display and the human eye, the contrast threshold function of the target after system processing is obtained:
[0105]
[0106] Where: H sys (ξ) is the modulation transfer function of the system display and the human eye.
[0107] Step 6: Calculation of target detection probability based on TTP model
[0108] Objective C tgt Horizontal and vertical spatial frequencies exceeding CTF sys The weighted integration of the two parts is performed to obtain the TTP values of the imaging system in the horizontal and vertical directions:
[0109]
[0110] Where: high is the integration starting frequency; ξ low is the integration cut-off frequency.
[0111] Then consider the target's observation distance Range and the target's critical feature size L tgt Impact on detection and recognition probability, calculation of target equivalent mission cycle number N resolved :
[0112]
[0113] Finally, the target task V 50 Criteria, calculate the target recognition probability:
[0114]
[0115] Where: V 50 The number of target equivalent cycles corresponding to the acquisition probability of 50% obtained from the visual experiment measurement;
[0116] The performance simulation and judgment method of complex scene infrared imaging system based on TTP criterion is based on the various performance parameters of the infrared imaging system. The imaging system component modules are analyzed and modeled by computer. At the same time, the contrast threshold function and system transfer function model of the system under different conditions are established. The detection probability of the system under different conditions is simulated and calculated using the TTP criterion. By inputting various performance indicators of the system, the system performance model can be established without the physical object, and the performance of the infrared detection system can be evaluated and predicted, providing a reference for optimal design.
Claims
1. A method for simulating and judging the performance of a complex scene infrared imaging system based on the TTP criterion, characterized in that: The following steps are involved: 1) Based on the type, physical characteristics and working status of the target, a target background temperature model is constructed, and the target background radiation value is obtained through the model; 2) Establish an atmospheric loss model, and obtain the target background radiation brightness received by the receiver through the target background radiation value and the atmospheric transmittance; 3) Establish an infrared sensor system simulation model, and simulate the target background under different conditions through the target background radiation brightness; 4) Calculate the target contrast according to the simulated image; 5) Based on the target contrast and other related parameters, establish the human eye contrast threshold function and system contrast threshold function model of the infrared imaging system; 6) Through the established function model and TTP model calculation formula, the target TTP value and target task cycle number are calculated, the target detection and identification probability is solved, and the infrared system performance is judged according to the probability value.
2. According to claim 1, a complex scene infrared imaging system performance simulation judgment method based on TTP criterion is characterized in that: The step 1) is specifically as follows: Based on the type, physical characteristics and working status of the target, the heat transfer mathematical model corresponding to the target is established using the heat conduction equation and the energy conservation equation. The mathematical model is solved by numerical calculation method to obtain the temperature field of the target, and the current temperature of the background, that is, the target background radiation value, is obtained through time and space parameters.
3. The method for simulating and judging the performance of a complex scene infrared imaging system based on the TTP criterion according to claim 1, characterized in that: The step 2) is specifically as follows: Lrec=Ltransτ(λ,z) Where Lrec is the radiance received by the receiver, Ltrans is the radiance of the target background radiation before transmission, and τ(λ,z) is the atmospheric transmittance, which is a function of the wavelength λ and the transmission path height z.
4. The method for simulating and judging the performance of a complex scene infrared imaging system based on the TTP criterion according to claim 1, characterized in that: The step 3) comprises the following steps: 3.1) Convert the three-dimensional target background radiation brightness into a two-dimensional energy signal, and use Fourier transform to convert the two-dimensional energy signal in the spatial domain into a frequency domain signal; 3.2) Using the principles of geometry and physical optics, computer modeling is performed on the diffraction, aberration, defocus and distortion effects of optical frequency domain signals during transmission in the optical system, and the energy distribution of the output signal is obtained; 3.3) The optical signal of energy distribution is converted into an electrical signal through photoelectric conversion simulation. The response characteristics, noise characteristics and filtering characteristics of the optical signal are modeled by simulating the time filtering, spatial filtering and low-pass filtering effects of the detector and signal processing circuit, and the target background signal after photoelectric conversion simulation processing is obtained; 3.4) Perform fast Fourier transform on the processed target background signal to obtain the target background simulation image and complete the establishment of the sensor simulation model.
5. The method for simulating and judging the performance of a complex scene infrared imaging system based on the TTP criterion according to claim 1, characterized in that: The step 4) comprises the following steps: 4.1) Extract all pixel gray values of the image and select the maximum gray value G max and the minimum gray value G min ; 4.2) Calculate the grayscale mean μ of the image, which is the sum of the grayscale values of all pixels Divide by the total number of pixels M×N, where M is the number of image rows and N is the number of image columns; 4.3) Subtract the minimum grayscale value from the maximum grayscale value and divide it by the grayscale mean μ to get the target contrast value C tgt :
6. The method for simulating and judging the performance of a complex scene infrared imaging system based on the TTP criterion according to claim 1, characterized in that: The step 5) is specifically as follows: Based on the bandpass filtering characteristics of the human eye, the human eye contrast threshold function CTF is established through grating target experimental fitting. eye (ξ): Where L0 is the average brightness of the display, a and b are coefficients; The simulated image is Fourier transformed and converted into a frequency domain signal. After attenuation through the display and human eye, the contrast threshold function CTF of the target after system processing is obtained. sys (ξ): Among them, H sys (ξ) is the modulation transfer function of the system display and the human eye.
7. The method for simulating and judging the performance of a complex scene infrared imaging system based on the TTP criterion according to claim 1, characterized in that: The step 6) is specifically as follows: Set the target contrast value C tgt Horizontal and vertical spatial frequencies exceeding CTF sys The weighted integration of the parts is performed to obtain the TTP values of the imaging system in the horizontal and vertical directions: Among them, ξ high is the integration starting frequency; ξ low is the integration cut-off frequency, CTFH sys (ξ) is the horizontal system contrast threshold function; CTFV sys (ξ) is the vertical system contrast threshold function; Calculate the target equivalent task cycle number N resolved : Among them, Range is the observation distance of the target and L tgt is the target critical feature size; Using the target task V 50 Criteria, calculate the target recognition probability P: Among them, V 50 The corresponding target equivalent cycle number when the acquisition probability is 50% obtained by visual experiment measurement, the intermediate variable