CN116448020B - Roughness measuring device and method based on pBRDF and dynamic TS algorithm - Google Patents
Roughness measuring device and method based on pBRDF and dynamic TS algorithm Download PDFInfo
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Abstract
The application discloses a roughness measuring device and method based on a pBRDF and dynamic TS algorithm, which belong to the field of polarization detection and surface property measurement, wherein the roughness measuring device based on the pBRDF and dynamic TS algorithm is used for realizing rapid acquisition of polarized images and acquisition of polarized information; substituting parameters into a linear polarization degree Dolp calculation formula based on the polarization hemispherical reflectivity of the parallel wave and the perpendicular wave, solving unknown parameters such as roughness and the like, and coupling an improved TS algorithm to further reverse the roughness value; meanwhile, other parameters such as specular reflectivity and the like can be inverted; the method solves the problem that the traditional roughness inversion process is in local optimum, and can improve the precision of the roughness inversion; in addition, references can be provided for measurement and parameter inversion of unknown quantities of materials in other fields.
Description
Technical Field
The application belongs to the field of polarization detection and the field of surface property measurement, and particularly relates to a roughness measurement device and method based on a pBRDF and dynamic TS algorithm.
Background
Roughness is an inherent property of the surface of an object and can only be measured by specialized equipment. However, specific values of roughness must be given in the simulation process of related field experiments, and the roughness is inverted by finding the optimal method within the controllable error rangeIs an urgent need for experiments in a plurality of fields.
At present, the surface roughness is tested by four methods: surface profilometer, roughness tester, comparison and test strip profiling. The surface profilometer method can measure the surface shape of a target, and the instrument records the peak-to-valley value; the roughness tester method is to record and measure the contour in a way that an arm rod contacts with a contact pin and the arm rod automatically moves on the surface; the comparison method uses a surface comparison plate to compare the newly sprayed surface with the well defined surface, mainly relies on vision and touch, and has lower test precision; the test strip profiling method is to use a plastic test strip made of foam material on the back surface, press the plastic test strip into the sprayed surface, and determine the surface roughness by measuring the test strip. Common to all four methods is that the test instrument is required to contact the surface to be tested, resulting in unstable test accuracy of the values.
The patent document of publication No. CN114076579A discloses a three-dimensional roughness detection device and method based on polarization imaging, which aims to solve the problem of reduced accuracy of material surface roughness test caused by the influence of flare on a metal surface in appearance detection, obtains images with different degrees of polarization by changing a camera angle and fuses the images so as to solve the problem of flare, but adopts a mechanical polarizer replacement method, which brings about larger error and is not suitable for the field of polarization imaging with higher precision; patent document CN114076579a discloses a three-dimensional roughness detecting device and method based on polarization imaging, which adopts a focal plane splitting detector to collect polarization images, performs three-dimensional reconstruction of surfaces on different polarization images, then uses a four-step phase shift method to perfect an obtained three-dimensional model, finally uses a least square method to calculate a reference plane, and calculates the plane roughness based on the reference plane. However, focal plane detectors require a large number of detectors and optics, complex structural patterns and high level registration requirements are difficult to achieve, and are bulky, costly to manufacture, and the least squares method is a linear estimation, which has limitations in terms of use. In the regression process, the regression correlation cannot pass through each regression data point completely, that is, the accuracy of the roughness test is the difference between the simulation value and the actual measurement value after the parameter is brought in, the obtained roughness inversion value is strongly correlated with the base number of the polarized image data, and is easy to sink into local optimum, and cannot represent the real roughness condition of each area of the material surface.
Disclosure of Invention
Aiming at the problem of surface roughness in the field of polarization detection, the application provides a roughness measurement device and method based on a pBRDF and dynamic TS algorithm, which are applied to the research of roughness of surfaces lacking a roughness measurement instrument or having high roughness precision or inconvenient roughness measurement. The dynamic TS algorithm is adopted in the inversion process, so that high inversion accuracy is ensured for the target surface of an unknown material, and the method has good practicability in the aspects of acquiring the roughness and other solidity parameters of a rough surface.
The technical scheme adopted by the application for achieving the purpose is as follows:
the application provides a roughness measuring device based on a pBRDF and dynamic TS algorithm, which is characterized by comprising a polarized image acquisition system for acquiring a polarized image, wherein the polarized image acquisition system comprises a polarized light emission system, an image acquisition system and an information receiving and controlling system, the information receiving and controlling system is respectively and electrically connected with the polarized light emission system and the image acquisition system, so as to regulate an incident zenith angle and a detection zenith angle, and the information receiving and controlling system is used for receiving information transmitted by the image acquisition system to the information receiving and controlling system; the polarized light emission system is used for emitting linearly polarized light with a polarization angle of 90 degrees and incident on the surface of the sample to be detected; the image acquisition system is used for acquiring reflected light of the surface of the sample to be detected and transmitting the reflected light to the information receiving and controlling system so as to obtain a polarized image with a polarization degree value, wherein the included angle between the incident light and the normal line of the surface of the sample to be detected is an incident zenith angle, and the included angle between the reflected light and the normal line of the surface of the sample to be detected is a detection zenith angle.
Further, the polarized light emission system is arranged on the emission end guide rail, the polarized light emission system comprises a tunable laser, a laser modulation system and a first driving motor, the laser modulation system is arranged on an emergent light path of the tunable laser, meanwhile, the laser modulation system and the tunable laser are fixed together to form a whole, the tunable laser is used for emitting laser beams and radiating to the laser modulation system, the tunable filter, the attenuation sheet, the polarization sheet and the collimation beam expander which are sequentially arranged along the light propagation direction are contained in the laser modulation system, the laser modulation system is used for modulating the received laser beams into linear polarized light with the polarization angle of 90 degrees and then incident on the surface of a sample to be detected, an electric signal input end of the tunable laser is connected with an output port of the first driving motor, an input port of the first driving motor is connected with an information receiving and controlling system, and the first driving motor sends motion control instructions through the information receiving and controlling system, and the first driving motor executes the motion control instructions, so that the whole formed by the laser modulation system and the tunable laser is driven to move on the emission end guide rail, and then the incident zenith angle is adjusted.
Further, the image acquisition system is arranged on the guide rail of the receiving end and comprises a polarization detector, a reflectivity integrating sphere, a second driving motor and an integrating sphere front collimating lens, wherein the polarization detector and the reflectivity integrating sphere are arranged side by side, the polarization detector, the reflectivity integrating sphere and the integrating sphere front collimating lens are fixed together to form a whole, the polarization detector is connected with the information receiving and controlling system, and the integrating sphere front collimating lens is used for focusing light rays reflected by the reflectivity integrating sphere and receiving the light rays through the information receiving and controlling system; the electric signal input end of the reflectivity integrating sphere is connected with the output port of the second driving motor, the input port of the second driving motor is connected with the information receiving and controlling system, a motion control instruction is sent to the second driving motor through the information receiving and controlling system, and the second driving motor executes the motion control instruction, so that the whole formed by the polarization detector, the reflectivity integrating sphere and the collimating lens in front of the integrating sphere is driven to move on the guide rail of the receiving end, and the zenith angle of detection is regulated.
Further, the information receiving and controlling system comprises a computer and a spectrometer, wherein a signal output port on the computer is connected with input ports of the first driving motor and the second driving motor through signal wires respectively; meanwhile, a signal receiving port of the computer and an input port of the polarization detector are used for acquiring a polarized image; the spectrometer is electrically connected with the reflectivity integrating sphere and is used for acquiring polarized reflectivity data.
The application also provides a roughness measurement method based on pBRDF and dynamic TS algorithm, which uses the roughness measurement device to perform the roughness measurement operation of the surface of the sample to be measured, and comprises the following steps:
step 1: numbering each parameter to be inverted, wherein A1, A2, A3 and A4 respectively represent unknown parameter roughnessSpecular reflectivity->Diffuse reflectance->Micro-surface element normal angle ++>；
Step 2: in matlab, 105 polarized pictures acquired in one round are classified, each 5 polarized pictures are defined as a data set in sequence, 21 groups of data sets S01-S21 are added, the data set S01 is defined as an initial solution set, and known parameters are calculated、/>、/>And->Is substituted into the linear polarization degree based on the polarized hemispherical reflectivity of the parallel wave and the perpendicular waveDSolving unknown parameter roughness in olp calculation formula>Specular reflectivity->Diffuse reflectance->Micro-surface element normal angle ++>Obtaining an initial feasible solution of a TS algorithm, wherein S02-S21 are used for inversion of the initial feasible solution;
the acquisition process of 105 polarization pictures acquired in one round is as follows: setting the degree interval of incident zenith angleDegree interval of zenith angle detection ++>Taking 20 degrees as a starting point and 60 degrees as an end point, accumulating one round of collection to obtain 5 times 21=105 polarized pictures, wherein the number is 001-105;
linear polarization based on polarized hemispherical reflectivity of both parallel and perpendicular waves for rough surfacesDThe olp calculation formula is:
wherein the parameters are knownFor incident zenith angle>For detecting zenith angle->And->Polarizing hemispherical reflectivities of the parallel wave and the perpendicular wave respectively; unknown parameter packageInclude roughness->Specular reflectivity->Diffuse reflectance->Micro-surface element normal angle ++>；/>Is a shadow masking function;
step 3: setting initial parameters of TS algorithm including tabu length, number Q of neighborhood solutions and maximum iteration times IT max And an adaptive weight coefficient w due to the linear polarization degree based on the polarization hemispherical reflectivity of the parallel wave and the perpendicular waveDIn the olp calculation formula, 4 unknown parameters are included, so the tabu length is [4,8 ]]Random numbers between the two, determining a neighborhood solution number Q=4, and the maximum iteration number IT max =200, adaptive weight coefficient w=1;
step 4: linear polarization due to hemispherical reflectivity of polarization based on parallel and perpendicular wavesDThe olp calculation formula is a nonlinear polynary function, and the adaptive value function is selected as a simulation valueAnd experimentally measured polarization degree valueDThe standard mean square error model of olp can ensure that the individual trend in the inversion process is correct, and the adaptive value function is as follows:
step 5: iterative preparation: the data set S01 formed by the polarized pictures 001-005 is used as a candidate solution set of the current solution, and a Tabu table is added to be used as a group of initial feasible solutions;
step 6: iterative optimization: selecting a solution which is optimal in objective function value and is not in the Tabu table from the candidate solution sets, as a new current solution, updating the Tabu table after adding the new current solution, and jumping out of an iteration link after the iteration termination condition is met, and outputting a search value, namely, the current round of optimal solution;
step 7: defining the data set S02 as an initial solution set, repeating the steps 2 to 6, and cycling the process until the step S21 is completed for the round of the initial solution set, so as to obtain the optimal inversion value of the roughness A1.
Further, the roughness measurement method based on pBRDF and dynamic TS algorithm further comprises the following steps of verifying:
after obtaining the optimal solution, the accuracy of the roughness inversion is expressed by the evaluation function of the solution whenNamely, the roughness inversion result is in a reasonable range, and the evaluation function is as follows: />
In the evaluation function, defineFor inverting the roughness arithmetic mean, +.>As an arithmetic mean of the actual roughness,for the error evaluation function value, the initial value i and the end value R in the formula are respectively the ith turn and the R turn of inversion; the inversion of the data set of 105 polarization pictures is completed each time, and the optimal value of the inversion result is obtained, wherein the optimal value is one round.
Through the design scheme, the application has the following beneficial effects: the application provides a roughness measuring device and method based on pBRDF and dynamic TS algorithm, which can realize the rapid acquisition of polarized image and the acquisition of polarized information; substituting parameters into a linear polarization degree calculation formula, solving unknown parameters such as roughness and the like, and coupling an improved TS algorithm to further invert roughness values; meanwhile, other parameters such as specular reflectivity and the like can be inverted; the method also well solves the problem that the traditional roughness inversion process is in local optimum, and can improve the precision of the roughness inversion; in addition, references can be provided for measurement and parameter inversion of unknown quantities of materials in other fields.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application, wherein:
FIG. 1 is a roughness inversion flow chart based on a dynamic TS algorithm;
FIG. 2 is a schematic diagram of a polarized image acquisition system;
FIG. 3 is a schematic diagram of the internal structure of a laser modulation system;
fig. 4 is an automatic control mode interface of the PC side of the image acquisition system.
The figures are marked as follows: 10-a tunable laser; 11-a laser modulation system; 12-a first drive motor; a 20-polarization detector; 21-a reflectivity integrating sphere; 22-a second drive motor; 23-an integrating sphere front collimator lens; 30-a computer; 31-spectrometer; 40-receiving end guide rail; 41-a transmitting end guide rail; a 111-tunable filter; 112-an attenuation sheet; 113-a polarizer; 114-collimation beam expander.
Description of the embodiments
In order to make the objects, features and advantages of the present application more obvious and understandable, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the present application is not limited by the following examples, and specific embodiments can be determined according to the technical scheme and practical situation of the present application. Well-known methods, procedures, flows, components and circuits have not been described in detail so as not to obscure the nature of the application. After determining the neighborhood and the Search rule, the TS algorithm (Tabu Search algorithm) substitutes the searched points into the fitness function to perform comparison and optimization, thereby determining the optimal solution of the objective function.
As shown in fig. 1 to 4, the roughness measurement device based on pBRDF and dynamic TS algorithm includes a polarized image acquisition system; as shown in fig. 2, the polarized image acquisition system comprises a polarized light emission system, an image acquisition system and an information receiving and controlling system; the polarized light emission system comprises a tunable laser 10, a laser modulation system 11 and a first driving motor 12, wherein the tunable laser 10, the laser modulation system 11 and the first driving motor 12 are arranged on an emission end guide rail 41, the laser modulation system 11 is positioned at the lower left part of the tunable laser 10, the laser modulation system 11 and the first driving motor are connected through bolts, an integral body can realize synchronous movement on the emission end guide rail 41, the tunable laser 10 emits laser beams, the laser beams modulated into polarized light with a polarization angle of 90 DEG by the laser modulation system 11 are incident on the surface of a sample to be detected, the first driving motor 12 is positioned at the inner side of the emission end guide rail 41, the bottom of the first driving motor is coplanar with the bottom of a sample table, an input port of the first driving motor 12 is connected with a computer 30, an output port of the first driving motor 12 is electrically connected with an electric signal input end of the tunable laser 10 in the polarized light emission system, and the integral body formed by the tunable laser 10 is controlled on the computer 30 to move on the emission end guide rail 41. The image acquisition system comprises a polarization detector 20, a reflectivity integrating sphere 21, a second driving motor 22 and an integrating sphere front collimating lens 23, wherein the polarization detector 20, the reflectivity integrating sphere 21, the second driving motor 22 and the integrating sphere front collimating lens 23 are arranged on a receiving end guide rail 40, the polarization detector 20 and the reflectivity integrating sphere 21 are arranged on the receiving end guide rail 40 and are used for receiving reflected light side by side, the integrating sphere front collimating lens 23 is positioned at the right lower part of the reflectivity integrating sphere 21 and is used for focusing reflected light, the polarization detector 20, the reflectivity integrating sphere 21 and the integrating sphere front collimating lens 23 are connected through bolts, the three components form a whole body and can synchronously move on the receiving end guide rail 40, the second driving motor 22 is positioned at the inner side of the receiving end guide rail 40, the bottom of the second driving motor 22 is coplanar with the bottom of a sample table, an input port of the second driving motor 22 is connected with a computer 30, an output port of the second driving motor 22 is electrically connected with an electric signal input end of the reflectivity integrating sphere 21 in the image acquisition system, every two adjacent parts in the image acquisition system are fixed through bolts, and the second driving motor 22 is controlled on the computer 30 and is used for driving the polarization detector 20, the reflectivity integrating sphere 21 and the integrating sphere front collimating lens 23 to form the receiving end of the whole body. The information receiving and controlling system comprises a computer 30 and a spectrometer 31, wherein the computer 30 is used for controlling the image acquisition process, and the spectrometer 31 is used for acquiring polarized reflectivity data. In addition, the included angle between the incident light and the normal line of the surface of the sample to be measured is an incident zenith angle, the included angle between the reflected light and the normal line of the surface of the sample to be measured is a detection zenith angle, and the computer 30 is respectively electrically connected with the polarized light emission system and the image acquisition system, and respectively controls the polarized light emission system and the image acquisition system to move on the emission end guide rail 41 and the receiving end guide rail 40, so as to adjust the incident zenith angle and the detection zenith angle.
The whole roughness measurement process is divided into a polarization information acquisition stage and a roughness inversion stage, wherein the technical scheme of the polarization information acquisition stage is as follows:
the tunable laser 10 emits laser light, the laser modulation system 11 emits linear polarized light with a polarization angle of 90 degrees, the linear polarized light irradiates the surface of a sample to be detected on a sample table, the image acquisition system acquires information, the polarization information of an image is displayed on the computer 30, and the spectrometer 31 is used for acquiring the polarization hemispherical reflectivity of the parallel wave and the vertical wave.
In order to quickly and conveniently acquire reflected light under different incident conditions, the system performs the whole image acquisition process, and adopts an automatic control method, as shown in fig. 4, the initial degrees of the incident zenith angle and the detected zenith angle are set on the computer 30, after the degrees are separated and ended, the system is automatically processed by clicking a 'determination' waiting system, photos are automatically stored in a preset folder, and the transmitting end guide rail 41 and the receiving end guide rail 40 can be restored to the initial angles to prepare for acquisition of the next group of images.
In the automatic mode, the number of image data can ensure the generality of inversion results, the initial value needs to be enough, the fixed selected relative azimuth angle is 180 degrees, the gray level error caused by shadow shielding is avoided, and the degree interval for setting the incident zenith angle is dynamically selectedDegree interval of zenith angle detection ++>All take 20 degrees as a starting point and 60 degrees as an end point, and 5 times 21=105 polarized pictures can be obtained by accumulating one round of acquisition, and the number is 001-105.
In addition, in order to eliminate the influence of the background stray light, the parallel and uniform light is irradiated onto the surface of the sample and then enters the image acquisition system, the light source modes of the natural light and the vertical linear polarized light irradiation are switched, and the laser modulation system 11 comprises a tunable filter 111, an attenuation sheet 112, a polarizing sheet 113 and a collimation beam expander 114 which are sequentially arranged according to the light propagation direction. The sample stage is a concave surface with the length of 20cm and the width of 15cm, and clamping grooves for fixing sample plates are formed in the periphery of the sample stage, so that the relative deviation of a region to be tested of a sample in comparison with the previous incident condition is avoided each time the transmitting end guide rail 41 and the receiving end guide rail 40 rotate.
In this example, an aluminum foil sheet 20cm long and 15cm wide was prepared, and 105 polarization degree images with polarization degree values were obtained by a polarization image acquisition system.
The technical scheme of the roughness inversion stage is as follows:
for a rough surface, the linear polarization degree Dolp calculation formula based on the polarization hemispherical reflectivity of the parallel wave and the perpendicular wave is as follows:
in the formula, the parameters are knownFor incident zenith angle>For detecting zenith angle->And->The polarization hemispherical reflectivity of the parallel wave and the vertical wave can be measured by the reflectivity integrating sphere 21; the unknown parameters include roughness->Specular reflectanceDiffuse reflectance->Micro-surface element normal angle ++>；/>Is a shadow mask function.
The roughness inversion flow of the linear polarization model coupling dynamic TS algorithm is shown in the figure 1, and the steps are as follows:
step 1: numbering each invertable parameter, A1, A2, A3 and A4 respectively represent unknown parameter roughnessSpecular reflectivity->Diffuse reflectance->Micro-surface element normal angle ++>；
Step 2: in matlab, 105 polarized pictures acquired in one round are classified, each 5 polarized pictures are defined as a data set in sequence, 21 groups of data sets S01-S21 are added, the data set S01 is defined as an initial solution set, and known parameters are calculated、/>、/>And->Is substituted into the linear polarization degree based on the polarized hemispherical reflectivity of the parallel wave and the perpendicular waveDSolving unknown parameter roughness in olp calculation formula>Specular reflectivity->Diffuse reflectance->Micro-surface element normal angle ++>Obtaining an initial feasible solution of a TS algorithm, wherein S02-S21 are used for inversion of the initial feasible solution;
step 3: setting initial parameters of TS algorithm including tabu length, number Q of neighborhood solutions and maximum iteration times IT max And an adaptive weight coefficient w due to the linear polarization degreeDIn the olp calculation formula, 4 unknown parameters are included, so the tabu length is [4,8 ]]Random numbers between the two, determining a neighborhood solution number Q=4, and the maximum iteration number IT max =200, adaptive weight coefficient w=1;
step 4: linear polarization degree of polarized hemispherical reflectivity based on parallel wave and perpendicular waveDThe olp calculation formula is a nonlinear polynary function, and the adaptive value function is selected as a simulation valueAnd experimentally measured polarization degree valueDThe standard mean square error model of olp can ensure that the individual trend in the inversion process is correct, and the adaptive value function is as follows:
step 5: iterative preparation: the data set S01 formed by the polarized pictures 001-005 is used as a candidate solution set of the current solution, and a Tabu table is added to be used as a group of initial feasible solutions;
step 6: iterative optimization: selecting a solution which is optimal in objective function value and is not in the Tabu table from the candidate solution sets, as a new current solution, updating the Tabu table after adding the new current solution, and jumping out of an iteration link after the iteration termination condition is met, and outputting a search value, namely, the current round of optimal solution;
step 7: defining a data set S02 as an initial solution set, repeating the steps 2 to 6, and cycling the process until S21 is the end of the turn of the initial solution set to obtain an optimal inversion value of the roughness A1;
step 8: verification
The accuracy of the roughness inversion is expressed by the evaluation function of the available solution after the optimal solution is obtained, whenNamely, the roughness inversion result is in a reasonable range, and the evaluation function is as follows: />
In the evaluation function, defineFor inverting the roughness arithmetic mean, +.>As an arithmetic mean of the actual roughness,for the error evaluation function value, the start value i and the end value R in the formula are the i-th run and the R-th run of the inversion, respectively. The inversion of the data set of 105 polarization pictures is completed each time, and the optimal value of the inversion result is obtained, wherein the optimal value is one round.
The application is characterized in that the data set is highly correlated with the inversion flow, and the inversion is performed for multiple times, so that the problem that the traditional algorithm falls into a local optimal solution is avoided, and the roughness value closest to the true value can be inverted at one time.
In order to facilitate understanding of the principles of the present application and the physical meaning of the parameters, the following sets forth some of the theories relating to the present application:
the Stokes vector in four dimensions can describe the polarization state of light, and after the light is incident on the rough surface, the change of the polarization state is represented by the Stokes vector:
wherein the method comprises the steps ofAnd->Four-dimensional Stokes vectors representing reflected light and incident light respectively,Mthe 4x4 form Muller matrix, m, representing pBRDF 00 Representing the strength characteristics of the surface to be measured, m 01 -m 03 Representing the bidirectional attenuation characteristics of the surface to be measured, m 10 -m 30 Representing the polarization characteristics of the surface to be measured, m 11 -m 33 Indicating the depolarization and phase retardation characteristics of the surface to be measured,S 0 is the sum of the horizontal component and the vertical component,S 1 is the difference between the horizontal component and the vertical component,S 2 is the difference between the + -45 DEG components,S 3 is the difference between the left and right circular polarization components, and the linear polarization degreeDThe olp measurement is expressed as: />
For the light vector vibrating in any direction, the light vector can be divided into an s component E vibrating perpendicular to the incident plane S And a p-component E vibrating parallel to the incident plane P Definition of E after incident light passes through a roughened surface S And E is P The corresponding reflectivity components are respectivelyAndwhen the laser modulation system 11 is built in with a vertical linear polarization device, the incident light is vertical linear polarized light, no s wave exists in the light vector, and the ratio of light intensity is the ratio of reflectivity, at this time +.>Expressed as Stokes parameters: />
According to the application, polarization information is acquired by collecting a polarization image, known parameters are substituted into a linear polarization degree calculation formula, unknown parameters such as roughness and the like are solved, an improved TS algorithm is utilized, an individual which enables an objective function to reach an optimal solution is found by judging an adaptation value function, and then the true value of the roughness is determined. Other parameters such as specular reflectivity can be inverted while the roughness parameters are obtained. The method well solves the problem that the traditional roughness inversion process is in local optimum, and can provide references for measurement and inversion of unknown materials in other fields.
It should be understood that the foregoing embodiments of the present application are merely illustrative of and not limiting on the embodiments of the present application, and that various other changes and modifications can be made by those skilled in the art based on the above description, and it is not intended to be exhaustive of all embodiments, and all obvious changes and modifications that come within the scope of the application are defined by the following claims.
Claims (2)
1. The roughness measurement method based on the pBRDF and dynamic TS algorithm is characterized in that the roughness measurement operation of the surface of a sample to be measured is performed by using a roughness measurement device, the roughness measurement device based on the pBRDF and dynamic TS algorithm comprises a polarized image acquisition system for acquiring a polarized image, the polarized image acquisition system comprises a polarized light emission system, an image acquisition system and an information receiving and controlling system, the information receiving and controlling system is respectively and electrically connected with the polarized light emission system and the image acquisition system, the incident zenith angle and the detection zenith angle are further adjusted, and meanwhile the information receiving and controlling system is used for receiving information transmitted by the image acquisition system to the system; the polarized light emission system is used for emitting linearly polarized light with a polarization angle of 90 degrees and incident on the surface of the sample to be detected; the image acquisition system is used for acquiring reflected light of the surface of the sample to be detected and transmitting the reflected light to the information receiving and controlling system so as to obtain a polarized image with a polarization degree value, wherein the included angle between the incident light and the normal line of the surface of the sample to be detected is an incident zenith angle, and the included angle between the reflected light and the normal line of the surface of the sample to be detected is a detection zenith angle;
the method comprises the following steps:
step 1: numbering each parameter to be inverted, wherein A1, A2, A3 and A4 respectively represent unknown parameter roughnessSpecular reflectivity->Diffuse reflectance->Micro-surface element normal angle ++>；
Step 2: in matlab, 105 polarized pictures acquired in one round are classified, each 5 polarized pictures are defined as a data set in sequence, 21 groups of data sets S01-S21 are added, the data set S01 is defined as an initial solution set, and known parameters are calculated、、/>And->Is substituted into the linear polarization degree based on the polarized hemispherical reflectivity of the parallel wave and the perpendicular waveDSolving unknown parameter roughness in olp calculation formula>Specular reflectivity->Diffuse reflectance->Micro-surface element normal angle ++>Obtaining an initial feasible solution of a TS algorithm, wherein S02-S21 are used for inversion of the initial feasible solution;
the acquisition process of 105 polarization pictures acquired in one round is as follows: setting the degree interval of incident zenith angleDegree interval of zenith angle detection ++>Taking 20 degrees as a starting point and 60 degrees as an end point, accumulating one round of collection to obtain 5 times 21=105 polarized pictures, wherein the number is 001-105;
linear polarization based on polarized hemispherical reflectivity of both parallel and perpendicular waves for rough surfacesDThe olp calculation formula is:
wherein the known parameter +.>For incident zenith angle>For detecting zenith angle->And->Polarizing hemispherical reflectivities of the parallel wave and the perpendicular wave respectively; the unknown parameters include roughness->Specular reflectivity->Diffuse reflectance->Micro-surface element normal angle ++>；/>Is a shadow masking function;
step 3: setting initial parameters of TS algorithm including tabu length, number Q of neighborhood solutions and maximum iteration times IT max And an adaptive weight coefficient w due to the linear polarization degree based on the polarization hemispherical reflectivity of the parallel wave and the perpendicular waveDIn the olp calculation formula, 4 unknown parameters are included, so the tabu length is [4,8 ]]Random numbers between the two, determining a neighborhood solution number Q=4, and the maximum iteration number IT max =200, adaptive weight coefficient w=1;
step 4: linear polarization due to hemispherical reflectivity of polarization based on parallel and perpendicular wavesDThe olp calculation formula is a nonlinear polynary function, and the adaptive value function is selected as a simulation valueAnd experimentally measured polarization degree valueDThe standard mean square error model of olp can ensure that the individual trend in the inversion process is correct, and the adaptive value function is as follows:
；
step 5: iterative preparation: the data set S01 formed by the polarized pictures 001-005 is used as a candidate solution set of the current solution, and a Tabu table is added to be used as a group of initial feasible solutions;
step 6: iterative optimization: selecting a solution which is optimal in objective function value and is not in the Tabu table from the candidate solution sets, as a new current solution, updating the Tabu table after adding the new current solution, and jumping out of an iteration link after the iteration termination condition is met, and outputting a search value, namely, the current round of optimal solution;
step 7: defining the data set S02 as an initial solution set, repeating the steps 2 to 6, and cycling the process until the step S21 is completed for the round of the initial solution set, so as to obtain the optimal inversion value of the roughness A1.
2. The roughness measurement method based on pBRDF and dynamic TS algorithm according to claim 1, further comprising verifying:
after obtaining the optimal solution, the accuracy of the roughness inversion is expressed by the evaluation function of the solution whenNamely, the roughness inversion result is in a reasonable range, and the evaluation function is as follows: />The method comprises the steps of carrying out a first treatment on the surface of the In the evaluation function, define->For inverting the roughness arithmetic mean, +.>For the arithmetic mean of the actual roughness +.>For the error evaluation function value, a start value i and a termination value R in the formula are respectively an ith round and an R round of inversion; the inversion of the data set of 105 polarization pictures is completed each time, and the optimal value of the inversion result is obtained, wherein the optimal value is one round.
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