A roughness anisotropy parameter measurement method based on normal vector statistical characteristics

Abstract

The application discloses a roughness anisotropy parameter measurement method based on normal vector statistical characteristics, which comprises the following steps: firstly, acquiring infrared images of a roughness contrast sample block at four polarization angles, and calculating polarization parameters of infrared radiation by solving Stokes vectors; secondly, selecting a sample block from the roughness contrast sample block and extracting an inner region of the sample block as a to-be-measured region; thirdly, establishing 180 spatial filters respectively containing test lines in different directions, extracting 180 groups of normal vectors, respectively, and calculating cosine similarity of all normal vectors on the test line and the average value of the normal vectors and taking the average value; and finally, sequentially acquiring average cosine similarity of the normal vectors in different directions, and obtaining roughness anisotropy parameters of the measured surface according to a two-dimensional fabric tensor model. The application has the advantages of high reliability, non-contact and accurate feature extraction, and meanwhile, an infrared polarization detection device is small in size, light in weight and simple in measurement system arrangement, and is more suitable for actual working conditions.

Description

Technical Field

[0001] This invention belongs to the field of computer vision technology, specifically relating to a method for measuring roughness anisotropy parameters based on the statistical properties of normal vectors. Background Technology

[0002] Basic machining methods for industrial objects mainly include turning, milling, planing, and grinding. These processes utilize the reciprocating motion between the cutting tool and the object's surface to achieve the desired surface texture ("turning, milling, planing, grinding"). During this reciprocating motion with the cutting tool, the object develops anisotropic surfaces, specifically exhibiting differences in surface roughness along different directions. Surface anisotropy is closely related to the contact characteristics of the object's mating surfaces, affecting friction and wear, contact damping, heat and electrical conduction, sealing performance, and mechanical properties. Therefore, measuring the anisotropy of surface roughness is of significant value in practical engineering.

[0003] Existing measurement methods are mainly divided into touch methods, sectioning methods, and computer vision methods. Touch methods involve experienced operators measuring manually by touch, which relies on the experience of the workers and is not very reliable. Sectioning methods involve cutting the object along different directions and measuring by studying the cut surface. This method is difficult to implement, and the cutting process may change the geometry of the cut surface, resulting in inaccurate measurement results. Existing computer vision methods measure by reconstructing the target surface in three dimensions. However, many industrial objects have smooth surfaces with sparse textures, which makes it difficult to detect feature points on the object surface, resulting in low accuracy and poor reliability of the three-dimensional reconstruction results.

[0004] The infrared polarization imaging-based measurement method is a non-contact approach. This method is based on the following experimental findings: the closer the angle of the test line module is to the direction of the actual trace on the object being measured, the lower the dispersion of the extracted normal vectors, and the closer the mean cosine similarity of the normal vectors is to "1"; conversely, the greater the deviation of the angle of the test line module from the direction of the actual trace on the object being measured, the higher the dispersion of the extracted normal vectors, and the closer the mean cosine similarity of the normal vectors is to "0". Therefore, by establishing a correlation model between the normal vector distribution information and roughness information of the object's surface along different directions, the roughness anisotropy parameters of the surface to be measured can be calculated using the model. This method is non-destructive to the surface of the measured part and has the advantages of low cost, high accuracy, and miniaturization of the measurement system. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a method for measuring roughness anisotropy parameters based on the statistical characteristics of normal vectors. First, infrared images of the surface of a roughness comparison sample at four polarization angles are acquired, and the polarization parameters of the infrared radiation are calculated by solving the Stokes vector. Second, a sample is selected from the roughness comparison samples, and the region within this sample is extracted as the area to be measured. Then, 180 spatial filters containing test lines in different directions are established, and 180 sets of normal vectors are extracted from each filter. The cosine similarity between all normal vectors on each test line and the average normal vector is calculated, and the average is taken. Finally, the average cosine similarity of normal vectors in different directions is obtained sequentially, and the roughness anisotropy parameters of the measured surface are obtained based on a two-dimensional structural tensor model. Compared with traditional touch, sectioning, and computer vision measurement methods, this invention has the advantages of high reliability, non-contact operation, and accurate feature extraction. Furthermore, the infrared polarization detection equipment is small in size, lightweight, and has a simple measurement system layout, making it more suitable for practical working environments.

[0006] The technical solution adopted by this invention to solve its technical problem includes the following steps:

[0007] Step 1: Obtain infrared images of the surface of the roughness comparison sample at four polarization angles, and calculate the polarization parameters of the infrared radiation by solving the Stokes vector;

[0008] Step 2: Extract an a×a pixel region A from the roughness comparison sample block as the region to be tested; calculate the direction angle and zenith angle distribution information of the normal vector in region A using the polarization parameters obtained in Step 1, and obtain the distribution information of the normal vector in region A;

[0009] Step 3: Construct 180 spatial filter modules, each containing test lines at different angles. Each spatial filter module has a pixel size of a′×a′, where a′... <a;

[0010] Step 4: Perform spatial filtering operations on the 180 spatial filter modules with region A respectively, and extract 180 sets of normal vectors respectively;

[0011] Step 5: Calculate the average value of each group of normal vectors, then calculate the cosine similarity between all normal vectors in the group and the average value of the normal vectors, and take the average value; obtain the roughness anisotropy parameters of the measured surface based on the two-dimensional structural tensor model.

[0012] Furthermore, the specific method of step 1 is as follows:

[0013] Infrared images of the roughness comparison sample surface at four polarization angles (0°, 45°, 90°, and 135°) were acquired using a focal plane infrared polarization camera or by mounting and rotating a linear polarizer in front of a regular infrared camera. The polarization phase angle and degree of polarization of the roughness comparison sample surface were calculated based on the Stokes vector.

[0014] Furthermore, the method for calculating the polarization parameters of infrared radiation by solving the Stokes vector is as follows:

[0015] The Stokes vector expression for the polarization state of light can be written in the following form:

[0016] S0 = 0.5 * (I0 + I 45 +I 90 +I 135 )

[0017] S1=I0-I 90

[0018] S2=I 45 -I 135

[0019] Among them, I0, I 45 I 90 I 135 These represent the radiation intensities at polarization directions of 0°, 45°, 90°, and 135°, respectively.

[0020] Based on the Stokes parameters, the polarization parameters, namely the degree of polarization DoLP and the polarization phase angle AoP, are further calculated using the following formulas:

[0021]

[0022]

[0023] Set the Stokes parameter S3 = 0.

[0024] Furthermore, the distribution information of the normal vector in region A is obtained as follows:

[0025] The azimuth angle θ is calculated using the following formula:

[0026]

[0027] Infrared radiation polarization degree DoLP and zenith angle The geometric expression is:

[0028]

[0029] in:

[0030]

[0031]

[0032] In the formula: n represents the real part of the refractive index of the object, and k represents the imaginary part of the refractive index of the object.

[0033] If the target surface is represented by z = f(x,y), then the normal vector of any point on the object surface is expressed by the following formula:

[0034]

[0035] Furthermore, the different angles in step 3 are 0°, 1°, ..., 179°.

[0036] Furthermore, the specific method for obtaining the object trace direction based on the maximum value of the average cosine similarity of the normal vectors at different angles in step 5 is as follows: sequentially obtain the average cosine similarity of the normal vectors in the directions of 0°, 1°, ..., 179°, draw a radar chart of the test line direction - the average cosine similarity of the normal vectors, and obtain the roughness anisotropy parameters of the object based on the two-dimensional assembly tensor model.

[0037] Furthermore, the two-dimensional tensor model T... ij Represented as:

[0038]

[0039]

[0040] Where A2 and B2 are components of the two-dimensional configuration tensor used to represent the degree of anisotropy of the roughness direction distribution, m represents the spatial filter containing the test line at that angle, N represents the total number of spatial filters, and δ θ It is the average cosine similarity of the normal vectors in the directions of 0°, 1°, ..., 179°. The values ​​of A2 and B2 are obtained through the above formula, and then the anisotropic parameters of the surface roughness of the object are calculated.

[0041] The beneficial effects of this invention are as follows:

[0042] Traditional methods for measuring roughness anisotropy parameters mainly include touch, sectioning, and computer vision methods. Touch methods rely on operator experience and have low reliability; sectioning methods may alter the geometry of the cut surface during the cutting process, leading to inaccurate results; and computer vision methods struggle with feature point detection and yield low accuracy when dealing with smooth, sparsely textured industrial objects. Compared to traditional touch, sectioning, and computer vision methods, this invention offers advantages such as high reliability (not relying on human subjective experience), non-contact measurement (avoiding the influence of the cutting process on the measurement results), and accurate feature extraction. Furthermore, the infrared polarization detection equipment is small, lightweight, and has a simple measurement system layout, making it more suitable for real-world working environments. Attached Figure Description

[0043] Figure 1 This is the object under test (lathe sample block) used in the embodiments of the present invention.

[0044] Figure 2 They are infrared images of the surface of the object to be measured at four polarization angles in the embodiments of the present invention.

[0045] Figure 3 It is the待测区域A extracted in the embodiments of the present invention.

[0046] Figure 4 They are test line modules at different angles in the embodiments of the present invention.

[0047] Figure 5 It is the radar chart of roughness anisotropy calculated in the embodiments of the present invention. Detailed implementation manners

[0048] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0049] The present invention provides a method for measuring roughness anisotropy parameters based on the statistical characteristics of normal vectors. As Figure 1 shown, a lathe sample block is used as the test object; as Figure 2 shown, infrared images of the surface of the roughness comparison sample block at four polarization angles of 0°, 45°, 90°, and 135° are obtained, and the polarization parameters of the infrared radiation are calculated by solving the Stokes vector; as Figure 3 shown, a sample block is selected from the roughness comparison sample block, and the area A within the sample block is extracted as the待测区域, and the pixel scale of the area A is a×a; as Figure 4 [[ID=3​​​​​​​​​​​​​​As shown, a sample block is selected from the roughness comparison sample block and region A within the sample block is extracted as the test area. The pixel scale of region A is a×a. The direction angle and zenith angle distribution information of the normal vector in A are calculated from the polarization phase angle and polarization degree information obtained in step 1. Finally, the distribution information of the normal vector in A is obtained.

[0053] Step 3, as follows Figure 4 As shown, 180 spatial filtering modules were established, each containing test lines in the directions of 0°, 1°, ..., 179°. The pixel size of each module is a′×a′(a′ <a);

[0054] Step 4: Perform spatial filtering operations on the 0°, 1°, ..., 179° test line modules and region A respectively, extract 180 sets of normal vectors, calculate the average value of the normal vector of one set, and then calculate the cosine similarity between all normal vectors on the test line of that set and the average value of the normal vectors and take the mean.

[0055] Step 5, as follows Figure 5 As shown, Figure 5 As shown, the mean cosine similarity of the normal vectors in the directions of 0°, 1°, ..., 179° is obtained sequentially, an angle-vector mean cosine similarity radar chart is plotted, and the roughness anisotropy parameters of the object are obtained according to the two-dimensional assembly tensor model.

[0056] In the technical solution of this invention, an infrared image of the surface of the target under test at four polarization angles (0°, 45°, 90°, and 135°) is acquired using a focal plane infrared polarization camera. The Stokes vector expression for the polarization state of light is written in the following form:

[0057] S0 = 0.5 * (I0 + I 45 +I 90 +I 135 )

[0058] S1=I0-I 90

[0059] S2=I 45 -I 135

[0060] Based on the Stokes parameters, the degree of polarization DoLP and the polarization phase angle AoP can be further calculated, as shown in the following formula:

[0061]

[0062]

[0063] This invention only considers the linear polarization case, therefore the Stokes parameter S3 = 0 is taken here.

[0064] Since the Stokes vector S1 gives the direction of the azimuth plane, this relationship can be used to eliminate the ambiguity between the polarization phase angle and the azimuth angle, and can be calculated using the following formula:

[0065]

[0066] Infrared radiation polarization degree DoLP and zenith angle The geometric expression is:

[0067]

[0068] That is, the zenith angle can be expressed by the refractive index and degree of polarization of the object as follows:

[0069]

[0070] In the above formula: n represents the real part of the refractive index of the object, and k represents the imaginary part of the refractive index of the object.

[0071] By determining the degree of polarization and phase angle of the infrared radiation from the target surface, the azimuth and zenith angles of the target surface can be calculated, thus obtaining the normal vector distribution of the target surface. The target surface is represented by z = f(x,y), and the normal vector at any point on the object surface can be expressed by the following formula:

[0072]

[0073] In the technical solution of this invention, based on the experimental finding that the closer the angle of the test line module is to the direction of the real trace of the object being measured, the lower the dispersion of the normal vector extracted by the test line module and the closer the mean cosine similarity of the normal vector is to "1"; and the further the angle of the test line module deviates from the direction of the real trace of the object being measured, the higher the dispersion of the normal vector extracted by the test line module and the closer the mean cosine similarity of the normal vector is to "0", a method for measuring the anisotropic parameters of object roughness is designed based on a test line module in the form of spatial filtering.

[0074] Roughness configuration tensor T in a two-dimensional plane ij It can be represented as:

[0075]

[0076]

[0077] δ θ The average cosine similarity value of the normal vectors in different directions is extracted using test line modules at angles of 0°, 1°, ..., 179°. When the δ of all θ angles... θ Once obtained, the values ​​of A2 and B2 can be obtained through the above formula, and then the anisotropic parameters of the surface roughness of the object can be calculated.

[0078] In the specific operation process of the present invention, first, infrared images of the surface of a roughness comparison sample block at four polarization angles are acquired, and the polarization parameters of the infrared radiation are calculated by solving the Stokes vector; secondly, a sample block is selected from the roughness comparison sample block, and area A within the sample block is extracted as the measurement area, and the pixel scale of area A is a×a; then, 180 spatial filters containing test lines in different directions are established, and the pixel scale of the module is a'×a' (a' < a). The test line modules in different directions are respectively subjected to spatial filtering operations with area A, 180 groups of normal vectors are respectively extracted, the average value of the normal vectors of one group is calculated, and then the cosine similarity between all the normal vectors on the test line of this group and the average value of the normal vectors is calculated and averaged; finally, the average cosine similarity of the normal vectors in different directions is sequentially obtained, and the roughness anisotropy parameter of the measured surface is obtained according to the two-dimensional fabric tensor model. Specific embodiment:

[0080] The camera used in this embodiment is an infrared polarization camera independently developed by the Key Laboratory of Information Fusion of Northwestern Polytechnical University, and the measurement target is a lathe sample block as Figure 1 shown; by using a split focal plane infrared polarization camera or by installing and rotating a linear polarizer in front of a common infrared camera, infrared images of the surface of the roughness comparison sample block at four polarization angles of 0°, 45°, 90°, and 135° are acquired as Figure 2 shown, and the polarization phase angle and polarization degree information of the surface of the roughness comparison sample block are calculated according to the Stokes vector; a sample block is selected from the roughness comparison sample block, and area A within the sample block is extracted as the measurement area, and the pixel scale of area A is a×a, as Figure 3 shown, and the direction angle and zenith angle distribution information of the normal vectors in A are calculated from the polarization phase angle and polarization degree information, and finally the distribution information of the normal vectors in A is obtained; 180 spatial filter modules containing test lines in the directions of 0°, 1°, ..., 179° are established, and the pixel scale of the module is a′×a′ (a′ < a) as Figure 4 shown; the test line modules are respectively subjected to spatial filtering operations with area A, 180 groups of normal vectors are respectively extracted, the average value of the normal vectors of one group is calculated, and then the cosine similarity between all the normal vectors on the test line of this group and the average value of the normal vectors is calculated and averaged; the average cosine similarity of the normal vectors in the directions of 0°, 1°, ..., 179° is sequentially obtained, a radar chart of the test line direction - average cosine similarity of the normal vectors is drawn, and the roughness anisotropy parameter of the object is obtained according to the two-dimensional fabric tensor model.

[0081] The two-dimensional roughness fabric tensor T is calculated by the formula : ij :

[0082]

[0083] From the calculation results of the two-dimensional roughness structure tensor and the radar image, it can be seen that the surface roughness of the measured object is anisotropic, and the dominant direction of its anisotropy is 90°. Figure 5 (In the direction of the major axis of the ellipse), the anisotropy parameters are:

[0084]

Claims

1. A method of measuring roughness anisotropy parameters based on statistical properties of normal vectors, characterized in that, The method comprises the following steps: Step 1: acquiring infrared images of the roughness contrast sample block surface at four polarization angles, and calculating the polarization parameters of the infrared radiation by solving the Stokes vector; Step 2: Extracting from the roughness contrast sample block The pixel-scale region A is taken as the region to be measured; the direction angle and zenith angle distribution information of the normal vector in the region A are calculated according to the polarization parameter obtained in step 1, and the distribution information of the normal vector in the region A is obtained. Step 3: Establish 180 spatial filter modules containing different angle test lines respectively, and the pixel scale of each spatial filter module is ; Step 4: performing spatial filtering operation on the 180 spatial filter modules and the region A respectively, and extracting 180 groups of normal vectors respectively; Step 5: calculating the average value of each group of normal vectors, and then calculating the cosine similarity of each group of normal vectors and the average value of the normal vectors and taking the average value; obtaining the roughness anisotropy parameters of the measured surface according to the two-dimensional fabric tensor model.

2. The method according to claim 1, wherein The specific method of step 1 is: Infrared polarization cameras with different focal planes or a linear polarizer installed in front of a common infrared camera and rotated are used to acquire infrared images of the roughness contrast sample block surface at four polarization angles of 0°, 45°, 90° and 135°, and the polarization phase angle and degree of polarization information of the roughness contrast sample block surface are calculated according to the Stokes vector.

3. The method according to claim 2, wherein The method for calculating the polarization parameters of the infrared radiation by solving the Stokes vector is as follows: The Stokes vector expression of the polarization state of light is written as follows: wherein I0, I45, I90, and I135represent the intensity of the radiation possessed by the polarization directions of 0°, 45°, 90°, and 135°, respectively. According to the Stokes parameters, further calculation obtains the polarization parameters: the degree of polarization DoLP and the polarization phase angle AoP , and the calculation formula is as follows: taking the stokes parameter .

4. The method according to claim 3, wherein The specific method for obtaining the distribution information of the normal vector in the region A is as follows: azimuth angle was calculated using the formula: Infrared radiation degree of polarization DoLP with zenith angle is given by the geometrical expression Wherein: wherein: represents the real part of the refractive index of the object, represents the imaginary part of the refractive index of the object; The target surface is represented by If the object surface is represented by then the normal vector at any point on the object surface is given by 。 5. The method according to claim 1, wherein The different angles in step 3 are 0°, 1°,..., 179°.

6. The method according to claim 1, wherein The specific method for obtaining the trace direction of the object according to the maximum value of the average cosine similarity of the normal vector at different angles in step 5 is: the average cosine similarity of the normal vector at 0°, 1°,..., 179° is obtained in sequence, a radar chart of the test line direction-average cosine similarity of the normal vector is drawn, and the roughness anisotropy parameters of the object are obtained according to the two-dimensional fabric tensor model.

7. The method according to claim 1, wherein In a two-dimensional fabric tensor model T ij is represented as: wherein, , is the degree of anisotropy of the roughness directional distribution represented by the components of the two-dimensional texture tensor, represents the spatial filter containing the angular test line, represents the total number of spatial filters, is the normal vector mean cosine similarity in the 0°, 1°,..., 179° directions, obtained by the formula , the values of which are then used to calculate the anisotropy parameters of the object surface roughness.

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