CN114396895A - Method for measuring surface roughness of tunnel lining concrete segment - Google Patents
Method for measuring surface roughness of tunnel lining concrete segment Download PDFInfo
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Abstract
The invention discloses a method for measuring the surface roughness of a tunnel lining concrete segment, which comprises the following steps: step 1, calibrating a measuring sub-surface: the measuring sub-surface comprises a calculating sub-surface and a characteristic sub-surface; step 2, collecting images: controlling the illumination intensity to be 250-400 lux during photographing; the same photographing equipment is adopted on the surface of the same tunnel lining concrete segment; the distance between a lens of the photographing equipment and the surface of the tunnel lining concrete segment is controlled to be 30-50 cm; step 3, cutting and preprocessing the image; step 4, measuring actual roughness; step 5, calculating image characteristic parameters; step 6, selecting a roughness calculation general formula; step 7, calculating a measured quantum surface roughness calculation value; step 8, determining an error correction coefficient; and 9, calculating the surface roughness of the lining concrete segment. The invention can reduce random error in the measuring process to the maximum extent, improves measuring precision, has high efficiency, easy operation and low cost, and has good application prospect and popularization value.
Description
Technical Field
The invention relates to the field of concrete material surface image processing application, in particular to a method for measuring the surface roughness of a tunnel lining concrete segment.
Background
The tunnel lining concrete segment is widely applied to slurry shield tunnel engineering and is a permanent structure for supporting and maintaining long-term stability of tunnels. The surface roughness of the concrete pipe piece is an important parameter for calculating the roughness of the wall surface of the tunnel and measuring the strength and the sealing degree of the pipe piece and peripheral rock soil or secondary lining concrete cementing surface. The method for researching the roughness measurement of the concrete pipe sheet with high precision and high efficiency has important engineering value and theoretical significance.
Currently, there are many measuring methods for concrete surface roughness, mainly including two types, contact type and non-contact type. The contact measurement method comprises a contact method, a sanding method, a comparison method, an impression method and the like, the method is a direct measurement of the surface roughness, and the operation is simple and visual. However, this measurement method may cause some damage to the surface of the concrete segment; in addition, the operation is time-consuming, labor-consuming and not suitable for large-area measurement. The non-contact measuring method relates to optical instruments such as a light cutting method, a laser scanning method, a holographic method and the like, has high precision and high speed, does not damage a surface to be measured, but has high acquisition cost and complex operation of measuring equipment, is mainly used for laboratory research or measurement of large and important projects at present, and has certain difficulty in popularization and application in small and medium projects.
With the rapid development of computers, computer vision and image processing technologies are rapidly popularized, and digital image technologies are gradually adopted by the engineering measurement field. The technology is combined with an image ranging principle, image acquisition can be completed by means of photographic equipment, and the technology is simple to operate and low in cost. In the prior art, the digital image technology has been introduced into the field of measuring the surface roughness of concrete, but in the actual engineering, the digital image technology has the following defects to be improved:
1. the digital image technology is easily limited by the measuring environment and the operation method, and cannot acquire high-quality and uniform image data, so that the accuracy of the calculated roughness cannot be ensured.
2. The digital processing method of the image data has not been realized, and the image characteristic parameter calculation method with simplicity, easy operability, high efficiency and high precision is yet to be researched.
2. The existing roughness calculation formula is hundreds of types, the cited parameters comprise more than ten types of roughness profile indexes, construction depths, fluctuating root mean square and the like, and the characteristics of various surfaces such as exposed concrete surfaces, new concrete surfaces and old concrete surfaces are all better applied, so that the uniqueness of different structural surfaces is shown, the difference also exists in the characterization calculation formula of the roughness, and the mature calculation formula aiming at the roughness of the lining surface does not exist at present, so that when the characterization calculation is carried out, the high-precision calculation formula is obtained by fitting by standing on the basis of the existing common general formula and combining with real conditions.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for measuring the surface roughness of a tunnel lining concrete segment, according to the characteristics and measurement conditions of the concrete segment, the method for measuring the surface roughness of the tunnel lining concrete segment collects and processes the surface image of the concrete segment, extracts image data such as pixel gray values and the like, introduces image characteristic parameter values to describe the image roughness of the concrete segment, and can obtain the high-precision surface roughness of the concrete segment.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for measuring the surface roughness of a tunnel lining concrete segment comprises the following steps.
Step 1A, determining the size and the number of the calculation sub-surfaces: determining the size and the number of the calculating sub-surfaces according to the whole surface area of the tunnel lining concrete segment to be measured; the calculation sub-surface is a sub-surface which is uniformly distributed on the surface of the tunnel lining concrete segment to be measured.
Step 1B, calibrating a calculation sub-surface: marking the calculation sub-surfaces sequentially from left to right and from bottom to top from the lower left corner of the surface of the tunnel lining concrete segment to be measured by adopting a marking wire frame with the same size as the calculation sub-surfaces determined in the step 1A, and numbering to obtain m calculation sub-surfaces; the spacing of the facets in the same row or column remains close or the same.
Step 1C, calibrating a characteristic sub-surface: marking the outlines of the characteristic sub-surfaces in sequence from left to right and from bottom to top by using a marking pen from the lower left corner of the surface of the tunnel lining concrete segment to be measured, and numbering to obtain n characteristic sub-surfaces; wherein n is more than or equal to 5 and less than m; the spacing of the feature facets in the same row or column remains close or the same; the characteristic sub-surface refers to an area where the roughness of the surface of the pipe piece to be measured has visible difference.
Step 4, measuring actual roughness K: respectively measuring the roughness of the n characterization sub-surfaces calibrated in the step 1 by adopting roughness measurement equipment, and further obtaining n roughness measurement values K; wherein the roughness measured value of the ith characteristic sub-surface is Ki，1≤i≤n。
Step 5, calculating image characteristic parameters fpAnd fz: respectively calculating an image characteristic parameter f aiming at each characterization sub-surface image and each calculation sub-surface image obtained by image preprocessingpAnd fz(ii) a Wherein f ispAnd fzRespectively the mean difference and the root mean square of the grey values in the pixel space.
Step 6, selecting a roughness calculation general formula, wherein the specific selection method comprises the following steps:
step 6A, when the n roughness measured values K in the step 4 do not exceed 2.5mm and the n roughness measured values K are close to or the same, the calculated roughness value JRC is related to fpA linear function of (a); f of the characteristic sub-surface image obtained by calculation in step 5 is adoptedpAnd fitting the roughness measured value K of the corresponding characterization sub-surface in the step 4 to obtain a fitting parameter of the linear function.
Step 6B, when the n roughness measured values K in the step 4 are other than the ones in the step 6A, the roughness calculated value JRC is related to fpAnd fzA binary multiple linear function of; f of the characteristic sub-surface image obtained by calculation in step 5 is adoptedpAnd fzAnd fitting the roughness measured value K of the corresponding characterization sub-surface in the step 4 to obtain a fitting parameter of the binary multiple linear function.
And 7, calculating a calculated value of the roughness of the measured quantum surface: calculating a general formula according to the roughness selected in the step 6, and using f obtained by calculation in the step 5pAnd fzCalculating roughness calculation values JRC respectively for each calculation sub-surface and each characteristic sub-surface; wherein the roughness calculation value of the ith characteristic sub-surface is JRCi(ii) a The roughness calculation value of the jth calculation sub-surface is JRCj，1≤j≤m。
Step 8, determining an error correction coefficient: and carrying out error analysis on the roughness calculated value JRC and the roughness measured value K of the characterization sub-surface to obtain an error correction coefficient xi, wherein the specific calculation formula is as follows:
step 9, calculating the surface roughness of the lining concrete segment
The specific calculation formula is as follows:
in step 1A, the size and number of the calculating sub-surfaces are specifically determined according to the actual surface area S of the segment to be measured, specifically:
when S is less than or equal to 10m2And if so, the sum of the areas of all the selected calculation facets is not less than S/10.
When S > 10m2And in the process, the sum of the areas of all the selected calculation facets is not less than S/100, and the number of the calculation facets on the surface of the lining concrete segment of the same tunnel is not less than 10 in the same collection environment.
In the step 1C, the number of the characteristic sub-surfaces is not less than 1/10 of the calculation sub-surfaces, and under the same collection environment, the number of the characteristic sub-surfaces on the surface of the same tunnel lining concrete segment is 5-20.
In step 2, the photographing device is a mobile phone with a photographing function, an IPAD with a photographing function, or a digital camera.
In step 2, when photographing, when the artificial light source is adopted to perform light supplement on the surface of the tunnel lining concrete segment to be measured, the included angle between the artificial light source and the surface of the tunnel lining concrete segment to be measured is 90 +/-20 degrees.
When the roughness of the surface of the tunnel lining concrete segment to be measured exceeds 2.5mm, the included angle between the artificial light source and the surface of the tunnel lining concrete segment to be measured is 90 +/-10 degrees.
Assuming that the lower left corner point of each representation sub-surface image and each calculation sub-surface image in the step 3 is a coordinate origin, and two side lengths passing through the coordinate origin are an x axis and a y axis respectively; in step 5, fpAnd fzThe calculation formulas of (A) and (B) are respectively as follows:
fp＝VP/A (3)
in the formula, VpA pixel space volume that is region D; d is a selected calculation sub-surface or a characteristic sub-surface; a is the area of the calculation sub-surface or the characteristic sub-surface; f. ofmaxThe maximum value of the point gray scale in the region D; f (x, y) is the gray value of the (x, y) coordinate point on the calculation sub-surface or the characteristic sub-surface; n is a radical ofx、NyThe number of the measuring points on the x axis and the y axis respectively; Δ x, Δ y represent the spacing of the measurement points on the x-axis and y-axis, respectively; k. l is the order number of the measuring point in the x-axis and y-axis directions, respectively, fk+1,l+1Gray values corresponding to the k +1 and l +1 serial number points; f. ofk,l+1The gray values corresponding to the k, l +1 serial number points; f. ofk+1,lThe gray values corresponding to the k +1 th and l serial number points; f. ofk,lThe gray values corresponding to the k-th and l-th sequence numbers are obtained.
In step 6A, the roughness calculation JRC is related to fpThe expression of the linear function of (a) is:
JRC＝afp+b (7)
in the formula, a and b are fitting parameters of a linear function.
In step 6B, the roughness calculation JRC is related to fpAnd fzThe expression of the binary multiple linear function of (a) is:
wherein:
F(fp)＝afp+b (8)
in the formula: a. b, a ', b' and c are fitting parameters of a binary multiple linear function; f (F)p) To form a roughness; g (f)z) Is the undulation roughness.
In step 6, when the fitting parameters of the linear function and the fitting parameters of the binary multiple linear function are fitted, the correlation coefficient R2It should be greater than 0.9 and the average relative error MRE less than 5%.
The invention has the following beneficial effects:
1. the invention is based on the principle of image ranging, can not damage or destroy the measured surface, and overcomes the weakness of the contact type measuring method. In addition, the measuring equipment adopted by the invention is easy to obtain, the operation process is simple, the automation degree is high, and the comprehensive cost performance is high.
2. The invention directly adopts the gray characteristic value of the pixel space to describe the surface roughness, and the surface roughness of the concrete pipe can be measured and calculated by analyzing the gray value of the pixel space of the image. The calculated roughness value can visually reflect the roughness of the surface of the concrete pipe piece, and can be restored into a visual three-dimensional image through point cloud data, so that the visualization of the three-dimensional roughness is realized. In addition, the invention strictly controls the operation steps and factor levels in the image acquisition, transmission, processing and information acquisition processes, thereby ensuring that the digital image technology can be normally used in the tunnel environment, acquiring high-quality images and reducing random errors in the measurement process to the maximum extent.
3. The invention requires that the collected measuring sub-surface is randomly covered on the whole surface, and the collecting operation is carried out according to a certain sequence, thereby not only ensuring that the collected sub-surface can cover all rough characteristics on the surface of the concrete pipe piece, but also facilitating the subsequent review and additional shooting and avoiding the occurrence of local blanks.
4. The invention adopts a mode of parallel calculation and characterization on the roughness characterization, increases the operations of measuring local roughness on the spot and fitting the characterization function, and combines other measuring methods with higher precision and more intuitive characterization on the digital image technology. By the steps, the conformity degree of the digital image measuring technology and the measured object can be obviously improved, so that the measuring result is more pertinent and the numerical value is more accurate.
In conclusion, the tunnel lining concrete segment surface roughness measurement and calculation method provided by the invention has the advantages of high speed, simplicity in operation, high cost performance, strong pertinence, high result accuracy and good application prospect and popularization value.
Drawings
Fig. 1 shows a flow chart of a method for measuring the surface roughness of a tunnel lining concrete segment according to the present invention.
Fig. 2 shows a graph of gray values of images photographed by different photographing devices and the number of pixels.
Fig. 3 shows a graph of gray scale values of the photographed image and the number of pixels under different illumination levels.
Figure 4 shows image contrast plots of three roughness planes taken with artificial light at different angles of incidence. FIG. 4(a) shows a contrast image taken at 0 for three roughness planes; FIG. 4(b) shows a contrast image taken at 90 for three roughness planes; figure 4(c) shows an image contrast plot taken at 180 deg. for the three roughness planes.
Fig. 5 shows a graph of the shooting distance versus different types of gray values.
Fig. 6 shows a plot of sample spacing versus computation time.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific preferred embodiments.
In the description of the present invention, it is to be understood that the terms "left side", "right side", "upper part", "lower part", etc., indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and that "first", "second", etc., do not represent an important degree of the component parts, and thus are not to be construed as limiting the present invention. The specific dimensions used in the present example are only for illustrating the technical solution and do not limit the scope of protection of the present invention.
As shown in fig. 1, a method for measuring the surface roughness of a tunnel lining concrete segment comprises the following steps.
The measuring sub-surface comprises a calculating sub-surface and a characteristic sub-surface, and the specific calibration method comprises the following steps.
Step 1A, determining the size and the number of the calculation sub-surfaces
The roughness and the surface area of the concrete pipe pieces of different tunnel projects have great difference, so that the number of the specific calibrated measuring sub-surfaces is not uniformly determined, and reasonable selection is required according to the requirements on whether the roughness of the surface is uniform and the required precision.
The calculation facet is the facet that is waiting to measure tunnel lining concrete section of jurisdiction surface evenly distributed, also can evenly cover the whole surface of lining cutting that awaits measuring to reduce the discreteness of data.
In the invention, the size and the number of the calculating sub-surfaces are specifically determined according to the actual surface area S of the segment to be measured, and specifically are as follows:
when S is less than or equal to 10m2And in order to ensure the basic precision, the sum of the areas of all the calculation facets is not less than S/10.
When S > 10m2In the process, the number of the sub-surfaces can be reduced according to actual conditions, surface areas and the fluctuation uniformity degree, so that the time cost is saved, the measurement efficiency is improved, the sum of the areas of all the selected calculation sub-surfaces is not less than S/100, and the number of the calculation sub-surfaces on the surface of the concrete segment lining the same tunnel is not less than 10 in the same collection environment.
Step 1B, calibrating a calculating sub-surface
In order to facilitate that a pixel surface constructed in the later stage corresponds to an actual rough surface, the size and the analysis precision of a quantum surface are measured uniformly, and therefore a calibration object needs to be prepared for calibrating the surface in advance. In the invention, a calibration wire frame with the same size as the calculation sub-surface determined in the step 1A is adopted, from the lower left corner of the surface of the tunnel lining concrete segment to be measured, the calculation sub-surfaces are sequentially marked by scribing from left to right and from bottom to top, and are numbered, so that m calculation sub-surfaces are obtained; the spacing of the facets in the same row or column remains close or the same.
Because the surface of the concrete pipe often has radian, the surface characteristics of the two-dimensional image can not be accurately recorded, the light of the artificial light source is concentrated, the illumination can be gradually reduced towards the periphery along with the center, the specified size needs to be limited, generally, for the smooth or nearly smooth surface of the concrete pipe, the artificial light source with more divergent light preferably adopts a fine wood pure white frame with the size of 100mm multiplied by 100mm, and the curvature of the lining surface is 0.003m-1Above or on the surface of the concrete pipe sheet with the curvature visible to the naked eye, a fine wood pure white frame with the size of 50 multiplied by 50mm is selected when the light is more concentrated.
In this embodiment, the total surface area of the concrete pipe piece to be measured is about 30m2The concrete size is 3m multiplied by 10m, the surface of the pipe piece is secondary mixed concrete with exposed aggregate, and the surfaceThe surface roughness is uniformly distributed and has larger roughness. Therefore a calibration wire frame of 100mm x 100mm was chosen. The calculation sub-surfaces are determined to be 60, the acquisition sequence is that 10 sub-surfaces are uniformly acquired from left to right, 6 layers are acquired from bottom to top, and the numbers are 1-60 in sequence.
Step 1C, calibrating the characteristic sub-surface
In addition, for the convenience of representing the actual roughness of the characteristic parameter values extracted after the digitization of the subsequent images, a plurality of representing sub-surfaces are required to be selected, the actual roughness of the sub-surfaces is measured, the representing sub-surfaces are selected to only cover the areas with obvious roughness difference on the surface of the concrete segment (the difference is judged through visual inspection, if an obvious depressed area exists, the surface roughness difference of the two areas is judged through visual inspection, and the like), then the even selection is carried out, the number of the characteristic sub-surfaces is preferably not less than 1/10 of the calculated sub-surfaces, and under the same acquisition environment, the number of the characteristic sub-surfaces on the surface of the concrete segment lined in the same tunnel is 5-20.
Marking the outlines of the characteristic sub-surfaces in sequence from left to right and from bottom to top by using a marking pen from the lower left corner of the surface of the tunnel lining concrete segment to be measured, and numbering to obtain n characteristic sub-surfaces; wherein n is more than or equal to 5 and less than m, and n is less than or equal to 20. The spacing of the feature facets in the same row or column remains close or the same; the characteristic sub-surface refers to an area where the roughness of the surface of the pipe piece to be measured has visible difference.
In this example, 6 sub-surfaces were characterized, outlined on the wall with white chalk, and numbered 1-6.
And photographing the surface of the tunnel lining concrete segment to be measured after the measurement sub-surface calibration is completed by adopting photographing equipment to obtain an image of the surface of the segment to be measured.
The photographing device is preferably a mobile phone with a photographing function, an IPAD or a digital camera with a photographing function, or the like. The type of the mobile phone is not limited, such as Huashi, millet or apple.
In FIG. 2, (a) is a graph showing the gray-scale values and the number of pixels of images taken by four photographing devices, camera/500(500 refers to the camera sensitivity value), camera/2000, camera/4000 and phone, respectively; (b) the graphs show the gray-scale values of the images taken by the four photographing devices, camera/500, camera/2000, camera/4000 and phone, respectively, versus the number of pixels.
The above-mentioned (a) and (b) in fig. 2 are from two different concrete surfaces, and the purpose is to verify that although there are differences in the peak value, the number of pixels, etc. of the surface image gray level histogram acquired by different shooting devices and intrinsic parameters, the overall trend of the histogram is the same, which indicates that the feature description for the surface roughness is the same.
The histogram has been converted to an image gray value distribution graph. Different photographing equipment, the distribution characteristics of the gray level histogram formed by the photographing equipment are very close, the trends are basically similar, and compared with a camera, the difference of the ISO value of the camera is obvious, but the characteristic of a rough surface can be well shown, so that each set of photographing equipment can collect qualified image data to calculate the roughness; in addition, the randomness of the measurement in the engineering field, namely the constraint of partial conditions, is considered, the image acquisition equipment of the measurement method can be flexibly selected, and only the same measurement work needs to be controlled and the same equipment is adopted. In this embodiment, a canon digital camera having a model number of EOS7D is preferably used.
When taking a picture, both the light environment and the shooting distance have a large influence on the measurement of the final roughness value. Wherein the light environment includes an illuminance of the light and an incident angle of the light. The priority values of the illuminance, the incident angle, and the shooting distance are described below, respectively.
1. Illuminance of light
Fig. 3 shows a graph of gray scale values of the photographed image and the number of pixels under different illumination levels. Wherein, six types of illumination environments are arranged, which respectively represent that the illumination intensity is above 40000lux at noon or afternoon when the sunlight is abundant; in the case of using a camera flash in darkness, the illuminance is above 40000 lux; in the morning or afternoon with sufficient sunlight, the illumination intensity is about 9680 lux; in rainy days with thin light, the illumination intensity is 3240 lux; artificial illumination in the dark, the illumination being 252 lux; indoor environment with sufficient light, and illuminance of 397 lux.
Under two schemes of artificial light and indoor light with the illumination intensity of 250-400 lux, the number distribution of gray value pixel points called by an image is extremely similar to the height distribution rule of coordinate points of an actual rough surface, while under two schemes of ordinary light and weak light with the illumination intensity of 3000-10000 lux, although the general trends are similar, the differences occur to a certain extent, and the histogram distribution of the rest two schemes with the illumination intensity of above 40000lux is obviously not practical.
Therefore, the illumination of the invention is selected to be 250-400 lux, and images obtained under strong light sources such as flash light and the like are avoided. The artificial illumination light source can replace a natural light source to achieve the illumination requirement, and in addition, the illumination also conforms to the tunnel environment. In this embodiment, the illumination is preferably selected to be 270lux, and the error fluctuation is about 20 lux.
2. Illumination angle
Because the rough surface has the tiny hillock shape of concave-convex fluctuation certainly, and under considering the tunnel environment, accomplish all-round even illumination hardly to make the rough surface can have a large amount of local shadows, disturb the image data and the rough surface calculation of rough surface, the controllable condition of the light source angle of reunion artificial light source transmission, can prove that research light incident angle has important meaning. In the embodiment, the artificial light source is preferably an Iphone8 five-grade flashlight of a mobile phone.
Figure 4 shows image contrast plots of three roughness planes taken with artificial light at different angles of incidence. FIG. 4(a) shows a contrast image taken at 0 for three roughness planes; FIG. 4(b) shows a contrast image taken at 90 for three roughness planes; figure 4(c) shows an image contrast plot taken at 180 deg. for the three roughness planes.
In FIG. 4, the first column is a roughness plane of the order S-1, the second column is a roughness plane of the order S-2, and the third column is a roughness plane of the order S-3. The roughness values are ordered as: s-1 is more than S-2 and more than S-3. As can be seen in fig. 4, the roughened surface is most clearly at 90 ° normal incidence.
Therefore, the incident light needs to be vertically irradiated to the rough surface, but in actual operation, the deviation is inevitable due to condition limitation, so that the grinding sand surface with smaller roughness can be controlled to be about 20 degrees deviated from the vertical direction; that is, the included angle between the artificial light source and the surface of the tunnel lining concrete segment to be measured is 90 +/-20 degrees.
However, for an occlusal surface with large roughness and obvious surface concave-convex fluctuation (for example, the roughness of the surface of a tunnel lining concrete segment to be measured exceeds 2.5mm), the angle needs to be controlled below 10 degrees; the included angle between the artificial light source and the surface of the tunnel lining concrete segment to be measured is 90 +/-10 degrees. The image obtained in this way can be closer to the image characteristics and data obtained by vertical irradiation, thereby meeting the precision requirement.
In this embodiment, the light source preferably generates light perpendicular to the measurement plane.
3. Shooting distance
The shooting distance is a key point in photography, and the imaging result is directly influenced by the change of the lens and the example distance or the change of the focal length. In actual shooting, the distance cannot be precisely controlled, and fluctuation of several centimeters always occurs.
Fig. 5 shows a graph of the shooting distance versus the respective image characteristic parameters and the calculation time. Wherein the image parameter comprises a gray level average value fmeanGray scale root mean square value fzStandard deviation of gray scale value StdMean value of gray level difference fpAnd a parameter calculation Time.
Experiments show that when the lens is 40 cm-50 cm away from the rough surface, the distribution amplitude of the result parameters and the histogram of the image is small. Therefore, the imaging distance is set within a range of 30cm to 50cm, and in the present embodiment, the imaging distance is preferably 40 cm. Under the shooting distance within the range, common camera lenses can meet requirements, and meanwhile, the collected sub-surface images can be guaranteed to have higher pixel levels. The light source should be controlled to be in front of and behind the camera lens by a distance of about 5cm, and in this embodiment, the light source is preferably located at about 3cm above the camera lens. By means of the distance control, the camera can be guaranteed not to be influenced by light source equipment when shooting, and meanwhile light rays emitted by the light source can be approximately perpendicular to the surface of the concrete pipe.
Therefore, the quality of the obtained image cannot deviate to a large extent due to the change of the shooting distance in the shooting process, and clear shooting can be directly realized by the camera lens circulating on the market at the distance. If the distance is reduced, some lenses cannot form images, and if the distance is increased, the images are blurred, and the quality straight line is reduced.
And (3) running an MATLAB program, cutting the tunnel lining concrete segment surface image to be measured acquired in the step (2) one by one according to the calibrated calculation sub-surface and the calibrated characteristic sub-surface, separately storing and operating according to the type of the sub-surface, and simultaneously keeping the number in the step (1).
Then, each cut image is preprocessed to be converted into a gray image meeting the set quality requirement.
Researches find that the sub-surface image obtained by conventional shooting is generally an RGB three-channel image, although the color is bright, if the roughness of the surface of the concrete pipe is directly described by image data, the gray value of a single channel is simpler and clearer than the RGB value of three channels; in addition, in the acquisition and transmission processes, due to some uncontrollable factors, the images have the problems of noise in different degrees, local blurring and the like, if the image data are not processed, the image data are directly extracted, and a larger error is generated as a result, so that the acquired images need to be preprocessed before data extraction.
The image preprocessing of the invention mainly comprises the processing of image graying, image noise reduction and sharpening. The image graying adopts an averaging theory, and the processing process is realized by writing an MATLAB program.
The smoothing noise reduction process is preferably gaussian filtering. The basic principle of gaussian noise reduction is to scan each pixel point of an image and replace the original value after weighted average by the value of each pixel point and the values of other pixel points in the peripheral field. The Gaussian noise can be eliminated mainly through Gaussian filtering, the noise is caused by the defects of circuit elements and an image sensor and is a main component of image noise, and the elimination of the Gaussian noise can greatly reduce the interference of the noise on image information.
The sharpening process is preferably a Sobel operator. The raised and recessed edges of the surface relief of the concrete segment are very important for describing the roughness of the concrete segment, and the raised and recessed edges often become the place where the pixel values are transitioned during imaging. Therefore, in order to avoid blurring such edge features, a sharpening process is required. The Sobel operator detects the edge according to the phenomenon that the weighted difference of the gray values of the upper and lower and left and right adjacent points of the pixel point reaches an extreme value at the edge, and can also play a smoothing role in noise.
The invention adopts a Gauss-Sobel combined algorithm to obviously reduce the input and transmission errors of the image, and the two operators supplement each other, so that the image data can reflect the surface roughness more truly. The surface roughness and the measured environment of the concrete segment are noisy construction environments, image noise is more serious than other working environments, and precision control in engineering application is generally millimeter level, so that the Gauss-Sobel combined algorithm can be a preferred scheme.
Before image information is extracted, in order to avoid the problem that poor images generated by improper operation cause error interference on a final measurement result, 2 image screening processes are required: before preprocessing, deleting, rechecking, retaking and the like the image with obvious defects; and after the second step of preprocessing, checking the image after the preprocessing is finished, and eliminating the image with poor preprocessing effect.
Based on the analysis, the preferable scheme of the main operation steps of the tunnel lining concrete segment surface image pretreatment and screening is as follows:
and manually reviewing the images generated by the cutting and shearing, deleting the unqualified images, and recording the label.
And running a program to perform image preprocessing on the image.
Examining all processed gray level images generated in a new file, deleting unqualified gray level images, and recording numbers; comprehensively analyzing all problem images on the comprehensive numbers by combining the distribution condition during collection, and if the number of the problem images is large or a local area blank appears in the collection of the surface of the concrete pipe slice after the images are eliminated, carrying out step-by-step forward review and re-operation; if the two phenomena do not appear, the two phenomena can be directly eliminated without repeated operation; and confirming that all the preprocessed images are arranged in a folder, and keeping the initial numbers of the preprocessed images.
It should be noted that the deletion operation of all images is only for the currently checked folder, the previous image information is retained, and in addition, if the steps of operation cutting, preprocessing and the like are repeated after the review, the images are directly covered without numbering.
The cropped image is preprocessed, digitized, and pixel data extracted, during which two image screens are performed. The calculated sub-surface image quality and data of the numbers 18 and 31 do not meet the requirements, the representation sub-surfaces meet the requirements, and the deletion of the images of the numbers 18 and 31 does not reduce the precision of the measurement result, so that the complementary shooting processing is not performed. The compensation processing may be performed as necessary.
In addition, compared with point cloud data obtained by 3D scanning of an image, the point cloud data amount of a digital surface formed after a rough surface of 50 x 50mm is scanned is about 17000 pieces of data, and the anti-interference capability of an image method is poorer than that of a scanning method, so that the amount of the point cloud data is properly increased, the data amount of the rough surface image of 100 x 100mm or 50 x 50mm used for calculation and analysis is not less than 80000/20000 pieces, and otherwise, the rough surface image is regarded as poor-quality data rejection. In this embodiment, the data amount of the data text is 8000 pieces, and the requirement of data analysis is met.
And then, continuously carrying out interpolation analysis on the image data text by using the SUFER15, selecting the sampling interval to be 0.1mm, selecting the minimum curvature method by using the interpolation method, wherein the single analysis time is about 0.5s, the analysis operation time of all the image data is about 300s, and storing the generated data report in a txt format.
When Sufer interpolates data, the sampling interval affects the calculation time and the calculation result, and fig. 6 shows a graph of the sampling interval versus the calculation time. As can be seen from fig. 6, the smaller the sampling pitch, the higher the accuracy, but the more expensive the time cost. Research shows that the cost performance is highest when the sampling interval of the rough surface image of 100mm multiplied by 100mm is selected to be 0.1mm, the accuracy is slightly improved when the sampling interval is further improved, but the time is definitely increased exponentially, and the opposite is true.
The interpolation method of the Sufer15 includes more than ten methods such as a kriging interpolation method, a minimum curvature method, an improved schild method, a natural neighbor interpolation method, a radial function interpolation method and the like, and for nine classic analysis directions, the calculation structures are close to each other, the deviation is extremely small, but the time difference is large and is different from several seconds to dozens of seconds, so that the traditional kriging interpolation method (43s) is abandoned, and the minimum curvature method (0.5s) is selected.
And after the image preprocessing is finished, obtaining n representation sub-surface images and m calculation sub-surface images.
Assuming that the left lower corner point of each representation sub-surface image and each calculation sub-surface image is a coordinate origin, and two side lengths passing through the coordinate origin are an x axis and a y axis respectively.
Step 4, measuring actual roughness K: respectively measuring the roughness of the n characterization sub-surfaces calibrated in the step 1 by adopting roughness measurement equipment, and further obtaining n roughness measurement values K; wherein the roughness measured value of the ith characteristic sub-surface is Ki，1≤i≤n。
The roughness measuring device adopts the high-precision roughness measuring device and technology accepted by the industry at present, and comprises but is not limited to micrometer measurement, 3D laser scanning and the like. In this embodiment, a dial indicator with a model of 0-1mm/0.001mm is preferably selected.
Step 5, calculating image characteristic parameters fpAnd fz
Respectively calculating image characteristic parameters c and f according to each characterization sub-surface image and each calculation sub-surface image obtained by image preprocessingz(ii) a Wherein f ispAnd fzRespectively the mean difference and the root mean square of the grey values in the pixel space.
A plurality of statistical parameter values including a maximum value, a minimum value, an average value, a root mean square, a mean difference value, a standard deviation and the like can be calculated through the surf, the values can reflect the gray value of each pixel point in the image pixel space most visually, and the roughness of the rough surface can be represented through related function calculation.
MATLAB software is adopted to obtain a pixel value f (x, y) corresponding to an image space coordinate (x, y) to obtain an image space curved surface pixel model, namely
F＝f(x,y) (1)
In the formula: x and y are coordinate values of the measuring points on the rough surface respectively, and are mm; f or F (x, y) is the gray value of the (x, y) coordinate point on the computation or feature sub-surface.
The pixel space curved surface pixel model of each point in a given area D on the rough surface is obtained by using the formula (1), and then the gray maximum value f can be calculatedmaxThe volume V enclosed between the planespI.e. by
In the formula: vpA pixel space volume that is region D; d is a selected calculation sub-surface or a characteristic sub-surface; a is the area of the calculation sub-surface or the characteristic sub-surface; f. ofmaxThe maximum value of the dot intensity in the region D.
The average value of the image gray differences is the ratio of the pixel spatial volume to the area A of a given region D, i.e. the ratio of the pixel spatial volume to the area A of the given region D
fp＝VP/A (3)
Mean value of the gray levels fmeanRoot mean square f of gray scale undulationzAnd mean value of gray level differences fpThe relationship of (1) is:
fp＝fmax-fmean (5)
in the formula, Nx、NyNumber of points measured on x-axis and y-axis respectivelyAn amount; Δ x, Δ y represent the spacing of the measurement points on the x-axis and y-axis, respectively; k. l is the order number of the measuring point in the x-axis and y-axis directions, respectively, fk+1,l+1Gray values corresponding to the k +1 and l +1 serial number points; f. ofk,l+1The gray values corresponding to the k, l +1 serial number points; f. ofk+1,lThe gray values corresponding to the k +1 th and l serial number points; f. ofk,lThe gray values corresponding to the k-th and l-th sequence numbers are obtained.
Each measuring point is actually a coordinate point for reading the gray value. The measuring point selection and extraction are automatically completed through the surfer software, and only a distance and interpolation method needs to be input.
The selection of the measurement points will be described in detail below, taking a 100mm × 100mm image as an example.
If the input distance is 1mm, the four sides (i.e., the four coordinate axes) of the image are divided into 100 equal parts, so that ninety-nine lines parallel to the x-axis and the y-axis appear, the intersection points of the lines and the coordinate axes are the measuring points, the intersection points are the serial number points in the interior, and the total number of 10000 points of 100 × 100 is generated. When measuring points are selected, the smaller the distance is, the more the number is, and therefore, the more accurate the calculation result is, but the higher the time cost of the calculation is. In the invention, the image data is processed by sufer software, and an interpolation method of sampling interval and minimum curvature of 0.1mm is adopted, so that the calculation efficiency is highest and the precision is high.
In the invention, the following three points are aimed at various operations and settings before the representation general formula is selected:
1. and establishing a good mapping relation between the image parameters and the geometric parameters of the rough surface.
2. The random error generation in the operation process is reduced, and therefore the possibility is provided for final unified error repair.
3. The measurement cost is further reduced on the premise of meeting the first two targets, and a choice is provided for large-scale measurement application in engineering.
In addition, after the image is preprocessed, the image is a gray-scale image, the distribution condition of the number of the pixel points of each gray-scale value (0-255) can be seen by drawing a gray-scale histogram, the maximum concavity of the actual rough surface is equally divided into 256 parts correspondingly, 256 concavity values are obtained, and the distribution condition of the number of the coordinate points of each concavity is obtained. Therefore, whether the roughness of the actual rough surface can be reflected by the image obtained under the image acquisition environment and operation and whether the image result parameter obtained by numerical calculation can be used for representing the actual roughness can be judged visually and qualitatively by analyzing the gray value histogram of the image, and the representing error is what.
Step 6, selecting a roughness calculation general formula
From previous studies, many formulas are available for studying surface roughness standards, but the formulas are not widely applicable and generally fall into three broad categories. A depth of construction, a curve fractal dimension method and a curved surface fractal dimension method. The three methods are used for directly analyzing and calculating the entity of the rough surface or the scanning surface, have higher precision and practicability, but need to be contrasted and corrected when being used in a digital image.
The corrected formula is directly related to the characteristics of the measuring environment and the rough surface, the obtained image characteristics of different cameras or mobile phones are not completely consistent, and if the formula on other engineering or research papers is directly used, the water and soil are difficult to avoid, and the measuring error is increased. The same formula is adopted for representing the roughness of the surface of the lining in different batches, roughness degrees or image acquisition environments, and the systematic error is inevitably increased, so that the most suitable method is to adopt the existing concepts, and select one from the common general formulas for fitting.
In the invention, the specific selection method of the roughness calculation general formula is as follows:
step 6A, when the n roughness measured values K in the step 4 do not exceed 2.5mm and the n roughness measured values K are close to or the same, the calculated roughness value JRC is related to fpA linear function of (a); f of the characteristic sub-surface image obtained by calculation in step 5 is adoptedpFitting the roughness measured value K of the corresponding characterization sub-surface in the step 4 to obtain a fitting parameter of a linear function; wherein, the n roughness measured values K are close to each other, which means that the difference between any two roughness measured values K does not exceed the valueThe larger of the two is 1/2.
The roughness calculated value JRC is related to fpThe expression of the linear function of (a) is:
JRC＝afp+b (7)
wherein a and b are fitting parameters of a linear function, and a correlation coefficient R is obtained during fitting2It should be greater than 0.9 and the average relative error MRE less than 5%.
Step 6B, when the n roughness measured values K in the step 4 are other than the ones in the step 6A, the roughness calculated value JRC is related to fpAnd fzA binary multiple linear function of; f of the characteristic sub-surface image obtained by calculation in step 5 is adoptedpAnd fzAnd fitting the roughness measured value K of the corresponding characterization sub-surface in the step 4 to obtain a fitting parameter of the binary multiple linear function.
The roughness calculated value JRC is related to fpAnd fzThe expression of the binary multiple linear function of (a) is:
wherein:
F(fp)＝afp+b (8)
in the formula: f (F)p) To form a roughness; g (f)z) Is the undulation roughness; a. b, a ', b' and c are fitting parameters of a binary multiple linear function; at fitting, the correlation coefficient R2It should be greater than 0.9 and the average relative error MRE less than 5%.
When the roughness calculation value is calculated using a binary multiple linear function, i.e., equation (10), wherein the undulation roughness G (f)z) During calculation, three general formulas in the formula (9) need to be calculated sequentially, and then the fitting result of the selected general formula is the best, namelyThe formula with the minimum MRE is used as the value of the roughness of the relief required by equation (10).
In this embodiment, the linear function in step 6A is preferably selected, the image characterization parameters are selected as the pixel gray level difference mean, the pixel gray level difference mean and the actual roughness of the characterization sub-surface image are extracted to perform fitting of the linear function, and R is the linear function2Is 0.98, and meets the requirement of fitting precision.
And 7, calculating a calculated value of the roughness of the measured quantum surface: calculating a general formula according to the roughness selected in the step 6, and using f obtained by calculation in the step 5pAnd fzCalculating roughness calculation values JRC respectively for each calculation sub-surface and each characteristic sub-surface; wherein the roughness calculation value of the ith characteristic sub-surface is JRCi(ii) a The roughness calculation value of the jth calculation sub-surface is JRCj，1≤j≤m。
Step 8, determining an error correction coefficient: and carrying out error analysis on the roughness calculated value JRC and the roughness measured value K of the characterization sub-surface to obtain an error correction coefficient xi, wherein the specific calculation formula is as follows:
in the present embodiment, the error correction coefficient is determined to be 0.9 using the above equation (11).
Step 9, calculating the surface roughness of the lining concrete segment
The specific calculation formula is as follows:
although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the embodiments, and various equivalent modifications can be made within the technical spirit of the present invention, and the scope of the present invention is also within the scope of the present invention.
Claims (9)
1. A method for measuring the surface roughness of a tunnel lining concrete segment is characterized by comprising the following steps: the method comprises the following steps:
step 1, calibrating a measuring sub-surface: the measuring sub-surface comprises a calculating sub-surface and a characteristic sub-surface; the specific calibration method comprises the following steps:
step 1A, determining the size and the number of the calculation sub-surfaces: determining the size and the number of the calculating sub-surfaces according to the whole surface area of the tunnel lining concrete segment to be measured; calculating the sub-surfaces which are uniformly distributed on the surface of the tunnel lining concrete segment to be measured;
step 1B, calibrating a calculation sub-surface: marking the calculation sub-surfaces sequentially from left to right and from bottom to top from the lower left corner of the surface of the tunnel lining concrete segment to be measured by adopting a marking wire frame with the same size as the calculation sub-surfaces determined in the step 1A, and numbering to obtain m calculation sub-surfaces; the spacing of the facets in the same row or column remains close or the same;
step 1C, calibrating a characteristic sub-surface: marking the outlines of the characteristic sub-surfaces in sequence from left to right and from bottom to top by using a marking pen from the lower left corner of the surface of the tunnel lining concrete segment to be measured, and numbering to obtain n characteristic sub-surfaces; wherein n is more than or equal to 5 and less than m; the spacing of the feature facets in the same row or column remains close or the same; wherein the characteristic sub-surface is an area where the roughness of the surface of the segment to be measured has visible difference;
step 2, collecting images: photographing the surface of the tunnel lining concrete segment to be measured after the measurement sub-surface calibration is completed by using photographing equipment to obtain an image of the surface of the segment to be measured; when photographing, controlling the illumination of the surface of the tunnel lining concrete segment to be measured to be 250-400 lux; the same photographing equipment is adopted on the surface of the same tunnel lining concrete segment; the distance between the lens of the photographing device and the surface of the tunnel lining concrete segment is controlled to be 30-50 cm;
step 3, cutting and preprocessing the image: cutting the surface images of the tunnel lining concrete segments to be measured acquired in the step 2 one by one according to the calibrated calculation sub-surface and the calibrated characteristic sub-surface, storing in a classified manner, and keeping the serial numbers in the step 1; then, preprocessing each cut image to convert the image into a gray image meeting the set quality requirement; after the image preprocessing is finished, n representation sub-surface images and m calculation sub-surface images are obtained;
step 4, measuring actual roughness K: respectively measuring the roughness of the n characterization sub-surfaces calibrated in the step 1 by adopting roughness measurement equipment, and further obtaining n roughness measurement values K; wherein the roughness measured value of the ith characteristic sub-surface is Ki，1≤i≤n；
Step 5, calculating image characteristic parameters fpAnd fz: respectively calculating an image characteristic parameter f aiming at each characterization sub-surface image and each calculation sub-surface image obtained by image preprocessingpAnd fz(ii) a Wherein f ispAnd fzRespectively the difference mean value and the root mean square of the gray value of the pixel space;
step 6, selecting a roughness calculation general formula, wherein the specific selection method comprises the following steps:
step 6A, when the n roughness measured values K in the step 4 do not exceed 2.5mm and the n roughness measured values K are close to or the same, the calculated roughness value JRC is related to fpA linear function of (a); f of the characteristic sub-surface image obtained by calculation in step 5 is adoptedpFitting the roughness measured value K of the corresponding characterization sub-surface in the step 4 to obtain a fitting parameter of a linear function;
step 6B, when the n roughness measured values K in the step 4 are other than the ones in the step 6A, the roughness calculated value JRC is related to fpAnd fzA binary multiple linear function of; f of the characteristic sub-surface image obtained by calculation in step 5 is adoptedpAnd fzFitting the roughness measurement value K corresponding to the characterization sub-surface in the step 4 to obtain a fitting parameter of a binary multiple linear function;
and 7, calculating a calculated value of the roughness of the measured quantum surface: calculating a general formula according to the roughness selected in the step 6, and using f obtained by calculation in the step 5pAnd fzTo, forCalculating roughness calculated values JRC of each calculation sub-surface and each characteristic sub-surface respectively; wherein the roughness calculation value of the ith characteristic sub-surface is JRCi(ii) a The roughness calculation value of the jth calculation sub-surface is JRCj，1≤j≤m；
Step 8, determining an error correction coefficient: and carrying out error analysis on the roughness calculated value JRC and the roughness measured value K of the characterization sub-surface to obtain an error correction coefficient xi, wherein the specific calculation formula is as follows:
step 9, calculating the surface roughness of the lining concrete segment
The specific calculation formula is as follows:
2. the method for measuring the surface roughness of the tunnel lining concrete segment according to claim 1, characterized in that: in step 1A, the size and number of the calculating sub-surfaces are specifically determined according to the actual surface area S of the segment to be measured, specifically:
when S is less than or equal to 10m2Then, the sum of the areas of all the selected calculation facets is not less than S/10;
when S > 10m2In the process, the sum of the areas of all the selected calculation facets is not less than S/100, and the number of the calculation facets on the surface of the lining concrete segment of the same tunnel is not less than 10 in the same collection environment;
in the step 1C, the number of the characteristic sub-surfaces is not less than 1/10 of the calculation sub-surfaces, and under the same collection environment, the number of the characteristic sub-surfaces on the surface of the same tunnel lining concrete segment is 5-20.
3. The method for measuring the surface roughness of the tunnel lining concrete segment according to claim 1, characterized in that: in step 2, the photographing device is a mobile phone with a photographing function, an IPAD with a photographing function, or a digital camera.
4. The method for measuring the surface roughness of the tunnel lining concrete segment according to claim 1, characterized in that: in step 2, when photographing, when the artificial light source is adopted to perform light supplement on the surface of the tunnel lining concrete segment to be measured, the included angle between the artificial light source and the surface of the tunnel lining concrete segment to be measured is 90 +/-20 degrees.
5. The method for measuring the surface roughness of the tunnel lining concrete segment according to claim 4, wherein the method comprises the following steps: when the roughness of the surface of the tunnel lining concrete segment to be measured exceeds 2.5mm, the included angle between the artificial light source and the surface of the tunnel lining concrete segment to be measured is 90 +/-10 degrees.
6. The method for measuring the surface roughness of the tunnel lining concrete segment according to claim 1, characterized in that: assuming that the lower left corner point of each representation sub-surface image and each calculation sub-surface image in the step 3 is a coordinate origin, and two side lengths passing through the coordinate origin are an x axis and a y axis respectively; in step 5, fpAnd fzThe calculation formulas of (A) and (B) are respectively as follows:
fp＝VP/A (3)
in the formula, VpA pixel space volume that is region D; d is a selected calculation sub-surface or a characteristic sub-surface; a is a meterThe area of a sub-facet or a feature sub-facet; f. ofmaxThe maximum value of the point gray scale in the region D; f (x, y) is the gray value of the (x, y) coordinate point on the calculation sub-surface or the characteristic sub-surface; n is a radical ofx、NyThe number of the measuring points on the x axis and the y axis respectively; Δ x, Δ y represent the spacing of the measurement points on the x-axis and y-axis, respectively; k. l is the order number of the measuring point in the x-axis and y-axis directions, respectively, fk+1,l+1Gray values corresponding to the k +1 and l +1 serial number points; f. ofk,l+1The gray values corresponding to the k, l +1 serial number points; f. ofk+1,lThe gray values corresponding to the k +1 th and l serial number points; f. ofk,lThe gray values corresponding to the k-th and l-th sequence numbers are obtained.
7. The method for measuring the surface roughness of the tunnel lining concrete segment according to claim 1, characterized in that: in step 6A, the roughness calculation JRC is related to fpThe expression of the linear function of (a) is:
JRC＝afp+b (7)
in the formula, a and b are fitting parameters of a linear function.
8. The method for measuring the surface roughness of the tunnel lining concrete segment according to claim 1, characterized in that: in step 6B, the roughness calculation JRC is related to fpAnd fzThe expression of the binary multiple linear function of (a) is:
wherein:
F(fp)＝afp+b (8)
in the formula: a. b, a ', b' and c are fitting parameters of a binary multiple linear function; f (F)p) To form roughness；G(fz) Is the undulation roughness.
9. The method for measuring the surface roughness of the tunnel lining concrete segment according to claim 1, characterized in that: in step 6, when the fitting parameters of the linear function and the fitting parameters of the binary multiple linear function are fitted, the correlation coefficient R2It should be greater than 0.9 and the average relative error MRE less than 5%.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115930847A (en) * | 2022-09-30 | 2023-04-07 | 中国科学院武汉岩土力学研究所 | Quantitative determination method for roughness evaluation index of three-dimensional structure surface |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140055792A1 (en) * | 2012-08-27 | 2014-02-27 | Canon Kabushiki Kaisha | Estimating material properties using speckle statistics |
| CN107990850A (en) * | 2017-10-16 | 2018-05-04 | 南京航空航天大学 | A kind of surface roughness on-line measurement system and method based on laser scattering method |
| DE102018008788A1 (en) * | 2018-11-08 | 2019-05-02 | Daimler Ag | Method for determining a road surface roughness |
| CN112001907A (en) * | 2020-08-24 | 2020-11-27 | 大连海事大学 | Method for measuring roughness of surrounding rock in tunnel construction site |
| CN113340241A (en) * | 2021-06-09 | 2021-09-03 | 河南德朗智能科技有限公司 | Binocular vision concrete joint surface roughness measurement method and system |
| CN113421245A (en) * | 2021-06-27 | 2021-09-21 | 王程 | Three-dimensional surface roughness calculation method for color reproduction |
-
2021
- 2021-12-20 CN CN202111564471.1A patent/CN114396895B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140055792A1 (en) * | 2012-08-27 | 2014-02-27 | Canon Kabushiki Kaisha | Estimating material properties using speckle statistics |
| CN107990850A (en) * | 2017-10-16 | 2018-05-04 | 南京航空航天大学 | A kind of surface roughness on-line measurement system and method based on laser scattering method |
| DE102018008788A1 (en) * | 2018-11-08 | 2019-05-02 | Daimler Ag | Method for determining a road surface roughness |
| CN112001907A (en) * | 2020-08-24 | 2020-11-27 | 大连海事大学 | Method for measuring roughness of surrounding rock in tunnel construction site |
| CN113340241A (en) * | 2021-06-09 | 2021-09-03 | 河南德朗智能科技有限公司 | Binocular vision concrete joint surface roughness measurement method and system |
| CN113421245A (en) * | 2021-06-27 | 2021-09-21 | 王程 | Three-dimensional surface roughness calculation method for color reproduction |
Non-Patent Citations (1)
| Title |
|---|
| 周玉海 等: "石墨加工表面粗糙度图像评定方法", 《工具技术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115930847A (en) * | 2022-09-30 | 2023-04-07 | 中国科学院武汉岩土力学研究所 | Quantitative determination method for roughness evaluation index of three-dimensional structure surface |
| CN115930847B (en) * | 2022-09-30 | 2023-09-22 | 中国科学院武汉岩土力学研究所 | Quantitative determination method for roughness evaluation index of three-dimensional structural surface |
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