CN110210359B - Speed measurement optimization method in spatial filtering speed measurement technology - Google Patents
Speed measurement optimization method in spatial filtering speed measurement technology Download PDFInfo
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Abstract
The invention relates to a speed measurement optimization method in a spatial filtering speed measurement technology, which is used for enhancing contrast ratio and facilitating extraction of characteristic points through image preprocessing, thereby having good improvement on measuring low-speed moving objects in a complex environment; eliminating the influence caused by partial background noise through differential operation; the proper grid spacing is obtained according to the size of the characteristic points, so that the power spectrum peak value can be conveniently extracted; by utilizing the Welch method, the power spectrum estimation with good smoothness and good precision can be obtained, and the peak value extraction is facilitated. Based on the existing spatial filtering speed measurement algorithm, more accurate speed is obtained through preprocessing of the sampled pictures and optimization of a spectrum estimation algorithm.
Description
Technical Field
The invention relates to a speed measuring technology, in particular to a speed measuring optimization method in a spatial filtering speed measuring technology.
Background
The spatial filtering velocimetry (Spatial Filtering Velocimetry, SFV) technology is receiving increasing attention because of its simple and stable optical and mechanical structure and optional light source. When the spatial filtering speed measurement is carried out, typical spatial filtering devices adopted include rectangular spatial filters, circular spatial filters, gaussian weighted spatial filters and the like, and the rectangular filters are easier to realize in practical application, so that the rectangular filters are most commonly used.
The rectangular spatial filter can be simulated through the area array CMOS camera, and the shot picture is analyzed and processed, so that a speed result can be obtained. Such as: non-contact measuring device for mud-rock flow surface velocity field distribution and measuring method thereof (application publication number: CN 108344879A) and the like, which directly obtains the power spectrum estimation by the Fourier transform of the autocorrelation, but the picture information obtained in the practical application always contains a large amount of noise, the randomness is strong, the power spectrum estimation error obtained by the method is larger, and more accurate velocity cannot be measured in a complex environment.
Disclosure of Invention
The invention provides a speed measurement optimization method in a spatial filtering speed measurement technology aiming at the problem that the speed measurement precision is affected in a complex environment, and obtains more accurate speed by preprocessing a sampling picture and optimizing a spectrum estimation algorithm on the basis of the existing spatial filtering speed measurement algorithm.
The technical scheme of the invention is as follows: a speed measurement optimization method in a spatial filtering speed measurement technology specifically comprises the following steps:
1) Under the illumination of LED white light, setting the acquisition frame rate of an area array CMOS camera to be 1000fps, acquiring an image mode to be gray, continuously photographing a region to be detected at a high speed by using the area array CMOS camera for 1 second, and transmitting a sampled picture to a computer to wait for processing, wherein the acquired picture sample;
2) Blocking the acquired image according to the resolution requirement of the user, selecting a subarea to be detected, carrying out minimum value filtering on the subarea to be detected, and carrying out binarization processing on the image by using a maximum inter-class variance method;
3) The analog space filtering process is realized by using an area array CMOS camera:
f (x) and f (y) are respectively horizontal gray value functions and longitudinal gray value functions in a matrix formed by gray values in the picture acquired by the pixel array of the camera and processed in the step 2); h (x) and h (y) are respectively the transverse and longitudinal transmission functions of the grating;
the spatial filtering theory formula is as follows:
s(x r )＝∫f(x r -x)h(x)dx
s(y r )＝∫f(y r -y)h(y)dy
wherein x is r ＝v x t+c 1 ,y r ＝v y t+c 2 ，v x For the size of the transverse average speed of the subarea to be measured, v y The longitudinal average speed of the subarea to be measured is; c 1 And c 2 The relative constant is determined by the magnification of an optical imaging system, t is exposure time, and r refers to an r frame picture; x is the transverse coordinate of the pixel point of the sub-region to be detected; y is the longitudinal coordinate of the pixel point of the sub-region to be detected; s (x) r ) And s (y) r ) Outputting gray values;
parameter processing for spatial filtering:
firstly, determining the size of a proper grating grid interval, and obtaining the average equivalent projection area diameter d of a feature point in a region to be detected by utilizing a picture with enhanced contrast, wherein the grating grid interval p=2d;
for h (x) and h (y), performing raster simulation by using a pixel array of an area array CMOS camera, namely sampling according to a grid interval p as a sampling interval in the process of acquiring a subarea image;
for h (x), 1, p+1, np+1 columns of the sub-region image pixel matrix of the r frame picture are gray information filtered by a 'grating', and each extracted column is recombined and named as a matrix A r The method comprises the steps of carrying out a first treatment on the surface of the For h (y), 1, p+1, of the sub-region image pixel matrix of the r frame picture, the np+1 row is subjected to raster filtering to obtain gray information, and each extracted row is recombined and named as matrix B r The method comprises the steps of carrying out a first treatment on the surface of the Matrix A here r 、B r I.e. the product of gray value function and transmission function of the frame;
a obtained in the selected frame r The gray values of the odd columns of the matrix are addedEven column gray values are added to obtainThe differentiated signal is +.>All frames are processed according to the method, and an output matrix can be obtained: />r＝1,2,...,1000；
B obtained in the selected frame r The gray values of the odd rows of the matrix are added up to obtainEven line gray values are added to get +.>The differentiated signal is +.>All frames are processed according to the method, and an output matrix can be obtained: />r＝1,2,...,1000；
The two-dimensional matrix is formed by gray value information filtered by the analog grating;
4) Dividing the matrix C with the length of 1000 obtained in the step 3) into K sections, wherein the lengths of each section are N and overlap each other, and windowing each section of signal by utilizing a Hamming window; the signal of the i-th segment can be expressed as:
C i (m)＝C(m+iE),m＝0,...,N-1；i＝0,...,K-1
m is the number of the arrangement in the signal, E is the overlapping length;
the periodogram power spectrum per segment is estimated as
w (m) is a Hamming window function, U is the energy of the Hamming window function, and f is the time domain signal C i A frequency of (m);
then average it to obtain the power spectrum estimation of the whole C, namely
Taking the frequency at peak amplitude from the whole power spectrum estimate as the maximum frequency is noted as f xmax ；
The same treatment is carried out on D, and the maximum frequency is obtained and is recorded as f ymax ；
5) A scale is arranged in a plane coplanar with the object to be measured, and imaging magnification is obtained after comparison and is recorded as M;
6) The velocity components in each direction are found: the following formula is used:
the transverse average speed v of the subarea to be detected can be obtained x Size and average longitudinal velocity v y Size of the material;
7) And (3) speed synthesis:
the velocity vector of the sub-region to be measured can be obtained.
The invention has the beneficial effects that: the speed measurement optimization method in the spatial filtering speed measurement technology of the invention enhances the contrast ratio through image preprocessing, is convenient for extracting the characteristic points, and has good improvement on measuring low-speed moving objects in complex environments; eliminating the influence caused by partial background noise through differential operation; by utilizing a Welch method, the power spectrum estimation with good smoothness and good precision can be obtained, and the peak value extraction is facilitated; and proper grid spacing is obtained according to the size of the characteristic points, so that the power spectrum peak value can be conveniently extracted.
Drawings
FIG. 1 is an original acquired image;
FIG. 2 is a pre-processed image of the present invention;
FIG. 3 is a spectrum diagram obtained by directly utilizing a conventional autocorrelation algorithm to a target region of an original image;
FIG. 4 is a graph of the present invention obtained by using an optimization algorithm on the target area after the pretreatment.
Detailed Description
A speed measurement optimization method in a spatial filtering speed measurement technology comprises the following steps:
under the illumination of LED white light with certain brightness, setting the acquisition frame rate of an area array CMOS camera to be 1000fps, setting an image mode to be gray acquisition, carrying out high-speed continuous photographing on a region to be detected by using the area array CMOS camera for 1 second, and transmitting a sampled picture into a computer to wait for processing, wherein the acquired picture sample;
step two, the acquired image can be segmented according to the requirement of the user resolution, the subareas to be detected are selected, the minimum value filtering is carried out on the subareas to be detected, and then the binarization processing of the image is carried out by utilizing the maximum inter-class variance method;
the minimum value filtering is an image processing means, firstly, the gray values of surrounding pixels and the gray values of central pixels are ordered, then the gray values of the central pixels are compared with the minimum gray values, if the gray values are smaller than the minimum gray values, the minimum gray values are replaced by the gray values of the central pixels, and the influence caused by uneven illumination can be eliminated by using the method;
the maximum inter-class variance method is a method of adaptive threshold determination. The method is characterized in that the image is divided into a background part and a target part according to the gray characteristic of the image. The larger the inter-class variance between the background and the object, the larger the difference between the two parts constituting the image, which results in a smaller difference between the two parts when the partial object is misclassified into the background or the partial background is misclassified into the object. Thus, a segmentation that maximizes the inter-class variance means that the probability of misclassification is minimal. The method can help find a proper threshold value so as to carry out binarization processing, enhance the contrast of the binarized image and facilitate the extraction of feature points;
step three, realizing an analog space filtering process by using an area array CMOS camera:
f (x) and f (y) are respectively horizontal gray value functions and longitudinal gray value functions in a matrix formed by gray values in the picture acquired by the pixel array of the camera and processed in the second step; h (x) and h (y) are respectively the transverse and longitudinal transmission functions of the grating. The spatial filtering theory formula is as follows:
s(x r )＝∫f(x r -x)h(x)dx
s(y r )＝∫f(y r -y)h(y)dy
wherein x is r ＝v x t+c 1 ,y r ＝v y t+c 2 ，v x For the size of the transverse average speed of the subarea to be measured, v y The longitudinal average speed of the subarea to be measured is; c 1 And c 2 The relative constant is determined by the magnification of an optical imaging system, t is exposure time, and r refers to an r frame picture; x is the transverse coordinate of the pixel point of the sub-region to be detected; y is the longitudinal coordinate of the pixel point of the sub-region to be detected; s (x) r ) And s (y) r ) And outputting the gray value.
Parameter processing for spatial filtering:
firstly, determining the size of a proper grating grid interval, and obtaining the average equivalent projection area diameter d of all feature points in a region to be detected by utilizing a picture with enhanced contrast, wherein the grating grid interval p=2d;
for h (x), h (y), the pixel array of the area array CMOS camera is used for raster simulation, i.e. sampling is performed in the sub-area image acquisition with the grid pitch p as the sampling interval.
For h (x), 1, p+1, np+1 columns of the sub-region image pixel matrix of the r frame picture are gray information filtered by a 'grating', and each extracted column is recombined and named as a matrix A r The method comprises the steps of carrying out a first treatment on the surface of the For h (y), 1, p+1, of the sub-region image pixel matrix of the r frame picture, the np+1 row is subjected to raster filtering to obtain gray information, and each extracted row is recombined and named as matrix B r . Matrix A here r 、B r I.e. the product of the gray value function and the transmission function (i.e. rectangular spatial filter) of the frame is visible. Due to the introduction of the additional light source, the signal needs to be subjected to differential processing to eliminate part of background noise.
A obtained in the selected frame r The gray values of the odd columns of the matrix are addedEven column gray values are added to obtainThe differentiated signal is +.>All frames are processed according to the method, and an output matrix can be obtained: />r＝1,2,...,1000。
B obtained in the selected frame r The gray values of the odd rows of the matrix are added up to obtainEven line gray value additionThe differentiated signal is +.>All frames are processed according to the method, and an output matrix can be obtained: />r＝1,2,...,1000。
The pixel matrix can be compressed into a gray value by the differential method, so that the computing efficiency of a computer is greatly improved.
Step four, power spectrum estimation is carried out on the output matrix:
dividing the matrix C with the length of 1000 obtained in the third step into K sections, wherein each section has the length of N and is mutually overlapped, and windowing each section of signal by utilizing a Hamming window. The signal of the i-th segment can be expressed as: c (C) i (m)＝C(m+iE),m＝0,...,N-1；i＝0,...,K-1
m is the number of permutations in the signal and E is the length of overlap. E=n/2 indicates that there is half overlap with each other, e=n indicates no overlap. Thus, the periodogram power spectrum per segment is estimated as
w (m) is a Hamming window function, U is the energy of the Hamming window function, and f is the time domain signal C i Frequency of (m):
then average it to obtain the power spectrum estimation of the whole C, namely
Taking the frequency at peak amplitude from the whole power spectrum estimate as the maximum frequency is noted as f xmax . The same treatment is carried out on D, and the maximum frequency is obtained and is recorded as f ymax 。
Fifthly, calculating imaging magnification by measuring the pixel size occupied by the characteristic object in the picture and comparing the actual size of the characteristic object by utilizing the pixel size parameter of the camera, and recording as M;
step six, solving the velocity components in all directions:
using the formula:
the sub-region to be measured can be obtainedIs the transverse average velocity v of (2) x Size and average longitudinal velocity v y Size of the material;
step seven, speed synthesis:
by means of
The velocity vector of the sub-region to be measured can be obtained.
In order to make the technical means, the creation characteristics, the achievement of the purposes and the effects of the present invention easy to understand, the speed measurement optimization algorithm provided by the present invention is specifically described below by way of examples and with reference to the accompanying drawings.
Firstly, preprocessing is utilized to enhance contrast ratio of an acquired picture, the average equivalent projection area diameter of a feature point in a region to be detected is obtained in the processed picture, and the grid interval p=2d is set.
The acquired 1000 frames of images are 1547×768, the useful area is 1300×600, and the acquired 1000 frames of images are divided by MATLAB to obtain sub-areas with the size of 135×50.
And aiming at the subareas to be detected, carrying out simulated shading treatment by utilizing MATLAB according to the size of the grid interval p, and carrying out accumulation summation on each row of data of the light transmission part to obtain a matrix of 1 x 135. The even lines and the odd lines are added respectively, and the added even lines and the added odd lines are subtracted to obtain the gray level difference value of the region in the frame image. The remaining frames are treated identically in the same way. The method is used to determine the longitudinal velocity v y V is calculated x In this case, the column operation is performed. The gray value sequence can be obtained in the link:
the speed measurement optimization algorithm using any one of the above, characterized by comprising the steps of:
setting the acquisition frame rate of an area array CMOS camera to be 1000fps, wherein an image mode is gray acquisition, and continuously acquiring images;
step two, collecting images for 1 second, wherein the collected pictures are 1000 frames, and storing the pictures to a computer to wait for processing, wherein the collected picture samples are shown in fig. 1;
thirdly, in a plane coplanar with an object to be detected, a card drawing a 1cm straight line is placed as a scale of a scale, and an area array CMOS camera is utilized to collect a picture for measuring the imaging magnification M;
step four, the sizes of the acquired 1000 frames of images are 1547 x 768, the useful area is 1300 x 600, the 1 st frame of image can be directly preprocessed by MATLAB self-contained functions ordfilt2 () and graythresh (), and the processed image is shown in fig. 2. Then, division is performed into sub-areas of 135 x 50. The divided subarea matrix is shown below;
and fifthly, sampling is carried out according to the grid interval p as a sampling interval in the process of acquiring the sub-region image. The 1 st, p+1, & gt, np+1 columns of the sub-region image pixel matrix are gray information filtered by a 'grating', and each extracted column is recombined and named as a matrix A 1 Will A 1 The gray values of the odd columns of the matrix are addedEven column gray values are added to get +.>The differentiated signal is +.>The other frames are similarly processed according to the method, and an output matrix can be obtained：Sampling by row in the same way, can obtain +.>
Step six, aiming at C, D, using MATLAB software, performing power spectrum estimation on the MATLAB software by using a Welch method, wherein parameters are set as follows: the window is a Hamming window, the length is 100 points, the FFT point number is 1024 points, so that the FFT processing is convenient to obtain a power spectrum estimation diagram, and the peak value in the obtained power spectrum estimation diagram (as shown in fig. 4, the abscissa is frequency f, and the peak value is more obvious and is more convenient to extract than that of the traditional spectrum estimation method (shown in fig. 3));
step seven, obtaining the frequency f at the peak amplitude xmax And f ymax Using the formula
Obtaining velocity components in two directions;
step eight, using vector summation
Obtaining a velocity vector of the subarea;
the flow of the speed measurement optimization algorithm in this embodiment includes the following steps:
step S1, setting parameters of an area array CMOS camera;
s2, image acquisition;
s3, placing a scale into the sample to obtain the magnification;
s4, preprocessing the picture and dividing the picture into subareas;
s5, carrying out differential processing on gray values of the subareas;
s6, carrying out Welch method power spectrum estimation on the output result after the difference;
s7, analyzing the power spectrogram, obtaining the frequency at the peak amplitude, and calculating the speed components in different directions;
step S8, vector summation is carried out on the speeds in two directions, and a speed vector is obtained;
the effects and effects of the examples:
the contrast is enhanced through pretreatment, and the characteristic points are conveniently extracted, so that the method has good improvement on measuring a low-speed moving object in a complex environment; eliminating the influence caused by partial background noise through differential operation; by utilizing a Welch method, the power spectrum estimation with good smoothness and good precision can be obtained, and the peak value extraction is facilitated; and proper grid spacing is obtained according to the size of the characteristic points, so that the power spectrum peak value can be conveniently extracted.
Claims (1)
1. The speed measurement optimizing method in the spatial filtering speed measurement technology is characterized by comprising the following steps of:
1) Under the illumination of LED white light, setting the acquisition frame rate of an area array CMOS camera to be 1000fps, acquiring an image mode to be gray, continuously photographing a region to be detected at a high speed by using the area array CMOS camera for 1 second, and transmitting a sampled picture to a computer to wait for processing, wherein the acquired picture sample;
2) Blocking the acquired image according to the resolution requirement of the user, selecting a subarea to be detected, carrying out minimum value filtering on the subarea to be detected, and carrying out binarization processing on the image by using a maximum inter-class variance method;
3) The analog space filtering process is realized by using an area array CMOS camera:
f (x) and f (y) are respectively horizontal gray value functions and longitudinal gray value functions in a matrix formed by gray values in the picture acquired by the pixel array of the camera and processed in the step 2); h (x) and h (y) are respectively the transverse and longitudinal transmission functions of the grating;
the spatial filtering theory formula is as follows:
s(x r )＝∫f(x r -x)h(x)dx
s(y r )＝∫f(y r -y)h(y)dy
wherein x is r ＝v x t+c 1 ,y r ＝v y t+c 2 ，v x For the size of the transverse average speed of the subarea to be measured, v y The longitudinal average speed of the subarea to be measured is; c 1 And c 2 The relative constant is determined by the magnification of an optical imaging system, t is exposure time, and r refers to an r frame picture; x is the transverse coordinate of the pixel point of the sub-region to be detected; y is the longitudinal coordinate of the pixel point of the sub-region to be detected; s (x) r ) And s (y) r ) Outputting gray values;
parameter processing for spatial filtering:
firstly, determining the size of a proper grating grid interval, and obtaining the average equivalent projection area diameter d of a feature point in a region to be detected by utilizing a picture with enhanced contrast, wherein the grating grid interval p=2d;
for h (x) and h (y), performing raster simulation by using a pixel array of an area array CMOS camera, namely sampling according to a grid interval p as a sampling interval in the process of acquiring a subarea image;
for h (x), 1, p+1, np+1 columns of the sub-region image pixel matrix of the r frame picture are gray information filtered by a 'grating', and each extracted column is recombined and named as a matrix A r The method comprises the steps of carrying out a first treatment on the surface of the For h (y), 1, p+1, of the sub-region image pixel matrix of the r frame picture, the np+1 row is subjected to raster filtering to obtain gray information, and each extracted row is recombined and named as matrix B r The method comprises the steps of carrying out a first treatment on the surface of the Matrix A here r 、B r I.e. the product of gray value function and transmission function of the frame;
the gray signal is subjected to differential processing to eliminate part of background noise, and the method is as follows:
a obtained in the selected frame r The gray values of the odd columns of the matrix are addedEven column gray values are added to obtainThe differentiated signal is +.>All frames are processed according to the method, and an output matrix can be obtained: />B obtained in the selected frame r The gray values of the odd rows of the matrix are added to get +.>Even line gray values are added to get +.>The differentiated signal is +.>All frames are processed according to the method, and an output matrix can be obtained: />
4) Dividing the matrix C with the length of 1000 obtained in the step 3) into K sections, wherein the lengths of each section are N and overlap each other, and windowing each section of signal by utilizing a Hamming window; the signal of the i-th segment can be expressed as:
C i (m)＝C(m+iE),m＝0,...,N-1；i＝0,...,K-1
m is the number of the arrangement in the signal, E is the overlapping length;
the periodogram power spectrum per segment is estimated as
w (m) is a Hamming window function, U is the energy of the Hamming window function, and f is the time domain signal C i A frequency of (m);
then average it to obtain the power spectrum estimation of the whole C, namely
Taking the frequency at peak amplitude from the whole power spectrum estimate as the maximum frequency is noted as f xmax ；
The same treatment is carried out on D, and the maximum frequency is obtained and is recorded as f ymax ；
5) A scale is arranged in a plane coplanar with the object to be measured, and imaging magnification is obtained after comparison and is recorded as M;
6) The velocity components in each direction are found: the following formula is used:
the transverse average speed v of the subarea to be detected can be obtained x Size and average longitudinal velocity v y Size of the material;
7) And (3) speed synthesis:
the velocity vector of the sub-region to be measured can be obtained.
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| CN109714513A (en) * | 2019-02-15 | 2019-05-03 | 江西省智成测控技术研究所有限责任公司 | Inhibit the method for velocity calculated noise in a kind of optics speed and mileage measuring instrument |
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| CN109714513A (en) * | 2019-02-15 | 2019-05-03 | 江西省智成测控技术研究所有限责任公司 | Inhibit the method for velocity calculated noise in a kind of optics speed and mileage measuring instrument |
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