CN105044785B - A kind of GPR Electromagnetic Survey of Underground Pipelines method based on fuzzy clustering Yu Hough transform - Google Patents
A kind of GPR Electromagnetic Survey of Underground Pipelines method based on fuzzy clustering Yu Hough transform Download PDFInfo
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Abstract
The present invention provides a kind of underground utilities inversion method, belongs to computer digital image process field.Its feature is comprehensively using hyperbola asymptote and the parameter such as fuzzy C-means clustering and cluster Hough transform inverting underground utilities position and radius.The present invention obtains radar data, then obtain a series of coordinates for inverting by target tracking based on offset mode GPR altogether by B-scan.The characteristics of variables contained using a series of this coordinate, extracts radar wave speed.With reference to fuzzy C-means clustering method, the purpose for obtaining accurate radar wave velocity of wave is reached.After radar wave velocity of wave is obtained, then a series of pipeline edge coordinate values are obtained by the way that triangle is similar, using Hough transform loop truss is clustered, the major parameter of pipeline is further finally inversed by exactly.The characteristics of present invention has accurate prediction, strong robustness.
Description
Technical field
The invention belongs to computer digital image process field, the characteristics of for ground penetrating radar image, using hyperbola gradually
Near line, fuzzy C-means clustering and cluster Hough transform inverting underground utilities parameter.
Background technology
GPR emitter launches ultrahigh frequency broadband short pulse electromagnetic wave to underground medium, runs into earth's surface and different medium
Buried target and during interface, partial pulse ripple will reflect back into ground, be received by reception antenna, then carry out data processing, when
Object is within the scope of aerial signal and signal to noise ratio is where appropriate, things concealed can be gone out by ground penetrating radar detection.GPR
Depth measurement and resolution ratio it is relevant with following factor：Antenna frequencies, transmission power, the electromagnetic property of propagation medium, object
Shapes and sizes.Using offset mode GPR altogether, offset mode is most common detection mode to this patent altogether, is being carried out
During data acquisition, radar transmit-receive antenna is moved along survey line with constant spacing and detected, and unbiased is referred to as when dual-mode antenna is at intervals of zero
Put pattern.A radar A sweep record is once obtained when dual-mode antenna is simultaneously mobile, dual-mode antenna is obtained along survey line synchronizing moving
One B-scan radar data record, abscissa records the horizontal displacement of reception antenna, ordinate record radar wave two-way time
Difference, the velocity of wave in the round trip time difference and underground medium can estimate buried target burying depth, in conjunction with abscissa information
Just can determine that target location.
Nineteen fifty-seven, Hugo Steinhaus proposed the thought of K mean cluster first, until, James in 1967
MacQueen just realizes the algorithm.K mean cluster, is rigid for the division of data, and each sample data is strict
Be divided into and belong to a certain class.But things is often unsatisfactory for the condition of " either-or " in practical problem, that will consider have
The fuzzy problem for existing, i.e., some things or feature are not only to only belong to a certain specific class, but " being this or that ", simply belong to
It is different in inhomogeneous degree.Therefore fuzzy mathematics theory is introduced into cluster analysis, uses the fuzzy cluster analysis can be with
Obtain more preferable effect.Fuzzy C-Means Cluster Algorithm, is a kind of clustering algorithm based on fuzzy division, and its thought is so that
Similarity is maximum between being divided into the object of same cluster, and the similarity between different clusters is minimum, and each is determined with degree of membership
Data point belongs to the degree of certain cluster.1973, J.C.Dunn and J.C.Bezdek proposed the algorithm, equal as early stage K
A kind of improvement of value clustering method.
The content of the invention
The purpose of the present invention is the hyperbola asymptote and Fuzzy C-Means Cluster Algorithm obtained by GPR B-scan
Accurate inverting underground utilities parameter.This method has good adaptability, Stability and veracity.
This method follows the steps below radar wave speed prediction：
Step (A1)：Radar data to underground utilities detection imaging have Hyperbolic Feature, the hyperbola only have half and
Open Side Down, and n element is had on hyperbola, and its abscissa is horizontal level xi, i=1,2 ... ..., n, ordinate are signal
Two-way time ti, i=1,2 ... ..., n, to reduce error information influence, remove the hyperbola summit (x0,t0) neighbouring data,
Remaining m data；
Step (A2)：The slope for asking this m data to constitute, is moved closer to asymptotic using the slope of element line on hyperbola
The characteristics of line, using on the hyperbola every a c line slope as asymptote estimation slope kj, j=1,2 ... ..., m-c；
Step (A3)：For a series of estimation slope k of asymptotesj, j=1,2 ... ..., m-c, we can be by public affairs
FormulaA series of estimate of velocities of wave is obtained, whereinIt is the estimate of velocity of wave,It is strong
Positive coefficient；
Step (A4)：M-c wave velocity estimation value is clustered using Fuzzy C-Means Cluster Algorithm, by Rational choice
Initial clustering quantity s and compensation coefficient λ, using the most class center of element as actual velocity of wave result
Inverting pipeline parameter is comprised the following steps that：
Step (B1)：Firstly the need of axial coordinate (xm, ym) in rough calculation pipeline, wherein, xm is for so that tiObtain minimum
X during valueiValue, i.e. x0, or it is several so that tiX when obtaining minimum valueiAverage,Wherein i=1,
2 ... ..., n,(xf,tf) it is apart from summit (x on curve0,t0) farther out a bit；
Step (B2)：Proportional according to the similar then corresponding sides of triangle, we can obtain pipeline edge point coordinates (xei,
yei), for the error for overcoming partial data to be produced when extracting, here will first to hyperbolic coordinate (xi,yi) carry out least square
Fitting, draws fitting result, substitutes into equation, wherein
Step (B3)：Pipeline edge point coordinates (xei,yei) may be on pipeline edge, it is also possible to not on pipeline edge,
Therefore we need to exclude the influence of error information by clustering Hough transform, and then obtain the estimate of pipeline radius
Step (B4)：The estimate of pipeline radiusAs, it is known that we are in conjunction with the ym being calculated before, just can be with
Obtain the new coordinate of axis of pipelineWhereinTo sum up, we just obtain
The parameter of pipeline detection
The hyperbola least square fitting step used in the present invention is as follows：
Step (C1)：By hyperbolic coordinate (xi,ti) obtain linear equation coordinate (Xi,Ti), wherein
Step (C2)：The parameter (A, B) of linear equation is calculated according to the principle of least square, wherein
Step (C3)：The hyp estimation parameter of digital simulationWherein
The cluster Hough transform step used in the present invention is as follows：
Step (D1)：Pipeline edge point coordinates (xei,yei), i=1,2 ... ..., n, as the defeated of Hough transform loop truss
Enter parameter, while the excursion (r of radius is also definedmin,rmax) and each step delta r for changing, mould is also predefined in addition
Paste the number s of the initial clustering of C mean clusters；
Step (D2)：Hough transform loop truss is by pipeline edge point coordinates (xei,yei), i=1,2 ... ..., n is used as circle
The heart, any two marginal point (xe1,ye1), (xe2,ye2) construction circle intersection point (xc1,yc1) and (xc2,yc2) can be by following
Formula is calculated,Meanwhile, Wherein
Round intersection point may have 1,2 or 0；
Step (D3)：Using Fuzzy C-Means Cluster Algorithm to the set (x of round intersection pointck,yck), k=1,2 ... ..., n
(n-1)/2 clustered, due to usual xckError is smaller, therefore main to yckClustered, we are by the most classification of element
Center as pipeline axis y-coordinate estimateRecycleObtain the estimate of caliber
The present invention just has the advantage that：
1st, radar wave speed prediction is accurate, the need for disclosure satisfy that pipeline parameter is calculated.
2nd, there is stronger robustness, be adapted to the treatment of various Coherent Noise in GPR Record.
3rd, memory requirements is relatively low, it is to avoid the situation of low memory.
Brief description of the drawings
Fig. 1 present invention prediction radar wave speed flow charts
Fig. 2 inverting pipeline parameter flow charts of the present invention
Fig. 3 present invention cluster Hough transform flow charts
Fig. 4 brief flowcharts of the present invention
Specific embodiment
The present invention is using offset mode GPR, the radar data obtained using B-scan, with reference to hyperbola asymptote altogether
Radar wave speed is predicted with fuzzy C-means clustering.Pipeline axle center coordinate is primarily determined that using velocity of wave, by the similar corresponding sides of triangle
It is proportional to obtain pipeline edge coordinate, then pipeline axle center is further determined that by clustering Hough transform loop truss, so as to be finally inversed by
Underground utilities parameter.
Prediction radar wave speed flow is as follows：
(1) as shown in figure 1, by extracting the radar data that B-scan is obtained, obtaining a series of coordinate (xi,ti).This is
Row coordinate has Hyperbolic Feature, and apex coordinate (x is found in coordinate0,t0), data near removal apex coordinate.
(2) using remaining data (xi,ti) estimate slope kj, and further obtain a series of radar wave speed v by calculatingj。
Radar wave speed v is calculated using fuzzy C-means clusteringjEstimate
Inverting pipeline parameter flow is as follows：
(1) as shown in Fig. 2 first with a series of coordinate (xi,ti) and radar wave speed estimateRough calculation pipe
Axial coordinate (xm, ym) in line, then further extracts pipeline marginal point coordinate (xei,yei)。
(2) to pipeline edge point coordinates (xei,yei) cluster Hough transform is done, obtain axial coordinate in accurate pipelineSo as to further determine that the estimate of pipeline radiusThe parameter of pipeline inverting is thus obtained
Cluster Hough transform flow is as follows：
(1) by pipeline edge point coordinates (xei,yei) as the center of circle, calculate its intersection point set (xck,yck)。
(2) using Fuzzy C-Means Cluster Algorithm antinode yckCluster, so as to obtain the estimate of pipeline axis y-coordinate
RecycleFurther obtain
Claims (4)
1. a kind of GPR Electromagnetic Survey of Underground Pipelines method based on fuzzy clustering Yu Hough transform, the method is set up in radar
Data are in the theoretical foundation of underground utilities detection imaging, it is characterised in that the hyperbola figure with theory deduction is contrasted, and is adopted
With asymptote slope as the basis of radar wave speed, obtain accurate using parameter and Fuzzy C-Means Cluster Algorithm is corrected
Radar wave speed, during radar wave speed is calculated, contains following steps successively：
Step (A1)：Radar data has Hyperbolic Feature to underground utilities detection imaging, and the hyperbola only has half and opening
Downwards, n element is had on hyperbola, its abscissa is horizontal level xi, i=1,2 ... ..., n, ordinate for signal come and go
Time ti, i=1,2 ... ..., n, to reduce error information influence, remove the hyperbola summit (x0,t0) neighbouring data, it is remaining
M data；
Step (A2)：The slope for asking this m data to constitute, asymptote is moved closer to using the slope of element line on hyperbola
Feature, using on the hyperbola every a c line slope as asymptote estimation slope kj, j=1,2 ... ..., m-c；
Step (A3)：For a series of estimation slope k of asymptotesj, j=1,2 ... ..., m-c, we can be by formulaA series of estimate of velocities of wave is obtained, whereinIt is the estimate of velocity of wave, λ is compensation coefficient；
Step (A4)：M-c wave velocity estimation value is clustered using Fuzzy C-Means Cluster Algorithm, it is initial by Rational choice
Number of clusters s and compensation coefficient λ, using the most class center of element as actual velocity of wave result
2. method as claimed in claim 1, it is characterised in that obtains pipeline edge coordinate according to triangle is similar, recycle poly-
Class Hough transform loop truss further determines that the middle axial coordinate of pipeline, so as to obtain pipeline radius in interior series of parameters, step
It is rapid as follows：
Step (B1)：Firstly the need of axial coordinate (xm, ym) in rough calculation pipeline, wherein, xm is for so that tiX when obtaining minimum valuei
Value, i.e. x0, or it is several so that tiX when obtaining minimum valueiAverage,Wherein i=1,2 ...,
N,(xf,tf) it is apart from summit (x on curve0,t0) farther out a bit；
Step (B2)：Proportional according to the similar then corresponding sides of triangle, we can obtain pipeline edge point coordinates (xei,yei),
For the error for overcoming partial data to be produced when extracting, here will first to hyperbolic coordinate (xi,yi) least square fitting is carried out,
Fitting result is drawn, equation is substituted into, wherein
I=1,2 ..., n；
Step (B3)：Pipeline edge point coordinates (xei,yei) may be on pipeline edge, it is also possible to not on pipeline edge, therefore
We need to exclude the influence of error information by clustering Hough transform, and then obtain the estimate of pipeline radius
Step (B4)：The estimate of pipeline radiusAs, it is known that we just can be managed in conjunction with the ym being calculated before
The new coordinate of axis of lineWhereinTo sum up, we have just obtained the ginseng of pipeline detection
Number
3. the hyperbola for being mentioned in claim 2 methods describedLeast square fitting is calculated, we will lead to
The method for crossing dimensionality reduction, least square fitting is realized to linear function, then draws hyp estimation parameterSpecific steps
It is as follows：
Step (C1)：By hyperbolic coordinate (xi,ti) obtain linear equation coordinate (Xi,Ti), wherein Xi=xi 2, Ti=ti 2；
Step (C2)：The parameter (A, B) of linear equation is calculated according to the principle of least square, wherein
Step (C3)：The hyp estimation parameter of digital simulationWherein
4. the cluster Hough transform for being mentioned in claim 2 methods described, it is characterised in that utilize fuzzy C-means clustering
Intersection point to Hough transform is clustered, and wherein the most classification of element regards the region that space intersection is most concentrated as, in the category
The heart as actual intersection point estimated location, this statistical problem for allowing for parameter space is converted to the clustering problem of space intersection,
Space is exchanged for the time, the memory consumption of calculating is drastically reduce the area, comprised the following steps that：
Step (D1)：Pipeline edge point coordinates (xei,yei), i=1,2 ... ..., n, the input as Hough transform loop truss are joined
Number the, while excursion (r of radius is also definedmin,rmax) and each step delta r for changing, Fuzzy C is also predefined in addition equal
It is worth the number s of the initial clustering of cluster；
Step (D2)：Hough transform loop truss is by pipeline edge point coordinates (xei,yei), i=1,2 ... ..., n appoint as the center of circle
Two marginal point (x of meaninge1,ye1), (xe2,ye2) construction circle intersection point (xc1,yc1) and (xc2,yc2) can be by below equation
Calculate,Meanwhile, Wherein Round intersection point
There may be 1,2 or 0；
Step (D3)：Using Fuzzy C-Means Cluster Algorithm to the set (x of round intersection pointck,yck), k=1,2 ... ..., n (n-1)/
2 are clustered, due to usual xckError is smaller, therefore main to yckClustered, we make the most class center of element
It is the estimate of pipeline axis y-coordinateRecycleObtain the estimate of caliber
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| CN109343022B (en) * | 2016-12-30 | 2022-11-15 | 北京师范大学 | Method for estimating interlayer soil water content |
| CN108415094B (en) * | 2018-01-24 | 2020-09-18 | 武汉智博创享科技股份有限公司 | Method for extracting buried pipeline attribute through ground penetrating radar result fitting comparison |
| CN108646229A (en) * | 2018-06-14 | 2018-10-12 | 北京师范大学 | Underground column reflector inclination angle detection method |
| CN111445515B (en) * | 2020-03-25 | 2021-06-08 | 中南大学 | Underground cylinder target radius estimation method and system based on feature fusion network |
| CN111323774B (en) * | 2020-03-30 | 2022-06-14 | 华南农业大学 | Method for extracting hyperbolic signal from ground penetrating radar map by adopting geometric cylindrical detection model |
| CN111679275A (en) * | 2020-08-06 | 2020-09-18 | 中南大学 | Underground pipeline identification method based on ground penetrating radar |
| CN116818057B (en) * | 2023-08-18 | 2023-11-17 | 江苏省计量科学研究院(江苏省能源计量数据中心) | Flowmeter on-site metering system and method |
| CN117951599B (en) * | 2024-01-16 | 2024-07-23 | 北京市科学技术研究院 | Underground piping diagram generation method and device based on radar image |
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