CN110306414A - A kind of pavement structural depth detection method - Google Patents
A kind of pavement structural depth detection method Download PDFInfo
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- CN110306414A CN110306414A CN201910633477.6A CN201910633477A CN110306414A CN 110306414 A CN110306414 A CN 110306414A CN 201910633477 A CN201910633477 A CN 201910633477A CN 110306414 A CN110306414 A CN 110306414A
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- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
- E01C23/00—Auxiliary devices or arrangements for constructing, repairing, reconditioning, or taking-up road or like surfaces
- E01C23/01—Devices or auxiliary means for setting-out or checking the configuration of new surfacing, e.g. templates, screed or reference line supports; Applications of apparatus for measuring, indicating, or recording the surface configuration of existing surfacing, e.g. profilographs
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Abstract
The invention discloses a kind of pavement structural depth detection methods, include: step S1: the data that laser sensor samples are filtered pretreatment, step S2: the data after filter preprocessing are subjected to regression fit, step S3: using the residual values of elevational point and fitting a straight line as final height value, step S4: pavement structural depth is calculated.A kind of pavement structural depth detection method disclosed by the invention has the characteristics that Information Meter is high, stability is strong, accuracy is high.
Description
Technical Field
The invention belongs to the technical field of pavement structure depth, and particularly relates to a pavement structure depth detection method.
Background
Based on the pavement structure depth detection method, at present, related works of a plurality of organizations and companies engaged in the research and development of pavement structure depth detection systems exist internationally. The detection system developed in foreign countries is advanced, and most of the detection systems in China are introduced in foreign countries.
At present, the pavement structure depth detection method mainly comprises a sand paving method and a vehicle-mounted laser detection method. Wherein, the method of sanding can directly obtain road surface structure depth value, and required equipment is few, nevertheless because it needs a large amount of manpower and material resources, the operation is wasted time and energy, and maneuverability is not high, receives the human factor influence simultaneously and makes the detection precision lower. The vehicle-mounted laser detection method is a detection method widely used in recent years to replace a sand paving method, can automatically complete detection of the structural depth, has the characteristics of convenience in use and good reliability, is widely applied to expressways and high-grade highways, but has higher price cost because most instruments are imported from abroad, and cannot detect small areas and low-speed limited areas such as country roads, non-motor-driven lanes, newly-built roads, airport runways, parking lots and the like in China due to the limitation of too large speed and regulation. The image processing method and the three-dimensional detection are research hotspots in recent years, the three-dimensional reproduction of the texture characteristics of the surface of the road surface can be realized, the road surface construction depth can be obtained more intuitively and accurately, but the feasibility and the applicability of an actual detection result are still to be verified due to higher algorithm requirements and larger influence of various environmental factors.
In addition, the current detection method in China has no definite definition on different road surface structures, the detection results of different road surface texture characteristics by adopting different methods have inconsistency, and meanwhile, due to the trend influence of the road surface and the noise interference of the measurement sensor, a reasonable data processing algorithm needs to be researched to accurately de-noise the data acquired by the sensor to realize a high-precision measurement result, so that the defects are overcome and improved.
Disclosure of Invention
The invention mainly aims to provide a pavement structure depth detection method which has the characteristics of high information degree, strong stability, high accuracy and the like.
In order to achieve the above object, the present invention provides a method for detecting a depth of a pavement structure, including:
step S1: carrying out filtering pretreatment on data sampled by a laser sensor;
step S2: performing regression fitting on the data after the filtering pretreatment;
step S3: taking the residual error value of the elevation point and the fitting straight line as a final elevation value;
step S4: and calculating the road surface structure depth.
As a further preferable embodiment of the above technical solution, the step S1 is specifically implemented as the following steps:
step S1.1: according to the formulaCalculating the number of sampling points in a detection unit, wherein n represents the number of sampling points in a detection unit, and taking an even number as a B tableThe length of each detection unit is shown, and l represents the interval of sampling points;
step S1.2: according to the formulaCalculating the number of elevation points in a filtering window, wherein M represents the number of elevation points in the filtering window, an odd number is taken, M represents the length of the filtering window, and l represents the interval of sampling points;
step S1.3: and filtering the elevation point according to the following formula:
wherein,and (4) representing the elevation value after the kth filtering process, wherein T represents the number of elevation points, and m represents the number of elevation points in a filtering window.
As a further preferable embodiment of the above technical solution, the step S2 is specifically implemented as the following steps:
step S2.1: the optimal fitting straight line of the elevation points in the detection unit is assumed as follows:
wherein, i represents the ith elevation point, i is 1 … n, and y represents the regression value of the ith elevation point;
step S2.2: linear fitting is carried out on the elevation points by adopting a least square method:
wherein,represents the average of the n elevation points,represents the elevation value after the ith filtering process,representing the mean of the n filtered elevation values, i.e.
As a further preferable technical solution of the above technical solution, the step S3 is specifically implemented as:
by fitting the regression optimal straight line, according to the formulaCalculating the residual value to obtain the final elevation value, wherein,representing the residual error at the ith elevation point.
As a more preferable embodiment of the above technical solution, the step S4 is to calculate an average cross-sectional depth, and is implemented by:
the average profile depth is calculated according to the following formula:
wherein, MPDSRepresenting the average profile depth of a detection cell;
MPD represents the average section depth of the road section to be measured;
representing the first half-length profile peak in one detection cell;
representing the peak of the second half-length profile in one detection cell;
represents the average value of all elevation points in one detection unit;
n represents the number of detection units.
The invention also provides a pavement structure depth detection method, which comprises the following steps:
step T1: carrying out filtering pretreatment on data sampled by a laser sensor;
step T2: performing quadratic curve fitting on the elevation value in each detection unit;
step T3: and calculating the road surface structure depth.
As a further preferable technical solution of the above technical solution, the step T1 is specifically implemented as the following steps:
step T1.1: according to the formulaCalculating the number of sampling points in one detection unit, wherein n represents the number of sampling points in one detection unit, an even number is taken, B represents the length of each detection unit, and l represents the interval of sampling points;
step T1.2: according to the formulaCalculating the number of elevation points in a filtering window, wherein M represents the number of elevation points in the filtering window, an odd number is taken, M represents the length of the filtering window, and l represents the interval of sampling points;
step T1.3: and filtering the elevation point according to the following formula:
wherein,and (4) representing the elevation value after the kth filtering process, wherein T represents the number of elevation points, and m represents the number of elevation points in a filtering window.
As a further preferable technical solution of the above technical solution, the step T2 is specifically implemented as the following steps:
step T2.1: fitting elevation points in one detection unit, assuming that an optimal quadratic curve exists, and setting:
f(i)＝a0+a1i+a2i2；
the mean square error of the fitting function f (i) and the filtered elevation values is:
step T2.2: obtaining the following linear equation set according to the multivariate function extremum theorem:
wherein i represents the ith elevation point in one detection unit, and i is 1 … N; y isiRepresenting the elevation value after the ith filtering processing in one detection unit; a is0,a1,a2The fitted regression coefficients are represented.
As a further preferable embodiment of the above technical solution, the step T3 is to calculate the laser measurement structure depth according to the following formula:
SMTDSa laser measurement build depth value representing a detection cell;
SMTD represents the laser measurement structure depth value of the road section to be measured;
m represents the number of test units in the road segment to be tested.
Drawings
Fig. 1 is a flowchart of a road surface structure depth detection method according to a first embodiment of the present invention.
Fig. 2 is a flowchart of a road surface structure depth detection method according to a second embodiment of the present invention.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art. The basic principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
Referring to fig. 1 of the drawings, fig. 1 is a flowchart of a road surface structure depth detection method according to a first embodiment of the present invention, and fig. 2 is a flowchart of a road surface structure depth detection method according to a second embodiment of the present invention.
In this first and second embodiments of the present invention, those skilled in the art note that regression fitting, elevation points, elevation values, MPD (Mean Profile Depth), SMTD (Sensor Mean texture Depth), and the like, which are related to the present invention, may be regarded as the prior art.
First embodiment (average profile depth).
The invention discloses a pavement structure depth detection method, which comprises the following steps:
step S1: carrying out filtering pretreatment on data sampled by a laser sensor;
step S2: performing regression fitting on the data after the filtering pretreatment;
step S3: taking the residual error value of the elevation point and the fitting straight line as a final elevation value;
step S4: and calculating the road surface structure depth.
It should be noted that the step S1 is specifically implemented as the following steps:
step S1.1: according to the formulaCalculating the number of sampling points in one detection unit, wherein n represents the number of sampling points in one detection unit, an even number is taken, B represents the length of each detection unit, and l represents the interval of sampling points;
step S1.2: according to the formulaCalculating the number of elevation points in a filtering window, wherein M represents the number of elevation points in the filtering window, an odd number is taken, M represents the length of the filtering window, and l represents the interval of sampling points;
step S1.3: and filtering the elevation point according to the following formula:
wherein,and (4) representing the elevation value after the kth filtering process, wherein T represents the number of elevation points, and m represents the number of elevation points in a filtering window.
Further, the step S2 is specifically implemented as the following steps:
step S2.1: the optimal fitting straight line of the elevation points in the detection unit is assumed as follows:
wherein, i represents the ith elevation point, i is 1 … n, and y represents the regression value of the ith elevation point;
step S2.2: linear fitting is carried out on the elevation points by adopting a least square method:
wherein,represents the average of the n elevation points,represents the elevation value after the ith filtering process,representing the mean of the n filtered elevation values, i.e.
Further, the step S3 is implemented as:
by fitting the regression optimal straight line, according to the formulaCalculating the residual value to obtain the final elevation value, wherein,representing the residual error at the ith elevation point.
Preferably, the step S4 is to calculate the average profile depth, and is implemented as:
the average profile depth is calculated according to the following formula:
wherein, MPDSRepresenting the average profile depth of a detection cell;
MPD represents the average section depth of the road section to be measured;
representing the first half-length profile peak in one detection cell;
representing the peak of the second half-length profile in one detection cell;
represents the average value of all elevation points in one detection unit;
n represents the number of detection units.
Second embodiment (preferred embodiment, laser measurement build depth).
The invention also discloses a pavement structure depth detection method, which comprises the following steps:
step T1: carrying out filtering pretreatment on data sampled by a laser sensor;
step T2: performing quadratic curve fitting on the elevation value in each detection unit;
step T3: and calculating the road surface structure depth.
It should be noted that the step T1 is implemented as the following steps:
step T1.1: according to the formulaCalculating the number of sampling points in one detection unit, wherein n represents the number of sampling points in one detection unit, an even number is taken, B represents the length of each detection unit, and l represents the interval of sampling points;
step T1.2:according to the formulaCalculating the number of elevation points in a filtering window, wherein M represents the number of elevation points in the filtering window, an odd number is taken, M represents the length of the filtering window, and l represents the interval of sampling points;
step T1.3: and filtering the elevation point according to the following formula:
wherein,and (4) representing the elevation value after the kth filtering process, wherein T represents the number of elevation points, and m represents the number of elevation points in a filtering window.
Further, the step T2 is specifically implemented as the following steps:
step T2.1: fitting elevation points in one detection unit, assuming that an optimal quadratic curve exists, and setting:
f(i)＝a0+a1i+a2i2；
the mean square error of the fitting function f (i) and the filtered elevation values is:
step T2.2: obtaining the following linear equation set according to the multivariate function extremum theorem:
wherein i represents the ith elevation point in one detection unit, and i is 1 … N; y isiRepresenting the elevation value after the ith filtering processing in one detection unit; a is0,a1,a2The fitted regression coefficients are represented.
Preferably, the step T3 is to calculate the laser measurement construction depth according to the following formula:
SMTDSa laser measurement build depth value representing a detection cell;
SMTD represents the laser measurement structure depth value of the road section to be measured;
m represents the number of test units in the road segment to be tested.
Preferably, the/sample points are spaced 1mm apart.
Preferably, the length B of each detection unit is 100 mm.
Preferably, the sampling method of the laser sensor is implemented by the laser sensor, the object to be measured and the receiver, and the laser sensor, the object to be measured and the receiver are located at three different positions in space to form a geometric triangle.
Preferably, the sampling method can be divided into a direct-injection structure and an oblique-injection structure according to different positions of the laser, wherein the direct injection structure is called when a 90-degree included angle is formed between a beam emitted by the laser and the surface of the object to be detected, and the oblique-injection structure is called when the angle formed between the beam emitted by the laser and the surface of the object to be detected is smaller than 90 degrees. The detection principle of the two modes is the same in nature, and only the expression forms are different, but the two modes also have different advantages and disadvantages, mainly comprising:
from the perspective of processing the light beam: for the oblique projection mode, laser not only generates scattering phenomenon but also generates reflection phenomenon on the surface of an object, so that a receiver can receive reflected light and scattered light; while the direct projection method can only receive scattered light for the receiver. Therefore, the direct beam is more demanding.
From the measurement accuracy: because the mode of direct-injection formula is structurally simpler, and the facula of projecting on the object that awaits measuring is littleer, and luminance can be more concentrated, simultaneously because the particularity of position, the facula of projecting on the object can be in synchronous corresponding state with the object that awaits measuring on moving, thereby the calculation of the distance of being convenient for improves measuring precision like this.
It should be noted that technical features such as regression fitting, elevation points, elevation values, MPD (Mean Profile Depth), SMTD (laser measurement Depth), and the like, which are related to the present patent application, should be regarded as the prior art, and specific structures, operation principles, control modes and spatial arrangement modes that may be related to these technical features may be conventionally selected in the field, and should not be regarded as the invention points of the present patent, and further detailed descriptions are not specifically provided for the present patent.
It will be apparent to those skilled in the art that modifications and equivalents may be made in the embodiments and/or portions thereof without departing from the spirit and scope of the present invention.
Claims (9)
1. A method for detecting a depth of a pavement structure, comprising:
step S1: carrying out filtering pretreatment on data sampled by a laser sensor;
step S2: performing regression fitting on the data after the filtering pretreatment;
step S3: taking the residual error value of the elevation point and the fitting straight line as a final elevation value;
step S4: and calculating the road surface structure depth.
2. The method for detecting the depth of a road surface structure according to claim 1, wherein step S1 is implemented as the following steps:
step S1.1: according to the formulaCalculating the number of sampling points in one detection unit, wherein n represents the number of sampling points in one detection unit, an even number is taken, B represents the length of each detection unit, and l represents the interval of sampling points;
step S1.2: according to the formulaCalculating the number of elevation points in a filtering window, wherein M represents the number of elevation points in the filtering window, an odd number is taken, M represents the length of the filtering window, and l represents the interval of sampling points;
step S1.3: and filtering the elevation point according to the following formula:
wherein,and (4) representing the elevation value after the kth filtering process, wherein T represents the number of elevation points, and m represents the number of elevation points in a filtering window.
3. The method for detecting the depth of a road surface structure according to claim 1, wherein step S2 is implemented as the following steps:
step S2.1: the optimal fitting straight line of the elevation points in the detection unit is assumed as follows:
wherein, i represents the ith elevation point, i is 1 … n, and y represents the regression value of the ith elevation point;
step S2.2: linear fitting is carried out on the elevation points by adopting a least square method:
wherein,represents the average of the n elevation points,represents the elevation value after the ith filtering process,representing the mean of the n filtered elevation values, i.e.
4. The method for detecting the depth of a road surface structure according to claim 1, wherein step S3 is specifically implemented as:
by fitting the regression optimal straight line, according to the formulaCalculating the residual value to obtain the final elevation value, wherein,representing the residual error at the ith elevation point.
5. The method for detecting the depth of a road surface structure according to claim 1, wherein the step S4 is to calculate an average profile depth, and is implemented as follows:
the average profile depth is calculated according to the following formula:
wherein, MPDSRepresenting the average profile depth of a detection cell;
MPD represents the average section depth of the road section to be measured;
representing the first half-length profile peak in one detection cell;
representing the peak of the second half-length profile in one detection cell;
represents the average value of all elevation points in one detection unit;
n represents the number of detection units.
6. A method for detecting a depth of a pavement structure, comprising:
step T1: carrying out filtering pretreatment on data sampled by a laser sensor;
step T2: performing quadratic curve fitting on the elevation value in each detection unit;
step T3: and calculating the road surface structure depth.
7. The method for detecting the depth of a road surface structure according to claim 6, wherein the step T1 is implemented as the following steps:
step T1.1: according to the formulaCalculating the number of sampling points in one detection unit, wherein n represents the number of sampling points in one detection unit, an even number is taken, B represents the length of each detection unit, and l represents the interval of sampling points;
step T1.2: according to the formulaCalculating the number of elevation points in a filtering window, wherein M represents the number of elevation points in the filtering window, an odd number is taken, M represents the length of the filtering window, and l represents the interval of sampling points;
step T1.3: and filtering the elevation point according to the following formula:
wherein,and (4) representing the elevation value after the kth filtering process, wherein T represents the number of elevation points, and m represents the number of elevation points in a filtering window.
8. The method for detecting the depth of a road surface structure according to claim 6, wherein the step T2 is implemented as the following steps:
step T2.1: fitting elevation points in one detection unit, assuming that an optimal quadratic curve exists, and setting:
f(i)＝a0+a1i+a2i2；
the mean square error of the fitting function f (i) and the filtered elevation values is:
step T2.2: obtaining the following linear equation set according to the multivariate function extremum theorem:
wherein i represents the ith elevation point in one detection unit, and i is 1 … N; y isiRepresenting the elevation value after the ith filtering processing in one detection unit; a is0,a1,a2The fitted regression coefficients are represented.
9. The method for detecting the depth of a road surface structure according to claim 6, wherein the step T3 is to calculate the laser measured structure depth according to the following formula:
SMTDSa laser measurement build depth value representing a detection cell;
SMTD represents the laser measurement structure depth value of the road section to be measured;
m represents the number of test units in the road segment to be tested.
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