CN103306186A - Algorithm for detecting structure depth of cement concrete pavement - Google Patents

Algorithm for detecting structure depth of cement concrete pavement Download PDF

Info

Publication number
CN103306186A
CN103306186A CN2013102472093A CN201310247209A CN103306186A CN 103306186 A CN103306186 A CN 103306186A CN 2013102472093 A CN2013102472093 A CN 2013102472093A CN 201310247209 A CN201310247209 A CN 201310247209A CN 103306186 A CN103306186 A CN 103306186A
Authority
CN
China
Prior art keywords
centerdot
data
filtering
value
matrix
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN2013102472093A
Other languages
Chinese (zh)
Other versions
CN103306186B (en
Inventor
李伟
孙朝云
郝雪丽
赵海伟
刘玉娥
任娜娜
刘晓鹏
包静
苏超
邹鹏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Changan University
Original Assignee
Changan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Changan University filed Critical Changan University
Priority to CN201310247209.3A priority Critical patent/CN103306186B/en
Publication of CN103306186A publication Critical patent/CN103306186A/en
Application granted granted Critical
Publication of CN103306186B publication Critical patent/CN103306186B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Length Measuring Devices By Optical Means (AREA)

Abstract

The invention discloses an algorithm for detecting the structure depth of a cement concrete pavement. The algorithm specifically comprises the following steps of: inputting image three-dimensional data matrixes, and filtering the data; sequentially taking the filtered three-dimensional data matrixes, obtaining each-line-corresponding structure depth line by line to obtain the structure depths C1, C2, -, Cm line by line, and averaging the m structure depths to obtain the structure depth of the pavement within an image capturing region. The algorithm disclosed by the invention is simple to compute, is short in running time, and can be carried out without labors. After the surface measurement is adopted, the structure depth of the pavement can be detected only by imputing the acquired three-dimensional data matrixes of the cement concrete pavement, so that the detecting algorithm is high in efficiency, and exact in detection.

Description

A kind of detection algorithm of cement concrete pavement construction depth
Technical field
The invention belongs to the highway construction field, refer to especially a kind of detection algorithm of cement concrete pavement construction depth.
Background technology
Along with the increase of the traffic volume, improving constantly of car speed causes traffic accident constantly to occur, so that the antiskid problem on road surface becomes increasingly conspicuous.The generation of traffic accident and the cling property on road surface have very large relation, and between the average texture on road surface and the road surface section construction depth good correlation are arranged.Construction depth is also referred to as texture depth, refers to the mean depth of the rough open pores of road surfaces of certain area.The construction depth of cement concrete surface has reacted the macrostructure of cement concrete surface, is the important indicator of estimating cement concrete pavement antiskid and drainage performance.In general, the cement pavement that surface texture depth is larger can provide higher frictional resistance, in the larger area of amount of precipitation, if road surfaces does not have enough construction depths to come retaining and draining, is easy to form moisture film and accident at road surfaces.Therefore to very important of the detection of cement concrete pavement construction depth and evaluation.
In present stage, the detection method of pavement structural depth and device are a lot, and method commonly used has artificial shop micromicrofarad, digital picture detection method, laser range finder.Wherein, manually spread the low and test structure of micromicrofarad efficient and be subjected to the factor of artificial disturbance a lot, poor reproducibility, being not easy to wet weather measures, for the long highway of mileage, can only select some highway section to carry out sample investigation, reduced the evaluation of test result to the pavement structural depth in whole highway section; The digital picture detection method is subjected to the impact of the intensity of illumination of external environment and lighting angle very large, and image processing algorithm remains further to be improved; The resulting result of laser range finder is discontinuous, and Micro texture feature that can not the true reappearance road surface is so error is larger.Although existing construction depth algorithm calculates simple, running time is short, also is adapted at adopting in the real-time system, and the precision of algorithm is not high enough.Therefore in sum, existing detection technique exists the problems such as error is large, efficient is low, studies that a kind of automaticity is high, easy to operate, efficient is high, detects accurately that high cement concrete pavement construction depth checkout gear extremely is necessary.
Summary of the invention
For the defective that exists in the above-mentioned prior art or deficiency, main purpose of the present invention is, a kind of detection algorithm of cement concrete pavement construction depth is provided, this algorithm has adopted correction function to revise when calculating construction depth, not only can be to the real-time detection of cement concrete pavement construction depth, and algorithm calculates simple, efficient detection, and testing result is accurate.
In order to achieve the above object, the present invention adopts following technical scheme:
A kind of detection algorithm of cement concrete pavement construction depth specifically comprises the steps:
Step 1: computer reads 3 d image data matrix O M * n
Step 2: the 3 d image data matrix is carried out filtering process, comprise two-way standard deviation filtering and morphologic filtering two parts, obtain the 3 d image data matrix after filtering is processed;
Step 3: the construction depth of asking every delegation;
Get the capable data of i of the 3 d image data matrix after filtering is processed, correspondence after the data trisection of this row is deposited among three one-dimension array A1, A2, the A3, data among A1, A2, the A3 are carried out respectively fitting a straight line, the corresponding match value correspondence that obtains is deposited among three one-dimension array B1, B2, the B3, respectively the corresponding data among the data among A1, A2, the A3 and B1, B2, the B3 is done poor three differences that obtain, and sequentially deposit among the one-dimension array C with the absolute value of these three differences and with it, the data among the C are averaged and be designated as C i, C then iIt is the capable construction depth of i;
Step 4: the construction depth of asking the image acquisition region road surface;
Construction depth to all row is averaged, and this average is brought among the correction function y=a*x+b revised, and x is this average, and y is correction value, is the pavement structural depth value.
Further, the 3 d image data matrix O in the described step 1 M * nAs follows:
O m × n = z 11 z 12 z 13 · · · z 1 j · · · z 1 n z 21 z 22 z 23 · · · z 2 j · · · z 2 n · · · · · · · · · · · · · · · z i 1 z i 2 z i 3 · · · z ij · · · z in · · · · · · · · · · · · · · · z m 1 z m 2 z m 3 · · · z mj · · · z mn , ( i = 1,2 · · · m , j = 1,2 · · · n )
z IjThe expression line number is i, and row number are the corresponding picture altitude data of j.
Further, described step 2 specifically comprises the steps:
(1) two-way standard deviation filtering: 1〉process line by line: to data calculation art average and the standard deviation of every delegation of 3 d image data matrix, then each data of this delegation is handled as follows: compare with the absolute value of the difference of former data and the arithmetic mean of instantaneous value threshold value divided by the resulting value of standard deviation and setting, if should be worth greater than threshold value, then former data are replaced with arithmetic mean of instantaneous value, otherwise keep former data constant; Described threshold value gets 3～8; 2〉process by column: on the basis of processing line by line, process by column again, data calculation art average and standard deviation to each row of matrix, then each data of these row is handled as follows: compare with the absolute value of the difference of former data and the arithmetic mean of instantaneous value threshold value divided by the resulting value of standard deviation and setting, if value is greater than threshold value, then former data are replaced with arithmetic mean of instantaneous value, otherwise keep former data constant; Described threshold value gets 3～8;
(2) morphologic filtering: morphologic filtering is carried out on the basis in two-way standard deviation filtering, and the choice structure element carries out opening operation to matrix, and then the choice structure element carries out expansion process to matrix; Obtain the 3 d image data matrix after filtering is processed.
The method that the present invention proposes has the following advantages:
1, need not artificial participation, overcome that the labour intensity that the manual detection method has is large, safety is low, driving is disturbed, inefficiency and detection accuracy lower shortcoming.
2, employing planar survey only needs input to collect the 3 d image data of cement concrete pavement, can finish the detection of road pavement construction depth, and therefore, this detection algorithm efficient is high, detection is accurate, is adapted at adopting in the real-time system.
3, by two-way standard deviation filtering image data matrix being carried out the two-way noise spot of row, column eliminates; Effectively take out the noise spot in the image data matrix by morphologic filtering, thereby can effectively eliminate the impact of noise spot, applied widely.Draw in conjunction with these two kinds of filtering methods by test and can eliminate better noise spot, and the speed of service is very fast.
4, in this algorithm, use correction function that the data that calculate are revised, thereby made calculated value more near actual value, also namely improved the accuracy of algorithm.
5, the maintenance management for cement concrete pavement provides strong information support, highway maintenance and managerial skills have been improved, simultaneously, for further developing the highway checkout equipment, change the present situation of highway engineering in China checkout equipment overwhelming majority dependence on import, saving resource, the research and development technical force of cultivating oneself has laid manpower and technical foundation.
Below in conjunction with the drawings and specific embodiments the present invention is further explained explanation.
Description of drawings
Fig. 1 is the general flow chart of algorithm of the present invention.
Fig. 2 is two-way standard deviation filtering algorithm flow chart.
Fig. 3 is the morphologic filtering algorithm flow chart.
Fig. 4 calculates the capable data construction depth of i algorithm flow chart.
The specific embodiment
Below be the specific embodiment that the inventor provides, need to prove, the embodiment that provides further explains to of the present invention, protection scope of the present invention be not limited to embodiment.
Referring to Fig. 1-Fig. 4, the detection algorithm of the cement concrete pavement construction depth of the present embodiment specifically comprises the steps:
Step 1: input picture three-dimensional data matrix;
The 3 d image data matrix O that image capture device (among the present invention adopt CCD area array cameras) is collected M * nThe input computer, computer reads the 3 d image data matrix, in the present embodiment, m=1000, n=1536.
Step 2: the 3 d image data matrix is carried out filtering process, comprise two-way standard deviation filtering and morphologic filtering two parts:
(1) two-way standard deviation filtering: as shown in Figure 2,1〉get line by line 3 d image data matrix O M * nEvery data line, such as the capable R of i i=(z I1, z I2... z I, 1536), i=(1,2 ... 1000), obtain R iArithmetic mean of instantaneous value
Figure BDA00003377275100031
With standard deviation S iGet successively each the data z in this row IjIf satisfy Then use arithmetic mean of instantaneous value
Figure BDA00003377275100042
Replace this data value zi j, otherwise z IjRemain unchanged, k also is threshold value for the row filter factor, here k=3; 2〉line by line behind the removal of images noise spot, use the same method and process by column, again eliminate noise spot, all row are disposed, obtain 3 d image data matrix Q;
(2) morphologic filtering: as shown in Figure 3, morphologic filtering is carried out on basis in two-way standard deviation filtering, choice structure element S E1=strel (' line', 11,9) matrix Q is carried out opening operation, obtain three-dimensional data matrix Q1, and then choice structure element S E2SE2=strel (' ball', 5,5) matrix Q1 is carried out expansion process, obtain 3 d image data matrix I.Utilize structural element that matrix is carried out opening operation and expansion process is the conventional means of Digital Image Processing, but be not the conventional means in three-dimensional matrice field, this is innovative point of the present invention.Opening operation and expansion process play the effect of removing noise spot.Noise spot be since road surface smoother and reflective or laser beat less than the place, perhaps machinery equipment vibrations etc. cause.Reached the purpose of effective removal noise through step 2.
Step 3: the construction depth of asking every delegation;
As shown in Figure 4, get the capable data of i of the 3 d image data matrix I after filtering is processed, correspondence after the data trisection of this row is deposited among three one-dimension array A1, A2, the A3, data among A1, A2, the A3 are carried out respectively fitting a straight line, the match value correspondence that obtains is deposited among three one-dimension array B1, B2, the B3, respectively the corresponding data among the data among A1, A2, the A3 and B1, B2, the B3 is asked poor three differences that obtain, and the absolute value of these three differences sequentially deposited among the one-dimension array C, the data among the C are averaged and be designated as C i, C then iIt is the capable construction depth of i.
Step 4: the construction depth of asking the image acquisition region road surface;
Construction depth to all row is averaged, and then will revise among this average substitution correction function y=a*x+b, and x is described average, and y is correction value, is the pavement structural depth value corresponding to 3-D view of step 1 input.
Described correction function y=a*x+b obtains: respectively the polylith road surface is carried out the calculating of construction depth value with sand patch method and this algorithm.Construction depth value by the resulting every block road of this algorithm forms one dimension matrix hh from small to large ord, forms from small to large ord one dimension matrix zz by the construction depth value of the resulting every block road of sand patch method; Then take matrix hh as abscissa, matrix zz obtains correction function as ordinate carries out fitting a straight line.This correction function has reflected the construction depth value on the road surface that is calculated by this algorithm and by the rule between the construction depth value on resulting this road surface of sand patch method.
In the present embodiment, choose 23 block roads, the construction depth value of the every block road that is obtained by this algorithm rearranges one dimension matrix hh=[0.648982,0.668880 from small to large ord, ..., 2.192089,2.311696], the construction depth value of the every block road that is obtained by sand patch method forms one dimension matrix zz=[0.97396 from small to large ord, 0.56185, ..., 1.11815,1.78051]; Take matrix hh as abscissa, matrix zz is ordinate, and the polyfit () function in the Calling MATLAB just can obtain correction function and be: y=0.3736*x+0.5774.
Obtain line by line 1000 construction depth C 1, C 2..., C 1000, and to these 1000 construction depths average into: avg=1.1095, in the above-mentioned correction function y=0.3736*x+0.5774 of this average substitution, the construction depth that obtains collection road surface corresponding to this image data matrix is: avg1=0.9919.
Cement concrete pavement sampling to highway section to be assessed gathers 3 d image data, obtain a plurality of 3-D views, for each 3-D view, obtain its corresponding pavement structural depth value according to above-mentioned algorithm, then the pavement structural depth value of a plurality of 3 d image data matrixes of collecting is asked the construction depth that just can obtain the pickup area cement concrete pavement after the arithmetic average.

Claims (3)

1. the detection algorithm of a cement concrete pavement construction depth is characterized in that, specifically comprises the steps:
Step 1: computer reads 3 d image data matrix O M * n
Step 2: the 3 d image data matrix is carried out filtering process, comprise two-way standard deviation filtering and morphologic filtering two parts, obtain the 3 d image data matrix after filtering is processed;
Step 3: the construction depth of asking every delegation;
Get the capable data of i of the 3 d image data matrix after filtering is processed, correspondence after the data trisection of this row is deposited among three one-dimension array A1, A2, the A3, data among A1, A2, the A3 are carried out respectively fitting a straight line, the corresponding match value correspondence that obtains is deposited among three one-dimension array B1, B2, the B3, respectively the corresponding data among the data among A1, A2, the A3 and B1, B2, the B3 is done poor three differences that obtain, and sequentially deposit among the one-dimension array C with the absolute value of these three differences and with it, the data among the C are averaged and be designated as C i, C then iIt is the capable construction depth of i;
Step 4: the construction depth of asking the image acquisition region road surface;
Construction depth to all row is averaged, and this average is brought among the correction function y=a*x+b revised, and x is this average, and y is correction value, is the construction depth on this image acquisition region road surface.
2. the method for claim 1 is characterized in that, the 3 d image data matrix O that described step 1 obtains M * nAs follows:
O m × n = z 11 z 12 z 13 · · · z 1 j · · · z 1 n z 21 z 22 z 23 · · · z 2 j · · · z 2 n · · · · · · · · · · · · · · · z i 1 z i 2 z i 3 · · · z ij · · · z in · · · · · · · · · · · · · · · z m 1 z m 2 z m 3 · · · z mj · · · z mn , ( i = 1,2 · · · m , j = 1,2 · · · n )
z IjThe expression line number is i, and row number are the corresponding picture altitude data of j.
3. the detection algorithm of cement concrete pavement construction depth as claimed in claim 1 is characterized in that, described step 2 specifically comprises the steps:
(1) two-way standard deviation filtering: 1〉process line by line: to data calculation art average and the standard deviation of every delegation of 3 d image data matrix, then each data of this delegation is handled as follows: compare with the absolute value of the difference of former data and the arithmetic mean of instantaneous value threshold value divided by the resulting value of standard deviation and setting, if value is greater than threshold value, then former data are replaced with arithmetic mean of instantaneous value, otherwise keep former data constant; Described threshold value gets 3～8; 2〉process by column: on the basis of processing line by line, process by column again, data calculation art average and standard deviation to each row of matrix, then each data of these row is handled as follows: compare with the absolute value of the difference of former data and the arithmetic mean of instantaneous value threshold value divided by the resulting value of standard deviation and setting, if should be worth greater than threshold value, then former data are replaced with arithmetic mean of instantaneous value, otherwise keep former data constant; Described threshold value gets 3～8;
(2) morphologic filtering: morphologic filtering is carried out on the basis in two-way standard deviation filtering, and the choice structure element carries out opening operation to matrix, and then the choice structure element carries out expansion process to matrix; Obtain the 3 d image data matrix after filtering is processed.
CN201310247209.3A 2013-06-20 2013-06-20 Algorithm for detecting structure depth of cement concrete pavement Expired - Fee Related CN103306186B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201310247209.3A CN103306186B (en) 2013-06-20 2013-06-20 Algorithm for detecting structure depth of cement concrete pavement

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201310247209.3A CN103306186B (en) 2013-06-20 2013-06-20 Algorithm for detecting structure depth of cement concrete pavement

Publications (2)

Publication Number Publication Date
CN103306186A true CN103306186A (en) 2013-09-18
CN103306186B CN103306186B (en) 2015-05-27

Family

ID=49131875

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201310247209.3A Expired - Fee Related CN103306186B (en) 2013-06-20 2013-06-20 Algorithm for detecting structure depth of cement concrete pavement

Country Status (1)

Country Link
CN (1) CN103306186B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104537651A (en) * 2014-12-17 2015-04-22 中交第一公路勘察设计研究院有限公司 Proportion detecting algorithm and system for cracks in road surface image
CN104537218A (en) * 2014-12-17 2015-04-22 长安大学 Road surface staggered platform quantity detection algorithm and system based on three-dimensional data
CN106204497A (en) * 2016-07-20 2016-12-07 长安大学 A kind of pavement crack extraction algorithm based on smooth smoothed curve and matched curve
CN106284035A (en) * 2016-08-09 2017-01-04 中公高科养护科技股份有限公司 The standard module of calibration depth measuring instrument for pavement structure and making and use method thereof
CN108693340A (en) * 2017-04-07 2018-10-23 交通运输部公路科学研究所 A method of detection porous asphalt pavement disperses disease
CN112729148A (en) * 2020-12-18 2021-04-30 深圳市广宁股份有限公司 Road construction depth detection method, system and device for constructing three-dimensional image

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11222808A (en) * 1998-02-05 1999-08-17 Mitsubishi Heavy Ind Ltd Road level difference measurement data analyzing method
CN1563891A (en) * 2004-04-20 2005-01-12 长安大学 System and method for discriminating road gap
CN103114514A (en) * 2013-01-31 2013-05-22 长安大学 Grooved texture depth detection algorithm for cement concrete pavement

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11222808A (en) * 1998-02-05 1999-08-17 Mitsubishi Heavy Ind Ltd Road level difference measurement data analyzing method
CN1563891A (en) * 2004-04-20 2005-01-12 长安大学 System and method for discriminating road gap
CN103114514A (en) * 2013-01-31 2013-05-22 长安大学 Grooved texture depth detection algorithm for cement concrete pavement

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104537651A (en) * 2014-12-17 2015-04-22 中交第一公路勘察设计研究院有限公司 Proportion detecting algorithm and system for cracks in road surface image
CN104537218A (en) * 2014-12-17 2015-04-22 长安大学 Road surface staggered platform quantity detection algorithm and system based on three-dimensional data
CN104537651B (en) * 2014-12-17 2017-05-24 中交第一公路勘察设计研究院有限公司 Proportion detecting method and system for cracks in road surface image
CN104537218B (en) * 2014-12-17 2017-06-16 长安大学 A kind of faulting quantity measuring method and system based on three-dimensional data
CN106204497A (en) * 2016-07-20 2016-12-07 长安大学 A kind of pavement crack extraction algorithm based on smooth smoothed curve and matched curve
CN106204497B (en) * 2016-07-20 2018-12-25 长安大学 A kind of pavement crack extraction algorithm based on smooth smoothed curve and matched curve
CN106284035A (en) * 2016-08-09 2017-01-04 中公高科养护科技股份有限公司 The standard module of calibration depth measuring instrument for pavement structure and making and use method thereof
CN106284035B (en) * 2016-08-09 2019-01-11 中公高科养护科技股份有限公司 Calibrate the standard module and its making and use method of depth measuring instrument for pavement structure
CN108693340A (en) * 2017-04-07 2018-10-23 交通运输部公路科学研究所 A method of detection porous asphalt pavement disperses disease
CN112729148A (en) * 2020-12-18 2021-04-30 深圳市广宁股份有限公司 Road construction depth detection method, system and device for constructing three-dimensional image

Also Published As

Publication number Publication date
CN103306186B (en) 2015-05-27

Similar Documents

Publication Publication Date Title
CN103310459B (en) A kind of detection method of the cement concrete pavement structure depth based on three-dimensional information
CN103306186B (en) Algorithm for detecting structure depth of cement concrete pavement
US11486548B2 (en) System for detecting crack growth of asphalt pavement based on binocular image analysis
CN106781476B (en) Vehicle dynamic position analysis method in traffic accident
CN103114514B (en) Grooved texture depth detection algorithm for cement concrete pavement
CN103778681B (en) A kind of vehicle-mounted highway cruising inspection system and data acquisition and disposal route
KR101394244B1 (en) Multi image acquisition apparatus, and probe car-based system for sensing road surface condition automatically using the same
CN103061237B (en) Detection algorithm of cement concrete pavement slab staggering quantity
CN106296814B (en) Highway maintenance detection and virtual interactive interface method and system
CN103617412A (en) Real-time lane line detection method
CN105261018A (en) Visibility detection method based on optical model and dark primary color priori theory
CN102635056A (en) Measuring method for construction depth of asphalt road surface
CN104964708A (en) Pavement pit detecting method based on vehicular binocular vision
CN203422305U (en) Road dust load measurement system based on vehicle-mounted instruments
CN101777261A (en) Method for measuring vehicle speed based on CMOS digital camera with belt-type shutter
CN115797338A (en) Panoramic road surface multi-performance index calculation method and system based on binocular vision
JP4025161B2 (en) Flow velocity distribution measurement system, flow velocity distribution measurement method, flow velocity distribution measurement program, flow rate measurement system, flow rate measurement method, and flow rate measurement program
Botha et al. Real time rut profile measurement in varying terrain types using digital image correlation
CN106012778A (en) Digital image collection and analysis method for freeway pavement strain measurement
CN114812450A (en) Machine vision-based asphalt pavement construction uniformity detection and evaluation method
CN114971166A (en) Lightweight road pavement service quality inspection system
CN114004950A (en) Intelligent pavement disease identification and management method based on BIM and LiDAR technology
CN111455787B (en) Pavement detection system based on pavement three-dimensional digitization
Laurent et al. 3D laser road profiling for the automated measurement of road surface conditions and geometry
CN112254701B (en) Vehicle-mounted bridge head vehicle jump detection and analysis system and method thereof

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20150527

Termination date: 20160620