CN102346013A - Tunnel lining crack width measuring method and device - Google Patents
Tunnel lining crack width measuring method and device Download PDFInfo
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- CN102346013A CN102346013A CN2010102405432A CN201010240543A CN102346013A CN 102346013 A CN102346013 A CN 102346013A CN 2010102405432 A CN2010102405432 A CN 2010102405432A CN 201010240543 A CN201010240543 A CN 201010240543A CN 102346013 A CN102346013 A CN 102346013A
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Abstract
The invention relates to a tunnel lining crack width measuring method, which comprises the follow steps of: (1) picking up crack images through a digital camera and measuring pickup distance through a laser distance measuring instrument; (2) selecting a region required to be measured on the picked images and converting selected region images into gray level images; (3) determining the threshold value of the gray level images selected in the second step, cutting binary image cutting, and obtaining a target region; (4) extracting a white region only comprising crack in the target region; (5) extracting the edge of the crack by a sub pixel method, and obtaining sub pixel level edge images; (6) circulating the pixel width of the crack by a minimum distance method; (7) circulating the actual width of the obtained crack according to the calibration proportion of the predetermined actual pixel size and the image pickup distance; and (8) storing the obtained actual width data into a crack data base. Compared with the prior art, the tunnel lining crack width measuring method has the advantages of low cost, high speed, convenience, high precision and the like.
Description
Technical field
The present invention relates to a kind of Tunnel Lining Cracks measuring technique, especially relate to a kind of measuring method and device of Tunnel Lining Cracks width.
Background technology
The quantification of defectives such as crack is the target that Non-Destructive Testing is pursued always.Progress along with detection technique; Traditional human eye that passes through is visual or use the method for the mensuration fracture width that simple instrument such as reading glass estimate; Because its personnel's subjectivity is bigger, and precision and efficient are lower, with being replaced by new method gradually.The fast development of Along with computer technology and correlation theory constantly perfect, digital image processing techniques receive extensive attention and have obtained great pioneering achievement in many applications.And be deep into already in the field of civil engineering such as fracture width measurement, deformation monitoring, the identification of rock mass rubble, and bringing into play noncontact, convenient relatively, directly perceived and accurate advantage based on the photographic measurement technology of Digital Image Processing.
2004, people's independent developments such as the Huang Zhanhua of University Of Tianjin, Li Meng one cover crack identification and analysis software.In the same year, the Zou Yiqun of BJ University of Aeronautics & Astronautics, Hou Guicang, Yang Feng have proposed a kind of surface crack detection method based on Digital Image Processing.The same year, Zhang Juan, Sha Aimin, Gao Huaigang, grandson towards cloud analysis based on the principle of work of the pavement crack of Digital Image Processing identification with evaluation system.2005, the integrated approach that bridge has proposed to judge concrete cracks was talked by the Liu Qing of Wuhan University of Technology unit.2006, the Yin Lan of Southeast China University, He Xiaoyuan utilized based on the digital image processing techniques on the flash spotting basis concrete surface crack width characteristics are measured and analyzed.
Yet, more than the method studied mainly be the crack enlarged image of gathering to through contact scanning or shooting at close range.And in the tunnel, for impalpable high-order crack pattern picture, hand-held contact gatherer process is loaded down with trivial details.And the image of wide-long shot has a strong impact on the image imaging quality to noise, light sensitive, makes the follow-up image pre-service grain granite that becomes.The area of crack in the wide-long shot image occupies the ratio of ratio in the picture that contact is gathered and wants much little; The background image that remaining large tracts of land is complicated and changeable; Add the defective of some edge detection methods; Make that the edge extracting method in roomy crack is inapplicable in the recent photograph, need to seek a kind of suitable new method.In addition, measure differently with the contact of fixed distance, the randomness that the position appears in the crack causes the randomness of shooting distance, needs to seek a kind of new scaling method and replace traditional spacing to demarcate.
Therefore, a kind of image detecting method and the detection system that in the tunnel, can measure the FRACTURE CHARACTERISTICS value convenient, quantitatively, quickly and accurately of research become one of pressing for of Tunnel Engineering structure Non-Destructive Testing field.
Summary of the invention
The object of the invention is exactly measuring method and the device that a kind of low cost, quick, convenient, high-precision Tunnel Lining Cracks width are provided for the defective that overcomes above-mentioned prior art existence.
The object of the invention can be realized through following technical scheme:
A kind of measuring method of Tunnel Lining Cracks width is characterized in that, may further comprise the steps:
(1) gathers the image in crack through digital camera, and pass through laser range finder and measure the collection distance;
(2) zone of on the image that collects, select to need measuring, and transfer the area image of choosing to gray level image;
(3) confirm the threshold values of the gray level image that step (2) is chosen, and carry out binary image and cut apart, obtain the target area;
(4) in the target area, extract the white portion that only comprises the crack;
(5) utilization sub-pix method is extracted the edge in crack, obtains sub-pix rank outline map;
(6) pixel wide of employing minimum distance method calculating fracture;
(7), calculate the developed width in crack according to the demarcation ratio of predetermined pixel physical size and photograph distance;
(8) the developed width data that obtain are deposited in the database of crack.
Digital camera then carries out the segmentation collection to large fracture in the described step (1).
The concrete steps of described step (3) are following:
(31) obtain the average gray value of entire image according to following formula:
T
ave＝∑Pixels/N
Wherein ∑ Pixels is the gray scale summation of each pixel in the image, and N is a total pixel number; With T
AveBe made as initial threshold T
k, this moment k=1;
(32) according to T
1Image segmentation is become two parts of target and background, with gray scale less than T
1The zone be called the target area, greater than T
1The zone be called the background area, and then obtain two the zone average gray be respectively T
LowAnd T
High
(33) obtain new threshold value according to following formula:
T
k+1＝(T
low+T
high)/2
If T
K+1Be not equal to T
k, then with T
K+1Replace T
k, return step (32), make k=k+1 simultaneously, up to T
K+1=T
k, execution in step (34);
(34) with T
K+1Be used as final threshold value, carry out binary image and cut apart, comprise the approximate pocket in crack area and noise spot, gray-scale value and crack in the target area, picture inversion.
The concrete steps of described step (4) are following:
(40) find out white portion maximum in the target area, remainder is treated to black, extract the zone that comprises the crack;
(41) operation is corroded in the zone that extracts of square unit matrix and the step (40) through a n*n, detects the zone that whether exists with its coupling, if yes, execution in step (42), if not, execution in step
(43)；
(42) average to carrying out gray scale corresponding to all points in the zone that is extracted in the original image: T
j=∑ Pixels
Target/ N
Target, again according to T
jThe image two-value is divided into two parts of target and background; After carrying out picture inversion; Corrode operation once more, judge whether it exists the zone of mating with square unit matrix, and circulate; Till the zone that in image, does not have to mate with square unit matrix, execution in step (43);
(43) the utilization morphological method is carried out open and close computing, cavity filling and is rejected burrs on edges image, seeks out maximum white portion, and all the other zones are treated to black, is only comprised the white portion in crack.
The concrete steps of described step (5) are following:
(51) { M20} carries out convolution algorithm to each pixel of image for M00, M11 through 7*7 Zernike template; To obtain corresponding image Zernike square { Z00, Z11, Z20}; Calculate parameter (φ, h, the l of each pixel through the Zernike square of image; K), to judge whether this pixel is marginal point
The M00 template:
M11 real number template:
M11 imaginary number template:
The M20 template:
(52) according to following formula
φ＝arctan[Im(Z11)/Re(Z11)]
Calculate angle φ, wherein Im (Z11) and Re (Z11) are respectively imaginary part and the real part of Z11;
(53) according to computes Z ' 11:
Z′11＝Re(Z11)cosφ+Im(Z11)sinφ
According to formula l=Z20/Z ' 11, obtain l then;
(54) according to computes step height k:
k＝3Z′11/2(1-l
2)
3/2；
(55) according to computes background gray scale h:
(56) obtained the edge parameters of each pixel after, if the parameter of pixel satisfies k>=k
t∩ l≤l
t, then this pixel is a marginal point, utilizes following formula:
x
s＝x+l·cos(φ)
y
s＝y+l·sin(φ)
Calculate the sub-pixel edge point coordinate and obtain final sub-pix rank outline map, wherein k
t, l
tBe decision threshold.
Described k
tBe 0.3, described l
tBe gradation of image peaked 1/10th.
Described step (7) concrete steps are following:
(71) try to achieve under the fixed lens focal length through test, when shooting distance was L, the corresponding physical size of each pixel was a in the image;
(72) with L as horizontal ordinate, a is an ordinate, makes calibration curve.
A kind of measurement mechanism of Tunnel Lining Cracks width; It is characterized in that; Comprise digital camera, laser range finder, connecting screw rod, metering computer and crack database; Described digital camera is located at the top of laser range finder through connecting screw rod, and is connected with metering computer through data line, and described crack database is connected with metering computer.
Described digital camera adopts sony α 350 single anti-digital cameras, and the camera lens of this digital camera is fixed as 300mm length.
Compared with prior art; The present invention is different from traditional closely hand-held contact fracture width measuring method; But the technology that digital photographing combines with Digital Image Processing applied to first the measurement of Tunnel Lining Cracks width; Utilize ordinary digital camera remote digital photographic means to collect the crack picture, to some harmful effects aspect Flame Image Process of remote captured picture, propose a kind of Tunnel Lining Cracks measuring method and device again based on Digital photographic and image processing techniques; For miscellaneous tunnel slot surveying work provides a kind of low cost, handled means fast, easily, and obtained satisfactory accuracy.
Description of drawings
Fig. 1 is a process flow diagram of the present invention;
Fig. 2 is a hardware configuration synoptic diagram of the present invention;
Fig. 3 is the synoptic diagram of the present invention through minimum distance method calculating fracture pixel wide;
The desirable step edge model synoptic diagram that Fig. 4 extracts for sub-pixel edge of the present invention.
Embodiment
Below in conjunction with accompanying drawing and specific embodiment the present invention is elaborated.
Embodiment
As shown in Figure 1, a kind of measuring method of Tunnel Lining Cracks width may further comprise the steps:
Step 101 is gathered the image in crack through digital camera, and measures the collection distance through laser range finder;
Step 103 is confirmed the threshold values of the gray level image that step 102 is chosen, and carries out binary image and cut apart, and obtains the target area;
Step 104 extracts the white portion that only comprises the crack in the target area;
Step 107 according to the demarcation ratio of predetermined pixel physical size and photograph distance, calculates the developed width in crack;
Step 108 deposits the developed width data that obtain in the database of crack in.
The concrete steps of described step 103 are following:
(31) obtain the average gray value of entire image according to following formula:
T
ave＝∑Pixels/N
Wherein ∑ Pixels is the gray scale summation of each pixel in the image, and N is a total pixel number; With T
AveBe made as initial threshold T
k, this moment k=1;
(32) according to T
1Image segmentation is become two parts of target and background, with gray scale less than T
1The zone be called the target area, greater than T
1The zone be called the background area, and then obtain two the zone average gray be respectively T
LowAnd T
High
(33) obtain new threshold value according to following formula:
T
k+1＝(t
low+T
high)/2
If T
K+1Be not equal to T
k, then with T
K+1Replace T
k, return step (32), make k=k+1 simultaneously, up to T
K+1=T
k, execution in step (34);
(34) with T
K+1Be used as final threshold value, carry out binary image and cut apart, comprise the approximate pocket in crack area and noise spot, gray-scale value and crack in the target area, picture inversion.
The concrete steps of described step 104 are following:
(40) find out white portion maximum in the target area, remainder is treated to black, extract the zone that comprises the crack;
(41) operation is corroded in the zone that extracts of square unit matrix and the step (40) through a n*n, detects the zone that whether exists with its coupling, if yes, execution in step (42), if not, execution in step
(43)；
(42) average to carrying out gray scale corresponding to all points in the zone that is extracted in the original image: T
j=∑ Pixels
Target/ N
Target, again according to T
jThe image two-value is divided into two parts of target and background; After carrying out picture inversion; Corrode operation once more, judge whether it exists the zone of mating with square unit matrix, and circulate; Till the zone that in image, does not have to mate with square unit matrix, execution in step (43);
(43) the utilization morphological method is carried out open and close computing, cavity filling and is rejected burrs on edges image, seeks out maximum white portion, and all the other zones are treated to black, is only comprised the white portion in crack.
The concrete steps of described step 105 are following:
(51) { M20} carries out convolution algorithm to each pixel of image for M00, M11 through 7*7 Zernike template; To obtain corresponding image Zernike square { Z00, Z11, Z20}; Calculate parameter (φ, h, the l of each pixel through the Zernike square of image; K), to judge whether this pixel is marginal point
The M00 template:
M11 real number template:
M11 imaginary number template:
The M20 template:
(52) according to following formula
φ＝arctan[Im(Z11)/Re(Z11)]
Calculate angle φ, wherein Im (Z11) and Re (Z11) are respectively imaginary part and the real part of Z11;
(53) according to computes Z ' 11:
Z′11＝Re(Z11)cosφ+Im(Z11)sinφ
According to formula l=Z20/Z ' 11, obtain l then;
(54) according to computes step height k:
k＝3Z′11/2(1-l
2)
3/2；
(55) according to computes background gray scale h:
(56) obtained the edge parameters of each pixel after, if the parameter of pixel satisfies k>=k
t∩ l≤l
t, then this pixel is a marginal point, utilizes following formula:
x
s＝x+l·cos(φ)
y
s＝y+l·sin(φ)
Calculate the sub-pixel edge point coordinate and obtain final sub-pix rank outline map, wherein k
t, l
tBe decision threshold.
Described k
tBe 0.3, described l
tBe gradation of image peaked 1/10th.
Like Fig. 4, (l k) can represent that more intuitively k is a step height through desirable step edge model to the parameter of pixel for φ, h; H is the background gray scale; L is the vertical range of disc centre to the edge; φ is edge and y axle angulation.
Crack of the present invention pixel width gauge is calculated and is adopted minimum distance method, is specially the last lower limb of distinguishing the crack, chooses the each point of coboundary respectively, adopts minimum distance method to calculate the width in target crack.
As shown in Figure 3; Be synoptic diagram through minimum distance method calculating fracture pixel wide; According to the coordinate of marginal point about the vertical direction, begin from the first point of coboundary earlier, the coordinate of using marginal point utilizes the range formula of the point-to-point transmission in the higher mathematics to calculate with each coordinate points of lower limb respectively; Calculate minimum value put the distance of lower limb as this, can be expressed as:
Wherein, k=0,1,2,3......
The i that formulate is got coboundary puts the minimum value and value that lower limb is had a few.
Calculate every of the coboundary distance value to lower limb successively, it is average or ask maximum that these are calculated distance value, is the mean value and the maximal value of fracture width, is expressed as:
w
max＝max(w
i)。
Described step 107 concrete steps are following:
(71) try to achieve under the fixed lens focal length through test, when shooting distance was L, the corresponding physical size of each pixel was a in the image;
(72) with L as horizontal ordinate, a is an ordinate, makes calibration curve.
The measurement mechanism of Tunnel Lining Cracks width of the present invention; Comprise digital camera 1, laser range finder 2, connecting screw rod 3, metering computer 4 and crack database 5; Described digital camera 1 is located at the top of laser range finder 2 through connecting screw rod 3; And be connected with metering computer 4 through a data line 6, described crack database 5 is connected with metering computer 4.
Described digital camera 1 adopts sony α 350 single anti-digital cameras, and its valid pixel is 1,420 ten thousand, adopts the 70-300mm camera lens, and the camera lens of this digital camera 1 is fixed as 300mm length.
Under the situation of conditions permit, can utilize utility appliance such as illuminating lamp and camera trivets to take, prevent because of light or artificial shake etc. former thereby cause the picture quality of clapping not high, influence the subsequent image processing effect.
Claims (8)
1. the measuring method of a Tunnel Lining Cracks width is characterized in that, may further comprise the steps:
(1) gathers the image in crack through digital camera, and pass through laser range finder and measure the collection distance;
(2) zone of on the image that collects, select to need measuring, and transfer the area image of choosing to gray level image;
(3) confirm the threshold values of the gray level image that step (2) is chosen, and carry out binary image and cut apart, obtain the target area;
(4) in the target area, extract the white portion that only comprises the crack;
(5) utilization sub-pix method is extracted the edge in crack, obtains sub-pix rank outline map;
(6) pixel wide of employing minimum distance method calculating fracture;
(7), calculate the developed width in crack according to the demarcation ratio of predetermined pixel physical size and photograph distance;
(8) the developed width data that obtain are deposited in the database of crack.
2. the measuring method of a kind of Tunnel Lining Cracks width according to claim 1 is characterized in that, the concrete steps of described step (3) are following:
(31) obtain the average gray value of entire image according to following formula:
T
ave＝∑Pixels/N
Wherein ∑ Pixels is the gray scale summation of each pixel in the image, and N is a total pixel number; With T
AveBe made as initial threshold T
k, this moment k=l;
(32) according to T
1Image segmentation is become two parts of target and background, with gray scale less than T
1The zone be called the target area, greater than T
1The zone be called the background area, and then obtain two the zone average gray be respectively T
LowAnd T
High
(33) obtain new threshold value according to following formula:
T
k+1＝(T
low+T
high)/2
If T
K+1Be not equal to T
k, then with T
K+1Replace T
k, return step (32), make k=k+1 simultaneously, up to T
K+1=T
k, execution in step (34);
(34) with T
K+1Be used as final threshold value, carry out binary image and cut apart, comprise the approximate pocket in crack area and noise spot, gray-scale value and crack in the target area, picture inversion.
3. the measuring method of a kind of Tunnel Lining Cracks width according to claim 1 is characterized in that, the concrete steps of described step (4) are following:
(40) find out white portion maximum in the target area, remainder is treated to black, extract the zone that comprises the crack;
(41) operation is corroded in the zone that extracts of square unit matrix and the step (40) through a n*n, detects the zone that whether exists with its coupling, if yes, execution in step (42), if not, execution in step
(43)；
(42) average to carrying out gray scale corresponding to all points in the zone that is extracted in the original image: T
j=∑ Pixels
Target/ N
TargetAccording to Tj the image two-value is divided into two parts of target and background again, carry out picture inversion after, corrode operation once more; Judge whether it exists the zone of mating with square unit matrix; And circulate, till the zone that in image, does not have to mate with square unit matrix, execution in step (43);
(43) the utilization morphological method is carried out open and close computing, cavity filling and is rejected burrs on edges image, seeks out maximum white portion, and all the other zones are treated to black, is only comprised the white portion in crack.
4. the measuring method of a kind of Tunnel Lining Cracks width according to claim 1 is characterized in that, the concrete steps of described step (5) are following:
(51) { M20} carries out convolution algorithm to each pixel of image for M00, M11 through 7*7 Zernike template; To obtain corresponding image Zernike square { Z00, Z11, Z20}; Calculate parameter (φ, h, the l of each pixel through the Zernike square of image; K), to judge whether this pixel is marginal point
The M00 template:
M11 real number template:
M11 imaginary number template:
The M20 template:
(52) according to following formula
φ＝arctan[Im(Z11)/Re(Z11)]
Calculate angle φ, wherein Im (Z11) and Re (Z11) are respectively imaginary part and the real part of Z11;
(53) according to computes Z ' 11:
Z′11＝Re(Z11)cosφ+Im(Z11)sinφ
According to formula l=Z20/Z ' 11, obtain l then;
(54) according to computes step height k:
k＝3Z′11/2(1-l
2)
3/2；
(55) according to computes background gray scale h:
(56) obtained the edge parameters of each pixel after, if the parameter of pixel satisfies k>=k
t∩ l≤l
t, then this pixel is a marginal point, utilizes following formula:
x
s＝x+l·cos(φ)
y
s＝y+l·sin(φ)
Calculate the sub-pixel edge point coordinate and obtain final sub-pix rank outline map, wherein k
t, l
tBe decision threshold.
5. the measuring method of a kind of Tunnel Lining Cracks width according to claim 4 is characterized in that, described k
tBe 0.3, described l
tBe gradation of image peaked 1/10th.
6. the measuring method of a kind of Tunnel Lining Cracks width according to claim 1 is characterized in that, described step (7) concrete steps are following:
(71) try to achieve under the fixed lens focal length through test, when shooting distance was L, the corresponding physical size of each pixel was a in the image;
(72) with L as horizontal ordinate, a is an ordinate, makes calibration curve.
7. the measurement mechanism of a Tunnel Lining Cracks width; It is characterized in that; Comprise digital camera, laser range finder, connecting screw rod, metering computer and crack database; Described digital camera is located at the top of laser range finder through connecting screw rod, and is connected with metering computer through data line, and described crack database is connected with metering computer.
8. the measurement mechanism of a kind of Tunnel Lining Cracks width according to claim 1 is characterized in that, described digital camera adopts sony α 350 single anti-digital cameras, and the camera lens of this digital camera is fixed as 300mm length.
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| CN114353670A (en) * | 2022-01-07 | 2022-04-15 | 深圳市北斗云信息技术有限公司 | Method for measuring cracks through visual multi-parameter intelligent crack meter |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006166212A (en) * | 2004-12-09 | 2006-06-22 | Seiko Epson Corp | Image processing method, system, apparatus and program, and digital camera |
| JP2008139285A (en) * | 2006-11-29 | 2008-06-19 | Korea Research Inst Of Standards & Science | Construct using picture processing technique, and crack width measuring method of product |
| CN101706259A (en) * | 2009-11-25 | 2010-05-12 | 天津大学 | Concrete crack width test method based on wavefront coding technology and hand-held tester |
| CN101957178A (en) * | 2009-07-17 | 2011-01-26 | 上海同岩土木工程科技有限公司 | Method and device for measuring tunnel lining cracks |
-
2010
- 2010-07-29 CN CN2010102405432A patent/CN102346013A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006166212A (en) * | 2004-12-09 | 2006-06-22 | Seiko Epson Corp | Image processing method, system, apparatus and program, and digital camera |
| JP2008139285A (en) * | 2006-11-29 | 2008-06-19 | Korea Research Inst Of Standards & Science | Construct using picture processing technique, and crack width measuring method of product |
| CN101957178A (en) * | 2009-07-17 | 2011-01-26 | 上海同岩土木工程科技有限公司 | Method and device for measuring tunnel lining cracks |
| CN101706259A (en) * | 2009-11-25 | 2010-05-12 | 天津大学 | Concrete crack width test method based on wavefront coding technology and hand-held tester |
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