CN107462182B - A kind of cross section profile deformation detecting method based on machine vision and red line laser - Google Patents
A kind of cross section profile deformation detecting method based on machine vision and red line laser Download PDFInfo
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- CN107462182B CN107462182B CN201710851620.XA CN201710851620A CN107462182B CN 107462182 B CN107462182 B CN 107462182B CN 201710851620 A CN201710851620 A CN 201710851620A CN 107462182 B CN107462182 B CN 107462182B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
Abstract
The invention discloses a kind of cross section profile deformation detecting method based on machine vision and red line laser mainly includes the calibration of laser smooth surface and shape changing detection two parts；Wherein, laser smooth surface calibration process includes calibration to camera and irradiates the smooth surface come to red line laser and demarcate, and makes after calibration three-dimensional coordinate of the laser rays that can be irradiated on effective expression picture in camera coordinates system；Shape changing detection process be in photo captured by camera by red line laser irradiate Lai red line detect, according still further to calibration result carry out profile reconstruct, finally compared with standard section profile, thus realize structural deformation detect.
Description
Technical field
The invention belongs to technical field of image processing, more specifically, are related to a kind of sharp based on machine vision and red line
The cross section profile deformation detecting method of light device.
Background technique
The detection of the cross-section profile shape of the object of large scale structure is raw for the quality testing of product or building body and safety
Production is of great significance.For example track, bridge, tunnel, girder steel etc. may meanings when subtle change occurs for its surface shape
There are biggish quality and security risks.
Traditional detection is usually artificial inspection, this depends not only upon artificial experience, and for most subtle
Deformation is manually imperceptible, thus can have missing inspection；Another kind is just to rely on Large-scale professional instrument, such as laser scanning
Measurement is typically fixed to certain point detection, is then being moved to next point, but this quasi-instrument is generally expensive, is detecting speed
It is relatively slow, it carries and uses nor very convenient.Consider that The present invention gives one for cost, precision, detection speed and ease for use
The rapid detection method of simple and easy object section profile deformation of the kind based on machine vision and red line laser.
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of based on machine vision and red line laser
Cross section profile deformation detecting method carries out object section profile deformation inspection in such a way that red line laser and machine vision combine
It surveys, there is preferable universality.
For achieving the above object, the present invention is a kind of cross section profile deformation based on machine vision and red line laser
Detection method, which comprises the following steps:
(1), laser smooth surface is demarcated
(1.1), camera and red line laser are fixed according to certain angle, guarantee red line laser illumination to object
The red line on surface is within the scope of camera fields of view；
(1.2), camera is demarcated using Zhang Zhengyou calibration method, writes down the internal reference coefficient matrix M of camera；
(1.3), camera shooting N group picture is set, every group two is opened, total 2N width picture, wherein every group of two pictures are respectively
Camera shoots to obtain when camera shoots to obtain and be the closing of red line laser when red line laser is opened, and the difference of two pictures is only
For whether there is or not open red line laser；
M point in every group in red line picture on red line is taken, total n=m*N point is calibrated often using Zhang Zhengyou calibration method
The outer ginseng coefficient matrix of group pictureInternal reference coefficient matrix M is being combined, the three-dimensional coordinate in n corresponding camera coordinates system is obtained
(xck,yck,zck)；
(1.4), by n three-dimensional coordinate (xck,yck,zck) optimization formula finds out smooth surface coefficient matrix W=[w below for substitution1,
w2,w3,w4]；
Wherein, η indicates regularization parameter；
(2), image preprocessing；
(2.1), setting camera acquisition is triple channel RGB image；
Calculate single channel red characteristic pattern R: in each pixel (i, j) pixel value R (i, j):
Wherein, R (i, j) is the pixel value of pixel (i, j)；D is dependent variable, has been reacted in a RGB image, as
Ratio shared by red in vegetarian refreshments (i, j), the bigger expression pixel of numerical value are redder；L is corresponding to pixel (i, j)
Brightness in Lab space；R, g, b are respectively three color components in rgb space corresponding to pixel (i, j)；
(2.2), each pixel (i, j) is filtered using multistage median filtering device, obtains pixel (i, j)
New pixel value P (i, j)；
(3), red line region ROI is extracted
(3.1), it according to the pixel value P (i, j) of each pixel (i, j) in single channel red characteristic pattern R, is thrown according to level
Shadow and upright projection extract red line region ROI；
Wherein, X_proj (x) indicate xth column pixel carry out floor projection after as a result, Y_proj (y) indicate y row
Pixel carries out the result after upright projection；Nrow, ncol are the line numbers and columns of single channel red characteristic pattern R；
(3.2), process of convolution is done to X_proj (x) and Y_proj (y):
Xconv=X_proj (x) * h
Yconv=Y_proj (y) * h
Wherein, h is warp factor, and * is convolution symbol, and Xconv and Yconv are the result after convolution respectively；
(3.3), Xconv is traversed from head to tail and from tail to first both direction respectively using threshold value thro1, wherein from
When head is traversed to tail, first position x for being greater than thro1 is recorded1, when being traversed from tail to head, record first and be greater than
The position y of thro11；
Similarly, identical processing is done to Yconv using threshold value thro2, respectively obtains two position x2、y2；
(3.4), according to position x1、y1And x2、y2, red line region ROI is obtained, R (x is expressed as2:y2, x1:y1)；
(4), the center line of red line region ROI is extracted
In R (x2:y2, x1:y1) every a line on, coordinate weighted average is asked to the point that all transverse directions are not 0；
Wherein, the value range of i are as follows: x2≤i≤y2；ρ (P (i, j)) indicates the weighting function of pixel value P (i, j)；J is indicated
The pixel of j column；
By R (x2:y2, x1:y1) in the coordinate weighted average X (i) that seeks of all rows be combined into vector X, as red line area
The center line of domain ROI；
(5), center line is smoothed
(5.1), vector X is normalized, obtains vector X '；
(5.2), vector X ' and the dot product of itself are sought, vector X ", X "=X ' .*X ' are obtained；
(5.3), the convolution for being all 1/ τ based on core is carried out to vector X ' and X ", respectively obtains mean_o and mean_o2,
In, the size of τ core；
(5.4), the smooth sequence q of center line is calculated；
Q=a ' .*X '+b '
Wherein, a ' and b ' is vector；
(6), by internal reference coefficient matrix M and smooth surface coefficient matrix W, following equation is brought into smooth sequence q:
Wherein, qiIndicate i-th of element of smooth sequence q
By solving above-mentioned equation, coordinate sequence (x of the section of object in camera coordinates system is obtainedci,yci,zci), x2
≤i≤y2；
Again by coordinate sequence (xci,yci,zci) and the calculated coordinate sequence of standard section profileCompare, such as
Fruit coordinate sequence changes, then deformation occurs for object section profile.
Goal of the invention of the invention is achieved in that
A kind of cross section profile deformation detecting method based on machine vision and red line laser of the present invention mainly includes laser
Smooth surface calibration and shape changing detection two parts；Wherein, laser smooth surface calibration process includes calibration to camera and to red line laser
It irradiates the smooth surface come to be demarcated, makes that the laser rays come can be irradiated in camera on effective expression picture after calibration
Three-dimensional coordinate in coordinate system；Shape changing detection process be in photo captured by camera by red line laser irradiate Lai
Red line is detected, and is carried out profile reconstruct according still further to the result of calibration, is finally compared with standard section profile, to realize
Structural deformation detection.
Meanwhile also having the present invention is based on the cross section profile deformation detecting method of machine vision and red line laser and following have
Beneficial effect:
(1), a kind of the advantages of present invention employs a kind of smoothing method based on wave filter, the filter is linear
Time complexity can guarantee that the space structure of original pixel collection is constant while generating smaller influence to pixel as far as possible, from
And achieve the purpose that smoothing denoising；
(2), the present invention firstly the need of what is done is demarcated to camera, using a kind of calibration side of nonlinear model
Method, the scaling method precision is high, can reach the stated accuracy of sub-pixel, while the equipment demarcated also needs for relatively simple
One gridiron pattern；
(3), in the present invention, the characteristics of for red line itself, the red feature graph model of use can be carried out it accurately
Expression.
Detailed description of the invention
Fig. 1 is the cross section profile deformation detecting method flow chart the present invention is based on machine vision and red line laser；
Fig. 2 is the cross section profile shape changing detection schematic diagram of machine vision and red line laser；
Fig. 3 is RGB image；
Fig. 4 is red characteristic pattern；
Fig. 5 is to enhance red characteristic pattern；
Fig. 6 is ROI interception result；
Fig. 7 is the extraction at red line center；
Fig. 8 is the smooth of red line center；
Fig. 9 be RGB image and its for contour images；
Figure 10 is defect part unusual part.
Specific embodiment
A specific embodiment of the invention is described with reference to the accompanying drawing, preferably so as to those skilled in the art
Understand the present invention.Requiring particular attention is that in the following description, when known function and the detailed description of design perhaps
When can desalinate main contents of the invention, these descriptions will be ignored herein.
Embodiment
Fig. 1 is the cross section profile deformation detecting method flow chart the present invention is based on machine vision and red line laser.
In the present embodiment, as shown in Figure 1, a kind of cross section profile shape based on machine vision and red line laser of the present invention
Become detection method, mainly includes the calibration of laser smooth surface and shape changing detection two parts, expansion explanation is carried out to this two parts below.
S1, the calibration of laser smooth surface
S1.1, camera calibration
Camera and red line laser are fixed according to certain angle, angle can determines according to actual conditions, as long as
Guarantee red line laser illumination to body surface red line within the scope of camera fields of view；For cutting for effective reconstruction of objects
Face, a word red line laser irradiate the red line come and must be able to effectively cut the cross section of object, as shown in Figure 2；
After the completion of camera and red line laser are fixed, camera is demarcated using Zhang Zhengyou calibration method, writes down camera
Internal reference coefficient matrix M；
S1.2, smooth surface calibration
Gridiron pattern is fixed on before camera, for shooting picture, holding camera and tessellated current location are constant, then
Red line laser is opened, its light can be irradiated on gridiron pattern, then shoot picture.
Assuming that camera shoots N group picture, every group two is opened, total 2N (N substantially 5 to 7 groups) width picture, wherein the two of every group
Camera shoots to obtain when camera shoots to obtain and be the closing of red line laser when picture is respectively red line laser opening, and two
The difference of picture is only that whether there is or not open red line laser；
Take m point in every group in red line picture on red line, since picture number is few, take method a little that can adopt here
Point (general 5 to 8 points) is taken with the method for manual amplification picture, total n=m*N point is calibrated often using Zhang Zhengyou calibration method
The outer ginseng coefficient matrix of group pictureAccording to camera projection formula, first inverse goes out the point coordinate under world coordinate system, is combining
Internal reference coefficient matrix M calculates the point coordinate under camera coordinates system, obtains the three-dimensional seat in n corresponding camera coordinates system altogether in this way
Mark (xck,yck,zck)；
By n three-dimensional coordinate (xck,yck,zck) optimization formula finds out smooth surface coefficient matrix W=[w below for substitution1,w2,w3,
w4]；
Wherein, w1,w2,w3,w4For smooth surface equation coefficient；η indicates regularization parameter, and value is tested according to the actual situation.For
Trivial solution is avoided, can fix w1Coefficient value is 1, and the smooth surface equation coefficient found out is similar as follows:
[w1,w2,w3,w4]=[1.000, -0.0107,0.3101,42.1917]
S2, shape changing detection
S2.1 image preprocessing；
S2.1.1, set camera acquisition be triple channel RGB image, as shown in Figure 3；
Calculate single channel red characteristic pattern R: in each pixel (i, j) pixel value R (i, j):
Wherein, R (i, j) is the pixel value of pixel (i, j)；D is dependent variable, has been reacted in a RGB image, as
Ratio shared by red in vegetarian refreshments (i, j), the bigger expression pixel of numerical value be it is redder, it subtracts green and red by red
Blue is subtracted to portray；L is the brightness in Lab space corresponding to pixel (i, j), and actual Lab brightness value range needs
255 ranges for being amplified to 0-255 are multiplied by after normalizing；R, g, b are respectively in rgb space corresponding to pixel (i, j)
Three color components；In the present embodiment, single channel red characteristic pattern R is as shown in Figure 4；
S2.1.2, the effect to enhance red characteristic pattern, carry out each pixel (i, j) using multistage median filtering device
Filtering processing obtains the new pixel value P (i, j) of pixel (i, j), and it is as shown in Figure 5 to enhance red characteristic pattern；
S2.2, red line region ROI is extracted
S2.2.1, red line shared region in picture is very small in a width picture, rather than object is cut in red line region
The reconstruct in face be do not have it is effective, it would therefore be desirable to according to the picture of each pixel (i, j) in single channel red characteristic pattern R
Element value P (i, j) extracts red line region ROI according to floor projection and upright projection；
Wherein, X_proj (x) indicate xth column pixel carry out floor projection after as a result, the value range of x be 0≤x≤
ncol；Y_proj (y) indicate y row pixel carry out upright projection after as a result, the value range of y be 0≤y≤nrow；
Nrow, ncol are the line numbers and columns of single channel red characteristic pattern R, eliminate the influence of picture of different sizes to result in this way；
S2.2.2, process of convolution is done to X_proj (x) and Y_proj (y):
Xconv=X_proj (x) * h
Yconv=Y_proj (y) * h
Wherein, h is warp factor, generally takes 10-20；It * is convolution symbol, after Xconv and Yconv are respectively convolution
As a result；
S2.2.3, Xconv is traversed from head to tail and from tail to first both direction respectively using threshold value thro1, wherein from
When head is traversed to tail, first position x for being greater than thro1 is recorded1, when being traversed from tail to head, record first and be greater than
The position y of thro11；
Similarly, identical processing is done to Yconv using threshold value thro2, respectively obtains two position x2、y2；
S2.2.4, according to position x1、y1And x2、y2, red line region ROI is obtained, R (x is expressed as2:y2, x1:y1), such as Fig. 6 institute
Show；
S2.3, the center line for extracting red line region ROI
In R (x2:y2, x1:y1) every a line on, coordinate weighted average is asked to the point that all transverse directions are not 0；
Wherein, the value range of i are as follows: x2≤i≤y2；ρ (P (i, j)) indicates the weighting function of pixel value P (i, j), ρ (P
(i, j)) function expression are as follows:σ is super ginseng coefficient, the range of value 0.5 to 5；J is indicated
The pixel of j column；
By R (x2:y2, x1:y1) in the coordinate weighted average X (i) that seeks of all rows be combined into vector X, acquired results are made
For the center line of red line region ROI, as shown in Figure 7；This step needs to have corresponded to X (i) and line number i always；
S2.4, center line is smoothed
S2.4.1, vector X is normalized, obtains vector X '；
S2.4.2, vector X ' and the dot product of itself are sought, obtains vector X ", X "=X ' .*X '；
S2.4.3, the convolution for being all 1/ τ based on core is carried out to vector X ' and X ", respectively obtain mean_o and mean_o2,
In, τ is the size of core, is determined by actual effect, this example value τ=9；
S2.4.4, the smooth sequence q for calculating center line；
Q=a ' .*X '+b '
Sequence after final smooth is q, as shown in Figure 8；
Wherein, the acquiring method of vector a ' and b ' are as follows:
1) vector a and b, are calculated:
A=(mean_o2-mean_o.*mean_o)/(mean_o2+ ε)
B=mean_o-a.*mean_o
Wherein, ε is smoothness parameter, and value 0.1-1.5 .* indicate dot product, and/expression point removes；
2), a and b is carried out to the convolution for being all 1/ τ based on core, respectively obtain vector a ' and b ', wherein the size of τ core；
S2.5, three-dimensional coordinate are established
By internal reference coefficient matrix M and smooth surface coefficient matrix W, following equation is brought into smooth sequence q:
Wherein, qiIndicate i-th of element of smooth sequence q
By solving above-mentioned equation, coordinate sequence (x of the section of object in camera coordinates system is obtainedci,yci,zci), x2
≤i≤y2；
S2.6, object section shape changing detection
If thinking whether detection object has deformation, by coordinate sequence (xci,yci,zci) calculated with standard section profile
Coordinate sequenceComparison, if coordinate sequence changes, being monitored object section, deformation occurs.Such as figure
9, left figure is RGB image, and solid line is the experimental result according to above step in right figure, and dotted line is the profile of standard item, is passed through
Comparison can find the rejected region at rectangle frame.Figure 10 is the picture for amplifying rejected region in Fig. 9.
Although the illustrative specific embodiment of the present invention is described above, in order to the technology of the art
Personnel understand the present invention, it should be apparent that the present invention is not limited to the range of specific embodiment, to the common skill of the art
For art personnel, if various change the attached claims limit and determine the spirit and scope of the present invention in, these
Variation is it will be apparent that all utilize the innovation and creation of present inventive concept in the column of protection.
Claims (3)
1. a kind of cross section profile deformation detecting method based on machine vision and red line laser, camera and red line laser are pressed
It is fixed according to certain angle, the red line of guarantee red line laser illumination to body surface is special within the scope of camera fields of view
Sign is, further comprising the steps of:
(1), laser smooth surface is demarcated
(1.1), camera is demarcated using Zhang Zhengyou calibration method, writes down the internal reference coefficient matrix M of camera；
(1.2), camera shooting N group picture is set, every group two is opened, total 2N width picture, wherein every group of two pictures are respectively red line
Camera shoots to obtain when camera shoots to obtain and be the closing of red line laser when laser is opened, and the difference of two pictures is only to have
Without opening red line laser；
M point in every group in red line picture on red line is taken, total n=m*N point calibrates every group picture using Zhang Zhengyou calibration method
The outer ginseng coefficient matrix of pieceInternal reference coefficient matrix M is being combined, the three-dimensional coordinate (x in n corresponding camera coordinates system is obtainedck,
yck,zck)；
(1.3), by n three-dimensional coordinate (xck,yck,zck) optimization formula finds out smooth surface coefficient matrix W=[w below for substitution1,w2,
w3,w4]；
Wherein, η indicates regularization parameter；
(2), image preprocessing；
(2.1), setting camera acquisition is triple channel RGB image；
Calculate single channel red characteristic pattern R: in each pixel (i, j) pixel value R (i, j):
Wherein, R (i, j) is the pixel value of pixel (i, j)；D is dependent variable, has been reacted in a RGB image, pixel
Ratio shared by red in (i, j), the bigger expression pixel of numerical value are redder；L is Lab corresponding to pixel (i, j)
Brightness in space；R, g, b are respectively three color components in rgb space corresponding to pixel (i, j)；
(2.2), each pixel (i, j) is filtered using multistage median filtering device, obtains the new of pixel (i, j)
Pixel value P (i, j)；
(3), red line region ROI is extracted
(3.1), according to the pixel value P (i, j) of each pixel (i, j) in single channel red characteristic pattern R, according to floor projection and
Upright projection extracts red line region ROI；
Wherein, X_proj (x) indicate xth column pixel carry out floor projection after as a result, Y_proj (y) indicate y row pixel
Point carries out the result after upright projection；Nrow, ncol are the line numbers and columns of single channel red characteristic pattern R；
(3.2), process of convolution is done to X_proj (x) and Y_proj (y):
Xconv=X_proj (x) * h
Yconv=Y_proj (y) * h
Wherein, h is warp factor, and * is convolution symbol, and Xconv and Yconv are the result after convolution respectively；
(3.3), Xconv is traversed from head to tail and from tail to first both direction respectively using threshold value thro1, wherein from head to
When tail traverses, first position x for being greater than thro1 is recorded1, when traversing from tail to head, first is recorded greater than thro1's
Position y1；
Similarly, identical processing is done to Yconv using threshold value thro2, respectively obtains two position x2、y2；
(3.4), according to position x1、y1And x2、y2, red line region ROI is obtained, R (x is expressed as2:y2, x1:y1)；
(4), the center line of red line region ROI is extracted
In R (x2:y2, x1:y1) every a line on, coordinate weighted average is asked to the point that all transverse directions are not 0；
Wherein, the value range of i are as follows: x2≤i≤y2；ρ (P (i, j)) indicates the weighting function of pixel value P (i, j)；J indicates j column
Pixel；
By R (x2:y2, x1:y1) in the coordinate weighted average X (i) that seeks of all rows be combined into vector X, as red line region ROI
Center line；
(5), center line is smoothed
(5.1), vector X is normalized, obtains vector X '；
(5.2), vector X ' and the dot product of itself are sought, vector X ", X "=X ' .*X ' are obtained；
(5.3), the convolution for being all 1/ τ based on core is carried out to vector X ' and X ", respectively obtains mean_o and mean_o2, wherein τ
The size of core；
(5.4), the smooth sequence q of center line is calculated；
Q=a ' .*X '+b '
Wherein, a ' and b ' is vector；
(6), by internal reference coefficient matrix M and smooth surface coefficient matrix W, following equation is brought into smooth sequence q:
Wherein, qiIndicate i-th of element of smooth sequence q
By solving above-mentioned equation, coordinate sequence (x of the section of object in camera coordinates system is obtainedci,yci,zci), x2≤i≤
y2；
Again by coordinate sequence (xci,yci,zci) and the calculated coordinate sequence of standard section profileCompare, if
Coordinate sequence changes, then deformation occurs for object section profile.
2. the cross section profile deformation detecting method according to claim 1 based on machine vision and red line laser, special
Sign is, the function expression of the ρ (P (i, j)) are as follows:
Wherein, σ is super ginseng coefficient.
3. the cross section profile deformation detecting method according to claim 1 based on machine vision and red line laser, special
Sign is, the acquiring method of a ' and b ' are as follows:
1) vector a and b, are calculated:
A=(mean_o2-mean_o.*mean_o)/(mean_o2+ ε)
B=mean_o-a.*mean_o
Wherein, ε is smoothness parameter, and .* indicates dot product, and/expression point removes；
2), a and b is carried out to the convolution for being all 1/ τ based on core, respectively obtain vector a ' and b ', wherein the size of τ core.
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