CN103337067B - The visible detection method of single needle scan-type screw measurement instrument probe X-axis rotating deviation - Google Patents
The visible detection method of single needle scan-type screw measurement instrument probe X-axis rotating deviation Download PDFInfo
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Abstract
The invention provides a kind of visible detection method of single needle scan-type screw measurement instrument probe X-axis rotating deviation, relate to technical field of image processing.Comprise following process: step 1, initialization are demarcated: step 2, in real time detector probe cusp: step 3, calculating deviation angle.The present invention can measure, regulate probe X-axis by image processing means rotates deviations size, realizes automatic measurement, under the prerequisite ensureing installation accuracy, raises the efficiency.
Description
Technical field
The invention provides a kind of visible detection method of single needle scan-type screw measurement instrument probe X-axis rotating deviation, relate to technical field of image processing.
Background technology
Threaded connector is the mechanical component of mechanical industry widespread use, its manufacturing accuracy directly affects reliability, assembly precision and the interchangeability that parts connect, particularly in the manufacturing and designing of aerospacecraft, a large amount of employing is threaded, connection between the part of nearly more than 50% will realize by threaded engagement, connects reliability and life-span that quality determines the industrial products such as aircraft.
For STRTHD, often adopt ring standard gauge (feeler gauge) to carry out and screw differentiation, but for the higher screw thread of accuracy requirement, as the measurement of ring gauge (feeler gauge) itself, just need instrument more accurately to detect.At present, on high precision screw surveying instrument equipment, product ripe is not both at home and abroad a lot.There is the MSXP screw measurement instrument that Dutch IAC company produces abroad, the contact pin type scanning profile method of employing.Domestic also do not have special manufacturer production contact screw measurement instrument.Breathe out the surface profile instrument that there is contact pin type in quantity set group, but be not special in thread measurement, the calculating of screw thread comprehensive parameters and error compensation also imperfection.Harbin Institute of Technology, Changchun University of Science and Technology, Zhejiang University etc. utilize laser scanner technique to obtain thread contour data and measure, but from the data announced at present, its measuring accuracy is still not as good as contact type scanning.University Of Tianjin, Nanjing Aero-Space University etc. detect the thread parameter based on image vision and did research, and its method has detection efficiency and significantly improves, but image vision measuring accuracy is also not as good as contact pin type scanning, and are difficult to the Measurement accuracy solving internal threads.
In stylus scans formula screw measurement, in order to overcome the measuring error that probe weares and teares and some other environmental change causes, usually adopt relative measurement.Namely first carry out calibration measurements with standard thread, then whorl work piece to be measured is measured.And this just requires that instrument has high positioning precision, otherwise when standard thread differs larger with thread size to be measured, the error of measurement result will be exaggerated.
Utilize the homogeneous transformation method of volume coordinate, can judge, the positioning error had the greatest impact to central diameter parameter measurements mainly contains the rotating deviation (under coordinate system shown in Fig. 1) of the deviation of Y direction and the upper and lower needle point line of probe and X-axis.But the existence of actual mismachining tolerance and rigging error, make these two deviations be difficult to control in allowed band.The axial deviation of Y-axis can be drawn by compensation calculation, and the rotating deviation of X-axis then needs to be controlled by effective detection method.Not yet find that related data display can carry out Automatic Detection and Control to this deviation at present.
Summary of the invention
The object of the present invention is to provide a kind of visible detection method that can realize the single needle scan-type screw measurement instrument probes probes X-axis rotating deviation that Aulomatizeted Detect controls.
Step 1, initialization are demarcated:
Step 1-1, collection the 1st frame comprise the RGB image in probe motion region, according to following formula, RGB image are converted into gray level image;
Gray=(R*19595+G*38469+B*7472)>>16
Wherein, R, G, B represent red, green, blue three color components of the correspondence in RGB image respectively, and Gray represents gray-scale value;
Step 1-2, utilize SUSAN Corner Detection Algorithm, Corner Detection is carried out to gray level image, obtains all angle point informations comprising upper and lower probe cusp;
Step 1-3, manual operation, choose two angle points that upper and lower probe cusp is corresponding;
Step 2, in real time detector probe cusp:
Step 2-1, collection the i-th+1 frame comprise the RGB image in probe motion region, are translated into gray level image, wherein i >=1;
Step 2-2, as i=1, with the position of previous frame image middle probe cusp for prediction cusp, i.e. P '
i+1=P
i; When i>=2, according to the position of the gray level image middle probe cusp of the i-th-1 frame and i frame, by the probe position of cusp in following predictor formula prediction i+1 two field picture;
P′
i+1=P
i+(P
i-P
i-1)=2P
i-P
i-1;
Wherein, P is the coordinate vector of probe cusp, the prediction coordinate vector that P ' is probe cusp, and i is the frame number of image;
Step 2-3, based on probe pinpoint point prediction coordinate vector P ', the i-th+1 frame gray level image builds local region of interest;
Step 2-4, by carrying out SUSAN Corner Detection to local region of interest, filter out upper and lower probe position of cusp;
Step 2-5, the upper and lower probe cusp filtered out carried out to distance verification;
Distance verification condition 1 is
|d(P
up,P
down)-d
0(P
up,P
down)|<ε
1
Wherein, d (P
up, P
down) represent in k+1 two field picture between upper and lower probe cusp distance; d
0(P
up, P
down) represent in k two field picture between upper and lower probe cusp distance; ε
1for error threshold;
Step 2-6, determining step 2-5 check results, as satisfied condition 1, then proceed to step 2-10, otherwise go to step 2-7;
Step 2-7, detect image edge information in local region of interest with Canny operator, utilize Hough transform to extract outline of straight line, obtain upper cusp and element of cone corresponding to lower cusp respectively according to following rule further:
A) in extracted straight line, in two probe element of cones and image, X-axis corner dimension is between 60 ° ~ 80 °;
B) angle between two element of cones meets
θ
1, θ
2two respectively
Bar probe element of cone and image X-axis angle;
C) element of cone intersection point Q is the peak of all successful matching straight-line intersections, and in image;
Step 2-8, according to cusp on gained and element of cone corresponding to lower cusp, calculate upper and lower profile intersection point, row distance of going forward side by side verifies;
Distance verification condition 2 is
|d(Q
up,Q
down)-d
0(Q
up,Q
down)|<ε
2
Wherein, d (Q
up, Q
down) represent in current frame image between upper and lower probe cusp distance; d
0(Q
up, Q
down) represent in previous frame image between upper and lower probe cusp distance; ε
2for error threshold;
Step 2-9, determining step 2-8 check results, as satisfied condition 2, then according to previous frame image middle ideal probe cusp Q
0with P
0distance and current frame image middle ideal probe cusp Q, be calculated as follows actual probes cusp P in present frame.
P=Q+P
0-Q
0, proceed to step 2-10;
If condition 2 does not also meet, then think that current frame image detects unsuccessfully, measurement data points stored in database, does not go to step 2-1;
Step 2-10 records position and the line slope k thereof of upper and lower probe cusp;
Step 3, calculating deviation angle
Step 3-1, the respectively upper and lower position of cusp of sniffing probe, obtain probe motion straight line, detailed process is as follows;
In the video sequence of probe once upper and lower capturing movement, if the effective probe pinpoint dot image number detected is N, utilize equation of linear regression
Obtain probe motion straight line; Wherein, k
mfor the slope of line of motion; x
i, y
ibe respectively horizontal stroke, the ordinate of the i-th two field picture middle probe cusp;
Step 3-2, according to the upper and lower probe position of cusp in every two field picture, obtain each two field picture middle probe place rectilinear direction; Namely
Wherein, k
ibe in the i-th two field picture, the line slope of the upper and lower cusp of probe; (x
up, y
up) be upper probe pinpoint point coordinate, (x
down, y
down) be lower probe pinpoint point coordinate;
The average of probe upper and lower cusp line slope k is
Utilize probe upper and lower cusp line average gradient k
lobtain the fitting a straight line of a upper and lower cusp;
The angle theta of step 3-3, calculating probe motion straight line and upper and lower cusp fitting a straight line
θ is deviation angle.
The present invention can measure, regulate probe X-axis by image processing means rotates deviations size, realizes automatic measurement, under the prerequisite ensureing installation accuracy, raises the efficiency.
Accompanying drawing explanation
Fig. 1 is the definition of workpiece coordinate system;
Fig. 2 is camera and probe relative position relation;
Fig. 3 is the Corner Detection result of entire image;
Fig. 4 is local region of interest Corner Detection result;
Fig. 5 is three kinds of Corner Detection Algorithm Corner Detection results to the area-of-interest near probe cusp when having powerful connections shadow interference.Wherein, (a) is the result of Harris Corner Detection Algorithm; B () is the result of SUSAN Corner Detection Algorithm; C () is the result of FAST Corner Detection Algorithm;
Fig. 6 is the position relationship schematic diagram of desirable probe cusp and actual probes cusp.Wherein Q represents desirable probe cusp, and P is actual probes cusp;
Fig. 7 is that three kinds of edge detection algorithms are to the edge detection results of probe topography.Wherein, (a) is the testing result of Sobel operator, and (b) is the testing result of Prewitt operator, and (c) is the testing result of Canny operator;
Fig. 8 is the schematic diagram of non-maximum value suppressing method;
Fig. 9 is Hough straight-line detection design sketch;
Figure 10 is probe element of cone testing result;
Schematic diagram when Figure 11 is probe cusp spilling area-of-interest;
Figure 12 is the video sequence sectional drawing that needle point point is followed the tracks of;
Number in the figure title: 1, the illusion cylinder of workpiece, 2, probe, 3, mounting panel, 4 cameras
Embodiment
In order to ensure the accuracy rate of probe recognizing cusp, the present invention adopts Corner Detection, outline identification and motion prediction three aspects comprehensively to identify, greatly enhance the accuracy of probe cusp, the probe cusp flase drop that effectively inhibit background interference to cause.Then by the fitting a straight line to position of cusp, the angle of line of motion (measuring instrument Z axis) and upper and lower cusp line is calculated.
Shown in Fig. 2, by the X-axis positive dirction of held at probe, parallel in Z-direction with probe.By the upper and lower motion of camera collection probe, by line and the motion of the upper and lower cusp of image recognition automatic capturing probe, calculate deviation angle.Regulate probe to rotate by device rear again, control rotating deviation.Because image is 2-D data, therefore in the derivation of equation of image procossing below, z-axis is replaced to state with y-axis.Software program steps is as follows:
Step 1, initialization are demarcated:
Step 1-1, gather the RGB image that a width comprises probe motion region, according to following formula, RGB image is converted into gray level image;
Gray=(R*19595+G*38469+B*7472)>>16
Wherein, R, G, B represent red, green, blue three color components of the correspondence in RGB image respectively, and Gray represents gray-scale value;
Step 1-2, utilize SUSAN Corner Detection Algorithm, Corner Detection is carried out to gray level image, obtains all angle point informations comprising upper and lower probe cusp;
Angle point does not have clear and definite mathematical definition, but people generally believe that angle point is the point that two dimensional image brightness changes curvature maximum value on violent point or image border curve.The detection of probe cusp can use the method for image Corner Detection, and the detection method of image angle point mainly contains three major types at present: 1. based on the angular-point detection method of boundary curve.These class methods mainly find out the curvature Local modulus maxima on the curve of image border.The performance of edge detection algorithm will directly affect the quality detecting angle point, and the situation of interrupting as there is edge line during rim detection will cause detecting false angle point.2. based on the angular-point detection method of template.First set up a series of angle point template with different angles, in certain window, then compare the similarity degree between testing image and standard form, carry out the angle point in detected image with this.Due to the complicacy of angle point structure, the template covering all directions and angle point can not be designed, the large and more complicated of this class angular-point detection method calculated amount.3. based on the angular-point detection method of gradation of image, the differential character mainly by calculating pixel carries out Corner Detection, as Harris algorithm, SUSAN algorithm, FAST Corner Detection Algorithm etc.
Step 1-3, manual operation, choose two angle points that upper and lower probe cusp is corresponding;
Step 2, in real time detector probe cusp:
Step 2-1, collection the i-th+1 frame comprise the RGB image in probe motion region, are translated into gray level image, wherein i >=1;
When step 2-2, structure local region of interest, centered by the position of a two field picture middle probe cusp only, build a rectangular area as area-of-interest.In the topography built, the Time Inconsistency needed for different images detects, can make the position of probe can fluctuated, when probe motion speed, likely makes probe cusp spilling area-of-interest, as shown in figure 11.
But, if increase the setting of local interested, calculated amount can be made again to increase, be unfavorable for the real-time process of image.Therefore, needing can according to the rectilinear motion of probe and the probe pinpoint detected dot information, and to the position of next frame image middle probe cusp, do a simply prediction, the cusp of basic guarantee probe is in the center of area-of-interest always.
Although the movement velocity of probe is change, in the very short consecutive image time, uniform motion process can be carried out.Then the position of cusp of next frame can calculate by following formula
As i=1, with the position of previous frame image middle probe cusp for prediction cusp, i.e. P '
i+1=P
i; When i>=2, according to the position of the gray level image middle probe cusp of the i-th-1 frame and i frame, by the probe position of cusp in following predictor formula prediction i+1 two field picture
P′
i+1=P
i(P
i-P
i-1=2P
i-P
i-1;
Wherein, P is the coordinate vector of probe cusp, the prediction coordinate vector that P ' is probe cusp, and i is the frame number of image;
Step 2-3, based on probe pinpoint point prediction coordinate vector P ', the i-th+1 frame gray level image builds local region of interest;
Step 2-4, by carrying out SUSAN Corner Detection to local region of interest, filter out upper and lower probe position of cusp;
Corner Detection is carried out to entire image, as shown in Figure 3, because background is not solid color, can produce and much detect angle point, this to probe cusp P search element and screening brings difficulty.Consider that image middle probe cusp only has two, build local region of interest, as shown in Figure 4.
Fig. 4 specifies correct probe cusp P by artificial, and faces in territory at it and carry out Corner Detection, effectively can reduce the interference of background color.But move up and down in process at probe, because the light-reflecting property of material and background have significant change, make conventional Corner Detection Algorithm often can not find the cusp P of probe.
Three images of Fig. 5 are that Harris, SUSAN and FAST tri-kinds of Corner Detection Algorithm are to the result of certain moment probe image respectively.
Can find out, real probe cusp P does not all detect by three kinds of algorithms.Analysis chart picture finds, background object exists color change, and just overlap with near probe cusp P, therefore, for the detection of probe cusp, the angular-point detection method based on gradation of image just cannot obtain correct result.For this reason, we consider profile information again.
Step 2-5, the upper and lower probe cusp filtered out carried out to distance verification;
Distance verification condition 1 is
|d(P
up,P
down)-d
0(P
up,P
down)|<ε
1
Wherein, d (P
up, P
down) represent in i+1 two field picture between upper and lower probe cusp distance; d
0(P
up, P
down) represent in i two field picture between upper and lower probe cusp distance; ε
1for error threshold;
Step 2-6, determining step 2-5 check results, as satisfied condition 1, then proceed to step 2-10, otherwise go to step 2-7;
Step 2-7, detect image edge information in local region of interest with Canny operator, utilize Hough transform to extract outline of straight line, obtain upper cusp and element of cone corresponding to lower cusp respectively according to following rule further:
A) in extracted straight line, in two probe element of cones and image, X-axis corner dimension is between 60 ° ~ 80 °;
B) angle between two element of cones meets
θ
1, θ
2two respectively
Bar probe element of cone and image X-axis angle;
C) element of cone intersection point Q is the peak of all successful matching straight-line intersections, and in image;
The ideal model of end of probe is a conical structure (actual head cusp is a radius is the spherical of 50um), and the shooting direction of visual pattern and its centerline parallel.If two element of cones can be detected, calculate its joining, can think desirable probe cusp Q.In probe motion process, the distance between desirable cusp Q and actual cusp P remains unchanged.Fig. 6 can find out the difference between P, Q point.
In order to extract probe outline line, first need to carry out rim detection to image.The method of gray-scale Image Edge Detection is mainly divided into two large classes: first differential Image Edge-Detection operator and second-order differential image edge detection operator.Wherein first differential edge detection operator comprises: Roberts operator, Sobel operator, Krisch operator, Prewitt operator etc., and second-order differential edge detection operator mainly contains: Laplacian operator, LOG operator; In addition the detection methods such as Canny, SUSAN, statistic discriminance are also had.
Fig. 7 is the result of several method to probe topography rim detection.
In effect, the effect of three operators is all good, but the edge that Sobel operator and Prewitt operator detect there will be multi-pixel widths question, asks element of cone intersection point to produce certain error to contour detecting below.And the edge of Canny operator is more clear, can accurate position probe profile.The basic thought of Canny operator is: first select certain smoothing filtering of Gauss wave filter to image, then adopts non-extreme value suppression technology to carry out processing and obtain last edge image.Its step is;
A) with Gauss filter smoothing image.
Here the Gaussian function H (x, y) of coefficient is omitted with one:
G(x,y)=f(x,y)*H(x,y)
Wherein f (x, y) is view data.
B) to assign to the amplitude of compute gradient and direction by the finite difference of single order local derviation.
Utilize first order difference convolution masterplate:
Can obtain:
Amplitude:
Direction:
C) non-maxima suppression is carried out to gradient magnitude.
The gradient only obtaining the overall situation is not sufficient to determine edge.For determining edge, the point that partial gradient is maximum must be retained, and suppress non-maximum value, by non local maximum point zero setting to obtain the edge of refinement.
As shown in Figure 8, the label of 4 sectors is 0 to 3, and 4 kinds of corresponding 3 × 3 neighborhoods may be combined.To a point, the center pixel M of neighborhood is compared with two pixels along gradient line.If the Grad of M is large unlike two neighbor Grad along gradient line, then make M=0.
D) detect with dual threshold algorithm and be connected edge.
Use two threshold value T
1and T
2(T
1<T
2), thus two threshold skirt image N can be obtained
1[i, j] and N
2[i, j].Due to N
2[i, j] uses high threshold to obtain, and thus containing little false edge, but has interruption (not closing).Dual-threshold voltage will at N
2in [i, j], edge conjunction is become profile, when arriving the end points of profile, this algorithm is just at N
1the edge that can be connected on profile is found in 8 adjoint point positions of [i, j], and like this, algorithm is constantly at N
1edge is collected, until by N in [i, j]
2till [i, j] couples together.T
2be used for finding every bar line segment, T
1be used in the both direction of these line segments, extend the breaking part finding edge, and connect these edges.
After utilizing Canny operator to obtain the edge of image, need to extract the probe profile that we are concerned about, i.e. two element of cones.The method that general outline of straight line extracts, based on Hough transform.Straight line Hough transform adopts the thought of " ballot " to detect straight line in digital picture or line segment, and it is the classic algorithm of an image procossing and straight-line detection.Any straight line in plane can be determined by ρ and θ two parameters.Wherein ρ determines the distance of straight line to initial point, and θ determines the orientation of straight line.Its funtcional relationship is
ρ=xcosθ+ysinθx∈[0,π]
Every bit (x in image space
i, y
i) being mapped to one group of totalizer C (ρ, θ) in Hough space, i.e. so-called voting process, C (ρ, θ) represents in image space the pixel count meeting formula (2).After poll closing, each local maximum just corresponding straight-line segment of C (ρ, θ), namely corresponding ρ and θ can determine this straight line uniquely.Fig. 9 is the result after Hough transform.
Analyze this topography's feature: a) in extracted straight line, in two probe element of cones and image, X-axis angle is basically identical, and size is between 60 ° ~ 80 °, and this just means θ
1, θ
2∈ [π/3,4 π/9]; B) angle between two element of cones is known at about 45 ° according to report of dispatching from the factory, and namely its slope is approximately 1.According to angle formulae setting testing conditions:
The element of cone obtained required by two can be filtered.Again to the element of cone extracted, extend, find desirable probe cusp Q, as shown in Figure 10.Certainly also needing to add a verification condition c) element of cone intersection point Q is the peak of all successful matching straight-line intersections, and in image.
So far, just detected desirable probe cusp Q.Ideally, the upper and lower needle point line of probe, both can use L (P
up, P
down) represent, also can use L (Q
up, Q
down) represent, two straight lines overlap.But due to the mismachining tolerance of reality, two straight lines can not overlap completely.And in measuring process, contact point is actual cusp, this is higher than Q point with regard to making the reliability of P point.Therefore, when cusp detects, L (P is also taked
up, P
down) be main, L (Q
up, Q
down) be auxiliary strategy.
Step 2-8, according to cusp on gained and element of cone corresponding to lower cusp, calculate upper and lower profile intersection point, row distance of going forward side by side verifies;
Distance verification condition 2 is
|d(Q
up,Q
down)-d
0(Q
up,Q
down)|<ε
2
Wherein, d (Q
up, Q
down) represent in current frame image between upper and lower probe cusp distance; d
0(Q
up, Q
down) represent in previous frame image between upper and lower probe cusp distance; ε
2for error threshold;
Step 2-9, determining step 2-8 check results, as satisfied condition 2, then according to previous frame image middle ideal probe cusp Q
0with P
0distance and current frame image middle ideal probe cusp Q, be calculated as follows actual probes cusp P in present frame.
P=Q+P
0-Q
0, proceed to step 2-10;
Proceed to step 2-10;
If condition 2 does not also meet, then think that current frame image detects unsuccessfully, measurement data points stored in database, does not go to step 2-1;
Step 2-10 records position and the line slope k thereof of upper and lower probe cusp;
Step 3, calculating deviation angle
Step 3-1, the respectively upper and lower position of cusp of sniffing probe, obtain probe motion straight line, detailed process is as follows;
In the video sequence of probe once upper and lower capturing movement, if the effective probe pinpoint detected is counted as N, utilize equation of linear regression
Obtain probe motion straight line; Wherein, k
mfor the slope of line of motion; x
i, y
ibe respectively horizontal stroke, the ordinate of the i-th two field picture middle probe cusp;
Step 3-2, according to the upper and lower probe position of cusp in every two field picture, obtain each two field picture middle probe place rectilinear direction; Namely
Wherein, k
ibe in the i-th two field picture, the line slope of the upper and lower cusp of probe; (x
up, y
up) be upper probe pinpoint point coordinate, (x
down, y
down) be lower probe pinpoint point coordinate;
The average of probe upper and lower cusp line slope k is
Utilize probe upper and lower cusp line average gradient k
lobtain the fitting a straight line of a upper and lower cusp;
The angle theta of step 3-3, calculating probe motion straight line and upper and lower cusp fitting a straight line
θ is deviation angle.
Claims (1)
1., to a visible detection method for the X-axis rotating deviation of single needle scan-type screw measurement instrument measuring probe, it is characterized in that, comprise following process:
Step 1, initialization are demarcated:
Step 1-1, collection the 1st frame comprise the RGB image in probe motion region, according to following formula, RGB image are converted into gray level image;
Gray=(R*19595+G*38469+B*7472)>>16
Wherein, R, G, B represent red, green, blue three color components of the correspondence in RGB image respectively, and Gray represents gray-scale value;
Step 1-2, utilize SUSAN Corner Detection Algorithm, Corner Detection is carried out to gray level image, obtains all angle point informations comprising upper and lower probe cusp;
Step 1-3, manual operation, choose two angle points that upper and lower probe cusp is corresponding;
Step 2, in real time detector probe cusp:
Step 2-1, collection the i-th+1 frame comprise the RGB image in probe motion region, are translated into gray level image, wherein i >=1;
Step 2-2, as i=1, with the position of previous frame image middle probe cusp for prediction cusp, i.e. P '
i+1=P
i; When i>=2, according to the position of the gray level image middle probe cusp of the i-th-1 frame and i frame, by the probe position of cusp in following predictor formula prediction i+1 two field picture;
P′
i+1=P
i+(P
i-P
i-1)=2P
i-P
i-1;
Wherein, P is the coordinate vector of probe cusp, the prediction coordinate vector that P ' is probe cusp, and i is the frame number of image;
Step 2-3, based on probe pinpoint point prediction coordinate vector P ', the i-th+1 frame gray level image builds local region of interest;
Step 2-4, by carrying out SUSAN Corner Detection to local region of interest, filter out upper and lower probe position of cusp;
Step 2-5, the upper and lower probe cusp filtered out carried out to distance verification;
Distance verification condition 1 is
|d(P
up,P
down)-d
0(P
up,P
down)|<ε
1
Wherein, d (P
up, P
down) represent in i+1 two field picture between upper and lower probe cusp distance; d
0(P
up, P
down) represent in i two field picture between upper and lower probe cusp distance; ε
1for error threshold;
Step 2-6, determining step 2-5 check results, as satisfied condition 1, then proceed to step 2-10, otherwise go to step 2-7;
Step 2-7, detect image edge information in local region of interest with Canny operator, utilize Hough transform to extract outline of straight line, obtain upper cusp and element of cone corresponding to lower cusp respectively according to following rule further:
A) in extracted straight line, in two probe element of cones and image, X-axis corner dimension is between 60 ° ~ 80 °;
B) angle between two element of cones meets
θ
1, θ
2two probe element of cones and image X-axis angle respectively;
C) element of cone intersection point Q is the peak of all successful matching straight-line intersections, and in image;
Step 2-8, according to cusp on gained and element of cone corresponding to lower cusp, calculate upper and lower profile intersection point, row distance of going forward side by side verifies;
Distance verification condition 2 is
|d(Q
up,Q
down)-d
0(Q
up,Q
down)|<ε
2
Wherein, d (Q
up, Q
down) represent in current frame image between upper and lower probe cusp distance; d
0(Q
up, Q
down) represent in previous frame image between upper and lower probe cusp distance; ε
2for error threshold;
Step 2-9, determining step 2-8 check results, as satisfied condition 2, then according to previous frame image middle ideal probe cusp Q
0with P
0distance and current frame image middle ideal probe cusp Q, be calculated as follows actual probes cusp P in present frame;
P=Q+P
0-Q
0, proceed to step 2-10;
If condition 2 does not also meet, then think that current frame image detects unsuccessfully, measurement data points stored in database, does not go to step 2-1;
Step 2-10 records position and the line slope k thereof of upper and lower probe cusp;
Step 3, calculating deviation angle
Step 3-1, the respectively upper and lower position of cusp of sniffing probe, obtain probe motion straight line, detailed process is as follows:
In the video sequence of probe once upper and lower capturing movement, if the effective probe pinpoint dot image number detected is N, utilize equation of linear regression
Obtain probe motion straight line; Wherein, k
mfor the slope of line of motion; x
i, y
ibe respectively horizontal stroke, the ordinate of the i-th two field picture middle probe cusp;
Step 3-2, according to the upper and lower probe position of cusp in every two field picture, obtain each two field picture middle probe place rectilinear direction; Namely
Wherein, k
ibe in the i-th two field picture, the line slope of the upper and lower cusp of probe; (x
up, y
up) be upper probe pinpoint point coordinate, (x
down, y
down) be lower probe pinpoint point coordinate;
The average of probe upper and lower cusp line slope k is
Utilize probe upper and lower cusp line average gradient k
lobtain the fitting a straight line of a upper and lower cusp;
The angle theta of step 3-3, calculating probe motion straight line and upper and lower cusp fitting a straight line
θ is deviation angle.
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CN201310217418.3A CN103337067B (en) | 2013-06-03 | 2013-06-03 | The visible detection method of single needle scan-type screw measurement instrument probe X-axis rotating deviation |
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CN201310217418.3A CN103337067B (en) | 2013-06-03 | 2013-06-03 | The visible detection method of single needle scan-type screw measurement instrument probe X-axis rotating deviation |
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CN109102507A (en) * | 2018-08-28 | 2018-12-28 | 珠海格力智能装备有限公司 | Screw thread detection method and device |
CN109785338A (en) * | 2018-12-26 | 2019-05-21 | 汕头大学 | The online visible sensation method of screw thread critical size parameter under a kind of movement background |
CN110243297B (en) * | 2019-06-13 | 2020-08-04 | 上海交通大学 | Pipe thread pitch diameter measurement correction method, system and medium based on image measurement |
CN110849287B (en) * | 2019-11-27 | 2021-05-25 | 陕西理工大学 | Machine vision thread form angle compensation method |
CN114820620B (en) * | 2022-06-29 | 2022-09-13 | 中冶建筑研究总院(深圳)有限公司 | Bolt loosening defect detection method, system and device |
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