CN107490342A - A kind of cell phone appearance detection method based on single binocular vision - Google Patents
A kind of cell phone appearance detection method based on single binocular vision Download PDFInfo
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- CN107490342A CN107490342A CN201710520300.6A CN201710520300A CN107490342A CN 107490342 A CN107490342 A CN 107490342A CN 201710520300 A CN201710520300 A CN 201710520300A CN 107490342 A CN107490342 A CN 107490342A
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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
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/022—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness by means of tv-camera scanning
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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
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
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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
- G01B11/08—Measuring arrangements characterised by the use of optical techniques for measuring diameters
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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
- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
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Abstract
The present invention relates to a kind of cell phone appearance detection method based on single binocular vision, pass through Binocular vision photogrammetry mobile phone thickness, mobile phone length and width dimensions and fillet information are measured by monocular vision, the cell phone appearance data detected are compared with standard value, calculating difference simultaneously generates form, product of failing will be considered as beyond the mobile phone of allowable error scope according to form, be sorted to by manipulator and reprocess area.The present invention has the advantages that testing result accurate rate is high, detection efficiency is high, testing cost is low, non-contact.
Description
Technical Field
The invention relates to the technical field of mobile phone detection, in particular to a mobile phone appearance detection method based on single and binocular vision.
Background
Electronic products have entered various aspects of people's lives today, and people are not enjoying the convenience and quickness brought by electronic products all the time. Taking a mobile phone as an example, the mobile phone has basically reached one part of the human hand in the world. Such a large market demand also presents more serious challenges to the handset manufacturer. The quality detection of the mobile phone becomes a decisive factor for occupying market share. Among them, appearance inspection of the mobile phone is an important part in the overall quality inspection of the mobile phone. The traditional appearance detection of the mobile phone is contact measurement, namely, a manipulator grabs the mobile phone and places the mobile phone into a standard mold to see whether the mobile phone is matched with the mold. The detection method has many defects, such as time and labor consumption, high cost and damage to the mobile phone caused by the grabbing of the manipulator. Therefore, it is necessary to provide a new non-contact detection.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a non-contact mobile phone appearance detection method based on single and binocular vision, which has the advantages of high detection result accuracy, high detection efficiency and low detection cost.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: the method comprises the steps of obtaining a standard value and an allowable error range, measuring the thickness of the mobile phone through binocular vision, measuring the length, the width and the fillet information of the mobile phone through monocular vision, comparing detected appearance data of the mobile phone with the standard value, calculating a difference value and generating a report, regarding the mobile phone beyond the allowable error range as a defective product according to the report, and sorting the defective product to a repair area through a manipulator.
The standard value and the allowable error range of the mobile phone size are obtained from a manufacturer, and the method specifically comprises the following steps: the mobile phone length, width dimension, thickness dimension, fillet radius, arc length and standard value and allowable error range of the radian.
Further, binocular vision measures cell-phone thickness, specifically includes the following step:
1) calibrating a binocular camera to obtain internal and external parameters and distortion coefficients of a left camera and a right camera;
2) correcting the camera, removing the influence of optical distortion, and changing the binocular camera into a standard form;
3) binocular matching, namely calculating matching points between cameras to obtain a disparity map;
4) calculating the thickness of the mobile phone according to the disparity map;
5) and converting the pixel size of the thickness of the mobile phone into an actual size.
Further, the specific steps of calibrating the binocular camera are as follows:
1-1) calibrating a left camera to obtain internal and external parameters of the camera;
1-2) calibrating a camera of a right camera to obtain internal and external parameters of the camera;
1-3) binocular calibration, and acquiring the translation and rotation relationship between the cameras.
Further, the specific steps of binocular matching are as follows:
3-1) calculating a matching error;
3-2) matching error integration;
3-3) calculating a disparity map;
3-4) disparity map processing.
Further, when the disparity map is calculated, the disparity map is calculated by specifically adopting a global stereo matching algorithm based on an image segmentation algorithm.
Further, the disparity map processing specifically includes: median filtering is used to remove salt-pepper noise of the disparity map and outliers of matching failures.
Further, monocular vision measures cell-phone length and width size and fillet information, and it uses above-mentioned right camera of demarcation to gather the image, and concrete step is:
(1, detecting edge pixels to obtain an outer contour line of the mobile phone;
(2, converting the outer contour line into a minimum circumscribed rectangle, and calculating the length and width dimensions of the rectangle;
(3 dividing a mobile phone fillet area, and calculating characteristic parameters of the mobile phone fillet area, including arc length, radian and radius;
(4 convert the resulting pixel size to real size.
The principle and the advantages of the scheme are as follows:
according to the scheme, the thickness of the mobile phone is measured through binocular vision, the length and width dimensions and the fillet information of the mobile phone are measured through monocular vision, detected appearance data of the mobile phone are compared with a standard value, a difference value is calculated and a report is generated, the mobile phone exceeding the allowable error range is regarded as a defective product according to the report, and the defective product is sorted to a repair area through a manipulator.
The scheme has the advantages of high accuracy of detection results, high detection efficiency, low detection cost, non-contact property and the like.
Drawings
FIG. 1 is a flow chart of a mobile phone appearance detection method based on monocular and binocular vision according to the present invention;
FIG. 2 is a flow chart of binocular vision measuring the thickness of a mobile phone in the mobile phone appearance detection method based on single and binocular vision;
fig. 3 is a flow chart of monocular vision measurement of the length and width dimensions and fillet information of a mobile phone in the mobile phone appearance detection method based on monocular and binocular vision of the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples:
referring to fig. 1 to 3, the method for detecting the appearance of a mobile phone based on single and binocular vision in this embodiment includes the following steps:
s1, obtaining the standard value and the allowable error range of the mobile phone size from a manufacturer, specifically comprising: the mobile phone length, width dimension, thickness dimension, fillet radius, arc length and standard value and allowable error range of the radian.
S2, binocular vision measures the thickness of the mobile phone, including:
s21, calibrating the binocular camera to obtain the internal and external parameters and distortion coefficients of the left camera and the right camera, specifically:
1) calibrating a camera of a left camera to obtain internal and external parameters of the camera, wherein the formula is as follows:
wherein, a left camera O-x is arrangedlylzlLocated at the origin of the world coordinate system without rotation, and the image coordinate system is Ol-XlYl,(Xl,Yl) Coordinates of the image taken by the left camera, flIs the effective focal length of the left camera.
2) Calibrating a camera of the right camera to obtain internal and external parameters of the camera, wherein the formula is as follows:
for the same reason, the right camera O-xryrzrLocated at the origin of the world coordinate system without rotation, and the image coordinate system is Or-XrYr,(Xr,Yr) Coordinates of the image taken by the right camera, frIs the effective focal length of the right camera.
3) Binocular calibration is carried out, the translation and rotation relation between cameras is obtained, and the calculation formula is as follows:
wherein,are each O-xlylzlCoordinate system and O-xryrzrA translation transformation vector between the origin and a rotation matrix between the coordinate systems.
For O-xlylzlThe corresponding relation between the space points in the coordinate system and the image surface points of the two cameras is as follows:
one point P on the mobile phone1(X1,Y1,Z1) The three-dimensional coordinates of (a) are:
wherein,Pl、Primage coordinates of the spatial point P in the left and right cameras, M, respectivelyl、MrProjection matrices, X, for left and right cameras, respectivelywThree-dimensional coordinates of the space points in a world coordinate system;
wherein,
Zc1、Zc2z-axis coordinates of the camera coordinate systems of the left and right cameras, respectively, (u)1,v11) and (u)2,v21) are each Pl、PrThe homogeneous coordinate of the point in each image, (X, Y, Z,1) is the homogeneous coordinate of the point P in a world coordinate system;representing the ith row and jth column element of the projection matrix.
In the same way, the characteristic point P corresponding to P on the mobile phone is obtained2(X2,Y2,Z2) Three-dimensional coordinates of (a).
And S22, correcting the camera, removing the influence of optical distortion, and changing the binocular camera into a standard form.
S23, binocular matching, and calculating matching points between cameras to obtain a disparity map; which comprises the following steps:
1) and (3) matching error calculation:
let the X-direction extraction precision of the left and right cameras be X1、X2The extraction precision in the Y direction is Y1、Y2Then, the measurement accuracy of a point P on the mobile phone in the X direction is:
the measurement accuracy of P in the Y direction is:
the measurement accuracy of P in the Z direction is:
the overall measurement accuracy of the point P is then:
2) and integrating the matching errors.
3) And (3) calculating a disparity map:
the pixel size of a target image I to be matched is set to be M × N, the pixel size of a template T is M × N, and a sub-image I with the pixel size of M × N is randomly selected from the target image Ix,yThe coordinates of the upper left corner in the image I are (x, y), wherein x is more than or equal to 0 and less than or equal to M-M, y is more than or equal to 0 and less than or equal to N-N, M and N are the number of rows and columns of the image pixels to be matched respectively, and M and N are the number of rows and columns of the template pixels respectively; r (x, y) is subfigure Ix,yAnd normalized cross-correlation value of template T, then:
wherein (i, j) is the coordinate of the pixel in the template,
is sub-diagram Ix,yThe pixel average value of (a);
is the pixel average of the template T.
4) Processing the disparity map: median filtering is used to remove salt-pepper noise of the disparity map and outliers of matching failures.
S24, calculating the thickness of the mobile phone according to the parallax map:
from P, P2 obtained by the above calculation, the spatial distance d between two points is calculated:
s25, converting the pixel size of the thickness of the mobile phone into an actual size:
D＝kd
where D is the actual size, D is the pixel size, and k is the corresponding linear coefficient.
S3, monocular vision measures the length, width and fillet information of the mobile phone, the calibrated right camera is used for collecting images, and the image processing flow is as follows:
s31, detecting edge pixels to obtain the mobile phone outer contour line:
1) detecting edge pixels to obtain a plane profile of the mobile phone;
2) and selecting the contour line with the maximum length as the mobile phone outer contour line.
S32, converting the outer contour line into a minimum external rectangle, and calculating the length and width dimensions of the rectangle:
and taking the minimum circumscribed rectangle, taking characteristic points of the upper left corner and the lower right corner, respectively recording the characteristic points as (Row1, Column1) and (Row2, Column2), subtracting the two rows to obtain the width of the rectangle, and subtracting the two columns to obtain the length of the rectangle, thereby obtaining the pixel size of the length and the width of the medicinal bagged infusion.
S33, dividing the mobile phone fillet area, and calculating characteristic parameters of the mobile phone fillet area, including arc length, radian and radius:
1) converting the outer contour line into a minimum convex polygon to obtain a central O point coordinate;
2) and taking the center O of the convex polygon as an end point, generating a transverse straight line parallel to the upper bottom edge of the convex polygon, wherein the direction of the transverse straight line is horizontal to the left, and the transverse straight line is intersected with the left side edge of the convex polygon at the point A. Similarly, a transverse straight line parallel to the upper bottom side of the convex polygon is generated by taking the center as an end point, the direction is horizontally rightward, and the transverse straight line intersects with the right side of the convex polygon at the point B. Similarly, a longitudinal straight line parallel to the left side of the convex polygon is generated by taking the center of the convex polygon as an end point, and the direction of the longitudinal straight line is horizontally upward and is intersected with the upper bottom edge of the convex polygon at the point C. Similarly, a longitudinal straight line parallel to the left side of the convex polygon is generated by taking the center of the convex polygon as an end point, and the longitudinal straight line is horizontally downward and intersects with the lower bottom edge of the convex polygon at the point D.
3) The upper left region is scanned. The generated transverse straight line OA is moved horizontally upward and scanned line by line. The length of the line segment OA is calculated one pixel at a time. Similarly, the generated longitudinal straight line OC is moved vertically to the left, and is scanned column by column, and the length of the line segment OC is calculated by moving one pixel at a time.
4) When the segment OA length value jumps (i.e., decreases significantly) from a steady state, the scan is stopped. Record the pixel coordinate A of the pointi(xi,ym) And Ci(xp,yj) The points are two end points of the fillet at the upper left corner of the mobile phone.
5) And the circular arc part is divided by the coordinates of the end points of the circular bead. And calculating characteristic parameters of the arc, including arc length, arc degree and radius.
6) The upper right, lower left, and lower right regions were scanned in the same manner. Determining the coordinates of the fillet end points, segmenting the arc part, and calculating the characteristic parameters of the arc part; wherein, rad is L/R, rad is radian, L is arc length, and R is radius.
And S34, converting the obtained pixel size into an actual size.
And S4, comparing the actual size data measured in the steps S2 and S3 with standard values, calculating difference values and generating a report.
And S5, according to the report, the mobile phone which exceeds the allowable error range is regarded as a defective product, and the defective product is sorted to a repair area by the manipulator.
The detection method has the advantages of high detection result accuracy, high detection efficiency, low detection cost, non-contact property and the like.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that variations based on the shape and principle of the present invention should be covered within the scope of the present invention.
Claims (7)
1. A mobile phone appearance detection method based on single and binocular vision is characterized by comprising the following steps: the thickness of the mobile phone is measured through binocular vision, the length and width of the mobile phone and the fillet information are measured through monocular vision, detected appearance data of the mobile phone are compared with a standard value, a difference value is calculated, a report is generated, the mobile phone beyond an allowable error range is considered as a defective product according to the report, and the defective product is sorted to a repair area through a manipulator.
2. The mobile phone appearance detection method based on the single and binocular vision according to claim 1, wherein the mobile phone appearance detection method comprises the following steps: the binocular vision measurement mobile phone thickness specifically comprises the following steps:
1) calibrating a binocular camera to obtain internal and external parameters and distortion coefficients of a left camera and a right camera;
2) correcting the camera, removing the influence of optical distortion, and changing the binocular camera into a standard form;
3) binocular matching, namely calculating matching points between cameras to obtain a disparity map;
4) calculating the thickness of the mobile phone according to the disparity map;
5) and converting the pixel size of the thickness of the mobile phone into an actual size.
3. The mobile phone appearance detection method based on the single and binocular vision according to claim 2, wherein the mobile phone appearance detection method comprises the following steps: the binocular camera calibration in the step 1) comprises the following specific steps:
1-1) calibrating a left camera to obtain internal and external parameters of the camera;
1-2) calibrating a camera of a right camera to obtain internal and external parameters of the camera;
1-3) binocular calibration, and acquiring the translation and rotation relationship between the cameras.
4. The mobile phone appearance detection method based on the single and binocular vision according to claim 2, wherein the mobile phone appearance detection method comprises the following steps: the specific steps of binocular matching in the step 3) are as follows:
3-1) calculating a matching error;
3-2) matching error integration;
3-3) calculating a disparity map;
3-4) disparity map processing.
5. The method for detecting the appearance of the mobile phone based on the monocular and binocular vision as claimed in claim 4, wherein the method comprises the following steps: the step 3-3) of calculating the disparity map specifically comprises the following steps: and calculating the disparity map by adopting a global stereo matching algorithm based on a graph cut algorithm.
6. The method for detecting the appearance of the mobile phone based on the monocular and binocular vision as claimed in claim 4, wherein the method comprises the following steps: the processing of the disparity map in the step 3-4) specifically comprises the following steps: median filtering is used to remove salt-pepper noise of the disparity map and outliers of matching failures.
7. The mobile phone appearance detection method based on the single and binocular vision according to claim 1, wherein the mobile phone appearance detection method comprises the following steps: the specific steps of measuring the length, width and fillet information of the mobile phone through monocular vision are as follows:
(1, detecting edge pixels to obtain an outer contour line of the mobile phone;
(2, converting the outer contour line into a minimum circumscribed rectangle, and calculating the length and width dimensions of the rectangle;
(3 dividing a mobile phone fillet area, and calculating characteristic parameters of the mobile phone fillet area, including arc length, radian and radius;
(4 convert the resulting pixel size to real size.
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| CN109297402A (en) * | 2018-08-21 | 2019-02-01 | 苏州图锐智能科技有限公司 | Tin cream detection device based on technique of binocular stereoscopic vision |
| CN109878113A (en) * | 2019-04-04 | 2019-06-14 | 成都联科航空技术有限公司 | For cutting the detection method of the automatic blanking machine cutting accuracy of carbon fiber prepreg |
| CN110069131A (en) * | 2019-03-01 | 2019-07-30 | 中国人民解放军军事科学院国防科技创新研究院 | A kind of more finger tip localization methods based on the detection of near infrared light round spot |
| CN110360934A (en) * | 2019-07-15 | 2019-10-22 | 北海市龙浩光电科技有限公司 | A method of measurement globoidal glass cover board |
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