CN114332066A - Pinhole alignment detection method and device based on image difference - Google Patents
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Abstract
The invention discloses a pinhole alignment detection method and a pinhole alignment detection device based on image difference. The invention can accurately detect the relative position of the positioning hole of the element to be detected and the positioning pin of the substrate, has high real-time performance and can greatly improve the production efficiency.
Description
Technical Field
The invention relates to the field of visual detection, in particular to a pinhole alignment detection method and a pinhole alignment detection device based on image difference.
Background
In the precision machining process, the workpiece alignment link is very important. Along with the continuous accumulation of mechanical motion errors, inaccurate positioning is caused; a workpiece alignment detection method and device with high reliability and good real-time property are urgently needed;
the existing detection method for workpiece alignment based on machine vision has poor adaptability to illumination and environmental change, high false detection rate, low reliability and poor real-time performance;
disclosure of Invention
In order to solve the technical problems, the invention provides a pinhole alignment detection method and a pinhole alignment detection device based on image difference, so that the relative position of a positioning hole of an element to be detected and a positioning needle of a substrate can be accurately detected, and the reliable detection of whether two workpieces are aligned is realized, thereby improving the real-time performance of detection and greatly improving the production efficiency.
In order to achieve the purpose, the invention adopts the following technical measures:
the invention relates to a pinhole alignment detection method based on image difference, which is characterized by comprising the following steps of:
s1: acquiring a substrate gray image B and a gray image F to be detected, and calculating a difference image D of the substrate gray image B and the gray image F to be detected according to the formula (1); determining a pixel coordinate set U { (U) of a localization area in the difference image Dr1,uc1),(ur2,uc2)…(urk,uck) And an edge pixel coordinate set C { (C) of the positioning holer1,cc1),(cr2,cc2)…(crt,cct)…(crm,ccm) Wherein (u)rk,uck) Indicating locationThe coordinates of the kth pixel point in the hole region, (c)rt,cct) Expressing the coordinate of the t-th pixel point at the edge of the positioning circular hole, wherein k is the number of the pixel points in the positioning circular hole area, and m is the number of the pixel points at the edge of the positioning circular hole; t is an element of [1, m ∈]；
In the formula (1), F (x, y) represents a pixel point at (x, y) position in the gray image F to be detected, B (x, y) represents a pixel point at (x, y) position in the substrate gray image B, D (x, y) represents a pixel point at (x, y) position in the differential image D, T is a non-negative threshold value, T1Is the gray value of the foreground point pixel in the difference image D, t2The pixel gray value of the background point in the differential image D is obtained;
s2: sequentially carrying out morphology opening operation, morphology closing operation, Gaussian filtering and bilateral filtering on the substrate gray level image B, and determining a pixel coordinate set A { (a) of the locator pin region by OTSU global threshold segmentationr1,ac1),(ar2,ac2)…(ari,aci) And the edge pixel coordinate set of the pin N { (N)r1,nc1),(nr2,nc2)…(nrs,ncs)…(nrj,ncj) Wherein (a)ri,aci) Coordinates of pixel points representing the ith stylus region, (n)rs,ncs) Expressing the coordinates of the s-th pixel point at the edge of the positioning pin, wherein i is the number of pixel points in the positioning pin area, and j is the number of pixel points at the edge of the positioning pin; s is an element of [1, j ]]；
S3: judging whether the positioning pin is aligned with the positioning hole according to the pixel coordinate set U, C, A, N;
s31: establishing a positioning hole target optimization function H by using a formula (2) according to the pixel coordinate set C of the positioning hole;
in the formula (2), X1,X2,X3,X4,X5Five parameters of the positioning hole are set according to an ellipse general equation;
establishing a target optimization function G of the positioning needle by using a formula (3) according to the pixel coordinate set N of the positioning needle;
in the formula (3), Y1,Y2,Y3,Y4,Y5Five parameters of the positioning needle are set according to an ellipse general equation;
solving the optimal solution X when the target optimization functions H and G take the minimum value1 *、X2 *、X3 *、X4 *X5 *And the optimal solution Y1 *、Y2 *、Y3 *、Y4 *、Y5 *；
S32: the center coordinates (X) of the positioning holes are obtained by using the formulas (4) to (6)C,YC) And major and minor axes ac,bc；
The center coordinates (X) of the positioning needle are obtained by using the expressions (7) and (9)N,YN) And major and minor axes an,bn；
S33: calculating a distance threshold value R between the positioning needle and the positioning hole by using the formula (10), and judging whether the formula (11) is satisfied, if so, indicating that the positioning needle and the positioning hole are aligned, otherwise, indicating that the positioning needle and the positioning hole are not aligned:
R＝Kc(β1ac+β2bc)+Kn(β3an+β4bn) (10)
(XN-XC)2+(YN-YC)2＜R2 (11)
in the formula (10), Kc,KnCoefficient, beta, representing the distance threshold between the pilot hole and the pilot pin1,β2,β3,β4And the weight coefficient of the radius of the positioning pin and the radius of the positioning hole is calculated.
The pinhole alignment detection method based on image difference is also characterized in that the method for judging whether the positioning needle is aligned with the positioning hole in the step S3 is replaced by the following steps:
counting the number P of the same pixel coordinates in the pixel coordinate set U and the pixel coordinate set A; if P > TbIf yes, the positioning pin is judged to be aligned with the positioning hole; otherwise, judging that the positioning pin is not aligned with the positioning hole, wherein TbIndicating a predetermined threshold.
The invention relates to a pinhole alignment detection device based on image difference, which is characterized by comprising the following components: the system comprises an image acquisition unit, a controller unit and an in-place detection unit;
the image acquisition unit acquires a current image on the detection area and sends the current image to the controller unit;
the in-place detection unit judges whether the element to be detected is placed in place or not by using a sensor; if no element to be detected exists, sending a no element to be detected signal Sig1 to the controller unit; if the element is to be detected and the element to be detected is placed completely, sending a placing-in-place signal Sig2 to the controller unit;
the method comprises the steps that if a signal Sig1 is received, a current image is grayed to be used as a substrate gray image B, if a signal Sig2 is received, an image acquisition unit is controlled to acquire the image and grayed to obtain a gray image F to be detected, a difference image D, a pixel coordinate set U of a positioning hole area and an edge pixel coordinate set C of a positioning hole are obtained according to the substrate gray image B and the gray image F to be detected, the substrate image B is preprocessed in sequence and then is subjected to OTSU global threshold segmentation, a pixel coordinate set A of the positioning pin area and an edge pixel coordinate set N of the positioning pin are obtained, and therefore whether the positioning pin is aligned with the positioning hole or not is judged according to the pixel coordinate set U, C, A, N.
Compared with the prior art, the invention has the beneficial effects that:
1) the invention combines the actual production process, and avoids the influence caused by illumination or environmental change by continuously updating the substrate image in real time; when the element to be detected covers the substrate, an image difference method is adopted, the outline of the positioning circular hole of the element to be detected is effectively divided, and therefore the detection precision is improved.
2) According to the invention, various filtering modes are combined according to the noise condition and the gray characteristic of the image without the element to be detected, so that the influence of fine scratches and reflective spots on the surface of the substrate image is effectively eliminated, the outline of the positioning pin is effectively extracted, and the detection precision is further improved.
3) The device comprises an image acquisition unit, a controller unit, an in-place detection unit, a display unit and an input unit, and the detection of the alignment of the pinhole is completed based on an image difference method.
Drawings
FIG. 1 is a schematic view of the overall structure of the detecting device of the present invention;
FIG. 2 is a flow chart of the detection method of the present invention;
reference numbers in the figures: 1 a controller unit; 2, a human-computer interaction unit; 3 an image acquisition unit; 4 to the bit detection unit.
Detailed Description
In this embodiment, as shown in fig. 1, an image difference-based pinhole alignment detection apparatus includes: the system comprises a controller unit 1, a human-computer interaction unit 2, an image acquisition unit 3 and an in-place detection unit 4;
the image acquisition unit 3 mainly comprises an image pickup element, wherein the image pickup element is an industrial camera of Maidword VCGE-503C; the system is connected with the controller unit 1 by using a communication bus of Ethernet communication, fixes the camera element right above the area to be detected, and sends the acquired current image on the detection substrate to the controller unit 1 and the in-place detection unit 4 frame by frame;
the controller unit 1 comprises an ARM chip based on a Linux system, the model of the ARM chip is BCM2711, and an OPENCV visual open source library and a QT application program are installed in the Linux system;
the in-place detection unit 4 comprises a first photoelectric sensor 41 and a second photoelectric sensor 42, wherein the first photoelectric sensor 41 is used for detecting whether a component to be detected exists or not, and if the component to be detected does not exist, a signal Sig1 of no component to be detected is sent to the controller unit 1; the second photoelectric sensor 42 is used for detecting whether the element to be detected is placed in place, and if the element to be detected is placed in place, sending a placing-in-place signal Sig2 to the controller unit 1;
if the controller unit 1 receives the signal Sig1, graying the current image to be used as a substrate grayscale image B, if the signal Sig2 is received, controlling the image acquisition unit to acquire the image and graying the image to obtain a grayscale image F to be detected, obtaining a difference image D, a pixel coordinate set U of the positioning hole area and an edge pixel coordinate set C of the positioning hole according to the substrate grayscale image B and the grayscale image F to be detected, sequentially preprocessing the substrate image B, and then dividing the preprocessed substrate image B through an OTSU global threshold value to obtain a pixel coordinate set a of the positioning pin area and an edge pixel coordinate set N of the positioning pin, thereby judging whether the positioning pin is aligned with the positioning hole according to the pixel coordinate set U, C, A, N.
The human-computer interaction unit 2 comprises input equipment such as a keyboard and a mouse and a display supporting an HDMI interface, is used for setting the acquisition frame rate of the image acquisition unit, selecting a detection area, setting parameters such as a detection threshold value and the like, displaying an acquired image sequence, detecting the distance deviation between the currently detected locating pin and the circle center of the locating round hole, detecting results and the like, and can compile a human-computer interaction program based on a QT open source library.
In this embodiment, a pinhole alignment detection method based on image difference, as shown in fig. 2, includes the following steps:
s1: acquiring a substrate gray image B and a gray image F to be detected, and calculating a difference image D of the substrate gray image B and the gray image F to be detected according to the formula (1);
in the formula (1), F (x, y) represents a pixel point at (x, y) position in the gray image F to be detected, B (x, y) represents a pixel point at (x, y) position in the substrate gray image B, D (x, y) represents a pixel point at (x, y) position in the differential image D, T is a non-negative threshold value, T1Is the gray value of the foreground point pixel in the difference image D, t2The pixel gray value of the background point in the differential image D is obtained; d (x, y) is a binary image after calculation, and the pixel value of the positioning round hole area of the image to be measured is changed into t1(ii) a In this example, t1Take 255, t2Taking 0, and taking 50 from T;
determining a pixel coordinate set U { (U) of a localization area in the difference image Dr1,uc1),(ur2,uc2)…(urk,uck) And performing CANNY edge detection on the difference image D, searching the peripheral outline of the positioning hole by using a FindContours function based on an OPENCV visual open source library, and determining the positioning holeIs equal to { (C)r1,cc1),(cr2,cc2)…(crt,cct)…(crm,ccm) Wherein (u)rk,uck) Representing the k-th pixel point coordinate in the positioning hole area; (c)rt,cct) Expressing the coordinate of the t-th pixel point at the edge of the positioning hole, wherein k is the number of the pixel points in the area of the positioning hole, and m is the number of the pixel points at the edge of the positioning hole; t is an element of [1, m ∈]；
S2: sequentially carrying out morphology opening operation, morphology closing operation, Gaussian filtering and bilateral filtering on the substrate gray level image B, removing the influence of noise points such as scratches and light spots, and determining a pixel coordinate set A { (a) of the locator pin region by OTSU global threshold segmentationr1,ac1),(ar2,ac2)…(ari,aci) And finally, searching the peripheral outline of the positioning pin by using an OPENCV visual open source library through CANNY edge detection and by using a FindContours function based on an OPENCV visual open source library, and determining an edge pixel coordinate set N { (N) of the positioning pinr1,nc1),(nr2,nc2)…(nrs,ncs)…(nrj,ncj) Wherein (a)ri,aci) (n) coordinates of a pixel point representing the ith stylus regionrs,ncs) Expressing the coordinates of the s-th pixel point at the edge of the positioning pin, wherein i is the number of the pixel points in the positioning pin area, and j is the number of the pixel points at the edge of the positioning pin; s is an element of [1, j ]]；
S3: respectively carrying out least square ellipse fitting on the pixel coordinate sets C and N to obtain circle center coordinates and radii of the positioning hole and the positioning needle, and judging whether the positioning hole and the positioning needle are aligned according to the relative positions of the positioning hole and the positioning needle;
s31: establishing a positioning hole target optimization function H according to the formula (2) by using the pixel coordinate set C of the positioning hole;
in the formula (2), X1,X2,X3,X4,X5According to the general square of an ellipseSetting five parameters of the positioning hole;
establishing a target optimization function G of the positioning needle according to the formula (3) by using the pixel coordinate set N of the positioning needle;
in the formula (3), Y1,Y2,Y3,Y4,Y5Five parameters of the positioning needle are set according to an ellipse general equation;
solving the optimal solution X when the target optimization functions H and G take the minimum value1 *、X2 *、X3 *、X4 *、X5 *And the optimal solution Y1 *、Y2 *、Y3 *、Y4 *、Y5 *；
S32: the center coordinates (X) of the positioning holes are obtained by using the formulas (4) to (6)C,YC) And major and minor axes ac,bc；
The center coordinates (X) of the positioning needle are obtained by using the expressions (7) and (9)N,YN) And major and minor axes an,bn；
S33: calculating a distance threshold value R between the positioning needle and the positioning hole by using the formula (10), and judging whether the formula (11) is satisfied, if so, indicating that the positioning needle and the positioning hole are aligned, otherwise, indicating that the positioning needle and the positioning hole are not aligned:
R＝Kc(β1ac+β2bc)+Kn(β3an+β4bn) (10)
(XN-XC)2+(YN-YC)2＜R2 (11)
in the formula (10), Kc,KnCoefficient, beta, representing the distance threshold between the pilot hole and the pilot pin1,β2,β3,β4And the weight coefficient of the radius of the positioning pin and the radius of the positioning hole is calculated. In this example, KcTaking 1, KnTake 0, beta1,β2,β3,β4All are taken as 0.5;
in this embodiment, another method may be used to determine whether the positioning pin is aligned with the positioning hole: counting the number P of the same pixel coordinates in the pixel coordinate set U and the pixel coordinate set A; if P > TbIf yes, the positioning pin is judged to be aligned with the positioning hole; otherwise, judging that the positioning pin is not aligned with the positioning hole, wherein TbIndicating a predetermined threshold.
Claims (3)
1. A pinhole alignment detection method based on image difference is characterized by comprising the following steps:
s1: acquiring a substrate gray image B and a gray image F to be detected, and calculating a difference image D of the substrate gray image B and the gray image F to be detected according to the formula (1); at the placeThe difference image D has a set of pixel coordinates U { (U) for determining the location hole regionr1,uc1),(ur2,uc2)…(urk,uck) And an edge pixel coordinate set C { (C) of the positioning holer1,cc1),(cr2,cc2)…(crt,cct)…(crm,ccm) Wherein (u)rk,uck) The coordinates of the kth pixel point in the positioning hole area are shown, (c)rt,cct) Expressing the coordinate of the t-th pixel point at the edge of the positioning circular hole, wherein k is the number of the pixel points in the positioning circular hole area, and m is the number of the pixel points at the edge of the positioning circular hole; t is an element of [1, m ∈]；
In the formula (1), F (x, y) represents a pixel point at (x, y) position in the gray image F to be detected, B (x, y) represents a pixel point at (x, y) position in the substrate gray image B, D (x, y) represents a pixel point at (x, y) position in the differential image D, T is a non-negative threshold value, T1Is the gray value of the foreground point pixel in the difference image D, t2The pixel gray value of the background point in the differential image D is obtained;
s2: sequentially carrying out morphology opening operation, morphology closing operation, Gaussian filtering and bilateral filtering on the substrate gray level image B, and determining a pixel coordinate set A { (a) of the locator pin region by OTSU global threshold segmentationr1,ac1),(ar2,ac2)…(ari,aci) And the edge pixel coordinate set of the pin N { (N)r1,nc1),(nr2,nc2)…(nrs,ncs)…(nrj,ncj) Wherein (a)ri,aci) Coordinates of pixel points representing the ith stylus region, (n)rs,ncs) Expressing the coordinates of the s-th pixel point at the edge of the positioning pin, wherein i is the number of pixel points in the positioning pin area, and j is the number of pixel points at the edge of the positioning pin; s is an element of [1, j ]]；
S3: judging whether the positioning pin is aligned with the positioning hole according to the pixel coordinate set U, C, A, N;
s31: establishing a positioning hole target optimization function H by using a formula (2) according to the pixel coordinate set C of the positioning hole;
in the formula (2), X1,X2,X3,X4,X5Five parameters of the positioning hole are set according to an ellipse general equation;
establishing a target optimization function G of the positioning needle by using a formula (3) according to the pixel coordinate set N of the positioning needle;
in the formula (3), Y1,Y2,Y3,Y4,Y5Five parameters of the positioning needle are set according to an ellipse general equation;
solving the optimal solution X when the target optimization functions H and G take the minimum value1 *、X2 *、X3 *、X4 *、X5 *And the optimal solution Y1 *、Y2 *、Y3 *、Y4 *、Y5 *；
S32: the center coordinates (X) of the positioning holes are obtained by using the formulas (4) to (6)C,YC) And major and minor axes ac,bc；
The center coordinates (X) of the positioning needle are obtained by using the expressions (7) and (9)N,YN) And major and minor axes an,bn；
S33: calculating a distance threshold value R between the positioning needle and the positioning hole by using the formula (10), and judging whether the formula (11) is satisfied, if so, indicating that the positioning needle and the positioning hole are aligned, otherwise, indicating that the positioning needle and the positioning hole are not aligned:
R＝Kc(β1ac+β2bc)+Kn(β3an+β4bn) (10)
(XN-XC)2+(YN-YC)2＜R2 (11)
in the formula (10), Kc,KnCoefficient, beta, representing the distance threshold between the pilot hole and the pilot pin1,β2,β3,β4And the weight coefficient of the radius of the positioning pin and the radius of the positioning hole is calculated.
2. The method for detecting pinhole alignment based on image difference according to claim 1, wherein the method for determining whether the alignment of the positioning pin and the positioning hole is determined in step S3 is replaced by:
counting the number P of the same pixel coordinates in the pixel coordinate set U and the pixel coordinate set A; if P > TbIf yes, the positioning pin is judged to be aligned with the positioning hole; otherwise, judging that the positioning pin is not aligned with the positioning hole, wherein TbIndicating a predetermined threshold.
3. An image difference-based pinhole alignment detection device, comprising: the system comprises an image acquisition unit, a controller unit and an in-place detection unit;
the image acquisition unit acquires a current image on the detection area and sends the current image to the controller unit;
the in-place detection unit judges whether the element to be detected is placed in place or not by using a sensor; if no element to be detected exists, sending a no element to be detected signal Sig1 to the controller unit; if the element is to be detected and the element to be detected is placed completely, sending a placing-in-place signal Sig2 to the controller unit;
the method comprises the steps that if a signal Sig1 is received, a current image is grayed to be used as a substrate gray image B, if a signal Sig2 is received, an image acquisition unit is controlled to acquire the image and grayed to obtain a gray image F to be detected, a difference image D, a pixel coordinate set U of a positioning hole area and an edge pixel coordinate set C of a positioning hole are obtained according to the substrate gray image B and the gray image F to be detected, the substrate image B is preprocessed in sequence and then is subjected to OTSU global threshold segmentation, a pixel coordinate set A of the positioning pin area and an edge pixel coordinate set N of the positioning pin are obtained, and therefore whether the positioning pin is aligned with the positioning hole or not is judged according to the pixel coordinate set U, C, A, N.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0930499A1 (en) * | 1998-01-16 | 1999-07-21 | NEC Corporation | Automatic visual inspection apparatus, automatic visual inspection method, and recording medium having recorded an automatic visual inspection program |
| US20090238433A1 (en) * | 2008-03-21 | 2009-09-24 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Method and device for automatically detecting collimation edges |
| CN111080582A (en) * | 2019-12-02 | 2020-04-28 | 易思维（杭州）科技有限公司 | Method for detecting defects on inner surface and outer surface of workpiece |
| CN111562260A (en) * | 2020-04-14 | 2020-08-21 | 江苏大学 | Lotus root mud hole detection method and device based on machine vision |
| CN113344990A (en) * | 2021-04-27 | 2021-09-03 | 成都飞机工业（集团）有限责任公司 | Hole site representation projection system and hole site alignment method of self-adaptive fitting |
-
2022
- 2022-01-05 CN CN202210005075.3A patent/CN114332066B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0930499A1 (en) * | 1998-01-16 | 1999-07-21 | NEC Corporation | Automatic visual inspection apparatus, automatic visual inspection method, and recording medium having recorded an automatic visual inspection program |
| US20090238433A1 (en) * | 2008-03-21 | 2009-09-24 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Method and device for automatically detecting collimation edges |
| CN111080582A (en) * | 2019-12-02 | 2020-04-28 | 易思维（杭州）科技有限公司 | Method for detecting defects on inner surface and outer surface of workpiece |
| CN111562260A (en) * | 2020-04-14 | 2020-08-21 | 江苏大学 | Lotus root mud hole detection method and device based on machine vision |
| CN113344990A (en) * | 2021-04-27 | 2021-09-03 | 成都飞机工业（集团）有限责任公司 | Hole site representation projection system and hole site alignment method of self-adaptive fitting |
Non-Patent Citations (2)
| Title |
|---|
| 岑誉;高健;曾友;简川霞;: "面向PCB圆形基准点的权重式椭圆拟合定位方法", 组合机床与自动化加工技术, no. 04, 20 April 2015 (2015-04-20) * |
| 王义文;屈冠彤;刘献礼;付鹏强;李博;: "钢球表面缺陷的图像差分检测算法", 计算机辅助设计与图形学学报, no. 10, 15 October 2016 (2016-10-15) * |
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