CN113720280A - Bar center positioning method based on machine vision - Google Patents
Bar center positioning method based on machine vision Download PDFInfo
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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
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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
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Abstract
The invention discloses a method for positioning the circle center of a bar stock based on machine vision, which is used for determining the circle center of a target round bar in a stacked round bar stock and comprises the following steps: calibrating a camera of a machine vision system for acquiring a round bar image; automatically adjusting the exposure time of a camera, and acquiring an image of which the ROI area meets the requirement; extracting the contour geometric characteristics of the end face of the round bar to be processed to form a characteristic template; searching and matching the ROI area image by using the characteristic template, and identifying the contour of the round bar and the pixel coordinates of the circle center of the round bar; determining the outline of the target round bar based on the pixel coordinates of the circle center; regular circle inverse fitting is adopted for the contour of the target round bar, and the final circle center pixel coordinate is determined; and converting the final circle center pixel coordinate into a space position coordinate to finish positioning. The invention can realize the accurate positioning of the circle center of the accumulated round bar material and can realize the control of the manipulator to accurately move to a preset position for taking the material.
Description
Technical Field
The invention relates to the technical field of machine vision, in particular to a method for positioning the circle center of a bar based on machine vision.
Background
Due to the characteristics of the round bars, the self-adaptive feeding of the round bars with multiple specifications becomes an urgent problem to be solved by an automatic feeding system. In order to cooperate with production and improve the feeding automation of the round bar stock, an X, Y-axis manipulator is needed to convey the round bar stock in the triangular stacking frame to a station one by one through a clamp; when the clamp takes materials, round bar materials with different specifications need to be accurately positioned so as to ensure that the round bar materials to be conveyed can be just placed at the preset position of the clamp. However, due to the particularity of the round bar, the requirement of the process is met when the common sensor is used for detecting and positioning, and the round bar cannot be placed at the preset position of the clamp.
Disclosure of Invention
The invention aims to provide a bar center positioning method based on machine vision aiming at the technical defects in the prior art, which is used for high-precision visual identification and positioning of round bars under various illumination conditions in a production field and realizes the precise identification and positioning of the round bars on an aviation forging production line through the machine vision technology.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a method for positioning the center of a circle of a bar stock based on machine vision is used for determining the center of a target round bar in a stacked round bar stock and comprises the following steps:
s1, calibrating a camera of a machine vision system for acquiring a round bar image;
s2, automatically adjusting the exposure time of the camera to obtain an image with an ROI meeting the requirement;
s3, extracting the contour geometric characteristics of the end face of the round bar to be processed to form a characteristic template;
s4, searching and matching the ROI area image by using the characteristic template, and identifying the contour of the round bar and the pixel coordinates of the circle center of the round bar;
s5, determining the outline of the target round bar based on the circle center pixel coordinates;
s6, performing inverse fitting on the contour of the target round bar by adopting a regular circle to determine a final circle center pixel coordinate;
and S7, converting the final circle center pixel coordinate into a space position coordinate to finish positioning.
As a preferred technical scheme, a nine-point calibration method is adopted, internal parameters and external parameters of the camera are obtained through affine transformation, the coordinate relation between the spatial position of a real object and a corresponding point in an image is solved, and the calibration of the camera is achieved.
As a preferable technical scheme, the exposure time of the camera is automatically adjusted according to the gray value of the ROI area image and the image entropy threshold value.
As a preferred technical solution, the step of automatically adjusting the exposure time of the camera is as follows:
dividing the ROI area into an upper region and a lower region according to the gray value difference:
respectively calculating the average gray value of the upper area and the lower area as the gray value of the area;
respectively calculating the image entropy of the upper region and the lower region;
and when the gray value of the ROI where the round bar is located is within the designated gray average value interval value and is larger than the designated image entropy threshold value, the camera exposure time is obtained.
As a preferred technical solution, the step of determining the round bar to be conveyed in the image based on the circle center coordinates is as follows:
and in the ROI area image, based on the identified circle center pixel coordinate of the circle center outline, taking the circle center outline corresponding to the circle center pixel coordinate with the maximum numerical value on the Z axis as the target circle center outline. .
According to the bar center positioning method based on machine vision, the center of the accumulated round bar can be accurately positioned through the technical scheme, the manipulator is conveniently controlled to accurately move to the corresponding feeding position so as to correspondingly take materials from the material frame, and the round bar is conveyed to the target position.
Drawings
FIG. 1 is a flow chart of a method for positioning the center of a bar based on machine vision according to an embodiment of the present invention;
FIG. 2 is a ROI area of a round bar stock of an embodiment of the invention;
FIG. 3 is a trained round bar profile geometry according to an embodiment of the present invention;
FIG. 4 is a result of matching round bar stock according to an embodiment of the present invention;
FIG. 5 is a diagram of a round bar profile and center of a circle resulting from a back-fit of an embodiment of the present invention;
FIG. 6 shows a visual recognition system apparatus and an object to be recognized using the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the embodiment of the invention, the stacked round bar stock 2 is stacked in the V-shaped stock frame 1 in a triangular shape as shown in fig. 6, the camera 3 of the machine vision system is arranged behind the round bar stock 2, and the image of the round bar stock in the ROI area is acquired to realize the center positioning of the round bar stock, so that the manipulator which is used for controlling the movement in the direction X, Y takes the clamp to move to a proper position to take the round bar stock which is well positioned and identified for conveying.
As shown in fig. 1 to 6, a method for positioning a center of a bar stock based on machine vision according to an embodiment of the present invention is used to determine a center of a target circular bar in a stacked circular bar stock, and includes the following steps:
s1, camera calibration:
in order to obtain accurate center coordinates of the bar, the preprocessing of coordinate conversion is needed before the analysis processing of the image, namely, the calibration of the camera is carried out.
As an optional embodiment, when calibrating the camera, a nine-point calibration method may be adopted, 9 points are selected from a circular bar region to perform camera calibration, internal parameters and external parameters of the camera are obtained by affine transformation, so as to solve a coordinate relationship between a spatial position of the real object and a corresponding point in the image, and then an accurate position of the real object in a real space may be analyzed according to the coordinate relationship in the image.
For example, assuming that the pixel coordinate of the center of a certain round bar in the image is (u, v), and the corresponding manipulator coordinate is (x, y), there are:
wherein S isxAnd TxScaling and translation transformation coefficients in the x-axis direction; syAnd TyScaling and translation transformation coefficients in the y-axis direction; and theta is the corresponding rotation angle. The formula is arranged as follows:
specifically, 9 groups of data are obtained by a nine-point calibration method, and each parameter (all transformation coefficients and rotation angle values) of affine transformation is further solved by a least square method, so that the identification precision of the camera is further improved, and the calibration of the camera is realized.
S2, automatically adjusting exposure time to obtain an image meeting the requirements:
as a preferred embodiment, the invention can use the ROI area (a sector area) gray level average value and the image entropy threshold value as the optimizing conditions, adjust the exposure time of the camera, and obtain the best round bar image when the image area where the round bar is located meets the gray level average value interval of the best area and is larger than the designated image entropy threshold value.
Specifically, the exposure time of the camera can be adjusted for multiple times by using the above-mentioned technology until the exposure time meeting the conditions is found as the optimal exposure time for photographing by the camera, and the image of the round bar stock is acquired.
Since there is a large difference in the natural illumination intensity of the upper region and the lower region of the ROI region (e.g., the V-shaped region shown in fig. 2), and the illumination difference of the regions in the same height is small, for an optimal round bar image, the embodiment of the present invention divides the ROI into the upper region (sector) and the lower region (sector) according to the gray value difference, and uses the average of the regional gray values as the gray value of the region. The calculation formula of the average gray value of each area is as follows:
wherein, M multiplied by N is the number of pixel points contained in the image area; g (m, n) is the gray scale value of the point (m, n) in the image.
Besides the gray values of the regions, the image entropy of each region is also one of indexes for describing image information and characteristics; in a gray image, the one-dimensional entropy can reflect the information amount contained in the image, and the larger the value of the image entropy is, the more information the image contains. Illustratively, the calculation formula is as follows:
wherein p isiThe proportion of the pixel points with the gray value i in the graph is represented; k represents the number of gray levels of the image.
After the average gray value and the image entropy of each region are determined, whether the circular bar stock image meets the requirements or not can be judged according to the average gray value and the image entropy of each region and the corresponding threshold value, and whether the circular bar stock image meeting the requirements can be formed or not can be judged so as to meet the requirements of later image processing.
The method can select a proper area gray value range and an image entropy threshold value through actual test as the basis for adjusting the exposure time of the camera.
S3, training the geometric characteristics of the round bar:
performing feature training on a round end face of a round bar to be processed in actual production, extracting profile shape features of the end face of the round bar, wherein the profile shape features are edge features of the round bar, and are obtained by performing edge extraction on an original image of the round bar, then dividing the obtained edge features into a plurality of geometric elements, and matching the divided geometric elements in an image to be searched, so that the image to be searched is searched and matched;
s4, identifying the round bar stock:
on the basis of the step S3, searching and matching the round bar materials in the target image according to the trained geometric shape characteristics of the profile, and identifying and matching to obtain a plurality of round bar material profiles and circle center pixel coordinates thereof;
it should be noted that, in step S4, the profile of the round bar obtained by searching and matching may be irregular, that is, not a regular circle, and there are many influencing factors for forming this phenomenon, where it is more intuitive that the views obtained by observing the round bar at different positions at the same position are not a standard circle; therefore, in this step, after the coordinates of the circle center pixels are preliminarily determined, the coordinates of the circle center pixels may be inaccurate, so that subsequent steps are required to further identify and position the circle center, so as to improve the identification precision;
wherein, the center pixel coordinate is the centroid of the irregular circle;
s5, circle center sequencing:
according to the circle center pixel coordinates of the plurality of circular bar material outlines in the target image identified in the step S4, finding out the circular bar material outline corresponding to the maximum coordinate of the Z axis (in the vertical direction, one side of the sector ROI area is taken as an X axis, and the other side is taken as a Y axis) to be used as a target bar material;
s6, inverse fitting:
performing inverse fitting operation on the profile of the round bar obtained in the step S5, fitting the profile of the bar by adopting a regular circle on the basis of the irregular round profile obtained by matching in the step S4, determining the final circle center pixel coordinate, and completing the positioning of the round bar; the accuracy of circle center identification of the round bar is further improved through inverse fitting;
s7, coordinate conversion:
and converting the final pixel coordinate of the circle center into a spatial position coordinate according to the coordinate relationship between the spatial position of the real object obtained in the step S1 and the corresponding point in the image, thereby completing circle center positioning.
According to the bar center positioning method based on machine vision, the center of the accumulated round bar can be accurately positioned through the technical scheme, the manipulator is conveniently controlled to accurately move to the corresponding feeding position so as to correspondingly take materials from the material frame, and the round bar is conveyed to the target position.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (8)
1. The method for positioning the circle center of the bar stock based on machine vision is characterized by being used for determining the circle center of a target round bar in stacked round bar stocks and comprising the following steps of:
s1, calibrating a camera of a machine vision system for acquiring a round bar image;
s2, automatically adjusting the exposure time of the camera to obtain an image with an ROI meeting the requirement;
s3, extracting the contour geometric characteristics of the end face of the round bar to be processed to form a characteristic template;
s4, searching and matching the ROI area image by using the characteristic template, and identifying the contour of the round bar and the pixel coordinates of the circle center of the round bar;
s5, determining the outline of the target round bar based on the circle center pixel coordinates;
s6, performing inverse fitting on the contour of the target round bar by adopting a regular circle to determine a final circle center pixel coordinate;
and S7, converting the final circle center pixel coordinate into a space position coordinate to finish positioning.
2. The method for positioning the circle center of the bar stock based on the machine vision as claimed in claim 1, wherein a nine-point calibration method is adopted, the internal parameter and the external parameter of the camera are obtained by affine transformation, and the coordinate relationship between the spatial position of the real object and the corresponding point in the image is solved to realize the calibration of the camera.
3. The method for positioning the circle center of the bar stock based on the machine vision as claimed in claim 1, wherein the exposure time of the camera is automatically adjusted according to the gray value of the ROI area image and the image entropy threshold.
4. The method for positioning the center of a bar according to claim 3, wherein the step of automatically adjusting the exposure time of the camera comprises the following steps:
dividing the ROI area into an upper region and a lower region according to the gray value difference:
respectively calculating the average gray value of the upper area and the lower area as the gray value of the area;
respectively calculating the image entropy of the upper region and the lower region;
and when the gray value of the ROI where the round bar is located is within the designated gray average value interval value and is larger than the designated image entropy threshold value, the camera exposure time is obtained.
5. The method for positioning the circle center of the bar stock based on the machine vision as claimed in claim 1, wherein the step of determining the round bar to be conveyed in the image based on the circle center coordinates comprises the following steps:
and in the ROI area image, based on the identified circle center pixel coordinate of the circle center outline, taking the circle center outline corresponding to the circle center pixel coordinate with the maximum numerical value on the Z axis as the target circle center outline.
6. The method for positioning the center of a bar according to claim 1, wherein the round bars are stacked in a V-shaped frame in a triangular shape.
7. The method for positioning the center of a bar according to claim 4, wherein the ROI area is a sector area.
8. The method for positioning the center of a bar according to claim 7, wherein the ROI area is divided into an upper sector and a lower sector.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH02162247A (en) * | 1988-12-16 | 1990-06-21 | Kawasaki Steel Corp | Automatic surface-shape measuring apparatus |
| JP2006049755A (en) * | 2004-08-09 | 2006-02-16 | Omron Corp | Rotation center calculation method and work positioning device using the same |
| CN101334263A (en) * | 2008-07-22 | 2008-12-31 | 东南大学 | Circular target circular center positioning method |
| CN104123542A (en) * | 2014-07-18 | 2014-10-29 | 大连理工大学 | Device and method for positioning wheel hub work piece |
| CN104457577A (en) * | 2014-12-19 | 2015-03-25 | 上海工业自动化仪表研究院 | Machine-vision-oriented non-contact type workpiece positioning and measuring method |
| CN105258647A (en) * | 2015-07-26 | 2016-01-20 | 湖北工业大学 | Visual detection method of automobile lock catch rivet point |
| CN107398750A (en) * | 2017-06-09 | 2017-11-28 | 湖北亚科微钻有限公司 | Fixture, bar plane verticality detection means and method are used in cutting tool production |
| CN108169246A (en) * | 2017-11-28 | 2018-06-15 | 威海华菱光电股份有限公司 | Pole detecting system and the detection method for pole |
| CN108844471A (en) * | 2018-08-02 | 2018-11-20 | 成都天衡智造科技有限公司 | Measurement method and device of a kind of pair of round piece marginal trough region to center of circle development length |
| CN109084675A (en) * | 2018-06-04 | 2018-12-25 | 哈尔滨工业大学 | Center of circle positioning device and method based on Embedded geometrical characteristic in conjunction with Zernike square |
| CN110766018A (en) * | 2019-12-30 | 2020-02-07 | 江苏金恒信息科技股份有限公司 | Automatic counting method and device for bars |
| CN111062940A (en) * | 2019-12-31 | 2020-04-24 | 西南交通大学 | Screw positioning and identifying method based on machine vision |
| CN111899296A (en) * | 2020-06-24 | 2020-11-06 | 广西大学 | Method and device for detecting log volume based on computer vision |
| CN112132886A (en) * | 2020-09-30 | 2020-12-25 | 哈尔滨理工大学 | Circular hole center rapid positioning and roundness detection method for aviation part |
| JP2021071371A (en) * | 2019-10-30 | 2021-05-06 | 株式会社ディスコ | Center positioning method |
| CN113034614A (en) * | 2021-03-30 | 2021-06-25 | 上海久航电子有限公司 | Five-circle center calibration method of five-circle calibration plate |
-
2021
- 2021-09-03 CN CN202111035491.XA patent/CN113720280A/en active Pending
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH02162247A (en) * | 1988-12-16 | 1990-06-21 | Kawasaki Steel Corp | Automatic surface-shape measuring apparatus |
| JP2006049755A (en) * | 2004-08-09 | 2006-02-16 | Omron Corp | Rotation center calculation method and work positioning device using the same |
| CN101334263A (en) * | 2008-07-22 | 2008-12-31 | 东南大学 | Circular target circular center positioning method |
| CN104123542A (en) * | 2014-07-18 | 2014-10-29 | 大连理工大学 | Device and method for positioning wheel hub work piece |
| CN104457577A (en) * | 2014-12-19 | 2015-03-25 | 上海工业自动化仪表研究院 | Machine-vision-oriented non-contact type workpiece positioning and measuring method |
| CN105258647A (en) * | 2015-07-26 | 2016-01-20 | 湖北工业大学 | Visual detection method of automobile lock catch rivet point |
| CN107398750A (en) * | 2017-06-09 | 2017-11-28 | 湖北亚科微钻有限公司 | Fixture, bar plane verticality detection means and method are used in cutting tool production |
| CN108169246A (en) * | 2017-11-28 | 2018-06-15 | 威海华菱光电股份有限公司 | Pole detecting system and the detection method for pole |
| CN109084675A (en) * | 2018-06-04 | 2018-12-25 | 哈尔滨工业大学 | Center of circle positioning device and method based on Embedded geometrical characteristic in conjunction with Zernike square |
| CN108844471A (en) * | 2018-08-02 | 2018-11-20 | 成都天衡智造科技有限公司 | Measurement method and device of a kind of pair of round piece marginal trough region to center of circle development length |
| JP2021071371A (en) * | 2019-10-30 | 2021-05-06 | 株式会社ディスコ | Center positioning method |
| CN110766018A (en) * | 2019-12-30 | 2020-02-07 | 江苏金恒信息科技股份有限公司 | Automatic counting method and device for bars |
| CN111062940A (en) * | 2019-12-31 | 2020-04-24 | 西南交通大学 | Screw positioning and identifying method based on machine vision |
| CN111899296A (en) * | 2020-06-24 | 2020-11-06 | 广西大学 | Method and device for detecting log volume based on computer vision |
| CN112132886A (en) * | 2020-09-30 | 2020-12-25 | 哈尔滨理工大学 | Circular hole center rapid positioning and roundness detection method for aviation part |
| CN113034614A (en) * | 2021-03-30 | 2021-06-25 | 上海久航电子有限公司 | Five-circle center calibration method of five-circle calibration plate |
Non-Patent Citations (1)
| Title |
|---|
| 林耀海;景林;王长缨;张泽均;罗超;: "一种渐进式圆心定位的原木端面轮廓识别方法", 福建师范大学学报(自然科学版), no. 04, pages 23 * |
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