CN113267502A - Micro-motor friction plate defect detection system and detection method based on machine vision - Google Patents
Micro-motor friction plate defect detection system and detection method based on machine vision Download PDFInfo
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Abstract
The invention provides a micromotor friction plate defect detection system and a detection method based on machine vision, which comprises the following steps: placing a part to be detected on an objective table, and acquiring an image of a friction plate to be detected through a camera; the industrial personal computer carries out binarization on the collected friction plate image to be detected to obtain a binary image; performing closed operation on the binary image, and filling gaps on the boundary of the friction plate; filling the image after the closing operation with overflowing water, extracting the outline of the image of the friction plate to be detected after the filling with overflowing water, and obtaining each pixel position of the outline of the outermost side of the friction plate to be detected; fitting a circle by adopting a least square method to obtain the circle center of the friction plate; and judging whether the friction plate to be detected is incomplete or not according to whether the distance change from the pixel point on the outline of the friction plate to be detected to the circle center has a peak value or not. The invention carries out defect detection through an image processing algorithm, can greatly reduce the labor cost, improves the detection efficiency and precision, and is beneficial to the development of enterprises towards the direction of intellectualization, automation and flexibility.
Description
Technical Field
The invention relates to the field of machine vision or friction plate detection, in particular to a micromotor friction plate defect detection system and method based on machine vision.
Background
The micro motor, which is called a micro motor as a whole, is a motor with a diameter less than 160mm or a rated power less than 750W, and is commonly used in a control system or a transmission mechanical load, and is used for interconversion between mechanical energy and electric energy to realize functions of power output, energy conversion, amplification, execution and the like. In a micromotor, a friction plate is commonly used for axially fixing a gear to prevent the gear from moving, and if the friction plate is incomplete, the gear is unevenly stressed to cause deformation, and finally the power output of the micromotor is influenced. At present, in the motor industry, the motor detection research based on machine vision is mainly limited to the two-dimensional size and defects of a motor shell, the defects of a motor rotor and the defects of a motor commutator, the defect detection of a friction plate of a micromotor is not researched in a targeted manner, and the manual detection is mainly used.
Because the friction disc size among the micromotor is little, only about 8mm, adopt artifical the detection to produce visual fatigue easily, lead to the false retrieval to inefficiency is unfavorable for automated production demand.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a micro-motor friction plate defect detection system and a detection method based on machine vision, which can be used for detecting defects through an image processing algorithm, greatly reduce the labor cost, improve the detection efficiency and precision and be beneficial to the development of enterprises in the directions of intellectualization, automation and flexibility.
The present invention achieves the above-described object by the following technical means.
A micromotor friction plate defect detection method based on machine vision comprises the following steps:
placing a part to be detected on an objective table, and acquiring an image of a friction plate to be detected through a camera; the camera transmits the acquired image to the industrial personal computer;
the industrial computer carries out binarization to the friction disc image of waiting of gathering, obtains binary image: displaying the target in white and displaying the background in black for extracting the target features;
performing closed operation on the binary image through the circular kernel, and filling gaps on the boundary of the friction plate to be detected on the premise of not changing the size and the position of the boundary of the friction plate to be detected;
filling the image after the closing operation with water, and filling a white area outside the boundary of the friction plate to be detected into black for removing an uninteresting area outside the boundary of the friction plate to be detected: selecting one point in a white area outside the boundary of the friction plate to be detected as a starting point, and filling the white area where the point is located into black, so that the filled area becomes a background and is used for reducing the interference of an uninteresting area on the extraction of the friction plate outline;
carrying out contour extraction on the image of the friction plate to be detected after flooding filling: detecting the outermost outline of the friction plate to be detected by adopting a 'RETR _ EXTERNAL' method of a findContours function, and avoiding the interference of an inner side area of the boundary of the friction plate to be detected; acquiring each pixel position of the outermost side contour of the friction plate to be detected by adopting a CHAIN _ APPROX _ NONE method of a findContours function;
and fitting a circle by adopting a least square method on the extracted outline of the friction plate to be detected: taking pixel points on the outline of the friction plate to be detected as sample points, finding out a circle which is closest to the outline of the friction plate to be detected by a least square method, and taking the center of the circle as the center of the circle of the friction plate to be detected;
and judging whether the friction plate to be detected is incomplete or not according to whether the distance change from the pixel point on the outline of the friction plate to be detected to the circle center has a peak value or not.
Further, whether there is a peak value according to waiting to examine the distance change of waiting pixel on the friction disc profile to the centre of a circle, judge whether to wait to examine the friction disc incomplete, specifically do:
sequentially calculating the distances from all pixel points on the outline to the circle center according to the shape of the outline of the friction plate to be detected;
the serial number of the pixel point is taken as an abscissa, and the direction of the abscissa is taken as the horizontal right direction; and taking the distance from the pixel point to the circle center as a vertical coordinate, and drawing a distance change image vertically downwards, wherein if the distance change image has a peak value, the friction plate is incomplete, otherwise, the friction plate is complete.
A detection system of a micro-motor friction plate defect detection method based on machine vision is characterized by comprising a friction plate defect detection device, a light source controller and an industrial personal computer;
the friction plate defect detection device comprises an objective table, a bracket, a light source, a lens and a camera; a friction plate to be detected is placed on the objective table, the support is fixedly provided with a lens and a light source, and the camera transmits the acquired friction plate image to the industrial personal computer; the light source is installed between the lens and the friction plate to be detected.
Further, the light source is red annular light source, the camera lens is telecentric lens, the camera is black and white CCD camera, two the common cover has first strip shaped plate and second strip shaped plate on the support, install the light source fixing base on the first strip shaped plate, the light source fixing base is used for fixing the light source, install the camera lens fixing base on the second strip shaped plate, the camera lens fixing base is used for fixing the camera lens, the camera lens is connected on the camera, light source, camera lens, camera are the perpendicular installation downwards.
Further, the first strip-shaped plate and the second strip-shaped plate can freely move on the support and are used for adjusting the installation height of the light source and the lens, so that the camera can acquire the clearest image.
Further, black sticker is pasted to the upper surface of objective table for the friction disc and background are waited to the differentiation to the friction disc characteristic in the friction disc image is waited to the prominent display, the objective table can place two wait to examine the friction disc, the camera can gather 2 images of waiting to examine the friction disc simultaneously.
The invention has the beneficial effects that:
1. the micromotor friction plate defect detection system and method based on machine vision make up for the defects of traditional manual detection, and adopt a machine vision technology to improve the detection efficiency and precision, thereby being beneficial to information integration and automatic production requirements.
2. The invention relates to a micromotor friction plate defect detection system and a detection method based on machine vision, wherein a friction plate defect detection algorithm is mainly characterized in that a circle which is closest to a contour is found out by extracting the contour of a friction plate according to pixel points on the contour by adopting a least square method, the center of the found circle is used as the center of the friction plate, whether the distance change from the pixel points on the contour of the friction plate to the center of the circle has a peak value or not is judged, if the peak value exists, the friction plate defect exists, and if not, the friction plate is complete. The algorithm is also suitable for detecting the defects of other round parts and has flexibility.
3. Compared with the method for comparing the areas of defective products and qualified products by adopting a machine vision technology, the system and the method for detecting the defect of the micromotor friction plate based on the machine vision have higher accuracy in detection by adopting the algorithm of the invention because the process error exists in the process of producing the friction plate and the processed areas of the friction plates of the same type cannot be completely equal, so that the areas of different friction plates are inconsistent, and the area is adopted to judge whether the friction plate is defective or not, and misjudgment is possible.
4. According to the micromotor friction plate defect detection system and method based on machine vision, the uninteresting regions outside the target region are removed by adopting the flooding filling algorithm, and only the outermost contour is detected by combining the RETR _ EXTERNAL method in the findContours function, so that the uninteresting regions inside and outside the target region are inhibited, the image processing difficulty is reduced, and the precision of contour extraction is improved.
Drawings
FIG. 1 is a schematic diagram of a micro-motor friction plate defect detection system based on machine vision.
Fig. 2 is a structural diagram of the friction plate defect detecting device of the present invention.
Fig. 3 is a schematic view of an objective table in the friction plate defect detecting device according to the present invention.
FIG. 4 is a flow chart of the method for detecting the defect of the friction plate of the micro-motor based on machine vision.
Fig. 5 is a grayscale image captured by the camera in the embodiment.
Fig. 6 is an image obtained by binarizing a grayscale image.
Fig. 7 is a result image of the closing operation performed on the binary image.
Fig. 8 is a result image of flood filling performed on the image after the closing operation.
Fig. 9 is a profile view of a friction plate extracted using the findContours function.
Figure 10 is a least squares fit circle based on the profile of the friction plate.
FIG. 11 is a graph showing the variation of the distance from the pixel point on the friction plate profile to the center of the circle.
In the figure:
1-incomplete detection device of friction disc; 11-an object stage; 110-well; 12-a scaffold; 120-a first strip plate; 121-a second strip; 13-a light source; 130-light source holder; 14-a lens; 140-lens holder; 15-a camera; 2-a light source controller; 3-an industrial personal computer; 4-display screen.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "axial," "radial," "vertical," "horizontal," "inner," "outer," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the present invention and for simplicity in description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
As shown in fig. 1 and 2, the micromotor friction plate defect detection system based on machine vision comprises a friction plate defect detection device 1, a light source controller 2, an industrial personal computer 3 and a display screen 4; the friction plate defect detection device 1 comprises an object stage 11, a support 12, a light source 13, a lens 14 and a camera 15; a friction plate to be detected is placed on the objective table 11, the bracket 12 is fixedly provided with a light source 13 and a lens 14, and the light source 13 is arranged between the lens 14 and the friction plate to be detected; the camera 15 transmits the acquired friction plate images to the industrial personal computer 3. The display screen 4 can display the result of the analysis of the image by the industrial personal computer 3.
The light source 13 is a red annular light source, the lens 14 is a telecentric lens, the camera 15 is a black-and-white CCD camera, two the bracket 12 is sleeved with a first strip-shaped plate 120 and a second strip-shaped plate 121 together, the first strip-shaped plate 120 is provided with a light source fixing seat 130, the light source fixing seat 130 is used for fixing the light source 13, the second strip-shaped plate 121 is provided with a lens fixing seat 140, the lens fixing seat 140 is used for fixing the lens 14, the lens 14 is connected to the camera 15, and the light source 13, the lens 14 and the camera 15 are all vertically installed downwards. The first bar-shaped plate 120 and the second bar-shaped plate 121 are both freely movable on the bracket 12 for adjusting the installation heights of the light source 13 and the lens 14, so that the camera 15 acquires the clearest image.
Friction plates are used in micromotors as gear assemblies for axially fixing gears. Therefore, when quality detection is carried out, the friction plate, the gear and the shaft are assembled into a whole and then are detected together, so that the detection efficiency is improved. In order to capture the front image of the friction plate, a hole 110 is formed in the stage 11, the image capture of the camera 15 is completed by inserting a shaft vertically into the hole 110 of the stage 11, and in addition, two holes may be formed in the stage 11 in order to improve the detection efficiency, and two friction plates may be detected simultaneously, as shown in fig. 3.
As shown in FIG. 4, the method for detecting the defect of the friction plate of the micromotor based on machine vision comprises the following steps:
s01: placing a part to be detected on an object stage 11, and acquiring an image of a friction plate to be detected through a camera 15; the camera 15 transmits the acquired image to the industrial personal computer 3;
s02: the industrial personal computer 3 binarizes the acquired image, sets a gray value T as 125 as a threshold value by taking the gray image acquired by the camera 15 as an example, compares the gray value of each pixel point with 125, and gives the gray value of the pixel point as 255 when the gray value of the pixel point is less than 125, and displays the gray value in white; when the gray value of the pixel point is greater than 125, the gray value is assigned as 0, and the pixel is displayed in black, and the binarization result is shown in fig. 6. The region of interest is separated from the background, so that the image data volume is compressed, the storage space is reduced, and the subsequent image processing steps are simplified.
S03: considering that a gap exists at the boundary of the friction plate, the binary image is subjected to closing operation, a 7 × 7 circular inner core is adopted to perform expansion operation on the binary image, then corrosion operation is performed on the binary image, the gap at the boundary of the friction plate is filled, and the closing operation result is shown in fig. 7.
S04: due to the fact that the friction plate detection scene is complex, other white areas exist besides the white area of the friction plate boundary, the white area which is not interested is removed by adopting a flooding filling algorithm, the white area outside the friction plate boundary is filled to be black by selecting one point in the white area outside the friction plate boundary as a starting point, aiming at fig. 7, the point (0,0) which is the upper left corner of an image can be selected as the starting point, the eight connected domain where the point (0,0) is located is filled to be black, the value of the pixel point of the white area outside the friction plate boundary is reset to be 0, and the result after flooding filling is shown in fig. 8.
S05: the outline of the friction plate is extracted from the image filled with the overflowing water by adopting a findContours function, and only the outermost outline is detected by adopting a RETR _ EXTERNAL method of the findContours function, so that the influence of an inner side area of the boundary of the friction plate can be avoided; each pixel position of the outermost profile is acquired using the "CHAIN _ APPROX _ NONE" method of the findContours function, and the friction lining profile image is extracted as shown in fig. 9.
S06: the pixel points on the friction plate outline are used as sample points, a least square method is adopted to fit a circle, the fitting result is shown in fig. 10, a red circle is the circle obtained through fitting, and the center of the fitted circle is used as the center of the friction plate.
S07: sequentially calculating the distances from all pixel points on the contour to the circle center according to the shape of the friction plate contour; and judging whether the friction plate is defective or not according to whether the distance change image has a peak value or not. Finding out a pixel point with the minimum distance to the center of a circle on the outline, sequentially calculating the distance from the rear pixel point to the center of the circle backwards by taking the pixel point as a starting point, wherein the distance from the partial pixel point behind the pixel point to the center of the circle tends to increase, and simultaneously sequentially calculating the distance from the front pixel point to the center of the circle forwards, and the distance from the partial pixel point in front of the pixel point to the center of the circle also tends to increase, so that the distance change image has a peak value. The distance change from the pixel point to the center of the circle on the contour is shown in fig. 11, the number of the pixel point is taken as the abscissa, the direction of the abscissa is horizontal to the right, the distance from the pixel point to the center of the circle is taken as the ordinate, the direction of the ordinate is vertical to the down, and the distance change of fig. 11 has a peak value, so that the detected friction plate is incomplete.
It should be understood that although the present description has been described in terms of various embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and those skilled in the art will recognize that the embodiments described herein may be combined as suitable to form other embodiments, as will be appreciated by those skilled in the art.
The above-listed detailed description is only a specific description of a possible embodiment of the present invention, and they are not intended to limit the scope of the present invention, and equivalent embodiments or modifications made without departing from the technical spirit of the present invention should be included in the scope of the present invention.
Claims (6)
1. A micromotor friction plate defect detection method based on machine vision is characterized by comprising the following steps:
placing a part to be detected on an objective table (11), and acquiring an image of a friction plate to be detected through a camera (15); the camera (15) transmits the acquired image to the industrial personal computer (3);
the industrial personal computer (3) binarizes the collected image of the friction plate to be detected to obtain a binary image: displaying the target in white and displaying the background in black for extracting the target features;
performing closed operation on the binary image through the circular kernel, and filling gaps on the boundary of the friction plate to be detected on the premise of not changing the size and the position of the boundary of the friction plate to be detected;
filling the image after the closing operation with water, and filling a white area outside the boundary of the friction plate to be detected into black for removing an uninteresting area outside the boundary of the friction plate to be detected: selecting one point in a white area outside the boundary of the friction plate to be detected as a starting point, and filling the white area where the point is located into black, so that the filled area becomes a background and is used for reducing the interference of an uninteresting area on the extraction of the friction plate outline;
carrying out contour extraction on the image of the friction plate to be detected after flooding filling: detecting the outermost outline of the friction plate to be detected by adopting a 'RETR _ EXTERNAL' method of a findContours function, and avoiding the interference of an inner side area of the boundary of the friction plate to be detected; acquiring each pixel position of the outermost side contour of the friction plate to be detected by adopting a CHAIN _ APPROX _ NONE method of a findContours function;
and fitting a circle by adopting a least square method on the extracted outline of the friction plate to be detected: taking pixel points on the outline of the friction plate to be detected as sample points, finding out a circle which is closest to the outline of the friction plate to be detected by a least square method, and taking the center of the circle as the center of the friction plate;
and judging whether the friction plate to be detected is incomplete or not according to whether the distance change from the pixel point on the outline of the friction plate to be detected to the circle center has a peak value or not.
2. The micro-motor friction plate defect detection method based on machine vision as claimed in claim 1, characterized in that whether the friction plate to be detected is defective or not is judged according to whether the distance change from the pixel point on the outline of the friction plate to be detected to the circle center has a peak value or not, and specifically:
sequentially calculating the distances from all pixel points on the outline to the circle center according to the shape of the outline of the friction plate to be detected;
the serial number of the pixel point is taken as an abscissa, and the direction of the abscissa is taken as the horizontal right direction; and taking the distance from the pixel point to the circle center as a vertical coordinate, and drawing a distance change image vertically downwards, wherein if the distance change image has a peak value, the friction plate is incomplete, otherwise, the friction plate is complete.
3. The detection system of the micro-motor friction plate defect detection method based on the machine vision is characterized by comprising a friction plate defect detection device (1), a light source controller (2) and an industrial personal computer (3);
the friction plate defect detection device (1) comprises an object stage (11), a support (12), a light source (13), a lens (14) and a camera (15); a friction plate to be detected is placed on the objective table (11), the support (12) is fixedly provided with a light source (13) and a lens (14), and the camera (15) transmits the acquired friction plate image to the industrial personal computer (3); and the light source (13) is arranged between the lens (14) and the friction plate to be detected.
4. The detection system of the micro-motor friction plate defect detection method based on the machine vision is characterized in that the light source (13) is a red annular light source, the lens (14) is a telecentric lens, the camera (15) is a black-and-white CCD camera, a first strip-shaped plate (120) and a second strip-shaped plate (121) are sleeved on the two brackets (12) together, the first strip-shaped plate (120) is provided with a light source fixing seat (130), the light source fixing seat (130) is used for fixing the light source (13), the second strip-shaped plate (121) is provided with a lens fixing seat (140), the lens fixing seat (140) is used for fixing the lens (14), the lens (14) is connected onto the camera (15), and the light source (13), the lens (14) and the camera (15) are all vertically installed downwards.
5. The detection system of the micro-motor friction plate defect detection method based on the machine vision is characterized in that the first strip-shaped plate (120) and the second strip-shaped plate (121) can freely move on the bracket (12) and are used for adjusting the installation height of the light source (13) and the lens (14), so that the camera (15) can acquire the clearest image.
6. The detection system of the micro-motor friction plate defect detection method based on machine vision as claimed in claim 3, characterized in that the upper surface of the objective table (11) is pasted with black paster for distinguishing the friction plate to be detected from the background; objective table (11) can place two wait to examine the friction disc, camera (15) can gather 2 images of waiting to examine the friction disc simultaneously.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101557271B1 (en) * | 2014-06-26 | 2015-10-06 | 한밭대학교 산학협력단 | Method for detecting a circle-type object and approximating a substitute circle based on Image processing |
| CN109872300A (en) * | 2018-12-17 | 2019-06-11 | 南京工大数控科技有限公司 | A kind of vision significance detection method of friction plate open defect |
| CN110261410A (en) * | 2019-05-24 | 2019-09-20 | 西安交通大学 | A kind of detection device and method of glass lens defect |
| CN110544300A (en) * | 2019-09-05 | 2019-12-06 | 佛山市玖章智能科技有限公司 | Method for automatically generating three-dimensional model based on two-dimensional hand-drawn image characteristics |
| WO2020019648A1 (en) * | 2018-07-24 | 2020-01-30 | 中山新诺科技股份有限公司 | Machine vision positioning method |
| CN111354047A (en) * | 2018-12-20 | 2020-06-30 | 精锐视觉智能科技（深圳）有限公司 | Camera module positioning method and system based on computer vision |
| CN111445511A (en) * | 2020-03-24 | 2020-07-24 | 杭州东信北邮信息技术有限公司 | Method for detecting circle in image |
| CN111999305A (en) * | 2020-08-17 | 2020-11-27 | 浙江师范大学 | Machine vision hardware system and hub spline combined gap vision detection method |
| CN112184639A (en) * | 2020-09-15 | 2021-01-05 | 佛山(华南)新材料研究院 | Round hole detection method and device, electronic equipment and storage medium |
| WO2021012370A1 (en) * | 2019-07-25 | 2021-01-28 | 深圳壹账通智能科技有限公司 | Pupil radius detection method and apparatus, computer device and storage medium |
-
2021
- 2021-05-11 CN CN202110511142.4A patent/CN113267502B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101557271B1 (en) * | 2014-06-26 | 2015-10-06 | 한밭대학교 산학협력단 | Method for detecting a circle-type object and approximating a substitute circle based on Image processing |
| WO2020019648A1 (en) * | 2018-07-24 | 2020-01-30 | 中山新诺科技股份有限公司 | Machine vision positioning method |
| CN109872300A (en) * | 2018-12-17 | 2019-06-11 | 南京工大数控科技有限公司 | A kind of vision significance detection method of friction plate open defect |
| CN111354047A (en) * | 2018-12-20 | 2020-06-30 | 精锐视觉智能科技（深圳）有限公司 | Camera module positioning method and system based on computer vision |
| CN110261410A (en) * | 2019-05-24 | 2019-09-20 | 西安交通大学 | A kind of detection device and method of glass lens defect |
| WO2021012370A1 (en) * | 2019-07-25 | 2021-01-28 | 深圳壹账通智能科技有限公司 | Pupil radius detection method and apparatus, computer device and storage medium |
| CN110544300A (en) * | 2019-09-05 | 2019-12-06 | 佛山市玖章智能科技有限公司 | Method for automatically generating three-dimensional model based on two-dimensional hand-drawn image characteristics |
| CN111445511A (en) * | 2020-03-24 | 2020-07-24 | 杭州东信北邮信息技术有限公司 | Method for detecting circle in image |
| CN111999305A (en) * | 2020-08-17 | 2020-11-27 | 浙江师范大学 | Machine vision hardware system and hub spline combined gap vision detection method |
| CN112184639A (en) * | 2020-09-15 | 2021-01-05 | 佛山(华南)新材料研究院 | Round hole detection method and device, electronic equipment and storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116840158A (en) * | 2023-08-31 | 2023-10-03 | 深圳明锐理想科技有限公司 | Flip AOI cleaning device |
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