CN105954301B - A kind of bottleneck quality detection method based on machine vision - Google Patents
A kind of bottleneck quality detection method based on machine vision Download PDFInfo
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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Abstract
The bottleneck quality detection method based on machine vision that the invention discloses a kind of, includes the following steps: the image for acquiring bottleneck to be detected, and be converted into grayscale image；The gradient vector for calculating each pixel of grayscale image, obtains the edge image of bottleneck grayscale image to be detected；Edge is split according to gray value threshold range；Divide the inner ring and outer rings of bottleneck to be detected as feature using area；Inner ring and outer rings central coordinate of circle and radius are calculated separately, central coordinate of circle is averaged to obtain the central coordinate of circle of bottleneck to be detected, radius value range is set according to outer ring and inner ring radius；Round parametric equation is obtained according to bottleneck central coordinate of circle to be detected and radius value range, circular scanning is carried out to annulus according to round parametric equation, average gray value is calculated, draws average gray value curve；Average gray value curve is analyzed, the variation range of annulus in a certain range, then determines annulus there is no breakage, the present invention improves bottleneck quality detection efficiency.
Description
Technical field
The present invention relates to technical field of quality detection, and in particular to a kind of bottleneck quality detection side based on machine vision
Method.
Background technique
The manufacturings such as drinks, beverage, medicine, food largely use filling production lines in production, and mostly make
Use vial packing as product.But vial be due to that inevitably will be contaminated and damage in transportational process in production, especially
It is needed as industries such as beer using recyclable vial, thus vial has to pass through the processes such as cleaning, detection, Cai Nengjin
Enter filling process.In order to overcome harm brought by foreign matter and damage, it is necessary to careful detection is carried out to filling preceding vial,
It is known as real bottle detection in industry.This detection be usually in darkroom under light by manually carrying out.
Visual detection robot is mainly the theory and technology for utilizing machine vision, to empty bottle quality on filling production lines
It is detected.Machine vision obtains people's extensive concern as a comprehensive front subject in recent years, be research hotspot it
One, it is all quite active to its research and application.
Existing empty bottle inspection can only be detected manually, not only low efficiency, but also precision is low, seriously affect production line effect
Rate.
Summary of the invention
In view of this, being provided a kind of based on machine vision it is an object of the invention to overcome the drawbacks described above of the prior art
Bottleneck quality detection method, solve the problems, such as to rely on artificial detection precision and low efficiency in the past, improve domestic manufacturing industry
The technology content of detection.
The present invention is solved the above problems by following technological means:
A kind of bottleneck quality detection method based on machine vision, includes the following steps:
The image of S1, acquisition bottleneck to be detected, and it is converted into grayscale image；
S2, using Sobel edge detection operator calculate grayscale image each pixel gradient vector, obtain to be detected
The edge image of bottleneck grayscale image；
S3, setting gray value threshold range, split edge according to gray value threshold range；
S4, according to the region after segmentation, divide the inner ring and outer rings of bottleneck to be detected as feature using area；
S5, inner ring and outer rings central coordinate of circle and radius are calculated separately using gravity model appoach, the central coordinate of circle of outer ring and inner ring
It averages, obtains the central coordinate of circle of bottleneck to be detected, radius value range is set according to outer ring and inner ring radius；
S6, round parametric equation is obtained according to bottleneck central coordinate of circle to be detected and radius value range, according to round ginseng
Number equation carries out circular scanning to annulus, calculates average gray value, draws average gray value curve；
S7, average gray value curve is analyzed, the variation range of annulus in a certain range, then determines that annulus is not deposited
Product of failing is classified as otherwise, it is determined that annulus has breakage in breakage.
Further, in step S1, the image of bottleneck to be detected, the illumination detection system are acquired using illumination detection system
System includes bottleneck to be detected, LED light source, baffle, CCD industrial camera, and the baffle, which corresponds to bottle mouth position to be detected, an opening,
LED light source light shines bottleneck to be detected, is reflected through opening and enters CCD industrial camera, obtains the figure of bottleneck to be detected
Picture.
Further, in step S2, the gradient of each pixel of grayscale image is calculated using Sobel edge detection operator
The specific method is as follows for vector:
Sobel edge detection operator includes the matrix of two groups of 3*3, respectively lateral warp factorAnd it is vertical
To warp factor
Original image is represented with A, Gx and Gy respectively represent the gray value of image through transverse direction and longitudinal direction edge detection, formula
It is as follows:
The gradient value size of each pixel of image is calculated by the following formula:
Wherein, the approximation that does not extract square root is used in order to improve efficiency:
| G |=| Gx|+| Gy|,
Gradient direction is calculated with following formula:
If above angle, θ is equal to zero, i.e., possess longitudinal edge at this of representative image, left is dark compared with right.
Further, in step S3, the gray value threshold range is (25,255).
Further, in step S4, a threshold value is set, if the bottleneck to be detected that is considered as that area is greater than threshold value is split
Line is directly judged as and fails.
Further, the threshold value is 60000.
Further, in step S5, detailed process is as follows:
It calculates the external minimum rectangle of outer ring and obtains the coordinate of four points in outer ring upper and lower, left and right, then pass through gravity model appoach meter
Calculate midpoint, that is, outer ring central coordinate of circle:
Outer ring radius calculation formula:
Wherein, x2For the abscissa put on the right of outer ring, x1For the abscissa of outer ring left side point, y2For under outer ring edge point it is vertical
Coordinate, y1For the ordinate of edge point on outer ring；
Inner ring central coordinate of circle: (center can be similarly obtained according to the method described aboveX (interior), centerY (interior)) and inner ring radius RIt is interior；
The central coordinate of circle of outer ring and inner ring is averaged, the central coordinate of circle of bottleneck to be detected: (center is obtainedX,
centerY)；
Radius value range is set as R according to outer ring and inner ring radius.
Further, in step S6, round parametric equation are as follows:
Wherein, R is [RIt is interior–3,ROutside+ 3], central angle ψ is [0,360 °]；
I (x, y) is the corresponding gray value of annulus (x, y) coordinate points, then:
meanIFor average gray value.
Further, in step S7, the variation range of annulus is within 40 pixels, then determining annulus, there is no broken
Damage.
Bottleneck quality detection method based on machine vision of the invention solves in the past by artificial detection precision and effect
The low problem of rate improves the technology content of domestic manufacturing industry detection.Meanwhile the successful implementation of this method is to industrial machine manpower
Eye system, logistics transportation industry, packing business, optical detection and processing and other fields have a good application prospect.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts of the bottleneck quality detection method of machine vision；
Fig. 2 is that illumination detection system structure used by the bottleneck quality detection method of the invention based on machine vision is shown
It is intended to；
Fig. 3 is that Fig. 2 bottleneck grayscale image collected is used in the embodiment of the present invention；
Fig. 4 is the corresponding average gray curve graph of width bottleneck grayscale image every in Fig. 3.
Specific embodiment
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, below in conjunction with attached drawing and specifically
Embodiment technical solution of the present invention is described in detail.It should be pointed out that described embodiment is only this hair
Bright a part of the embodiment, instead of all the embodiments, based on the embodiments of the present invention, those of ordinary skill in the art are not having
Every other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
As shown in Figure 1, a kind of bottleneck quality detection method based on machine vision, includes the following steps:
The image of S1, acquisition bottleneck to be detected, and it is converted into grayscale image；
As shown in Fig. 2, acquire the image of bottleneck to be detected using illumination detection system, the illumination detection system include to
Bottleneck, LED light source, baffle, CCD industrial camera are detected, the baffle, which corresponds to bottle mouth position to be detected, an opening, LED light source
Bottleneck to be detected is light shone, opening is reflected through and enters CCD industrial camera, obtains the image of bottleneck to be detected, and turn
Grayscale image is turned to, as shown in Figure 3.
S2, using Sobel edge detection operator calculate grayscale image each pixel gradient vector, obtain to be detected
The edge image of bottleneck grayscale image；
Technically, it is a discreteness difference operator, for the approximation of the gray scale of operation brightness of image function.Scheming
Any point of picture uses this operator, it will generates corresponding gray scale vector or its law vector；
Sobel edge detection operator includes the matrix of two groups of 3*3, respectively lateral warp factorAnd it is vertical
To warp factorIt is made into planar convolution with image, can obtain the brightness of transverse direction and longitudinal direction respectively
Difference approximation value；
If representing original image with A, Gx and Gy respectively represent the gray value of image through transverse direction and longitudinal direction edge detection,
Formula is as follows:
The gradient value of each pixel of image is calculated by the following formula:In general, in order to
It improves efficiency using the approximation not extracted square root:
| G |=| Gx+Gy|,
Then following formula can be used to calculate gradient direction:
If above angle, θ is equal to zero, i.e. representative image possesses longitudinal edge at this, and left is dark compared with right.
S3, setting gray value threshold range, split edge according to gray value threshold range；
The gray value threshold range is (25,255).
S4, according to the region after segmentation, divide the inner ring and outer rings of bottleneck to be detected as feature using area；
According to the region after segmentation, divide the inner ring and outer rings of bottleneck as feature using area.In general, same
Bottle is criticized under identical illumination detection system, annulus area has fixed range, i.e. the area at edge is also to have fixed model
It encloses.When bottleneck is cracked, the inner ring and outer rings after edge segmentation can connect together, and can be seen that from the b in Fig. 3；This
In can set a threshold decision, if area is greater than threshold value and is considered as cracked situation, can directly be judged as not
It passes, according to experimental data, sets area threshold here as 60000.
S5, inner ring and outer rings central coordinate of circle and radius are calculated separately using gravity model appoach, the central coordinate of circle of outer ring and inner ring
It averages, obtains the central coordinate of circle of bottleneck to be detected, radius value range is set according to outer ring and inner ring radius；
After extracting annular edge, calculates the external minimum rectangle of outer ring and obtain the coordinate of four points in outer ring upper and lower, left and right, so
Midpoint, that is, outer ring central coordinate of circle is calculated by gravity model appoach afterwards:
Outer ring radius calculation formula:
Wherein, x2For the abscissa put on the right of outer ring, x1For the abscissa of outer ring left side point, y2For under outer ring edge point it is vertical
Coordinate, y1For the ordinate of edge point on outer ring；
Inner ring central coordinate of circle: (center can be similarly obtained according to the method described aboveX (interior), centerY (interior)) and inner ring radius RIt is interior；
The central coordinate of circle of outer ring and inner ring is averaged, the central coordinate of circle of bottleneck to be detected: (center is obtainedX,
centerY)；
Radius value range is set as R according to outer ring and inner ring radius.
S6, round parametric equation is obtained according to bottleneck central coordinate of circle to be detected and radius value range, according to round ginseng
Number equation carries out circular scanning to annulus, calculates average gray value, draws average gray value curve；
Since bottleneck image can substantially regard the annulus of a standard as, the annulus is justified according to round parametric equation
Week scanning, calculates average gray value, draws average gray value curve, as shown in figure 4,
Round parametric equation are as follows:
Wherein, R is (RIt is interior–3,ROutside+ 3), central angle ψ is (0,360 °)；
I (x, y) is the corresponding gray value of annulus (x, y) coordinate points, then:
meanIFor average gray value.
S7, average gray value curve is analyzed, the variation range of annulus in a certain range, then determines that annulus is not deposited
Product of failing is classified as otherwise, it is determined that annulus has breakage in breakage.
The variation range of annulus is within 40 pixels, then determining annulus, there is no breakages.
A in Fig. 3 is the doughnut of standard, can be seen that its variation from a in its average gray curve graph Fig. 4
Range is 16 pixels, for qualifying product；And the variation range of remaining 4 width doughnut is all larger, it is known that the circle of that a few width figure
All there is breakage in ring, therefore corresponding bottleneck can be classified as product of failing.
Bottleneck quality detection method based on machine vision of the invention solves in the past by artificial detection precision and effect
The low problem of rate improves the technology content of domestic manufacturing industry detection.Meanwhile the successful implementation of this method is to industrial machine manpower
Eye system, logistics transportation industry, packing business, optical detection and processing and other fields have a good application prospect.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (7)
1. a kind of bottleneck quality detection method based on machine vision, which comprises the steps of:
The image of S1, acquisition bottleneck to be detected, and it is converted into grayscale image；
S2, using Sobel edge detection operator calculate grayscale image each pixel gradient vector, obtain bottleneck to be detected
The edge image of grayscale image；
S3, setting gray value threshold range, split edge according to gray value threshold range；
S4, according to the region after segmentation, divide the inner ring and outer rings of bottleneck to be detected as feature using area；
S5, inner ring and outer rings central coordinate of circle and radius are calculated separately using gravity model appoach, the central coordinate of circle of outer ring and inner ring is asked flat
Mean value obtains the central coordinate of circle of bottleneck to be detected, sets radius value range according to outer ring and inner ring radius；
S6, round parametric equation is obtained according to bottleneck central coordinate of circle to be detected and radius value range, according to round parameter side
Journey carries out circular scanning to annulus, calculates average gray value, draws average gray value curve；
S7, average gray value curve is analyzed, in a certain range, then determining annulus, there is no broken for the variation range of annulus
Damage is classified as product of failing otherwise, it is determined that annulus has breakage；
In step S5, detailed process is as follows:
It calculates the external minimum rectangle of outer ring and obtains the coordinate of four points in outer ring upper and lower, left and right, then by gravity model appoach calculating
Point is outer ring central coordinate of circle:
Outer ring radius calculation formula:
Wherein, x2For the abscissa put on the right of outer ring, x1For the abscissa of outer ring left side point, y2For the ordinate of edge point under outer ring,
y1For the ordinate of edge point on outer ring；
Inner ring central coordinate of circle: (center can be similarly obtained according to the method described aboveX (interior), centerY (interior)) and inner ring radius RIt is interior；
The central coordinate of circle of outer ring and inner ring is averaged, the central coordinate of circle of bottleneck to be detected: (center is obtainedX, centerY)；
Radius value range is set as R according to outer ring and inner ring radius；
In step S6, round parametric equation are as follows:
Wherein, R is (RIt is interior–3,ROutside+ 3), central angle ψ is (0,360 °)；
I (x, y) is the corresponding gray value of annulus (x, y) coordinate points, then:
meanIFor average gray value.
2. the bottleneck quality detection method according to claim 1 based on machine vision, which is characterized in that in step S1,
Acquire the image of bottleneck to be detected using illumination detection system, the illumination detection system include bottleneck to be detected, LED light source,
Baffle, CCD industrial camera, the baffle, which corresponds to bottle mouth position to be detected, an opening, and LED light source light shines bottle to be detected
Mouthful, it is reflected through opening and enters CCD industrial camera, obtain the image of bottleneck to be detected.
3. the bottleneck quality detection method according to claim 2 based on machine vision, which is characterized in that in step S2,
Calculating the gradient vector of each pixel of grayscale image using Sobel edge detection operator, the specific method is as follows:
Sobel edge detection operator includes the matrix of two groups of 3*3, respectively lateral warp factorAnd longitudinal volume
The product factor
Original image is represented with A, Gx and Gy respectively represent the gray value of image through transverse direction and longitudinal direction edge detection, and formula is such as
Under:
The gray value of each pixel of image is calculated by the following formula:
Wherein, the approximation that does not extract square root is used in order to improve efficiency:
| G |=| Gx+Gy|,
Gradient direction is calculated with following formula:
If above angle, θ is equal to zero, i.e. representative image possesses longitudinal edge at this, and left is dark compared with right.
4. the bottleneck quality detection method according to claim 3 based on machine vision, which is characterized in that in step S3,
The gray value threshold range is (25,255).
5. the bottleneck quality detection method according to claim 4 based on machine vision, which is characterized in that in step S4,
Set a threshold value, if area be greater than threshold value to be considered as bottleneck to be detected cracked, be directly judged as and fail.
6. the bottleneck quality detection method according to claim 5 based on machine vision, which is characterized in that the threshold value is
60000。
7. the bottleneck quality detection method according to claim 1 based on machine vision, which is characterized in that in step S7,
The variation range of annulus is within 40 pixels, then determining annulus, there is no breakages.
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| CN108876773A (en) * | 2018-06-06 | 2018-11-23 | 杭州电子科技大学 | LED glass lamp cup aperture online test method based on machine vision |
| CN109406539B (en) * | 2018-11-28 | 2024-04-09 | 广州番禺职业技术学院 | Transparent medicine bottle bottom accumulated material defect detection system and method |
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