CN110441318A - A kind of chemical fibre spinneret hole defect inspection method based on machine vision - Google Patents
A kind of chemical fibre spinneret hole defect inspection method based on machine vision Download PDFInfo
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- CN110441318A CN110441318A CN201910780504.2A CN201910780504A CN110441318A CN 110441318 A CN110441318 A CN 110441318A CN 201910780504 A CN201910780504 A CN 201910780504A CN 110441318 A CN110441318 A CN 110441318A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/08—Measuring arrangements characterised by the use of optical techniques for measuring diameters
- G01B11/12—Measuring arrangements characterised by the use of optical techniques for measuring diameters internal diameters
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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Abstract
The invention discloses a kind of chemical fibre spinneret hole defect inspection method based on machine vision, comprising: defects detection algorithm, light source, industrial camera, computer system, control and communication system, execution system etc..By carrying out series of preprocessing and defects detection algorithm to industrial camera acquired image, the detection in the defects of realizing to the blocking of chemical fibre spinneret hole, burr and aperture and hole count, the efficiency and accuracy of detection can be improved, reduce the labor intensity of worker, the production cost for reducing enterprise simultaneously, can real-time display testing result.
Description
Technical field
The present invention relates to machine vision defects detection field more particularly to a kind of chemical fibre spinneret holes based on machine vision
Defect inspection method.
Background technique
Defects detection is essential link in production product, and chemical fibre spinneret hole is in the manufacturing and the mistake of spinneret
Be easy to exist in journey blocking, burr, it is non-round the defects of, and spinning head aperture is small, and hole count is more, and traditional artificial detection is by means of showing
The problems such as micro mirror is completed, and low, labor intensity of workers that there are detection efficiencies is greatly, testing result is not objective, testing cost is high, it is clear that
It is not able to satisfy the needs of modern business development.Machine vision has many advantages, such as that speed is fast, and precision is high, it has also become defects detection is most
Good means.
Summary of the invention
The present invention provides a kind of chemical fibre spinneret hole defect inspection method based on machine vision, has detection efficiency height,
It is at low cost, the advantages that labor intensity of workers is low, testing result precision is high.
In order to achieve the above object, using following technical scheme herein:
A kind of chemical fibre spinneret hole defect inspection method based on machine vision, specifically includes the following steps:
The image that step 1. obtains industrial camera is acquired；
Step 2. demarcates camera, to obtain the relationship of Pixel Dimensions and actual size；
Step 3. carries out greyscale transformation to collected color image；
Step 4. pair carries out image progress area-of-interest (Regions of Interest, ROI) after greyscale transformation and mentions
It takes；
Step 5. turns off Connected area disposal$ to the ROI region extracted；
Connected domain after step 6. pair disconnects carries out edge enhancing；
Step 7. carries out edge extracting to each connected domain；
Step 8. is fitted circle to each connected domain；
Step 9. judges whether there is defect, is then qualified product if it does not exist, is then rejected product otherwise；
Step 10. calculated hole diameters and hole count, if aperture in predetermined tolerance range, for qualified product, otherwise, then not conform to
Lattice product；
Step 11. counts and shows result；
Correspondingly, also providing a kind of chemical fibre spinneret hole defect detecting system based on machine vision, comprising:
Light-source illuminating system, for striation part needed for providing acquisition image；
Image capturing system, for acquiring image and handling image；
Computer system, the display for image procossing and testing result；
Motion control and communication system, for production line locking and trigger camera and take pictures；
Execution system, crawl and transmission for spinning head；
Compared with prior art, the invention has the following advantages that
Reduce worker labor intensity and enterprise production cost, improve detection efficiency, can real-time display testing result,
The accuracy of testing result is also ensured simultaneously.
Detailed description of the invention
Fig. 1 is a kind of chemical fibre spinneret hole defect inspection method flow chart based on machine vision that embodiment one provides；
Fig. 2 is the original image of chemical fibre spinneret hole provided by the invention；
Fig. 3 is a kind of chemical fibre spinneret hole defect detecting system structure chart based on machine vision that embodiment two provides；
Specific embodiment
To further illustrate that each embodiment, the present invention are provided with attached drawing.These attached drawings are that the invention discloses one of content
Point, mainly to illustrate embodiment, and cooperate the associated description of specification to explain the operation principles of embodiment.In conjunction with these
Content, those of ordinary skill in the art can easily recognize embodiment and advantages of the present invention；
Those skilled in the art of the present technique are appreciated that unless otherwise defined, term used herein above (including technical term
And scientific term) there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention；
In order to facilitate understanding of embodiments of the present invention, further solution is done by taking specific embodiment as an example below in conjunction with attached drawing
It releases, and embodiment does not constitute the restriction to the embodiment of the present invention；
Those of ordinary skill in the art are it should be understood that attached drawing is the schematic diagram of one embodiment, the component in figure
It is not drawn to scale, and component in attached drawing or device are not necessarily implemented necessary to the present invention.
Embodiment one
The present embodiment provides a kind of chemical fibre spinneret hole defect inspection method based on machine vision, as shown in Figure 1, it is special
Sign is, including following below scheme step:
The image that step 1. obtains industrial camera is acquired；
Step 2. demarcates camera, to obtain the relationship of Pixel Dimensions and actual size；
Step 3. carries out greyscale transformation to collected color image；
Step 4. pair carries out image progress area-of-interest (Regions of Interest, ROI) after greyscale transformation and mentions
It takes；
Step 5. turns off Connected area disposal$ to the ROI region extracted；
Connected domain after step 6. pair disconnects carries out edge enhancing；
Step 7. carries out edge extracting to each connected domain；
Step 8. is fitted circle to each connected domain；
Step 9. judges whether there is defect, is then qualified product if it does not exist, is then rejected product otherwise；
Step 10. calculated hole diameters and hole count, if aperture in predetermined tolerance range, for qualified product, otherwise, then not conform to
Lattice product；
Step 11. shows result.
The executing subject of the chemical fibre spinneret hole defect inspection method based on machine vision of the present embodiment is to be equipped with figure
As the terminal device such as computer of processing software, the terminal device and industrial camera and light source etc. are connect.
Specifically, obtaining the image of the spinneret hole of industrial camera acquisition first.
Specifically, then being demarcated to camera, to obtain the relationship of Pixel Dimensions and actual size.
It should be noted that camera calibration is the internal and external parameter and distortion parameter in order to obtain camera.The present invention uses
Zhang Zhengyou calibration method captures several chessboard table images with camera, detects Harris feature in the picture, then carries out the meter of parameter
It calculates and optimizes.
Specifically, carrying out greyscale transformation to collected color image.
It should be noted that color image includes tri- channels RGB, data volume to be treated is larger, therefore will collect
Image switch to the speed that processing image can be improved for gray level image according to 0.299*R+0.587*G+0.114*B.
Specifically, carrying out area-of-interest (Regions of Interest, ROI) to the image after progress greyscale transformation
It extracts.
It should be noted that setting minimum gradation value and maximum gradation value control parameter, selection meets within control parameter
Gray value, if all pixels point gray value meets this threshold value, just formed a ROI region, computer can be reduced
Operand.
Specifically, turning off Connected area disposal$ to the ROI region extracted.
It should be noted that carrying out 8 connected domain judgements to ROI region, 8 connected domains, there are the regions of gray value to be classified as one
A region can disconnect ROI region in this way, facilitate subsequent calculating hole count.
Specifically, carrying out edge enhancing to the connected domain after disconnection.
It should be noted that edge is the part of gray scale value mutation, it is the place most comprising characteristic information, using height
This-Laplace operator enhances the edge of each connected domain, facilitate subsequent edge extracting.
Specifically, carrying out edge extracting to each connected domain.
It should be noted that due to signal-to-noise ratio and high positioning performance that Canny operator has had, so the present invention uses
Canny operator carries out edge extracting.
Specifically, being fitted circle to each connected domain.
It should be noted that using least square method fitting circle to each connected domain.
Then it is qualified product if it does not exist specifically, judging whether there is defect, is then rejected product otherwise.
It should be noted that for the spinneret hole that defect is not present, the profile of edge extracting and the profile of fitting circle
It almost coincides together, biggish spinneret hole image is differed for two profile distances, can judge this spinneret hole
Existing defects.
It should be noted that defect includes blocking, burr and non-round etc., these can all influence circularity to a certain extent,
Spinneret hole can be judged with the presence or absence of defect by calculating circularity.
Specifically, calculated hole diameters and hole count, if aperture in predetermined tolerance range, for qualified product, otherwise, then not conform to
Lattice product.
It should be noted that the number to connected domain counts, the total number of connected domain is spinning head hole count, fitting
Diameter of a circle is spinning head aperture.
It counts and shows result.
It should be noted that result includes aperture, hole count and qualification rate etc..
Embodiment two
The present embodiment provides a kind of chemical fibre spinneret hole defect detecting system based on machine vision, as shown in figure 3, it is special
Sign is, including following below scheme step:
Light-source illuminating system, for striation part needed for providing acquisition image；
Image capturing system, for acquiring image and handling image；
Computer system, the display for image procossing and testing result；
Motion control and communication system, for production line locking and trigger camera and take pictures；
Execution system, crawl and transmission for spinning head.
Specifically, in general, wavelength is longer, the diffraction of light is bigger, therefore the coaxial back that the present invention is shorter using wavelength
Light source.
Specifically, using parallel coaxial backlight source lighting, image space since detection target is the hole of diameter 50-70um
Telecentric lens and high resolution industrial camera.
It should be noted that authenticity and testing result precision in order to guarantee image, to light source, spinning head and industrial phase
Machine has stringent concentricity requirement.
Specifically, the image of acquisition judge by drawbacks described above detection algorithm and calculated hole diameters and hole count.
It should be noted that including the visual software for carrying out user's interaction, achievable detection in computer system
And count display result.
Technical solution of the present invention is exemplarily described invention above in conjunction with attached drawing, it is clear that present invention specific implementation
It is not subject to the restrictions described above, changes as long as using the various unsubstantialities that the inventive concept and technical scheme of the present invention carry out
Into, or it is not improved the conception and technical scheme of invention are directly applied into other occasions, in protection scope of the present invention
Within.
Claims (12)
1. a kind of chemical fibre spinneret hole defect inspection method based on machine vision, which is characterized in that comprising steps of
The image that step 1. obtains industrial camera is acquired；
Step 2. demarcates camera, to obtain the relationship of Pixel Dimensions and actual size；
Step 3. carries out greyscale transformation to collected color image；
Step 4. pair carries out the image after greyscale transformation and carries out area-of-interest (Regions of Interest, ROI) extraction；
Step 5. turns off Connected area disposal$ to the ROI region extracted；
Connected domain after step 6. pair disconnects carries out edge enhancing；
Step 7. carries out edge extracting to each connected domain；
Step 8. is fitted circle to each connected domain；
Step 9. judges whether there is defect, is then qualified product if it does not exist, is then rejected product otherwise；
Step 10. calculated hole diameters and hole count, if otherwise aperture is then unqualified for qualified product in predetermined tolerance range
Product；
Step 11. counts and shows result.
2. a kind of chemical fibre spinneret hole defect inspection method based on machine vision according to claim 1, feature exist
In the spinneret hole is in a horizontal state in described image.
3. a kind of chemical fibre spinneret hole defect inspection method based on machine vision according to claim 1, feature exist
In, the defect include blocking, burr and non-round etc..
4. a kind of chemical fibre spinneret hole defect inspection method based on machine vision according to claim 1, feature exist
In the display result includes aperture, hole count and qualification rate etc..
5. a kind of chemical fibre spinneret hole defect inspection method based on machine vision according to claim 1, feature exist
In being timely responded to for the spinneret hole of existing defects.
6. a kind of chemical fibre spinneret hole defect detecting system based on machine vision characterized by comprising
Light-source illuminating system, for striation part needed for providing acquisition image；
Image capturing system, for acquiring image and handling image；
Computer system, the display for image procossing and testing result；
Motion control and communication system, for production line locking and trigger camera and take pictures；
Execution system, crawl and transmission for spinneret hole.
7. a kind of chemical fibre spinneret hole defect detecting system based on machine vision according to claim 6, feature exist
In the spinning head includes the different model of 50~70um of aperture and hole count 30~100.
8. a kind of chemical fibre spinneret hole defect detecting system based on machine vision according to claim 6, feature exist
In the industrial camera and light-source system of the acquisition picture system.
9. a kind of chemical fibre spinneret hole defect detecting system based on machine vision according to claim 6, feature exist
In the selection and polishing mode of the image space telecentric lens.
10. a kind of chemical fibre spinneret hole defect detecting system based on machine vision according to claim 6, feature exist
In the user interaction software is write.
11. a kind of chemical fibre spinneret hole defect detecting system based on machine vision according to claim 6, feature exist
In the spinneret hole is in a horizontal state in described image.
12. a kind of chemical fibre spinneret hole defect detecting system based on machine vision according to claim 6, feature exist
In the crawl and transmission of the spinning head.
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| CN201910780504.2A CN110441318A (en) | 2019-08-22 | 2019-08-22 | A kind of chemical fibre spinneret hole defect inspection method based on machine vision |
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Cited By (3)
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| CN113262551A (en) * | 2021-04-08 | 2021-08-17 | 中国铝业股份有限公司 | Filtering condition monitoring method of aluminum oxide flat disc filter and related equipment |
| CN113390367A (en) * | 2021-06-10 | 2021-09-14 | 江苏丽倍达精密机械有限公司 | Spinneret orifice detection method for spinneret plate |
| CN114170132A (en) * | 2021-10-20 | 2022-03-11 | 中国航发四川燃气涡轮研究院 | Machine vision-based method and system for detecting quality of static pressure hole of flow tube |
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