CN109087286A - A kind of detection method and application based on Computer Image Processing and pattern-recognition - Google Patents
A kind of detection method and application based on Computer Image Processing and pattern-recognition Download PDFInfo
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- CN109087286A CN109087286A CN201810783605.0A CN201810783605A CN109087286A CN 109087286 A CN109087286 A CN 109087286A CN 201810783605 A CN201810783605 A CN 201810783605A CN 109087286 A CN109087286 A CN 109087286A
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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
- G06T7/0008—Industrial image inspection checking presence/absence
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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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
- G06T7/11—Region-based segmentation
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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
- G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
Abstract
Disclosed by the invention to belong to technical field of image processing, specially a kind of detection method and application based on Computer Image Processing and pattern-recognition is somebody's turn to do the detection method based on Computer Image Processing and pattern-recognition and includes the following steps: S1: sample Image Acquisition；S2: image preprocessing；S3: image segmentation；S4: image boundary tracking and extraction: use empties interior point method and comes out the contours extract of image first, and then one marginal point from gray level image, successively searches for and connect neighboring edge point, realize the tracking of image boundary；S5: detection image acquisition；S6: defect analysis, the quick detection to product defects is realized in conjunction with modern photoelectron technology and computer disposal and mode identification technology, it can quickly and accurately determine the location and shape of product defects, pass through the processing to image, improve the arithmetic speed of computer, it greatly improves work efficiency, the invention is easy to use, convenient for promoting.
Description
Technical field
The present invention relates to technical field of image processing, specially a kind of inspection based on Computer Image Processing and pattern-recognition
Survey method and application.
Background technique
Visual pattern detection is exactly to replace human eye with machine to measure and judge.With computer technology and information technology
Development, image recognition technology is more and more widely used.The digital processing of image be centered on computer, including
It is carried out on digital image processing system including various inputs, output and display equipment, is to become continuous analog image
After discrete digital picture, the process control worked out on the basis of specific physical model and mathematical model with foundation, operation is simultaneously
Realize the processing of various requirements.
During industrial production, need to detect the product produced, so that it is guaranteed that there is defects
Product is separated, and the detection method detection efficiency of existing method is low, and labor intensity is high, and there is missing inspections and false detection rate to compare
Height, in addition, detecting intuitive in speed, ease for operation and detection process etc., conveniently there is also many problems.For this purpose, we
It is proposed a kind of detection method and application based on Computer Image Processing and pattern-recognition.
Summary of the invention
The purpose of the present invention is to provide a kind of detection method and application based on Computer Image Processing and pattern-recognition,
To solve the problems mentioned in the above background technology.
To achieve the above object, the invention provides the following technical scheme: a kind of known based on Computer Image Processing and mode
Other detection method is somebody's turn to do the detection method based on Computer Image Processing and pattern-recognition and is included the following steps:
S1: sample Image Acquisition: being realized using image capture device and carry out Image Acquisition to the qualified product of standard, and
The qualified product of the standard of acquisition is subjected to image image as image sample；
S2: image preprocessing: the image sample in step S1 is subjected to image grayscale and binary conversion treatment, is then carried out again
Picture smooth treatment and image sharpening processing；
S3: image segmentation: the image pre-processed is split, by interested foreground image from uninterested back
It is split in scape image；
S4: image boundary tracking and extraction: use empties interior point method and comes out the contours extract of image first, then from ash
A marginal point sets out in degree image, successively searches for and connects neighboring edge point, the tracking of image boundary is realized, finally to image
The measurement of perimeter and area；
S5: detection image acquisition: the image information for the product that acquisition needs to acquire, and installation steps S2 is carried out at image
Reason；
S6: defect analysis: will test image and sample image penetrates and carries out image analysis and classification in classifier, thus real
Now to the defect dipoles of detection image.
Preferably, the image capture device in the step S1 includes one saturating with light source, CCD camera, CCD imaging
The packaging body of mirror, the packaging body connect the image pick-up card being plugged on expanded slot of computer by video line.
Preferably, image grayscale and binary conversion treatment in the step S1 method particularly includes: by the image sample of acquisition
Whole pixels carry out gray-scale statistical, then in plane coordinate system carry out curve graph drafting, which is indicated with ordinate
Possessed number of pixels indicates gray value with abscissa, so that the drafting to intensity profile histogram is realized, then according to ash
It spends distribution histogram and carries out binary conversion treatment.
Preferably, image segmentation in the step S3 method particularly includes: each zonule in image is carried out first
Label, a label indicate the presence in a region, and the minimum as gradient is forced in the region for then going to these sides, then is shielded
Other minimums in gradient image are covered, then using this processing as basis, carry out going for noise further according to morphological operation
It removes, the segmentation of image is finally carried out using watershed segmentation method.
Preferably, the specific steps of interior point method are emptied in the step S4 are as follows: the image to be extracted is carried out two-value first
Change processing is converted into binary image, then judges 8 pixels around each pixel one by one, if 8 of surrounding
The gray value of pixel is identical as this gray value, then this pixel must be internal point, then carries out the deletion of internal point, if
It is not it is determined that marginal point, is retained, until all pixels have all been handled, the image that remaining pixel is constituted is
The image outline for needing to extract.
Preferably, the defects of described step S6 judgment method are as follows: establish a tested production by containing all kinds of defects
The sample space image of the image composition of product, analyzes images all in sample space, finds out the main spy of all kinds of defects
The inner link sought peace between them, finally extracts best feature composition characteristic vector, is established according to extracted feature
Classifying rules, and classifying rules is converted into threshold rule, measurement space is divided into the region not overlapped, each correspondence
Just the object is included into corresponding classification if characteristic value falls in some region in one or more regions.
Preferably, when defect is not identified accurately, the modification to the threshold value of characteristic parameter is needed.
A kind of application of the detection method based on Computer Image Processing and pattern-recognition should be based on Computer Image Processing
It is applied to the defects detection of product with the detection method of pattern-recognition.
Compared with prior art, the beneficial effects of the present invention are: one kind that the invention proposes is based on Computer Image Processing
With the detection method and application of pattern-recognition, realized pair in conjunction with modern photoelectron technology and computer disposal and mode identification technology
The quick detection of product defects can quickly and accurately determine the location and shape of product defects, by the processing to image,
The arithmetic speed for improving computer, greatly improves work efficiency, and the invention is easy to use, convenient for promoting.
Detailed description of the invention
Fig. 1 is detection method flow chart.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Referring to Fig. 1, the present invention provides a kind of technical solution: a kind of inspection based on Computer Image Processing and pattern-recognition
Survey method is somebody's turn to do the detection method based on Computer Image Processing and pattern-recognition and is included the following steps:
S1: sample Image Acquisition: being realized using image capture device and carry out Image Acquisition to the qualified product of standard, and
The qualified product of the standard of acquisition is subjected to image image as image sample；
S2: image preprocessing: the image sample in step S1 is subjected to image grayscale and binary conversion treatment, is then carried out again
Picture smooth treatment and image sharpening processing；
S3: image segmentation: the image pre-processed is split, by interested foreground image from uninterested back
It is split in scape image；
S4: image boundary tracking and extraction: use empties interior point method and comes out the contours extract of image first, then from ash
A marginal point sets out in degree image, successively searches for and connects neighboring edge point, the tracking of image boundary is realized, finally to image
The measurement of perimeter and area；
S5: detection image acquisition: the image information for the product that acquisition needs to acquire, and installation steps S2 is carried out at image
Reason；
S6: defect analysis: will test image and sample image penetrates and carries out image analysis and classification in classifier, thus real
Now to the defect dipoles of detection image.
Wherein, the image capture device in the step S1 includes one with light source, CCD camera, CCD imaging len
Packaging body, the packaging body connects the image pick-up card that is plugged on expanded slot of computer, the step S1 by video line
Middle image grayscale and binary conversion treatment method particularly includes: whole pixels of the image sample of acquisition are subjected to gray-scale statistical, so
The drafting for carrying out curve graph in plane coordinate system afterwards, indicates number of pixels possessed by the gray scale with ordinate, with abscissa
It indicates gray value, to realize the drafting to intensity profile histogram, is then carried out at binaryzation according to intensity profile histogram
It manages, image segmentation in the step S3 method particularly includes: each zonule in image is marked first, a label
Indicate the presence in a region, the minimum as gradient is forced in the region for then going to these sides, then is shielded in gradient image
Other minimums carry out the removal of noise further according to morphological operation then using this processing as basis, finally use and divide
Water ridge split plot design carries out the segmentation of image, the specific steps of interior point method is emptied in the step S4 are as follows: first the figure to be extracted
It is converted into binary image as carrying out binary conversion treatment, then judges 8 pixels around each pixel one by one, if
The gray value of 8 pixels of surrounding is identical as this gray value, then this pixel must be internal point, then carries out internal point
It deletes, if not it is determined that marginal point, is retained, until all pixels have all been handled, remaining pixel is constituted
The image image outline that as needs to extract, the defects of described step S6 judgment method are as follows: establish one by containing all kinds of
The sample space image of the image composition of the tested product of defect, analyzes images all in sample space, finds out each
The main feature of class defect and the inner link between them finally extract best feature composition characteristic vector, according to institute
The feature of extraction establishes classifying rules, and classifying rules is converted into threshold rule, will measure space and is divided into and does not overlap
Region, each corresponds to one or more regions and the object is just included into corresponding classification if characteristic value falls in some region
In, when defect is not identified accurately, need the modification to the threshold value of characteristic parameter.
A kind of application of the detection method based on Computer Image Processing and pattern-recognition should be based on Computer Image Processing
It is applied to the defects detection of product with the detection method of pattern-recognition.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (8)
1. a kind of detection method based on Computer Image Processing and pattern-recognition, it is characterised in that: computer picture should be based on
The detection method of processing and pattern-recognition includes the following steps:
S1: it sample Image Acquisition: is realized using image capture device and Image Acquisition is carried out to the qualified product of standard, and will adopted
The qualified product of the standard of collection carries out image image as image sample；
S2: image preprocessing: the image sample in step S1 is subjected to image grayscale and binary conversion treatment, then carries out image again
Smoothing processing and image sharpening processing；
S3: image segmentation: the image pre-processed is split, by interested foreground image from uninterested Background
It is split as in；
S4: image boundary tracking and extraction: use empties interior point method and comes out the contours extract of image first, then from grayscale image
A marginal point sets out as in, successively searches for and connects neighboring edge point, the tracking of image boundary is realized, finally to image perimeter
With the measurement of area；
S5: detection image acquisition: the image information for the product that acquisition needs to acquire, and installation steps S2 carries out image procossing；
S6: defect analysis: will test image and sample image penetrates and carries out image analysis and classification in classifier, thus realization pair
The defect dipoles of detection image.
2. a kind of detection method based on Computer Image Processing and pattern-recognition according to claim 1, feature exist
In: the image capture device in the step S1 includes the packaging body for having light source, CCD camera, CCD imaging len,
The packaging body connects the image pick-up card being plugged on expanded slot of computer by video line.
3. a kind of detection method based on Computer Image Processing and pattern-recognition according to claim 1, feature exist
In image grayscale and binary conversion treatment in the step S1 method particularly includes: by whole pixels of the image sample of acquisition into
Then row gray-scale statistical carries out the drafting of curve graph in plane coordinate system, indicates pixel possessed by the gray scale with ordinate
Number indicates gray value with abscissa, so that the drafting to intensity profile histogram is realized, then according to intensity profile histogram
Carry out binary conversion treatment.
4. a kind of detection method based on Computer Image Processing and pattern-recognition according to claim 1, feature exist
In: image segmentation in the step S3 method particularly includes: each zonule in image is marked first, a label
Indicate the presence in a region, the minimum as gradient is forced in the region for then going to these sides, then is shielded in gradient image
Other minimums carry out the removal of noise further according to morphological operation then using this processing as basis, finally use and divide
The segmentation of water ridge split plot design progress image.
5. a kind of detection method based on Computer Image Processing and pattern-recognition according to claim 1, feature exist
In: the specific steps of interior point method are emptied in the step S4 are as follows: the image to be extracted is carried out binary conversion treatment first and is converted into
Then binary image judges 8 pixels around each pixel one by one, if the gray scale of 8 pixels of surrounding
Be worth it is identical with this gray value, then this pixel must be internal point, then progress internal point deletion, if not it is determined that
Marginal point is retained, until the figure that all pixels have all been handled, and the image that remaining pixel is constituted as needs to extract
As profile.
6. a kind of detection method based on Computer Image Processing and pattern-recognition according to claim 1, feature exist
In: the defects of described step S6 judgment method are as follows: establish an image by the tested product containing all kinds of defects and form
Sample space image, images all in sample space are analyzed, find out all kinds of defects main feature and they between
Inner link, finally extract best feature composition characteristic vector, classifying rules established according to extracted feature, and will
Classifying rules is converted into threshold rule, measurement space is divided into the region not overlapped, each corresponds to one or more areas
Just the object is included into corresponding classification if characteristic value falls in some region in domain.
7. a kind of detection method and application based on Computer Image Processing and pattern-recognition according to claim 6,
It is characterized in that: when defect is not identified accurately, needing the modification to the threshold value of characteristic parameter.
8. a kind of application of the detection method based on Computer Image Processing and pattern-recognition, it is characterised in that: should be based on calculating
Machine image procossing and the detection method of pattern-recognition are applied to the defects detection of product.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110335233A (en) * | 2019-04-24 | 2019-10-15 | 武汉理工大学 | Express-way guard-rail plates defect detecting system and method based on image processing techniques |
| CN110400315A (en) * | 2019-08-01 | 2019-11-01 | 北京迈格威科技有限公司 | A kind of defect inspection method, apparatus and system |
| CN111462053A (en) * | 2020-03-18 | 2020-07-28 | 深圳科瑞技术股份有限公司 | Image morphology processing method and system |
| CN111487192A (en) * | 2020-04-26 | 2020-08-04 | 天津海融科技有限公司 | Machine vision surface defect detection device and method based on artificial intelligence |
| CN111583084A (en) * | 2020-04-01 | 2020-08-25 | 杭州优视泰信息技术有限公司 | Workpiece defect positioning method |
| CN111583190A (en) * | 2020-04-16 | 2020-08-25 | 浙江浙能技术研究院有限公司 | Automatic identification method for hidden crack defect of internal cascade structure component |
| CN112359748A (en) * | 2020-11-11 | 2021-02-12 | 泰州锐比特智能科技有限公司 | Automatic opening system of return channel |
| CN112598652A (en) * | 2020-12-25 | 2021-04-02 | 凌云光技术股份有限公司 | Liquid crystal display edge broken line detection method based on gradient transformation |
| CN112862747A (en) * | 2020-12-07 | 2021-05-28 | 英特尔产品（成都）有限公司 | Method and image processing system for processing and analyzing images of chip tray stack and chip tray stack detection device |
| CN113052829A (en) * | 2021-04-07 | 2021-06-29 | 深圳市磐锋精密技术有限公司 | Mainboard AOI detection method based on Internet of things |
| CN114928720A (en) * | 2022-05-13 | 2022-08-19 | 重庆云凯科技有限公司 | System and method for detecting state of parking barrier gate rod |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100158345A1 (en) * | 2008-12-22 | 2010-06-24 | Tadashi Kitamura | Defect And Critical Dimension Analysis Systems And Methods For A Semiconductor Lithographic Process |
| CN102253050A (en) * | 2011-03-14 | 2011-11-23 | 广州市盛通建设工程质量检测有限公司 | Automatic detection method and device for magnetic tile surface defect based on machine vision |
| CN105139027A (en) * | 2015-08-05 | 2015-12-09 | 北京天诚盛业科技有限公司 | Capsule head defect detection method and apparatus |
| CN105447851A (en) * | 2015-11-12 | 2016-03-30 | 刘新辉 | Glass panel sound hole defect detection method and system |
| CN105588845A (en) * | 2016-01-04 | 2016-05-18 | 江苏科技大学 | Weld defect characteristic parameter extraction method |
| CN106570511A (en) * | 2016-11-02 | 2017-04-19 | 上海大学 | Keyboard character defect online detection system based on computer vision and keyboard character defect online detection method thereof |
| CN106897994A (en) * | 2017-01-20 | 2017-06-27 | 北京京仪仪器仪表研究总院有限公司 | A kind of pcb board defect detecting system and method based on layered image |
| CN108169236A (en) * | 2016-12-07 | 2018-06-15 | 广州映博智能科技有限公司 | A kind of cracks of metal surface detection method of view-based access control model |
| CN108171242A (en) * | 2016-12-07 | 2018-06-15 | 广州映博智能科技有限公司 | A kind of efficient baffle ring detecting system |
-
2018
- 2018-07-17 CN CN201810783605.0A patent/CN109087286A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100158345A1 (en) * | 2008-12-22 | 2010-06-24 | Tadashi Kitamura | Defect And Critical Dimension Analysis Systems And Methods For A Semiconductor Lithographic Process |
| CN102253050A (en) * | 2011-03-14 | 2011-11-23 | 广州市盛通建设工程质量检测有限公司 | Automatic detection method and device for magnetic tile surface defect based on machine vision |
| CN105139027A (en) * | 2015-08-05 | 2015-12-09 | 北京天诚盛业科技有限公司 | Capsule head defect detection method and apparatus |
| CN105447851A (en) * | 2015-11-12 | 2016-03-30 | 刘新辉 | Glass panel sound hole defect detection method and system |
| CN105588845A (en) * | 2016-01-04 | 2016-05-18 | 江苏科技大学 | Weld defect characteristic parameter extraction method |
| CN106570511A (en) * | 2016-11-02 | 2017-04-19 | 上海大学 | Keyboard character defect online detection system based on computer vision and keyboard character defect online detection method thereof |
| CN108169236A (en) * | 2016-12-07 | 2018-06-15 | 广州映博智能科技有限公司 | A kind of cracks of metal surface detection method of view-based access control model |
| CN108171242A (en) * | 2016-12-07 | 2018-06-15 | 广州映博智能科技有限公司 | A kind of efficient baffle ring detecting system |
| CN106897994A (en) * | 2017-01-20 | 2017-06-27 | 北京京仪仪器仪表研究总院有限公司 | A kind of pcb board defect detecting system and method based on layered image |
Non-Patent Citations (1)
| Title |
|---|
| 周显恩等: "基于机器视觉的瓶口缺陷检测方法研究", 《电子测量与仪器学报》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110335233A (en) * | 2019-04-24 | 2019-10-15 | 武汉理工大学 | Express-way guard-rail plates defect detecting system and method based on image processing techniques |
| CN110335233B (en) * | 2019-04-24 | 2023-06-30 | 武汉理工大学 | Highway guardrail plate defect detection system and method based on image processing technology |
| CN110400315A (en) * | 2019-08-01 | 2019-11-01 | 北京迈格威科技有限公司 | A kind of defect inspection method, apparatus and system |
| CN110400315B (en) * | 2019-08-01 | 2020-05-05 | 北京迈格威科技有限公司 | Defect detection method, device and system |
| CN111462053A (en) * | 2020-03-18 | 2020-07-28 | 深圳科瑞技术股份有限公司 | Image morphology processing method and system |
| CN111583084A (en) * | 2020-04-01 | 2020-08-25 | 杭州优视泰信息技术有限公司 | Workpiece defect positioning method |
| CN111583190A (en) * | 2020-04-16 | 2020-08-25 | 浙江浙能技术研究院有限公司 | Automatic identification method for hidden crack defect of internal cascade structure component |
| CN111583190B (en) * | 2020-04-16 | 2022-07-22 | 浙江浙能技术研究院有限公司 | Automatic identification method for hidden crack defect of internal cascade structure component |
| CN111487192A (en) * | 2020-04-26 | 2020-08-04 | 天津海融科技有限公司 | Machine vision surface defect detection device and method based on artificial intelligence |
| CN112359748A (en) * | 2020-11-11 | 2021-02-12 | 泰州锐比特智能科技有限公司 | Automatic opening system of return channel |
| CN112862747A (en) * | 2020-12-07 | 2021-05-28 | 英特尔产品（成都）有限公司 | Method and image processing system for processing and analyzing images of chip tray stack and chip tray stack detection device |
| CN112598652A (en) * | 2020-12-25 | 2021-04-02 | 凌云光技术股份有限公司 | Liquid crystal display edge broken line detection method based on gradient transformation |
| CN112598652B (en) * | 2020-12-25 | 2024-01-30 | 凌云光技术股份有限公司 | Gradient transformation-based liquid crystal display edge broken line detection method |
| CN113052829A (en) * | 2021-04-07 | 2021-06-29 | 深圳市磐锋精密技术有限公司 | Mainboard AOI detection method based on Internet of things |
| CN114928720A (en) * | 2022-05-13 | 2022-08-19 | 重庆云凯科技有限公司 | System and method for detecting state of parking barrier gate rod |
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Application publication date: 20181225 |