CN107478657A - Stainless steel surfaces defect inspection method based on machine vision - Google Patents
Stainless steel surfaces defect inspection method based on machine vision Download PDFInfo
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- CN107478657A CN107478657A CN201710469603.XA CN201710469603A CN107478657A CN 107478657 A CN107478657 A CN 107478657A CN 201710469603 A CN201710469603 A CN 201710469603A CN 107478657 A CN107478657 A CN 107478657A
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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
- 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 present invention relates to the stainless steel surfaces defect inspection method based on machine vision, step is：S1. the surface image of stainless steel to be detected is gathered using CCD industrial cameras；S2. the two-dimensional defect detection based on Blob analyses is carried out to the stainless steel surfaces image to be detected collected；S3. the 3 D defects detection of the Fourier transform based on frequency domain is carried out to the stainless steel surfaces image to be detected collected；S4. according to two and three dimensions defects detection result, there will be the stainless steel of defect to separate.The present invention is directed to two-dimensional defect, it is proposed a kind of surface defects detection algorithm based on Blob analyses, for 3 D defects, it is proposed a kind of fourier transform algorithm based on frequency domain, stainless steel product surface common deficiency can effectively be detected, such as cut, greasy dirt, iron mold, bubble, crackle, magazine, roll mark, slivering, moreover, this programme testing result is accurate, detection efficiency is high.
Description
Technical field
The present invention relates to the technical field of steel and iron manufacturing, more particularly to the stainless steel watch planar defect inspection based on machine vision
Survey method.
Background technology
The production and manufacture of steel are a highly important factors for influenceing national national economy and the modernization of industry.Example
Such as, in daily life, stainless steel product is applied to various aspects.So the quality testing to stainless steel is particularly important.
Stainless steel watch planar defect is generally divided into two-dimensional defect and 3 D defects.The surface defects detection of traditional stainless steel is by examining
Survey personnel are completed by human eye range estimation.But this method is there is many deficiencies, for example：1st, testing result is easily examined
Survey personnel subjective factor influences；2nd, it is only used for detecting the very slow stainless steel surfaces of the speed of service；3rd, it is difficult to detect small lack
Fall into.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of testing result is accurate, detection efficiency is high, energy
Detect that stainless steel surfaces are two-dimentional and the stainless steel surfaces defect inspection method based on machine vision of three-dimensional tiny defect.
To achieve the above object, technical scheme provided by the present invention is：Method comprises the following steps：
S1. the surface image of stainless steel to be detected is gathered using CCD industrial cameras；
S2. the two-dimensional defect detection based on Blob analyses is carried out to the stainless steel surfaces image to be detected collected；
S3. the 3 D defects inspection of the Fourier transform based on frequency domain is carried out to the stainless steel surfaces image to be detected collected
Survey；
S4. according to two and three dimensions defects detection result, there will be the stainless steel of defect to separate.
Further, step S2 stainless steel surfaces two-dimensional defect detecting step is as follows：
S21. ROI region is selected：
ROI region, i.e. stainless steel plate region segmentation are come out using Global thresholding, then extract stainless steel plate connection
Domain.If image to be split is f (x, y), the image after Threshold segmentation is S (x, y), and formula is
Wherein, T is segmentation threshold；
S22. image preprocessing；
S23. segmentation figure picture：
Pretreated gray-scale map is split using improved dual-threshold voltage, divided the image into as foreground image (i.e.
Defect area) and background image pixel set；
If foreground image is p (x, y), the image after Threshold segmentation is q (x, y), carries out image segmentation as follows：
Wherein T1And T2For for the improvement threshold value set by stainless steel plate imaging effect；
S24. feature is extracted：
Connected region extraction is carried out to target area, draws the area of defect part, circularity, gray average parameter.
Areal calculation formula is：
Wherein, R represents image-region, and m, n represent that image-region has m rows n row, and f (i, j) represents point (i, j) place in region
Pixel value；
Circularity calculation formula is：
Wherein, P represents the girth in region, and A represents the area in region；
Gray average calculation formula is：
Wherein, L is that gray level is total, ziRepresent i-th of gray level, h (zi) represent that the gray scale that counts is z in histogrami's
Number of pixels；
Further, step S3 stainless steel surfaces 3 D defects detecting step is as follows：
S31. Gaussian filter is created：
Two Gaussian filters are created, and subtraction process is done to the image after gaussian filtering；
Formula is described as：
O (i, j)=| I1(i, j)-I2(i, j) |,
Wherein, O (i, j) be subtract each other after image, I1(i, j), I2(i, j) is respectively two images after gaussian filtering；
S32. image preprocessing：
RGB triple channel image graphs are converted into gray-scale map；
If the gray-scale map after changing is into Gray (i, j), calculation formula：
Gray (i, j)=0.11*R (i, j)+0.59*G (i, j)+0.3*B (i, j),
Wherein, Gray (i, j) is gray value of the image at (i, j) point after conversion；
S33. pretreated image changes to frequency domain processing from transform of spatial domain：
Gray-scale map is subjected to Fourier transform, frequency domain processing is changed to from transform of spatial domain；
Two-dimension fourier transform calculation formula is：
Wherein, f (x, y) is space area image, and F (u, v) is image after two-dimensional Fourier transform；
S34. convolution algorithm is carried out to frequency domain figure picture：
Convolution algorithm is carried out in frequency domain with a wave filter to image, calculation formula is：
Wherein, g (i, j) is input picture, and h is referred to as related core, and f (i, j) is output image；
S35. frequency domain figure picture is transformed into spatial domain processing again：
Inverse fourier transform is carried out to the image after convolution algorithm, is transformed into spatial domain processing again；
Calculation formula is：
Two-dimension fourier inverse transformation calculation formula is:
Wherein,For image after two-dimensional inverse Fourier transform, F (u, v) is two-dimentional Fourier's image；
S36. space area image is split：
Step S35 inversefouriertransform image is split using improved dual-threshold voltage, extracts defect area.
If foreground image is p (x, y), the image after Threshold segmentation is q (x, y), carries out image segmentation as follows：
Wherein T1And T2For for the improvement threshold value set by stainless steel plate imaging effect；
S37. connected domain is chosen.
Further, step S22 image preprocessings comprise the following steps that：Selected ROI region is entered with greyscale transformation
Row image preprocessing, mean filter denoising is carried out to gray-scale map using mask.
This programme principle and advantage are as follows：
This programme is directed to two-dimensional defect, proposes a kind of surface defects detection algorithm based on Blob analyses, is lacked for three-dimensional
Fall into, propose a kind of fourier transform algorithm based on frequency domain, can effectively detect stainless steel product surface common deficiency, such as draw
Trace, greasy dirt, iron mold, concave point, crackle, impurity, roll mark, slivering etc., moreover, this programme testing result is accurate, detection efficiency is high.
Brief description of the drawings
Fig. 1 is the algorithm flow chart that two-dimensional defect detects in the present invention；
Fig. 2 is the algorithm flow chart that 3 D defects detect in the present invention；
Fig. 3 is stainless steel two-dimensional defect Detection results figure；
Fig. 4 is stainless steel 3 D defects Detection results figure.
Embodiment
With reference to specific embodiment, the invention will be further described：
Referring to shown in accompanying drawing 1-2, the stainless steel surfaces defect inspection method based on machine vision described in the present embodiment, wrap
Include following steps：
S1. the surface image of stainless steel to be detected is gathered using CCD industrial cameras；
S2. the two-dimensional defect detection based on Blob analyses, step are carried out to the stainless steel surfaces image to be detected collected
It is as follows：
S21. ROI region is selected：
ROI region, i.e. stainless steel plate region segmentation are come out using Global thresholding, then extract stainless steel plate connection
Domain.If image to be split is f (x, y), the image after Threshold segmentation is S (x, y), then
Wherein, T is segmentation threshold；
S22. image preprocessing：
Image preprocessing is carried out to selected ROI region with greyscale transformation, gray-scale map carried out using 21*21 mask equal
Value filtering denoising；
S23. segmentation figure picture：
Pretreated gray-scale map is split using improved dual-threshold voltage, divided the image into as foreground image (i.e.
Defect area) and background image pixel set；
If foreground image is p (x, y), the image after Threshold segmentation is q (x, y), carries out image segmentation as follows：
Wherein T1And T2For for the improvement threshold value set by stainless steel plate imaging effect；
S24. feature is extracted：
Connected region extraction is carried out to target area, draws the area of defect part, circularity, gray average parameter；
Areal calculation formula is：
Wherein, R represents image-region, and m, n represent that image-region has m rows n row, and f (i, j) represents point (i, j) place in region
Pixel value；
Circularity calculation formula is：
Wherein, P represents the girth in region, and A represents the area in region；
Gray average calculation formula is：
Wherein, L is that gray level is total, ziRepresent i-th of gray level, h (zi) represent that the gray scale that counts is z in histogrami's
Number of pixels；
S3. the 3 D defects inspection of the Fourier transform based on frequency domain is carried out to the stainless steel surfaces image to be detected collected
Survey, step is as follows：
S31. Gaussian filter is built：
Two Gaussian filters are created, and subtraction process is done to the image after gaussian filtering；
Formula is described as：
O (i, j)=| I1(i, j)-I2(i, j) |,
Wherein, O (i, j) be subtract each other after image, I1(i, j), I2(i, j) is respectively two images after gaussian filtering；
S32. image preprocessing：
RGB triple channel image graphs are converted into gray-scale map；
If the gray-scale map after changing is Gray (i, j), then calculation formula is：
Gray (i, j)=0.11*R (i, j)+0.59*G (i, j)+0.3*B (i, j),
Wherein, Gray (i, j) is gray value of the image at (i, j) point after conversion；
S33. pretreated image changes to frequency domain processing from transform of spatial domain：
Gray-scale map is subjected to Fourier transform, frequency domain processing is changed to from transform of spatial domain；
Two-dimension fourier transform calculation formula is：
Wherein, f (x, y) is space area image, and F (u, v) is image after two-dimensional Fourier transform；
S34. convolution algorithm is carried out to frequency domain figure picture：
Convolution algorithm is carried out in frequency domain with a wave filter to image, calculation formula is：
Wherein, g (i, j) is input picture, and h is referred to as related core, and f (i, j) is output image；
S35. frequency domain figure picture is transformed into spatial domain processing again：
Inverse fourier transform is carried out to the image after convolution algorithm, is transformed into spatial domain processing again；
Calculation formula is：
Two-dimension fourier inverse transformation calculation formula is:
Wherein,For image after two-dimensional inverse Fourier transform, F (u, v) is two-dimentional Fourier's image；
S36. space area image is split：
Step S35 inversefouriertransform image is split using improved dual-threshold voltage, extracts defect area.
If foreground image is p (x, y), the image after Threshold segmentation is q (x, y), carries out image segmentation as follows：
Wherein T1And T2For for the improvement threshold value set by stainless steel plate imaging effect；
S37. connected domain is chosen；
S4. according to two and three dimensions defects detection result, there will be the stainless steel of defect to separate.
The present embodiment testing result is accurate, and detection efficiency is high, can detect that stainless steel surfaces are two-dimentional and three-dimensional tiny defect,
Such as cut, greasy dirt, iron mold, concave point, crackle, impurity, roll mark, slivering.
Examples of implementation described above are only the preferred embodiments of the invention, and the implementation model of the present invention is not limited with this
Enclose, therefore the change that all shape, principles according to the present invention are made, it all should cover within the scope of the present invention.
Claims (4)
1. the stainless steel surfaces defect inspection method based on machine vision, it is characterised in that：Comprise the following steps：
S1. the surface image of stainless steel to be detected is gathered using CCD industrial cameras；
S2. the two-dimensional defect detection based on Blob analyses is carried out to the stainless steel surfaces image to be detected collected；
S3. the 3 D defects detection of the Fourier transform based on frequency domain is carried out to the stainless steel surfaces image to be detected collected；
S4. according to two and three dimensions defects detection result, there will be the stainless steel of defect to separate.
2. according to the stainless steel surfaces defect inspection method based on machine vision described in claim 1, it is characterised in that：The step
Rapid S2 stainless steel surfaces two-dimensional defect detecting steps are as follows：
S21. ROI region is selected；
S22. image preprocessing；
S23. segmentation figure picture；
S24. feature is extracted.
3. the stainless steel surfaces defect inspection method according to claim 1 based on machine vision, it is characterised in that：It is described
Step S3 stainless steel surfaces 3 D defects detecting steps are as follows：
S31. Gaussian filter is created；
S32. image preprocessing；
S33. pretreated image changes to frequency domain processing from transform of spatial domain；
S34. convolution algorithm is carried out to frequency domain figure picture；
S35. frequency domain figure picture is transformed into spatial domain processing again；
S36. space area image is split；
S37. connected domain is chosen.
4. the stainless steel surfaces defect inspection method based on machine vision described in claim 2, it is characterised in that：The step
S22 image preprocessings comprise the following steps that：Image preprocessing is carried out to selected ROI region with greyscale transformation, using mask
Mean filter denoising is carried out to gray-scale map.
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| CN108037135A (en) * | 2018-01-15 | 2018-05-15 | 佛山职业技术学院 | A kind of magnet ring surface defect detection apparatus and method |
| CN108181324A (en) * | 2018-01-05 | 2018-06-19 | 佛山职业技术学院 | A kind of board surface detection method based on machine vision |
| CN108921861A (en) * | 2018-05-15 | 2018-11-30 | 佛山市南海区广工大数控装备协同创新研究院 | A kind of portable computer touch tablet edge detection method based on machine vision |
| CN109211919A (en) * | 2018-09-03 | 2019-01-15 | 珠海格力智能装备有限公司 | Method and device for identifying magnetic tile defect area |
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| CN109682820A (en) * | 2018-11-05 | 2019-04-26 | 苏州佳智彩光电科技有限公司 | A kind of defect automatic optical detection method of display screen |
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| CN110111301A (en) * | 2019-03-21 | 2019-08-09 | 广东工业大学 | Metal based on frequency-domain transform aoxidizes surface defect visible detection method |
| CN110189290A (en) * | 2019-04-08 | 2019-08-30 | 广东工业大学 | Metal surface fine defects detection method and device based on deep learning |
| CN110211120A (en) * | 2019-06-04 | 2019-09-06 | 北京航天宏图信息技术股份有限公司 | The corrosion degree of historical relic determines method, apparatus and electronic equipment |
| CN110223296A (en) * | 2019-07-08 | 2019-09-10 | 山东建筑大学 | A kind of screw-thread steel detection method of surface flaw and system based on machine vision |
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| CN110827235A (en) * | 2019-09-24 | 2020-02-21 | 苏州苏相机器人智能装备有限公司 | Steel plate surface defect detection method |
| CN111028215A (en) * | 2019-12-06 | 2020-04-17 | 上海大学 | Method for detecting end surface defects of steel coil based on machine vision |
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Application publication date: 20171215 |