CN105414206A - Online rapid positioning method for cold-rolled strip steel edge - Google Patents
Online rapid positioning method for cold-rolled strip steel edge Download PDFInfo
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- CN105414206A CN105414206A CN201410483796.0A CN201410483796A CN105414206A CN 105414206 A CN105414206 A CN 105414206A CN 201410483796 A CN201410483796 A CN 201410483796A CN 105414206 A CN105414206 A CN 105414206A
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- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 51
- 239000010959 steel Substances 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000001360 synchronised effect Effects 0.000 claims abstract description 8
- 230000006835 compression Effects 0.000 claims abstract description 4
- 238000007906 compression Methods 0.000 claims abstract description 4
- 238000001914 filtration Methods 0.000 claims abstract description 4
- 238000000205 computational method Methods 0.000 claims description 12
- 239000000758 substrate Substances 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 abstract description 6
- 230000007547 defect Effects 0.000 abstract description 5
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- 238000005286 illumination Methods 0.000 abstract description 4
- 238000009776 industrial production Methods 0.000 abstract description 2
- 238000001514 detection method Methods 0.000 description 8
- 238000003708 edge detection Methods 0.000 description 6
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 description 2
- 230000004075 alteration Effects 0.000 description 2
- 239000011701 zinc Substances 0.000 description 2
- 229910052725 zinc Inorganic materials 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 1
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- 238000013461 design Methods 0.000 description 1
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- 238000005516 engineering process Methods 0.000 description 1
- 238000003703 image analysis method Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
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Abstract
An online rapid positioning method for the edge of cold-rolled strip steel comprises the steps of processing a strip steel real-time image collected by a camera to obtain a strip steel synchronous gray image; carrying out longitudinal median filtering on the gray image, wherein the gray value of each pixel point on the image is equal to the gray intermediate value of the longitudinally adjacent point; layering images, and respectively determining left and right fluctuation maximum blocks of each layer from the left and right directions; performing image longitudinal compression to respectively form left and right compressed image sequences; respectively generating left and right edge fluctuation sequences; then, determining an edge fluctuation threshold value of each fluctuation sequence, respectively searching a fluctuation sequence value of which the first one is larger than the corresponding edge fluctuation threshold value in the fluctuation sequence from the left direction and the right direction, and determining the left edge position and the right edge position of the layer and a step value; and finally, determining the statistical boundaries of the left and right sides. The invention has the advantages of reliable work, simple algorithm, high running speed, good processing effect, no influence of strip steel texture, color difference, defects and illumination and suitability for industrial production and various computing equipment applications.
Description
Technical field
The invention belongs to industrial machine vision technique field, particularly the online method for rapidly positioning of a kind of cold-strip steel edge.
Background technology
It is a key issue in belt steel rolling process that edge with steel detects, and the processing links such as centering process, trimming process, strip width detection, steel strip surface defect Information locating for band steel all plays vital effect.
Traditional steel edge portion detection method has mechanical probe method, photodetection method etc.Mechanical detection method by contact zones steel direct-detection, but very easily causes damage to the edge of band steel and forms crimping.Photodetection method, by photoelectric combination formula equipment, non-contact detecting can go out steel edge portion, but needs to rely on basic automatization means, equipment relative complex and costliness, and because system element is more, easily breaks down, difficult maintenance.
Along with the development of machine vision technique, obtain industry based on image analysis method determination steel edge portion position and more and more paid close attention to.Image Edge-Detection is one of the most basic problem of image procossing and computer vision field, and how to extract image edge information is fast, accurately study hotspot both domestic and external always, and the detection at edge simultaneously is also a difficult problem in image procossing.
" a kind of method of Image Edge-Detection " that Authorization Notice No. CN1O2156996B provides is the improvement to canny edge detection method, can the edge of positioning image more accurately, and the edge blurry avoiding image again can not be too responsive to noise.Application publication number CN1O2521836A discloses " a kind of edge detection method based on certain kinds gray level image ", and the method can improve rim detection precision and the degree of accuracy, effectively realizes the detection at edge.Above two kinds of methods are all improve the one of conventional method, but method amount of calculation is comparatively large, the quick running environment that uncomfortable crossed belt steel is produced.
Application publication number CN1O3226829A discloses one " method for detecting image edge based on edge enhancement operator ", and the method can realize the precision location at edge, and the edge of different size can respond preferably, and can reduce undetected as far as possible.But the method the cold-strip steel edge not being suitable for substrate complexity (be such as with zinc flower galvanized sheet or aluminium plating zinc plate etc.) detect.Application publication number is " a kind of method for detecting image edge average based on gray scale difference value " of CN1O3150735A, propose a kind ofly utilize the method identification object edge of the average combining adaptive threshold value of gray scale difference value and extract profile, although the method processing speed comparatively fast, is not suitable for substrate complexity and may there is the cold-strip steel edge detection of edge defect equally.
Summary of the invention
Object of the present invention aim to provide a kind of can when cold-strip steel high-speed runs, determine non-contacting, online, instant steel edge portion position, and the online method for rapidly positioning of all kinds of cold-strip steel edge of the not situation such as tape steel superficial makings, aberration, uneven illumination, edge defect impact.
For this reason, the solution that the present invention takes is:
The online method for rapidly positioning of a kind of cold-strip steel edge, it is characterized in that, position fixing process is divided into the location of left lateral and the location in the right portion, and concrete grammar and step are:
1, edge Image Acquisition: by collected by camera band steel real-time synchronization image, then turn gray proces to the image collected, obtains the synchronous gray level image of band steel.
2, Image semantic classification: first carry out longitudinal medium filtering to the synchronous gray level image of band steel obtained, namely on image, the gray value of each pixel equals the gray scale median of its longitudinal point of proximity.
3, search fluctuation largest block: for the search of the left-hand side undulated layer largest block, from image upper left quarter, horizontal M row pixel, the capable pixel of longitudinal N, with the size of M × N, divide block check from left to right, according to gray scale initial survey situation determination the left-hand side undulated layer largest block in block; For right undulated layer largest block then from image upper right quarter, with the size of M × N, divide block check from right to left, make to use the same method and determine right undulated layer largest block; Every one deck largest block longitudinal extent below the first floor is still the capable pixel of N, and lateral extent is then equal to the lateral extent of last layer largest block;
4, image longitudinal compression is carried out: left lateral is located, the left undulated layer largest block determined in step (3) expands to 3M × n-quadrant to the left and right, left and right extension length is identical, calculates the longitudinal mean value often arranged, and forms the left compressed image sequence FY of 3M × 1 size
l(x, y), wherein x=0,1,2 ..., y is the number of plies; For location, the right portion, method is identical, forms right compressed image sequence FY
r(x, y);
5, volatility series is generated:
For left lateral location, volatility series FB
lthe computational methods of (x, y) are:
FB
L(i,y)=|FY
L(i,y)-FY
L(i-1,y)|(i>0)
For location, the right portion, volatility series FB
rx the computational methods of () are:
FB
r(i, y)=| FY
r(i, y)-FY
r(i+1, y) | (i< compressed image sequence length)
6, a layer marginal position is determined:
For left lateral location, first calculate volatility series FB
lruffling threshold k B in (x, y)
l(y):
KB
L(y)=AVG
L(y)+SDV
L(y)
Wherein, AVG
ly () is volatility series FB
lthe mean value of (x, y), SDV
ly () is volatility series FB
lthe standard deviation of (x, y);
Then, volatility series FB is searched for from left to right
lin (x, y), first is greater than KB
ly the volatility series value of (), original image horizontal pixel point position (x) corresponding to this value is this layer of left hand edge position LB
l(Y) the volatility series value, corresponding to this position is designated as this layer of left hand edge and jumps rank value FS
l(y);
For location, the right portion, first calculate volatility series FB
rruffling threshold k B in (x, y)
r(y):
KB
R(y)=AVG
R(y)+SDV
R(y)
Wherein, AVG
ry () is volatility series FB
rthe mean value of (x, y), SDV
ry () is volatility series FB
rthe standard deviation of (x, y);
Then, volatility series FB is searched for from right to left
rin (x, y), first is greater than KB
ry the volatility series value of (), original image horizontal pixel point position (x) corresponding to this value is this layer of right hand edge position LB
r(Y) the volatility series value, corresponding to this position is designated as this layer of right hand edge and jumps rank value FS
r(y);
7, statistical boundary is determined: for the situation calculating multi-layer image statistical boundary, left lateral statistical boundary TGB
lcomputational methods be:
Wherein, y is the number of plies.
The right portion statistical boundary TGB
rcomputational methods be:
In described step 3, be if the following test condition of this block first fit according to gray scale initial survey situation decision method in gray scale initial survey situation determination the left-hand side undulated layer largest block in block, be then defined as the left-hand side undulated layer largest block: test condition:
N
M>T
K
Wherein, C
kgray scale for strip substrate and non-band steel part fluctuates predetermined minimum, T
kfor judging constant.
Beneficial effect of the present invention is:
1, be applicable to industrial production running environment, reliable operation, algorithm is simple, is easy to realize.
2, for all kinds of camera design, treatment effect is good, and the speed of service is fast.
3, the various computing equipment such as computer and single-chip microcomputer is applicable to.
4, the impact of the present invention's not situation such as tape steel superficial makings, aberration, uneven illumination, edge defect.
5, steel edge portion is detected roughly and meticulous detection all applicable.
Accompanying drawing explanation
Fig. 1 is the online quick positioning system pie graph of cold-strip steel edge;
Fig. 2 is the online method for rapidly positioning flow chart of cold-strip steel edge;
Fig. 3 is cold-strip steel edge online method for rapidly positioning effect figure.
In figure: band steel 1, image capture device 2, lighting device 3, light belt 4.
Detailed description of the invention
Below in conjunction with accompanying drawing, the invention will be further described.
The online quick positioning system of cold-strip steel edge of the present invention is formed as shown in Figure 1: system carries out Real-time Collection by the band steel 1 of image capture device 2 to motion, is thrown light on by lighting device 3.Image capture device 2 can be single or multiple camera (camera) or other image capture devices.Lighting device 3 can be the various light sources such as general light modulation, LED integrated lamp, laser scanning.Lighting device 3 projects belt steel surface and forms light belt 4, and IMAQ point is completely wrapped in light belt 4.The flip flop equipment of camera can be equipped with sychronisation, also can asynchronously trigger.System is captured in line image by image acquisition units, and communication modes can be digital or analog.System carries out pretreatment by graphics processing unit to image, and graphics processing unit can be separate unit (as singlechip equipment etc.) or integrated unit (program module as in computer).System is confirmed steel edge portion by edge detecting unit, and edge detecting unit also can be separate unit (as singlechip equipment etc.) or integrated unit (program module as in computer).
The concrete steps following (flow process is shown in Fig. 2) of the online method for rapidly positioning of cold-strip steel edge of the present invention:
Step one: edge Image Acquisition.Be with steel 1 synchronous images in real time by collected by camera, then gray proces turned to the image collected, obtain band, 1 synchronous gray level image.
Step 2: Image semantic classification.First carry out longitudinal medium filtering to the synchronous gray level image of the band steel 1 obtained, namely on image, the gray value of each pixel equals the gray scale median of its longitudinal point of proximity.Now the selection range of longitudinal point of proximity can be upper two pixels of analysis site and lower two pixels.
Step 3: search fluctuation largest block.For the search of the left-hand side undulated layer largest block, from image upper left quarter, size (laterally 50 row with 50 × 10, longitudinally 10 row), divide block check from left to right, if a certain piece of following test condition of first fit, be then defined as the left-hand side undulated layer largest block.
N
M>T
K
Wherein, C
kgray scale for strip substrate and non-band steel part fluctuates predetermined minimum, is set as 5; T
kfor judging constant, T
kget 150.
For right undulated layer largest block then from image upper right quarter, with the size of 50 × 10, divide block check from right to left, make to use the same method and determine right undulated layer largest block.Every one deck largest block height below the first floor is still 10, and lateral extent is then directly equal to last layer lateral extent.
Step 4: carry out image longitudinal compression.For left lateral location, in 150 × 10 regions that the left undulated layer largest block determined in previous step is expanded to the left and right, calculate the longitudinal mean value often arranged, form the left compressed image sequence FY of 150 × 1 sizes
l(x, y).For location, the right portion, method is identical, forms right compressed image sequence FY
r(x, y).
Step 5: generate volatility series.
For left lateral location, volatility series FB
lthe computational methods of (x, y) are as follows:
FB
L(i,y)=FY
L(i,y)-FY
L(i-1,y)(i>0)
For location, the right portion, volatility series FB
rx the computational methods of () are as follows:
FB
r(i, y)=FY
r(i, y)-FY
r(i+1, y) (i< compressed image sequence length)
Step 6: determine a layer marginal position.
For left lateral location, first calculate volatility series FB
lruffling threshold k B in (x, y)
l(y).
KB
L(y)=AVG
L(y)+SDV
L(y)
Wherein, AVG
ly () is volatility series FB
lthe mean value of (x, y), SDV
ly () is volatility series FB
lthe standard deviation of (x, y).Then, volatility series FB is searched for from left to right
lin (x, y), first is greater than KB
ly the volatility series value of (), original image horizontal pixel point position (x) corresponding to this value is this layer of left hand edge position LB
l(Y) the volatility series value, corresponding to this position is this layer of left hand edge and jumps rank value FS
l(y).
For location, the right portion, first calculate volatility series FB
rruffling threshold k B in (x, y)
r(y).
KB
R(y)=AVG
R(y)+SDV
R(y)
Wherein, AVG
ry () is volatility series FB
rthe mean value of (x, y), SDV
ry () is volatility series FB
rthe standard deviation of (x, y); Then, volatility series FB is searched for from right to left
rin (x, y), first is greater than KB
ry the volatility series value of (), original image horizontal pixel point position (x) corresponding to this value is this layer of right hand edge position LB
r(Y) the volatility series value, corresponding to this position is designated as this layer of right hand edge and jumps rank value FS
r(y).
Step 7: repeat step 3 to step 6, successively calculate, until calculated the left and right edges position of bottom layer image and the rank value that jumps.
Step 8: determine statistical boundary.For the situation calculating multi-layer image statistical boundary, left lateral statistical boundary TGB
lcomputational methods as follows:
Wherein, y is the number of plies.
The right portion statistical boundary TGB
rcomputational methods as follows:
Fig. 3 display be edge Detection results for lvanized cold rolled strip.Inconsistent from image the right and left portion band steel illumination condition, left lateral is comparatively dark, and the right portion is brighter, and substrate steel sheet has more obvious texture, and part edge band steel gradation of image is near or below non-band steel district gradation of image.The method can determine the edge position of galvanized sheet preferably under such condition.
Claims (1)
1. the online method for rapidly positioning of cold-strip steel edge, is characterized in that, position fixing process is divided into the location of left lateral and the location in the right portion, and concrete grammar and step are:
(1) edge Image Acquisition: by collected by camera band steel real-time synchronization image, then turn gray proces to the image collected, obtains the synchronous gray level image of band steel;
(2) Image semantic classification: first carry out longitudinal medium filtering to the synchronous gray level image of band steel obtained, namely on image, the gray value of each pixel equals the gray scale median of its longitudinal point of proximity;
(3) search fluctuation largest block: for the search of the left-hand side undulated layer largest block, from image upper left quarter, horizontal M row pixel, the capable pixel of longitudinal N, with the size of M × N, divide block check from left to right, according to gray scale initial survey situation determination the left-hand side undulated layer largest block in block; For right undulated layer largest block then from image upper right quarter, with the size of M × N, divide block check from right to left, make to use the same method and determine right undulated layer largest block; Every one deck largest block longitudinal extent below the first floor is still the capable pixel of N, and lateral extent is then equal to the lateral extent of last layer largest block;
(4) image longitudinal compression is carried out: left lateral is located, the left undulated layer largest block determined in step (3) expands to 3M × n-quadrant to the left and right, left and right extension length is identical, calculates the longitudinal mean value often arranged, and forms the left compressed image sequence FY of 3M × 1 size
l(x, y), wherein x=0,1,2 ..., y is the number of plies; For location, the right portion, method is identical, forms right compressed image sequence FY
r(x, y);
(5) volatility series is generated:
For left lateral location, volatility series FB
lthe computational methods of (x, y) are:
FB
L(i,y)=|FY
L(i,y)-FY
L(i-1,y)|(i>0)
For location, the right portion, volatility series FB
rx the computational methods of () are:
FB
r(i, y)=| FY
r(i, y)-FY
r(i+1, y) | (i< compressed image sequence length)
(6) a layer marginal position is determined:
For left lateral location, first calculate volatility series FB
lruffling threshold k B in (x, y)
l(y):
KB
L(y)=AVG
L(y)+SDV
L(y)
Wherein, AVG
ly () is volatility series FB
lthe mean value of (x, y), SDV
ly () is volatility series FB
lthe standard deviation of (x, y);
Then, volatility series FB is searched for from left to right
lin (x, y), first is greater than KB
ly the volatility series value of (), original image horizontal pixel point position (x) corresponding to this value is this layer of left hand edge position LB
l(Y) the volatility series value, corresponding to this position is designated as this layer of left hand edge and jumps rank value FS
l(y);
For location, the right portion, first calculate volatility series FB
rruffling threshold k B in (x, y)
r(y):
KB
R(y)=AVG
R(y)+SDV
R(y)
Wherein, AVG
ry () is volatility series FB
rthe mean value of (x, y), SDV
ry () is volatility series FB
rthe standard deviation of (x, y);
Then, volatility series FB is searched for from right to left
rin (x, y), first is greater than KB
ry the volatility series value of (), original image horizontal pixel point position (x) corresponding to this value is this layer of right hand edge position LB
r(Y) the volatility series value, corresponding to this position is designated as this layer of right hand edge and jumps rank value FS
r(y);
(7) statistical boundary is determined: for the situation calculating multi-layer image statistical boundary, left lateral statistical boundary TGB
lcomputational methods be:
Wherein, y is the number of plies;
The right portion statistical boundary TGB
rcomputational methods be:
In described step (3), be if the following test condition of this block first fit according to gray scale initial survey situation decision method in gray scale initial survey situation determination the left-hand side undulated layer largest block in block, be then defined as the left-hand side undulated layer largest block: test condition:
N
M>T
K
Wherein, C
kgray scale for strip substrate and non-band steel part fluctuates predetermined minimum, T
kfor judging constant.
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Cited By (5)
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CN107309545A (en) * | 2017-07-10 | 2017-11-03 | 首钢京唐钢铁联合有限责任公司 | Method and device for controlling centering camera with head and tail of laser welding machine |
CN107367232A (en) * | 2016-05-12 | 2017-11-21 | 鞍钢股份有限公司 | Strip steel width detection method based on embedded processing system and gigabit Ethernet |
CN107796309A (en) * | 2016-09-06 | 2018-03-13 | 鞍钢股份有限公司 | Strip steel width information detection method for embedded system and gigabit Ethernet |
CN113866183A (en) * | 2021-09-15 | 2021-12-31 | 北京首钢股份有限公司 | Fault detection method and device of strip steel surface detector |
CN115213241A (en) * | 2022-09-20 | 2022-10-21 | 山东钢铁股份有限公司 | Online identification method and system for thick hot steel plate |
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CN107367232A (en) * | 2016-05-12 | 2017-11-21 | 鞍钢股份有限公司 | Strip steel width detection method based on embedded processing system and gigabit Ethernet |
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CN115213241A (en) * | 2022-09-20 | 2022-10-21 | 山东钢铁股份有限公司 | Online identification method and system for thick hot steel plate |
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