CN100593716C - On-line detecting method of machine vision system for printed calico flaw - Google Patents
On-line detecting method of machine vision system for printed calico flaw Download PDFInfo
- Publication number
- CN100593716C CN100593716C CN200710135139A CN200710135139A CN100593716C CN 100593716 C CN100593716 C CN 100593716C CN 200710135139 A CN200710135139 A CN 200710135139A CN 200710135139 A CN200710135139 A CN 200710135139A CN 100593716 C CN100593716 C CN 100593716C
- Authority
- CN
- China
- Prior art keywords
- image
- calico
- standard picture
- real
- flaw
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Landscapes
- Image Processing (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention relates to an online detecting method of a machine vision system for detecting blemishes of a printed cloth. The invention aims at solving the problem that the labor intensity of manualonline detecting blemishes of the printed cloth is high, and the detecting quality is bad. Clear and thorough, and spotless graphs are shot on the running printed cloth through industrial camera. Theclear and thorough, and spotless graphs are stored in the calculating machine as a standard graph molding board. The blemish area and graphic gray value are taken as the detecting parameter of the blemish. The precision scope of the detecting parameter is arranged according to the requirement of the consumer. The graphs of the running printed cloth are shot in real-time, and are conveyed to the calculating machine. The real-timely shot graphs are compared with the standard graph molding board and are dealt with. If a blemish appears, the operating personnel are indicated to deal with the blemish of the printed cloth through an alarm. The alarm is started through the calculating machine. The invention can carry out light intensity self-compensating of the standard graph molding board, and dynamically correcting the shift of the pixel of the real-timely shot printed cloth graphs. The invention has the advantages of high precision and fast speed of detecting blemishes of the printed cloth, and greatly reduced labor intensity of manual detecting.
Description
Technical field
The present invention relates to utilize Vision Builder for Automated Inspection to carry out the technical field of online detection, relate in particular to, utilize Vision Builder for Automated Inspection that the flaw of calico is carried out on-line detection method in the calico production scene.
Background technology
At the workshop scene of line production printing and dyeing calico, need carry out online detection to the flaw of calico.In the prior art, the online detection of calico flaw relied on manually detect, be provided with 2-4 people in the both sides of printing machine the flaw of calico is estimated and handled, and establish tracking, the detection that 1 people carries out product quality at oven dry cropping place.The shortcoming of its existence has: on-the-spot humiture is very high, and workman's testing environment is abominable, and labour intensity is big; The workman works long hours and is easy to generate visual fatigue, can't guarantee to detect quality, therefore can't guarantee the qualification rate of product export.
Summary of the invention
Big and be easy to generate visual fatigue, can't guarantee to detect quality and the low problem of product export qualification rate at prior art to the online manual detection labour intensity of calico flaw, the invention provides the online test method of a kind of Vision Builder for Automated Inspection to the calico flaw, it reduces workman's detection labour intensity greatly, the degree of accuracy height of Defect Detection, the qualification rate of the product that can effectively guarantee to dispatch from the factory.
The technical scheme of the inventive method is as follows:
A kind of Vision Builder for Automated Inspection of utilizing is carried out on-line detection method to the calico flaw, may further comprise the steps:
(1). utilize industrial camera and control the image that described industrial camera is taken the on-line operation calico by outer triggering signal;
(2). according to the requirement of user, adjust aperture size, time shutter and the amount of gain of described industrial camera, so that the calico image of shooting clear to calico color, decorative pattern size and fineness;
(3). select complete display, flawless calico image is stored in computing machine as the standard picture template, every described industrial camera stores a width of cloth standard picture template;
(4). flaw area and gradation of image value are set at the detection parameter of calico flaw, and the accuracy rating of described detection parameter is set according to customer requirements;
(5). control described industrial camera by outer triggering signal, take the image of on-line operation calico in real time, and the image of taking is transferred to computing machine for detecting;
Meanwhile, by following step described standard picture template of (3) step is carried out the dynamic auto compensation of image intensity: the gradation of image mean value of the on-line operation calico image that above-mentioned real-time shooting is obtained compares with (3) the gradation of image mean value that goes on foot the standard picture template, variation has taken place in the gradation of image mean value that obtains as real-time shooting, then the gradation of image value with described standard picture template multiply by the luminosity compensation coefficient automatically, be stored in computing machine as new standard picture template, enter following (6) step again, contrast is used when detecting the calico image of taking in real time for (6) step, and wherein the luminosity compensation coefficient is the ratio of the average gray of the average gray of real-time photographic images and standard picture template image; Do not change as gradation of image mean value, then the standard picture template is not carried out the compensation of image intensity, directly enter following (6) step;
And meanwhile, proofread and correct the displacement of taking on-line operation calico image pixel in real time dynamically by following step:
A). the position of image pixel compares in the position of the on-line operation calico image pixel that described real-time shooting is obtained and described (3) the step reference image template, judge whether to occur the displacement of real-time photographic images pixel location, if displacement, then turn to following b) to c) step, otherwise the correction that is not shifted directly enters following (6) step;
B). will judge that the coordinate position of the real-time photographic images pixel of displacement appears in the back through a) step, by Computing, in (3) step standard picture template the coordinate position of corresponding pixel move;
C). by b) step moves after the pixel coordinates position, the pixel location in the standard picture template that obtains with above-mentioned (3) step compares again, and still whether the pixel location of judging calico displacement, if no longer displacement then enters described (6) step; If still displacement then continues above b) step, the pixel location in the standard picture template that obtains with (3) step relatively back does not have displacement, and the correction that then no longer is shifted enters following (6) step then;
(6). process the image intensity that the real-time shooting that computing machine obtained for (5) step and the image of calibrated pixel shift and (5) step obtain compensates standard picture template afterwards automatically and compares, analyzes, differentiates, occur if any the flaw outside the detection parameter accuracy rating of (4) step setting, then by the computer starting warning device, alert is handled the calico flaw, continues the operation in (5) step afterwards; As do not have above-mentioned flaw appearance, then directly continue the operation in (5) step.
The technique effect of the inventive method:
The present invention adopts Vision Builder for Automated Inspection that the calico flaw is carried out online detection, replace manual detection, occur if any flaw, then in image, identify the position of flaw by friendly man-machine interface, and give the acousto-optic electric alarm, alert is carried out the flaw processing, reduces workman's detection labour intensity greatly; According to customer requirements on the calico of operation, select complete display, flawless image is stored in computing machine as the standard picture template, rather than set up complicated flaw model storehouse in advance as the standard picture template according to customer requirements, more suit the actual conditions that consumer products detects, improve the accuracy and the qualification rate of user's calico Defect Detection; Flaw area and gradation of image value are set at the detection parameter of calico flaw, and the accuracy rating of described detection parameter is set according to customer requirements, and avoid setting up various Defect Detection parameter, can reach customer requirements, can effectively improve detection speed again, check speed can reach the 100m/ branch; When taking the intensity of illumination variation of on-line operation calico image in real time, the inventive method can be carried out the automatic compensation of image intensity to the standard picture template, thereby can guarantee the accuracy of standard picture template, can accurately judge product defect; When the calico of on-line operation is mobile all around when real-time the shooting, make easily the image pixel of clapping displacement, with the pixel of standard picture template relatively the time, influence the accuracy of Defect Detection, the inventive method can be taken the displacement of on-line operation calico image pixel in real time from normal moveout correction, realize taking in real time the automatic contraposition of on-line operation calico image pixel, the image and (5) that real-time shooting and calibrated pixel are shifted goes on foot the automatic compensation of the process image intensity that obtains standard picture template afterwards and carries out correct contrast, analyze and differentiation, effectively improve accuracy the calico Defect Detection.
Description of drawings
Fig. 1 is the pattern of calico standard picture template;
Fig. 2 is " dislocation " flaw pattern in the calico image;
Fig. 3 is " disappearance " flaw pattern in the calico image;
Fig. 4 is " stain " flaw pattern in the calico image;
Fig. 5 is the synoptic diagram of the automatic contraposition of calico image;
Fig. 6 is a process sequence diagram of the present invention.
Embodiment
Below to the detection of calico flaw for example, further specify the inventive method.
Embodiment 1, to the detection of " dislocation " flaw in the calico image:
A. adopt white cotton, image as shown in Figure 1, as detecting embodiment cloth.
B. with 4 of the sincere 1300UM type industry in Xi'an side cameras, laterally evenly arrange stuck-at-.5m height along calico, camera uses 8mmCOMPUTAR type camera lens, aperture is transferred to maximal value, and the time shutter is adjusted to 0.58ms, and yield value (the contrast effect of its decision image) is adjusted to 0; Adopt the white light source of 2.65MHZ, directly over irradiation so that to the irradiation relative insensitivity of the color and the decorative pattern of cloth, and to the irradiation sensitivity of flaw; The power supply of display light source is a constant pressure and flow, is the stabilized light source that tool does not become frequently or high frequency becomes, so that can photograph flaw image clearly more stablely, and is shown in the screen of computing machine.
C. start external trigger and control signal automatically by computing machine, start camera and take the image of the calico of on-line operation, select calico image complete display, flawless to be stored in the computing machine by operating personnel as the standard picture template, every camera is taken respectively simultaneously and is stored a width of cloth standard picture template (see Fig. 1, calculating in 4 cameras respectively by computing machine in the present embodiment, the standard picture average gray as shown in Figure 1 of storage is respectively 127) in the memory of computing machine;
D. flaw area and gradation of image value are set at two of flaw and detect parameters, getting calico, to walk cloth speed be 100 meters/minute, the accuracy rating that the detection of flaw area is set according to customer requirements is 3 square millimeters, the accuracy rating of gradation of image value is 50, the difference that is the flaw area gradation of image value that surpasses 3 square millimeters or gradation of image value and standard picture template then is judged to be underproof flaw above 50;
E. start outer triggering signal automatically by computing machine and control described shooting camera, take the image of on-line operation calico in real time, meanwhile, respectively the image of standard picture template and shooting is in real time handled by following step:
A, light intensity when the standard picture template is taken is carried out dynamic auto compensation: the gradation of image mean value during to real-time photographic images (the gradation of image mean value when calculating 4 real-time photographic images of camera by computing machine in the present embodiment is respectively 75) compares with above-mentioned standard picture template image average gray (127), because variation has taken place in gradation of image mean value, the gradation of image value of described each pixel of standard picture template be multiply by luminosity compensation coefficient 75/127 respectively automatically, as new standard picture template be stored in computing machine in, at this moment, the gradation of image mean value of new standard picture template is compensated for as 75 (that is: 127 * 75/127) automatically; (annotate: do not change as light intensity, then need not the standard picture template is carried out the compensation of image intensity, the image that computing machine will be taken in real time compares, analyzes and differentiate with standard picture template for the first time.The flaw that detects outside the parameter accuracy rating if any two of above-mentioned setting occurs, and then by the computer starting warning device, after alert is handled the calico flaw, continues to take in real time the image of calico; As do not have the flaw that two of above-mentioned setting detects outside the parameter accuracy ratings and occur, then continue to take in real time the image of calico).
B, proofread and correct the position of real-time photographic images pixel as follows, to carry out the automatic contraposition of image pixel in the real-time photographic images:
A). the pixel location that will take in real time in the standard picture template that pixel location in the whole breadth image of calico and top a step obtain compares, judge whether to occur the displacement of pixel location, if displacement shown in Figure 5, then turn to following b) to c) step carries out automatic contraposition, otherwise directly enters described (6) step;
B). with the coordinate position of the pixel of the appearance of above-mentioned shooting displacement, the computerized algorithm X by known technology (i, j)=X (i+1, j) and X (i, j)=X (i, j+1) loop computation in the standard picture template the coordinate position of corresponding pixel move;
C). by b) step moves after the pixel coordinates position, the pixel location in the standard picture template that obtains with above-mentioned a step compares again, and still whether the pixel location of judging calico displacement, if no longer displacement then enters described (6) step; If still displacement then continues above b) step, the pixel location in the standard picture template that obtains with a step relatively back does not have displacement, enters described (6) step then.
In the present embodiment,, the displacement of image pixel position do not occur, therefore directly enter described (6) step through after the above-mentioned a) step:
The standard picture template that the on-line operation calico image taken in real time and above-mentioned a step are obtained compares, analyzes and differentiates, wherein camera photographs " dislocation " flaw image of calico pattern shown in Figure 2, and the dash area among Fig. 2 in the dashed region is the dislocation defect areas.By COMPUTER CALCULATION and the gradation of image mean value that demonstrates each pixel in " dislocation " shown in Figure 2 flaw is 138, the difference of the gradation of image value 75 of itself and above-mentioned new standard picture template is 63, the flaw area is 9 square millimeters, surpass the accuracy rating that sets, this moment is by the computer starting warning device, operating personnel after the flaw defective that causes this calico flaw is handled, continue to take in real time the image of calico by checking.(annotate: in the difference and flaw area two parameters as above-mentioned gradation of image value, only a parameter surpasses accuracy rating, and computing machine also can start warning device.)
The control system (hardware and software) of image acquisition of using in the present embodiment (shooting) and storage device (camera, radiation source, power supply etc.), image processing equipment (hardware of image processor and software), image display (hardware and software), warning device and each part mentioned above all adopts prior art to design and produce or directly adopts the commercially available prod, the above-mentioned Related product that for example DVT, SONY, Siemens, NI, CCS, Xi'an Fang Cheng, Beijing are littlely looked, the big identical company in Beijing produces.
Embodiment 2: to the detection of calico pattern " disappearance " flaw:
It is identical with embodiment 1 to detect embodiment cloth, capture apparatus and operation steps.With 4 in Xi'an side's sincere 1300UM type camera, laterally evenly to arrange along calico, stuck-at-.5m height, camera use 8mmCOMPUTAR type camera lens, and aperture is transferred to 2/3 position, and the time shutter is adjusted to 0.8ms, and yield value is adjusted to 100.
In real-time shooting process, the dynamic auto compensation situation of standard picture template light intensity and also identical with embodiment 1 to the correction situation of real-time photographic images pixel displacement.Photograph calico pattern shown in Figure 3 and exceed " disappearance " flaw image of accuracy requirement, part among Fig. 3 in the dashed region is the disappearance zone, computer screen displayed map 3 images " disappearance " flaw area value is that 20 square millimeters and gradation of image are 240, and the difference of display image average gray is 165, surpass the accuracy rating that sets, reported to the police by computer controlled automatic acousto-optic electric alarm device this moment, operating personnel handle the calico flaw, continue to take in real time the image of on-line operation embodiment calico afterwards.
Remaining explanation is identical with embodiment 1.
Embodiment 3: to the detection of calico pattern " stain " flaw:
It is identical with embodiment 1 to detect embodiment cloth, capture apparatus and operation steps.With 4 in Xi'an side's sincere 1300UM type camera, laterally evenly to arrange along calico, stuck-at-.5m height, camera use 8mmCOMPUTAR type camera lens, and aperture is transferred to 1/3 position, and the time shutter is adjusted to 1ms, and yield value is 50.
In real-time shooting process, the dynamic auto compensation situation of standard picture template light intensity and also identical with embodiment 1 to the correction situation of real-time photographic images pixel displacement.Get access to calico pattern shown in Figure 4 and exceed " stain " flaw image of accuracy requirement, dash area is the stain zone among Fig. 4, the gradation of image value is 5, and the difference of display image gray-scale value is 70, " stain " flaw area is 5 square millimeters, surpasses the accuracy rating that sets, and reported to the police by computer controlled automatic acousto-optic electric alarm device this moment, operating personnel handle the calico flaw, continue to take in real time the image of on-line operation embodiment calico afterwards.Remaining explanation is identical with embodiment 1.
The above processing step is shown in Fig. 6.
Claims (1)
1, a kind of Vision Builder for Automated Inspection of utilizing is carried out on-line detection method to the calico flaw, it is characterized in that may further comprise the steps:
(1). utilize industrial camera and control the image that described industrial camera is taken the on-line operation calico by outer triggering signal;
(2). according to the requirement of user, adjust aperture size, time shutter and the amount of gain of described industrial camera, so that the calico image of shooting clear to calico color, decorative pattern size and fineness;
(3). select complete display, flawless calico image is stored in computing machine as the standard picture template, every described industrial camera stores a width of cloth standard picture template;
(4). flaw area and gradation of image value are set at the detection parameter of calico flaw, and the accuracy rating of described detection parameter is set according to customer requirements;
(5). control described industrial camera by outer triggering signal, take the image of on-line operation calico in real time, and the image of taking is transferred to computing machine for detecting;
Meanwhile, by following step described standard picture template of (3) step is carried out the dynamic auto compensation of image intensity: the gradation of image mean value of the on-line operation calico image that above-mentioned real-time shooting is obtained compares with (3) the gradation of image mean value that goes on foot the standard picture template, variation has taken place in the gradation of image mean value that obtains as real-time shooting, then the gradation of image value with described standard picture template multiply by the luminosity compensation coefficient automatically, be stored in computing machine as new standard picture template, enter following (6) step again, contrast is used when detecting the calico image of taking in real time for (6) step, and wherein the luminosity compensation coefficient is the ratio of the average gray of the average gray of real-time photographic images and standard picture template image; Do not change as gradation of image mean value, then the standard picture template is not carried out the compensation of image intensity, directly enter following (6) step;
And meanwhile, proofread and correct the displacement of taking on-line operation calico image pixel in real time dynamically by following step:
A). the position of image pixel compares in the position of the on-line operation calico image pixel that described real-time shooting is obtained and described (3) the step reference image template, judge whether to occur the displacement of real-time photographic images pixel location, if displacement, then turn to following b) to c) step, otherwise the correction that is not shifted directly enters following (6) step;
B). will judge that the coordinate position of the real-time photographic images pixel of displacement appears in the back through a) step, by Computing, in (3) step standard picture template the coordinate position of corresponding pixel move;
C). by b) step moves after the pixel coordinates position, the pixel location in the standard picture template that obtains with above-mentioned (3) step compares again, and still whether the pixel location of judging calico displacement, if no longer displacement then enters described (6) step; If still displacement then continues above b) step, the pixel location in the standard picture template that obtains with (3) step relatively back does not have displacement, and the correction that then no longer is shifted enters following (6) step then;
(6). process the image intensity that the real-time shooting that computing machine obtained for (5) step and the image of calibrated pixel shift and (5) step obtain compensates standard picture template afterwards automatically and compares, analyzes, differentiates, occur if any the flaw outside the detection parameter accuracy rating of (4) step setting, then by the computer starting warning device, alert is handled the calico flaw, continues the operation in (5) step afterwards; As do not have above-mentioned flaw appearance, then directly continue the operation in (5) step.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN200710135139A CN100593716C (en) | 2007-11-09 | 2007-11-09 | On-line detecting method of machine vision system for printed calico flaw |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN200710135139A CN100593716C (en) | 2007-11-09 | 2007-11-09 | On-line detecting method of machine vision system for printed calico flaw |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101158650A CN101158650A (en) | 2008-04-09 |
| CN100593716C true CN100593716C (en) | 2010-03-10 |
Family
ID=39306810
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN200710135139A Expired - Fee Related CN100593716C (en) | 2007-11-09 | 2007-11-09 | On-line detecting method of machine vision system for printed calico flaw |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100593716C (en) |
Families Citing this family (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101592672B (en) * | 2008-05-30 | 2011-10-26 | 安徽宏实光机电高科有限公司 | Alignment method of shooting line and object moving direction based on linear array CCD camera velocity measurement |
| CN101865819B (en) * | 2009-04-14 | 2012-01-25 | 上海天祥质量技术服务有限公司 | Test method of textile printing fastness |
| CN102221559B (en) * | 2011-03-05 | 2012-08-29 | 河海大学常州校区 | Online automatic detection method of fabric defects based on machine vision and device thereof |
| CN102673177B (en) * | 2011-03-16 | 2016-01-20 | 杭州宏华数码科技股份有限公司 | A kind of based on cloth textured textile design Method of printing |
| CN102288615B (en) * | 2011-07-12 | 2015-08-26 | 王万年 | The detection method of middle line adhesive on filter rod |
| CN103234980B (en) * | 2012-08-20 | 2015-07-29 | 苏州大学 | Online fabric flaw detection and alarm system based on machine vision |
| CN103226106B (en) * | 2013-03-15 | 2016-02-24 | 江南大学 | Based on the bearing dustproof cover quality monitoring method of machine vision |
| FR3005042B1 (en) * | 2013-04-26 | 2016-01-01 | Snecma | WEAVING OR WINDING MACHINE FOR FIBROUS TEXTURE FOR ANOMALY CONTROL BY IMAGE ANALYSIS |
| CN103451846B (en) * | 2013-09-13 | 2015-11-18 | 福州大学 | A kind of grey cloth broken yarn online test method based on computer vision and system |
| CN104749181A (en) * | 2013-12-27 | 2015-07-01 | 波司登羽绒服装有限公司 | Fabric examination system for identifying defect point of fabric |
| CN104346807A (en) * | 2014-06-05 | 2015-02-11 | 苏州大学 | Detection method of online fabric flaw detection and alarm system based on machine vision |
| CN106276372A (en) * | 2016-10-11 | 2017-01-04 | 凌云光技术集团有限责任公司 | A kind of method and device positioning package packaging defect |
| CN107643297B (en) * | 2017-08-08 | 2018-07-20 | 广州森展纺织品有限公司 | A kind of method of real-time monitoring quality of textile products |
| CN107901591A (en) * | 2017-11-10 | 2018-04-13 | 苏州苏大维格光电科技股份有限公司 | Transfer device and printing transferring method |
| CN108387585B (en) * | 2018-01-25 | 2021-10-22 | 西安理工大学 | Frequency automatic adjustment type printing picture stroboscopic detection device and detection method |
| CN108449525B (en) * | 2018-03-26 | 2019-06-28 | 京东方科技集团股份有限公司 | The acquisition methods and automatic optical checking equipment of automatic visual inspection Plays image |
| CN108956614B (en) * | 2018-05-08 | 2020-12-29 | 太原理工大学 | Mining steel wire rope dynamic flaw detection method and device based on machine vision |
| CN109537205A (en) * | 2018-11-22 | 2019-03-29 | 肇庆广惠地毯化纤制造有限公司 | A kind of 4 color chinampa jacquard control mechanism and the tufting machine with the mechanism |
| CN109765174A (en) * | 2018-12-29 | 2019-05-17 | 莱茵技术（上海）有限公司 | A kind of printing in textiles fastness to washing performance test methods |
| CN109781741A (en) * | 2019-01-17 | 2019-05-21 | 张家港永和包装印务有限公司 | A kind of picture on surface detection method of printed matter |
| CN109916907A (en) * | 2019-03-04 | 2019-06-21 | 播种者工业智能科技(苏州)有限公司 | A kind of positioning control system of fabric flaw point |
| CN109916913A (en) * | 2019-04-04 | 2019-06-21 | 哈尔滨理工大学 | A kind of intelligence manufacture product identification and detection method based on machine vision |
| CN109975323B (en) * | 2019-04-08 | 2021-07-23 | 中原工学院 | Texture and printing pattern matching system based on automatic optical detection |
| CN110570397B (en) * | 2019-08-13 | 2020-12-04 | 创新奇智(重庆)科技有限公司 | Method for detecting ready-made clothes printing defects based on deep learning template matching algorithm |
| CN110672209A (en) * | 2019-10-24 | 2020-01-10 | 福建屹立智能化科技有限公司 | Online detection method for warp knitting cloth color difference |
| CN111709915A (en) * | 2020-05-28 | 2020-09-25 | 拉萨经济技术开发区美第意户外用品有限公司 | Automatic detection method and system for quick-drying fabric defects |
| CN111721772A (en) * | 2020-07-06 | 2020-09-29 | 宁波申洲针织有限公司 | Cloth flaw visual detection device and detection method |
| CN112406325A (en) * | 2020-10-26 | 2021-02-26 | 东华大学 | Device and method for repairing dyed fabric defects in digital printing machine |
| CN112680872B (en) * | 2020-12-17 | 2022-03-08 | 常州市新创智能科技有限公司 | Warp yarn winding roller broken yarn detection method |
| CN113063791A (en) * | 2021-03-23 | 2021-07-02 | 南通大学 | Cloth defect identification method based on image processing |
| CN113218955A (en) * | 2021-05-06 | 2021-08-06 | 宁波星帆信息科技有限公司 | Detection device and method |
| CN115100206B (en) * | 2022-08-26 | 2022-12-09 | 南通永安纺织有限公司 | Printing defect identification method for textile with periodic pattern |
| CN116026860B (en) * | 2023-03-28 | 2023-06-30 | 和峻（广州）胶管有限公司 | Quality control method and system for steel wire braided tube |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0741290A1 (en) * | 1995-05-04 | 1996-11-06 | Mahlo GmbH & Co. KG | Method and apparatus for determining the distortion angles on moving fabrics or the like |
| CN101059451A (en) * | 2007-05-15 | 2007-10-24 | 上海多路信息技术有限公司 | On-line flaw real-time detection system of printing machine based on machine vision |
-
2007
- 2007-11-09 CN CN200710135139A patent/CN100593716C/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0741290A1 (en) * | 1995-05-04 | 1996-11-06 | Mahlo GmbH & Co. KG | Method and apparatus for determining the distortion angles on moving fabrics or the like |
| CN101059451A (en) * | 2007-05-15 | 2007-10-24 | 上海多路信息技术有限公司 | On-line flaw real-time detection system of printing machine based on machine vision |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101158650A (en) | 2008-04-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN100593716C (en) | On-line detecting method of machine vision system for printed calico flaw | |
| CN100561146C (en) | Vision Builder for Automated Inspection is to the online test method of printing dye aberration | |
| CN102253048B (en) | Machine vision detection method and system for detection of various products | |
| KR101256369B1 (en) | Flat display pannel test equipment and test method using multi ccd camera | |
| CN103218961A (en) | Method and system for liquid crystal display (LCD) defect online detection | |
| CN102262093A (en) | Machine vision-based on-line detection method for printing machine | |
| CN102374996B (en) | Multicast detection device and method for full-depth tooth side face defects of bevel gear | |
| JP2018179939A (en) | Sheet inspection device, and inspection system | |
| CN101762589A (en) | On-line monitoring method of machine vision on stationery combined set flaw and equipment thereof | |
| CN107515481A (en) | Display panel detection method and device | |
| CN111591716A (en) | Photoelectric integrated intelligent detection device for belt damage condition of conveyor | |
| JPS6293637A (en) | Automatic cloth inspecting machine using image processing | |
| KR20160118722A (en) | 3D vision inpection system | |
| CN113838043A (en) | Machine vision-based quality analysis method in metal foil manufacturing | |
| KR20230137584A (en) | Vision inspection system for defect detection of fabric | |
| CN114612423A (en) | Chip packaging defect detection method | |
| CN206146851U (en) | Intellectual detection system industrial part surface quality's device | |
| CN103091332B (en) | Detection method and detection system of U-shaped powder pipe based on machine vision | |
| CN113793321B (en) | Casting surface defect dynamic detection method and device based on machine vision | |
| CN112258466A (en) | Cold-rolled strip vibration line online detection system and method based on gray level processing | |
| JP5634390B2 (en) | Glass container defect inspection method and apparatus | |
| CN204373688U (en) | A kind of electroplating assembly line outward appearance and dimension control equipment | |
| CN104713887B (en) | A kind of method for detecting planar disfigurement | |
| Zhang et al. | Design of solder quality inspection system based on machine vision | |
| TWM619409U (en) | Intelligent processing system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20100310 Termination date: 20111109 |