CN104537390A - Intelligent fabric weave type detection method - Google Patents
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- CN104537390A CN104537390A CN201510021219.4A CN201510021219A CN104537390A CN 104537390 A CN104537390 A CN 104537390A CN 201510021219 A CN201510021219 A CN 201510021219A CN 104537390 A CN104537390 A CN 104537390A
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- 238000003707 image sharpening Methods 0.000 claims description 24
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Abstract
The invention relates to an intelligent fabric weave type detection method. The method comprises the steps that (1), an intelligent fabric weave type detection system is provided, wherein the detection system comprises a stereoscopic microscope, photographing equipment, image processing equipment and embedded processing equipment, the photographing equipment is connected with the stereoscopic microscope to carry out image acquisition on a microscopic detection result, obtained by the stereoscopic microscope, of fabric to be detected so as to obtain a fabric texture image, the image processing equipment is connected with the photographing equipment to carry out image processing on the fabric texture image so as to obtain a fabric element structure image, and the embedded processing equipment is connected with the photographing equipment to match the fabric element structure image with various fabric element reference images one by one so as to determine the weave type of the fabric to be detected based on the matching result; (2), the system is run.
Description
Technical field
The present invention relates to textile inspection field, particularly relate to a kind of intelligent fabric tissue type detection method.
Background technology
The daily life of textile to the mankind is of crucial importance, in the past in the economics of underdevelopment epoch, people are just limited to screening body and heating to the requirement of textile, and it is highly developed and pursue the personalized epoch at current economic, people are also increasingly strict and concrete to the requirement of textile, the field of textile also presents refinement trend day by day, has occurred increasing new fabric type.
In the process manufacturing textile, manufacturer's cost that different fabric type causes is also different, in order to the manufacturer that keeps under strict control adopts cheap fabric type to replace the high price fabric type of original publicity to cheat consumer, obtain the economic interests of great number, textile inspection department needs the regular raw materials for production to textile manufacturer and finished product to carry out determination and analysis, according to determination and analysis result, judge whether manufacturer exists the economic behaviour of swindle.
In prior art, general employing manual mode of operation identifies fabric type, and exper ienced testing staff relies on manual mode of operation, by contrast and the experience accumulation of oneself of fabric, judges the type of fabric voluntarily.Although this mode has certain recognition effect, there is the technological deficiency that efficiency is low, error is large.
Therefore, need a kind of intelligent fabric tissue type detection method, substitute traditional manual detection mode, realized the Weigh sensor of fabric tissue by the mode of machine examination, thus improve efficiency and the accuracy of fabric tissue identification.
Summary of the invention
In order to solve the problem, the invention provides a kind of intelligent fabric tissue type detection method, transform existing manual detection pattern, quote microtechnic effectively to amplify fabric structure, quote the robotization identification that image acquisition recognition technology and template matching technique targetedly realize fabric tissue type, and economized on resources by the co-operating of image processor and embedded processing equipment, for textile inspection department provides more valuable reference data.
According to an aspect of the present invention, provide a kind of intelligent fabric tissue type detection method, the method comprises following: 1) provide a kind of intelligent fabric tissue typing systems, described detection system comprises stereomicroscope, picture pick-up device, image processing equipment and embedded processing equipment, described photographic equipment is connected that with described stereomicroscope the micrograph results of described stereomicroscope to fabric to be detected is realized image acquisition, obtain cloth textured image, described image processing equipment is connected with described picture pick-up device, image procossing is performed to obtain fabric unit structural drawing to described cloth textured image, described embedded processing equipment is connected with described image processing equipment, described fabric unit structural drawing is mated one by one with all kinds of fabric unit benchmark image, the organization type of described fabric to be detected is determined based on matching result, and 2) run described system.
More specifically, in described intelligent fabric tissue typing systems, also comprise: usb communication interface, for inserting outside USB flash disk, automatically to read all kinds of fabric unit benchmark images stored in outside USB flash disk, each kind fabric regulator image is the imaging of the minimum unit structure of benchmark fabric that take in advance, corresponding types, and described minimum unit structure refers to the cellular construction that multiplicity is maximum in benchmark fabric, static storage device, be connected with described usb communication interface, for reading and storing described all kinds of fabric unit benchmark image, described static storage device is also for storing default fabric upper limit gray threshold and default fabric lower limit gray threshold, and described default fabric upper limit gray threshold and described default fabric lower limit gray threshold are used for the fabric in image to separate with background, voice playing equipment, with described embedded processing equipment connection, for playing the voice prompted file corresponding with the organization type of described fabric to be detected or described it fails to match signal, image display, with described embedded processing equipment connection, for showing the prompt text corresponding with the organization type of described fabric to be detected or described it fails to match signal, power-supply unit, with described embedded processing equipment connection, under the control of described embedded processing equipment, for described detection system provides various different electric power supply pattern, described various different electric power supply pattern comprises battery saving mode and normal mode, described picture pick-up device is high-definition camera, and its resolution is 1280 × 720, for gathering described cloth textured image, described image processing equipment comprises contrast-enhancement module, wavelet filtering module, image sharpening module, gray processing processing module, fabric segmentation module and fabric unit identification module, described contrast-enhancement module is connected with described picture pick-up device, for performing contrast enhancement processing to described cloth textured image, obtain and strengthen image, described wavelet filtering module is connected with described contrast-enhancement module, for performing wavelet filtering to described enhancing image based on Harr wavelet filter, obtain filtering image, described image sharpening module and described wavelet filtering model calling, for performing image sharpening process to described filtering image, obtain sharpening image, described gray processing processing module and described image sharpening model calling, for performing gray processing process to described sharpening image, obtain gray level image, described fabric segmentation module is connected respectively with described static storage device and described gray processing processing module, so that the pixel identification of gray-scale value in described gray level image between default fabric upper limit gray threshold and default fabric lower limit gray threshold is formed fabric construction image, described fabric unit identification module and described fabric split model calling, add up in described fabric construction image the maximum cell picture of number of times of going on a journey to export as fabric unit structural drawing, described embedded processing equipment is connected respectively with described static storage device and described image processing equipment, receive described fabric unit structural drawing and described all kinds of fabric unit benchmark image, described fabric unit structural drawing is mated one by one with all kinds of fabric unit benchmark image, if the match is successful, type corresponding to the fabric unit benchmark image that the match is successful is exported as the organization type of described fabric to be detected, if it fails to match, then output matching failure signal, wherein, described image processing equipment and the operation of described embedded processing equipment collaboration, described embedded processing equipment is when resources occupation rate own is less than 30%, replace all operations performing described image processing equipment, described embedded processing equipment, when resources occupation rate own is more than 30%, terminates the replacement of all operations to described image processing equipment, described image pre-processing module, described contrast-enhancement module, described wavelet filtering module, described image sharpening module, described gray processing processing module, described fabric segmentation module and described fabric unit identification module adopt independently fpga chip to realize all respectively, described image processing equipment also comprises image pre-processing module, described image pre-processing module is connected respectively with described picture pick-up device and described embedded processing equipment, whether the fabric to be detected prejudged in described cloth textured image based on image recognition technology exists breakage or pollution, when identifying the fabric to be detected in described cloth textured image and there is damaged or pollution, improper detection echo signal is sent to described embedded processing equipment, described embedded processing equipment is when receiving described improper detection echo signal, stop contrast-enhancement module in described image processing equipment, wavelet filtering module, image sharpening module, gray processing processing module, the respective operations of fabric segmentation module and fabric unit identification module, when identifying the fabric to be detected in described cloth textured image and there is not breakage or pollute, send to described embedded processing equipment and normally detect echo signal, described embedded processing equipment is when receiving described normal detection echo signal, recover contrast-enhancement module in described image processing equipment, wavelet filtering module, image sharpening module, gray processing processing module, the respective operations of fabric segmentation module and fabric unit identification module.
More specifically, in described intelligent fabric tissue typing systems, all comprise in described fabric construction image, described fabric unit structural drawing and described all kinds of fabric unit benchmark image fabric through interlacing point, latitude interlacing point, through flotation line and latitude flotation line.
More specifically, in described intelligent fabric tissue typing systems, substitute the way of realization adopting independently fpga chip respectively, described image pre-processing module, described contrast-enhancement module, described wavelet filtering module, described image sharpening module, described gray processing processing module, described fabric segmentation module and described fabric unit identification module are all integrated in one piece of fpga chip.
More specifically, in described intelligent fabric tissue typing systems, described voice playing equipment is two-way speaker, and described image display is LCDs.
Accompanying drawing explanation
Below with reference to accompanying drawing, embodiment of the present invention are described, wherein:
Fig. 1 is the block diagram of the intelligent fabric tissue typing systems illustrated according to an embodiment of the present invention.
Fig. 2 is the block diagram of the image processing equipment of the intelligent fabric tissue typing systems illustrated according to an embodiment of the present invention.
Embodiment
Below with reference to accompanying drawings the embodiment of intelligent fabric tissue typing systems of the present invention is described in detail.
It is an important test item of textile industry supervision department that fabric tissue detects, its testing result be related to textile quality good or not and to human body with or without harm, be also related to weaving manufacturer whether sincerity is abide by the law, is standardized the management simultaneously.
Fabric tissue of the prior art is detected mainly with coming in the previous experiences of testing staff, the testing result that this manual detection mode obtains there will be error unavoidably, or cause the misjudged illegal operation of weaving manufacturer, or concealed illegal act, bring loss to consumer.
In order to improve the accuracy that fabric tissue detects, the operation of specification textile industry, need the manual detection mode in the past of changing, adopt detection of electrons mode that precision is higher to improve the detection of fabric tissue, but adopt the detection of electrons mode of which kind of type to be still a problem.
Intelligent fabric tissue typing systems of the present invention, adopt the detection of electrons mode based on image recognition, fabric to be detected after microscope process is carried out to the detection of organization type, also add image processor and flush bonding processor cooperative mechanism and various Image semantic classification means in whole system, accurately can obtain the particular type of fabric to be detected.
Fig. 1 is the block diagram of the intelligent fabric tissue typing systems illustrated according to an embodiment of the present invention, described detection system comprises: stereomicroscope 1, picture pick-up device 2, image processing equipment 3 and embedded processing equipment 4, described photographic equipment 2 is connected respectively with described stereomicroscope and described image processing equipment 3, and described embedded processing equipment 4 is connected respectively with described picture pick-up device 2 and described image processing equipment 3.
Wherein, the micrograph results of described stereomicroscope 1 to fabric to be detected is realized image acquisition by picture pick-up device 2, obtain cloth textured image, described image processing equipment 3 performs image procossing to obtain fabric unit structural drawing to described cloth textured image, described fabric unit structural drawing mates with all kinds of fabric unit benchmark image by described embedded processing equipment 4 one by one, determines the organization type of described fabric to be detected based on matching result.
Then, continue to be further detailed the concrete structure of intelligent fabric tissue typing systems of the present invention.
Also comprise in described detection system: usb communication interface, for inserting outside USB flash disk, automatically to read all kinds of fabric unit benchmark images stored in outside USB flash disk, each kind fabric regulator image is the imaging of the minimum unit structure of benchmark fabric that take in advance, corresponding types, and described minimum unit structure refers to the cellular construction that multiplicity is maximum in benchmark fabric.
Also comprise in described detection system: static storage device, be connected with described usb communication interface, for reading and storing described all kinds of fabric unit benchmark image, described static storage device is also for storing default fabric upper limit gray threshold and default fabric lower limit gray threshold, and described default fabric upper limit gray threshold and described default fabric lower limit gray threshold are used for the fabric in image to separate with background.
Also comprise in described detection system: voice playing equipment, with described embedded processing equipment connection, for playing the voice prompted file corresponding with the organization type of described fabric to be detected or described it fails to match signal.
Also comprise in described detection system: image display, with described embedded processing equipment connection, for showing the prompt text corresponding with the organization type of described fabric to be detected or described it fails to match signal.
Also comprise in described detection system: power-supply unit, with described embedded processing equipment connection, for under the control of described embedded processing equipment, for described detection system provides various different electric power supply pattern, described various different electric power supply pattern comprises battery saving mode and normal mode.
Described picture pick-up device 2 is high-definition camera, and its resolution is 1280 × 720, for gathering described cloth textured image.
As shown in Figure 2, described image processing equipment 3 comprises image pre-processing module 31, contrast-enhancement module 32, wavelet filtering module 33, image sharpening module 34, gray processing processing module 35, fabric segmentation module 36 and fabric unit identification module 37, described contrast-enhancement module 32 is connected with described picture pick-up device 2, for performing contrast enhancement processing to described cloth textured image, obtain and strengthen image, described wavelet filtering module 33 is connected with described contrast-enhancement module 32, for performing wavelet filtering to described enhancing image based on Harr wavelet filter, obtain filtering image, described image sharpening module 34 is connected with described wavelet filtering module 33, for performing image sharpening process to described filtering image, obtain sharpening image, described gray processing processing module 35 is connected with described image sharpening module 34, for performing gray processing process to described sharpening image, obtain gray level image, described fabric segmentation module 36 is connected respectively with described static storage device and described gray processing processing module 35, so that the pixel identification of gray-scale value in described gray level image between default fabric upper limit gray threshold and default fabric lower limit gray threshold is formed fabric construction image, described fabric unit identification module 37 and described fabric are split module 36 and are connected, add up in described fabric construction image the maximum cell picture of number of times of going on a journey to export as fabric unit structural drawing.
Described embedded processing equipment 4 is connected respectively with described static storage device and described image processing equipment 3, receive described fabric unit structural drawing and described all kinds of fabric unit benchmark image, described fabric unit structural drawing is mated one by one with all kinds of fabric unit benchmark image, if the match is successful, type corresponding to the fabric unit benchmark image that the match is successful is exported as the organization type of described fabric to be detected, if it fails to match, then output matching failure signal.
Wherein, described image processing equipment 3 and the co-operating of described embedded processing equipment 4, described embedded processing equipment 4 is when resources occupation rate own is less than 30%, replace all operations performing described image processing equipment 3, described embedded processing equipment 4, when resources occupation rate own is more than 30%, terminates the replacement of all operations to described image processing equipment 3, described image pre-processing module 31, described contrast-enhancement module 32, described wavelet filtering module 33, described image sharpening module 34, described gray processing processing module 35, described fabric segmentation module 36 and described fabric unit identification module 37 adopt independently fpga chip to realize all respectively, described image pre-processing module 31 is connected respectively with described picture pick-up device 2 and described embedded processing equipment 4, whether the fabric to be detected prejudged in described cloth textured image based on image recognition technology exists breakage or pollution, when identifying the fabric to be detected in described cloth textured image and there is damaged or pollution, improper detection echo signal is sent to described embedded processing equipment 4, described embedded processing equipment 4 is when receiving described improper detection echo signal, stop contrast-enhancement module 32 in described image processing equipment 3, wavelet filtering module 33, image sharpening module 34, gray processing processing module 35, the respective operations of fabric segmentation module 36 and fabric unit identification module 37, when identifying the fabric to be detected in described cloth textured image and there is not breakage or pollute, send to described embedded processing equipment 4 and normally detect echo signal, described embedded processing equipment 4 is when receiving described normal detection echo signal, recover contrast-enhancement module 32 in described image processing equipment 3, wavelet filtering module 33, image sharpening module 34, gray processing processing module 35, the respective operations of fabric segmentation module 36 and fabric unit identification module 37.
Wherein, in described detection system, described fabric construction image, all comprise in described fabric unit structural drawing and described all kinds of fabric unit benchmark image fabric through interlacing point, latitude interlacing point, through flotation line and latitude flotation line, substitute the way of realization adopting independently fpga chip respectively, can by described image pre-processing module 31, described contrast-enhancement module 32, described wavelet filtering module 33, described image sharpening module 34, described gray processing processing module 35, described fabric segmentation module 36 and described fabric unit identification module 37 are all integrated in one piece of fpga chip, described voice playing equipment is chosen as two-way speaker, described image display is chosen as LCDs.
In addition, stereomicroscope is also known as stereomicroscope, he can produce the three dimensions image of attentioning when observing object, stereoscopic sensation is strong, imaging clearly and broad, having long reach again, and the feature of thing according to the observation can select different reflections and projection light illumination, is scope of application conventional microscopy widely.
The purposes of stereomicroscope is mainly manifested in the following aspects: (1) is as the research of zoology, botany, entomology, histology, mineralogy, archaeology, geology and dermatology etc. and dissecting tool; (2) inspection of textile industry Raw and cotton wool fabric is carried out; (3) in the electronics industry, the device work such as auxiliary transistor; (4) in the inspection of the surface phenomena such as the crack of various material formation, pore shape corrosion condition; (5) device of lathe and instrument is made when manufacturing small-sized precise part, to the observation of the course of work, the inspection of precision component and the instrument as assembly work; (6) to the surface quality of lens, prism or other transparency materials, and the quality check of accurate scale; (7) to the true and false judgement of word bank note.
Adopt intelligent fabric tissue typing systems of the present invention, too rely on artificial experience for existing fabric tissue type detection pattern and brought the technical matters of multiple error, introduce micro-remarkably productive stereomicroscope to amplify fabric to be checked, introducing image acquisition and treatment facility carry out image acquisition to the fabric to be checked after amplification and high-precision structural images is extracted, so that mate one by one with all kinds of fabric unit benchmark images prestored, thus obtain final fabric tissue type.
Be understandable that, although the present invention with preferred embodiment disclose as above, but above-described embodiment and be not used to limit the present invention.For any those of ordinary skill in the art, do not departing under technical solution of the present invention ambit, the technology contents of above-mentioned announcement all can be utilized to make many possible variations and modification to technical solution of the present invention, or be revised as the Equivalent embodiments of equivalent variations.Therefore, every content not departing from technical solution of the present invention, according to technical spirit of the present invention to any simple modification made for any of the above embodiments, equivalent variations and modification, all still belongs in the scope of technical solution of the present invention protection.
Claims (5)
1. an intelligent fabric tissue type detection method, the method comprises following:
1) a kind of intelligent fabric tissue typing systems is provided, described detection system comprises stereomicroscope, picture pick-up device, image processing equipment and embedded processing equipment, described photographic equipment is connected that with described stereomicroscope the micrograph results of described stereomicroscope to fabric to be detected is realized image acquisition, obtain cloth textured image, described image processing equipment is connected with described picture pick-up device, image procossing is performed to obtain fabric unit structural drawing to described cloth textured image, described embedded processing equipment is connected with described image processing equipment, described fabric unit structural drawing is mated one by one with all kinds of fabric unit benchmark image, the organization type of described fabric to be detected is determined based on matching result, and
2) described system is run.
2. the method for claim 1, is characterized in that, described detection system also comprises:
Usb communication interface, for inserting outside USB flash disk, automatically to read all kinds of fabric unit benchmark images stored in outside USB flash disk, each kind fabric regulator image is the imaging of the minimum unit structure of benchmark fabric that take in advance, corresponding types, and described minimum unit structure refers to the cellular construction that multiplicity is maximum in benchmark fabric;
Static storage device, be connected with described usb communication interface, for reading and storing described all kinds of fabric unit benchmark image, described static storage device is also for storing default fabric upper limit gray threshold and default fabric lower limit gray threshold, and described default fabric upper limit gray threshold and described default fabric lower limit gray threshold are used for the fabric in image to separate with background;
Voice playing equipment, with described embedded processing equipment connection, for playing the voice prompted file corresponding with the organization type of described fabric to be detected or described it fails to match signal;
Image display, with described embedded processing equipment connection, for showing the prompt text corresponding with the organization type of described fabric to be detected or described it fails to match signal;
Power-supply unit, with described embedded processing equipment connection, under the control of described embedded processing equipment, for described detection system provides various different electric power supply pattern, described various different electric power supply pattern comprises battery saving mode and normal mode;
Described picture pick-up device is high-definition camera, and its resolution is 1280 × 720, for gathering described cloth textured image;
Described image processing equipment comprises contrast-enhancement module, wavelet filtering module, image sharpening module, gray processing processing module, fabric segmentation module and fabric unit identification module, described contrast-enhancement module is connected with described picture pick-up device, for performing contrast enhancement processing to described cloth textured image, obtain and strengthen image, described wavelet filtering module is connected with described contrast-enhancement module, for performing wavelet filtering to described enhancing image based on Harr wavelet filter, obtain filtering image, described image sharpening module and described wavelet filtering model calling, for performing image sharpening process to described filtering image, obtain sharpening image, described gray processing processing module and described image sharpening model calling, for performing gray processing process to described sharpening image, obtain gray level image, described fabric segmentation module is connected respectively with described static storage device and described gray processing processing module, so that the pixel identification of gray-scale value in described gray level image between default fabric upper limit gray threshold and default fabric lower limit gray threshold is formed fabric construction image, described fabric unit identification module and described fabric split model calling, add up in described fabric construction image the maximum cell picture of number of times of going on a journey to export as fabric unit structural drawing,
Described embedded processing equipment is connected respectively with described static storage device and described image processing equipment, receive described fabric unit structural drawing and described all kinds of fabric unit benchmark image, described fabric unit structural drawing is mated one by one with all kinds of fabric unit benchmark image, if the match is successful, type corresponding to the fabric unit benchmark image that the match is successful is exported as the organization type of described fabric to be detected, if it fails to match, then output matching failure signal;
Wherein, described image processing equipment and the operation of described embedded processing equipment collaboration, described embedded processing equipment is when resources occupation rate own is less than 30%, replace all operations performing described image processing equipment, described embedded processing equipment, when resources occupation rate own is more than 30%, terminates the replacement of all operations to described image processing equipment;
Wherein, described image pre-processing module, described contrast-enhancement module, described wavelet filtering module, described image sharpening module, described gray processing processing module, described fabric segmentation module and described fabric unit identification module adopt independently fpga chip to realize all respectively;
Wherein, described image processing equipment also comprises image pre-processing module, described image pre-processing module is connected respectively with described picture pick-up device and described embedded processing equipment, whether the fabric to be detected prejudged in described cloth textured image based on image recognition technology exists breakage or pollution, when identifying the fabric to be detected in described cloth textured image and there is damaged or pollution, improper detection echo signal is sent to described embedded processing equipment, described embedded processing equipment is when receiving described improper detection echo signal, stop contrast-enhancement module in described image processing equipment, wavelet filtering module, image sharpening module, gray processing processing module, the respective operations of fabric segmentation module and fabric unit identification module, when identifying the fabric to be detected in described cloth textured image and there is not breakage or pollute, send to described embedded processing equipment and normally detect echo signal, described embedded processing equipment is when receiving described normal detection echo signal, recover contrast-enhancement module in described image processing equipment, wavelet filtering module, image sharpening module, gray processing processing module, the respective operations of fabric segmentation module and fabric unit identification module.
3. method as claimed in claim 2, is characterized in that:
All comprise in described fabric construction image, described fabric unit structural drawing and described all kinds of fabric unit benchmark image fabric through interlacing point, latitude interlacing point, through flotation line and latitude flotation line.
4. method as claimed in claim 2, is characterized in that:
Substitute the way of realization adopting independently fpga chip respectively, described image pre-processing module, described contrast-enhancement module, described wavelet filtering module, described image sharpening module, described gray processing processing module, described fabric segmentation module and described fabric unit identification module are all integrated in one piece of fpga chip.
5. method as claimed in claim 2, is characterized in that:
Described voice playing equipment is two-way speaker, and described image display is LCDs.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108053399A (en) * | 2017-08-08 | 2018-05-18 | 卜风雷 | Real-time pattern identifying system |
| CN114253199A (en) * | 2022-03-01 | 2022-03-29 | 江苏苏美达纺织有限公司 | Weaving control method and system |
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| WO1997046969A2 (en) * | 1996-05-22 | 1997-12-11 | Georgia Tech Research Corporation | Automated apparatus for counting pillings in textile fabrics |
| CN101424680A (en) * | 2008-12-11 | 2009-05-06 | 东华大学 | Computer automatic recognition apparatus and method for profile fiber |
| CN103604937A (en) * | 2013-11-22 | 2014-02-26 | 青岛大学 | Fabric analysis system and method based on biaxial mechanical stretch processing |
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2015
- 2015-01-15 CN CN201510021219.4A patent/CN104537390B/en not_active Expired - Fee Related
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| WO1997046969A2 (en) * | 1996-05-22 | 1997-12-11 | Georgia Tech Research Corporation | Automated apparatus for counting pillings in textile fabrics |
| CN101424680A (en) * | 2008-12-11 | 2009-05-06 | 东华大学 | Computer automatic recognition apparatus and method for profile fiber |
| CN103604937A (en) * | 2013-11-22 | 2014-02-26 | 青岛大学 | Fabric analysis system and method based on biaxial mechanical stretch processing |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108053399A (en) * | 2017-08-08 | 2018-05-18 | 卜风雷 | Real-time pattern identifying system |
| CN108053399B (en) * | 2017-08-08 | 2020-02-11 | 宿州市徽腾知识产权咨询有限公司 | Real-time pattern recognition system |
| CN114253199A (en) * | 2022-03-01 | 2022-03-29 | 江苏苏美达纺织有限公司 | Weaving control method and system |
| CN114253199B (en) * | 2022-03-01 | 2022-05-06 | 江苏苏美达纺织有限公司 | Weaving control method and system |
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