WO1992008967A1 - Procede et appareil de verification de tissus - Google Patents

Procede et appareil de verification de tissus Download PDF

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Publication number
WO1992008967A1
WO1992008967A1 PCT/GB1991/002001 GB9102001W WO9208967A1 WO 1992008967 A1 WO1992008967 A1 WO 1992008967A1 GB 9102001 W GB9102001 W GB 9102001W WO 9208967 A1 WO9208967 A1 WO 9208967A1
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WO
WIPO (PCT)
Prior art keywords
image
fabric
line
area image
defect
Prior art date
Application number
PCT/GB1991/002001
Other languages
English (en)
Inventor
Leonard Norton-Wayne
Original Assignee
De Montfort University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by De Montfort University filed Critical De Montfort University
Priority to JP3518207A priority Critical patent/JPH06502489A/ja
Publication of WO1992008967A1 publication Critical patent/WO1992008967A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/89—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles
    • G01N21/892—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles characterised by the flaw, defect or object feature examined
    • G01N21/898—Irregularities in textured or patterned surfaces, e.g. textiles, wood
    • G01N21/8983—Irregularities in textured or patterned surfaces, e.g. textiles, wood for testing textile webs, i.e. woven material
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30124—Fabrics; Textile; Paper

Definitions

  • This invention relates to fabric inspection.
  • Fabric is inspected routinely in the textile industry so that fabric faults do not persist through processing and aking-up into the final textile article. Fabric inspection is however a tedious business and inspectors are prone to variable performance through fatigue and other factors so that a consistent quality cannot be ass re ⁇ .
  • the present invention provides methods and apparatus for inspecting plain web fabric for faults which involve methodology which can be implemented on equipment which is economical to instal and maintain.
  • the invention comprises a method for inspecting plain web fabric for faults comprising moving the fabric relatively to a line scan camera forming a line pixel image across the width of the fabric so as to form successive line images spaced apart comparably to the pixels of the line image and storing a succession of such line images in a frame store so as to constitute an area image of a rectangular area of the fabric, processing said area image by thresholding to isolate trigger areas from background areas, filtering the thresholded area image to eliminate (as noise) trigger areas of small pixel number leaving only defect triggers, and generating a defect signal if at least one ⁇ efect trigger is present in the filtered image.
  • Said thresholding, filtering and defect signal generating steps may be " performed while the next area image is being built up from a succession of line images. These steps may be always performed within a predetermined time which is less than the time taken to form an area image having a given number of line images. However, the area image may be of variable length, imaging being terminated when the thresholding, filtering and defect signal generating steps for a previous area image are completed. Thus 'no-defect' area images may be processed in a short time while when a defect is encountered the image processing time may be extended by delaying the capturing of the next area image by taking more lines. This next image will take longer to process, of course, but if, as would normally be expected, it contains no defect, its processing time can still be relatively short.
  • successive area images can be processed in separate processors so that image processing time can be longer than area image formation time.
  • the area image may be processed by a thresholding method which is adaptive to the fabric (generally speaking to its colour and surface texture) and also to the levelness of illumination of the fabric.
  • a thresholding method which is adaptive to the fabric (generally speaking to its colour and surface texture) and also to the levelness of illumination of the fabric.
  • fabrics will be produced in numerous knitting constructions and it will be desirable not to have to reset the inspection equipment each time a different fabric is encountered.
  • Levelness of illumination is a factor which cannot be guaranteed to remain constant in time even if it can be achieved in the first place; it is at least difficult over a width of a metre, and it is desirable that ageing of lamps and changing ambient lighting do not affect the equipment.
  • One method according to the invention of adaptive thresholding is to create an average line image from the area image.
  • Each column of the area image is summed and divided by the number of lines in it to give an average brightness for that column; the line image which is reconstituted from these column averages is the average line.
  • Thresholding is then a matter of setting a bright ⁇ ness level as the threshold value for any particular pixel of the line image which is at a predetermined brightness level away from the corresponding pixel of the average line.
  • Threshold values may be set above and below the average line to detect hole faults, with back lighting, which show up bright, and yarn contamination, slubs and the like, which show up dark.
  • the thresholded area image which will now consist of a uniformly gray background, with light and dark pixels occuring either singly (due mainly to noise) or in clusters (indicative of a fault) is now filtered to remove noise.
  • One method is to associate each pixel with its eight neighbours and reset its brightness level to a new value unless four or more of those neighbours have the same brightness value. This will effectively remove single and small groups of pixels.
  • the operation (which involves convolving each pixel with the unchanged value of its neighbours) is best carried out by writing new values to a separate frame store. This operation will affect defect areas by removing some edge pixels, of course, but if the system resolution is adequate the method successfully eliminates noise, and hence false alarms, while detecting fabric faults reliably.
  • a satisfactory resolution for knitted fabric is 0.5 mm per pixel side; a 2000 pixel line scan camera is thus suitable for a 1 metre fabric width.
  • a processed area image can also be analysed to locate faults - it is again a relatively simple matter to locate the centroid of a pixel cluster and post its coordinates together with the frame number to a hard copy record or, by computer graphics, to superimpose a flashing symbol, such as cross wires, on to the screen to highlight the fault.
  • the processed area image may also be further analysed to identify faults.
  • the area image region containing a fault may be associated with a set of masks representative of typical faults for the fabric until a "match' • is obtained according to predetermined criteria.
  • This further analysis may be quite time consuming but need be performed only when a fault is found, and it can be carried out off line if required.
  • fault data can be reduced for convenient storage and this analysis carried out on the fault data at the end of each 100 metres fabric piece run, for example.
  • the invention also comprises apparatus for inspecting plain web fabric for faults comprising a line scan camera arranged to form a line pixel image across the width of a relatively moving fabric so as to form successive line images spaced apart comparably to the pixels of the line image, frame store means adapted to store a succession of such line images so as to constitute an area image of a rectangular area of fabric, image processing means adapted to threshold the area image to isolate trigger areas from background areas and to filter the threshold area image to eliminate (as noise) trigger areas of small pixel number leaving only defect triggers, and defect signal generating means generating a defect signal if at least one defect trigger is present in the filtered image.
  • Said frame store may be comprised in memory of a computer which is also loaded with software so as to constitute said image processing and defect signal generating means.
  • Said memory may accommodate two frame stores, one being a frame store written to by the line scan camera and the other being written to by said image processing means.
  • the computer may comprise two image processing arrangements operating on alternate area images.
  • the image processing means may be operative to threshold the image adaptively.
  • the apparatus may comprise fabric drive means controlled in accordance with the image processing means, so that the fabric speed can be adjusted to the processing rate.
  • the apparatus may also comprise lighting means, which may comprise back lighting means so that a hole defect appears bright against the fabric background while a slub fault appears dark.
  • the lighting means may comprise a floating lamp pod, which may be powered by on-board battery means or by comprising fluorescent tubes powered by an alternating magnetic field.
  • the apparatus may comprise a monitor displaying the processed area images.
  • the apparatus may also comprise off-line defect identification means comprising data processing means connected to receive defect data and programmed to associate said defect data with mask data representative of typical defects.
  • Figure 1 is a diagrammatic illustration of fabric inspection apparatus
  • Figure la is a semi-processed area image
  • Figure 2 is a view of an arrangement for a tubular fabric
  • Figure 3 is a depiction of a line image produced by a line scan camera
  • Figure 4 is a depiction of an area image built up from a plurality of line images
  • FIG. 5 is a diagrammatic illustration of an adaptive thresholding method
  • the apparatus illustrated in Figure 1 comprises a 2000 + element line scan camera 11 connected to a frame store 12 which is controlled by a computer 13 through suitable software.
  • a display monitor 14 displays the contents of a second framestore 15 into which data representative of a processed image is fed by the computer.
  • the framestores 12,15 are in fact memory areas of said computer 13.
  • the computer 13 has a VDU 16 and keyboard input 17 and a printer 18 for hard copy output.
  • Fabric 19 is run from a roll 21 on a stand 22 to a take-up roll 23 driven by a motor 24 which can be controlled by the computer 13 as indicated by the broken line or which can simply run so as to feed the fabric at constant speed - a roller supporting and driving arrangement is depicted which for constant motor speed gives a constant web speed.
  • the fabric 19 is illuminated by any convenient method - here in Figure 1 a battery of lamps 25 is illustrated on the same side of the fabric as the camera, though back-lighting can, of course, be used.
  • a battery of lamps 25 is illustrated on the same side of the fabric as the camera, though back-lighting can, of course, be used.
  • a floating lamp pod 26 is contained within the moving fabric tube 19 supported on rollers 29 and comprises a circular fluorescent lamp 27 powered by a battery and inverter arrangement 28 - this gives backlighting, naturally.
  • the lamp 27 could alternatively be powered by a powerful externally applied alternating magnetic field provided adequate shielding for the line scan camera can be arranged.
  • Figure 3 is a typical line scan image, though showing only twenty pixels whereas a typical line scan camera output can be 2048 pixels long.
  • the camera 11 can be arranged to "see" all around the fabric by carefully arranging mirrors 30.
  • Figure 4 is a typical area scan built up from the successive line scans as illustrated in Figure 3.
  • the arrows indicate that the area scan can be longer.
  • the camera 11 is so arranged in relation to the fabric 19 that the desired image resolution is obtained along the scan line.
  • there is obviously some slight distortion because of the fact that the edges of the fabric 19 are further away than the middle of the fabric from the centrally placed camera, and there will be even more distortion in the arrangement of Figure 2.
  • the image of Figure 3 has pixels representing image areas spaced 0.5 mm apart, which is an adequate resolution for most knitted fabrics.
  • the speed of the fabric 19 is selected in relation to the scanning rate of the camera 11 so that the spacing between consecutive line images is also 0.5 mm so that the area image shown is Figure 4 is "square" in the sense that the vertical scale is not squashed nor expanded in relation to the horizontal scale.
  • the actual image need not, of course, be square, in the sense that if it is 2048 pixels wide it need be 2048 pixels long. There is in general no need to have the area image any longer than the length of the largest hole defect expected to be found in the fabric - lengthwise runs, ladders and other vertical faults excepted, of course, which can persist over quite long intervals.
  • the camera 11 can be adjusted so that it images the entire width of the fabric, no more and no less, and it can be guaranteed that the edges do not wobble, it will be necessary to deal with the image areas beyond the fabric edges. Perhaps the simplest way to deal with this is to begin reading into the framestore 12 when the first fabric image pixel is detected for each line scan - this will be indicated by a marked change in brightness. Then assuming that the fabric width remains constant, the image width will correspond to the fabric width despite any variations in tracking of the fabric.
  • Fabric width can also be measured by detecting the change in intensity at the end of the scan, of course. Width measurements can be analysed to monitor departures from nominal width. Fabric inspection is sometimes associated with stentering, and automated inspection according to the present invention is particularly suitable for combination with stentering, the fabric width measurement being useful in the control of the stenter to ensure that the finished width is within a desired specification, any width error being input to a feedback loop controlling the stenter width.
  • FIG 1 the box depicting the ' memory 12 written to by the camera 11, is a simplified illustration of a typical area image assembled from line images. It is to be understood here that the image will usually be a virtual rather than a visual image although this image could be displayed on a monitor, in. practice it will not need to be.
  • the image will comprise pixels each having one of an allowed range of brightness values.
  • Two faults, a hole defect 101 and a slub defect 102 are shown against a generally gray, generally uniform background 103, the hole defect 101 (assuming backlighting, as in Figure 2) showing up as a relatively bright area while the slub defect 102 appearing dark.
  • single pixels 104 or very small clusters 105 are present due to background noise, whether this arises within the camera and associated electronics or as a result of the texture of the fabric.
  • Faults in knitted fabrics include vertical and horizontal defects as well as area defects. Examples of defects include holes resulting from press offs, slubs and knots as well as drop stitches and dropstitch runs, irregular- ities of appearance due to thick and thin yarn, barre, tuck needle and rip needle faults.
  • a needle line fault is manifested as a think vertical line, while most of the other faults extend both vertically and horizontally, or, in knitting terminology, walewise and coursewise.
  • the first processing step performed on the area image is thresholding in which the image is segmented, in this case, into three gray scale levels, of which a middle level represents the general fabric background, a bright level represents holes and a dark level represents slubs.
  • the segmenting is carried out by a thresholding method adaptive to the fabric.
  • Fabrics can of course be presented to the camera in different colours (including white and black) and will have different surface textures as well as densities. It is desirable that the system is adaptive to different fabric colours and types and whilst completely automatic adaptation covering the whole range of possible colours and types may be difficult, if not impossible, to achieve with apparatus of reasonable capital cost, the present invention provides automatic adaptation over a wide range of colours and fabric types.
  • Lighting is also a problem dealt with by the invention. It is difficult - again, if not impossible - to achieve absolutely uniform lighting across a metre width of fabric. Lamps, moreover, are subject to changing intensity with age and with variations in the electric supply, and ambient lighting may change according as the room lights are on or off and with the time of day.
  • the adaptive thresholding method comprises calculating from the area image an average line image by summing for each column of the area image the brightness levels of the pixels of the column and dividing the sum by the number of lines in the image (i.e. the number of pixels in the column).
  • Figure 5 shows a typical average line AL derived from a white fabric which indicates an increased brightness in the centre of the line.
  • the next step is to normalize the average line, which is to say bring it to a middle gray scale level without losing the transverse variation.
  • NAL normalized average line
  • Threshold levels are now set above and below the average line by adding and subtracting, respectively, constant values, say 32, to and from the pixel brightness levels - the upper threshold is shown as UT, and the lower threshold as LT.
  • a typical line signal LS is now converted (by adding 64 to each gray scale level) into a normalised line signal NLS which is then segmented into gray scale levels of 0, 128 and 255 according as the signal is above UT, between UT and LT or below LT. This results in a segmented image as shown in Figure la - this will be the processed image stored in the framestore 12.
  • the normalized average line NAL can be computed at the beginning of a fabric run and used throughout the run. Or, if long fabric runs are contemplated so that lighting conditions may vary significantly during the run, or if different fabrics, especially of different colours and/or albedos are to be joined together to run through on a continous basis, the computation can be updated periodically or whenever a change of fabric requires.
  • the constant value added to and subtracted from the normalized average line to set the two thresholds may be chosen on the basis of experience with given systems or by calibration against known fabrics.
  • the area image is then segmented using the upper and lower thresholds and will then contain small isolated pixel groups or single pixels at gray scale level zero or level 255 which are assumed to be due to noise, either system noise or noise caused by non-defect signals from the fabric, e.g. reflections from individual fibres or pinholes or shadows from stitches, and larger pixel clusters indicative of a defect.
  • This thresholded area image is filtered by convolving each pixel of the image with its eight neighbours and resetting its brightness level to a new value (which is written into the corresponding address in framestore 15) unless four or more of its neighbours had the same gray scale level, otherwise the present value is written to the corresponding address in framestore 15.
  • the resulting area image is shown inset in the box depicting framestore 15 in Figure 1. All or most of the noise signals have been eliminated, leaving only the defect signals, slightly reduced in size.
  • This image is displayed on the monitor 14. Each time an area image is processed it replaces the current display so that the monitor 14 can be used by an inspector to watch out for fabric defects which will show up more clearly than they appear in the fabric because of the increased contrast and the elimination of non-defect fabric generated noise.
  • the appearance of a fabric effect in the processed image as displayed on the monitor 14 can be detected by assuming that the presence of an arbitarily small number of extreme (0 or 255) gray scale level pixels, say ten pixels, in the area image indicates a fabric defect and arranging that the computer sounds an audible alarm whenever ten or more extreme value pixels are present in the image.
  • an arbitarily small number of extreme (0 or 255) gray scale level pixels, say ten pixels, in the area image indicates a fabric defect and arranging that the computer sounds an audible alarm whenever ten or more extreme value pixels are present in the image.
  • the inspector's attention will be drawn to the monitor 16.
  • the alarm can also effect other operations if desired, including the slowing down or even stopping of the fabric feed to allow visual inspection of the fabric itself at the defect position, or an automatic marker can be placed on the fabric's edge so that the inspected, marked roll can be re-wound stopping at the markers to further inspect or deal with indicated defects (as by cutting them out and sewing together or by removing slubs or knots or repairing drop stitches where this is possible).
  • the processed area image can be further analysed to locate any faults - some, especially if small to begin with, may be even smaller in the image as a result of filtering - and a flashing cursor or cross-wires can be generated by graphics software and superimposed on the defect centroid, which can be determined by standard methods.
  • location data can be posted to some useful output, for example a log for a roll of fabric in which length along the fabric is derived from image frame number (or 21 [frame member x image length] in the case of variable length images) plus y-coordinate.
  • image frame number or 21 [frame member x image length] in the case of variable length images
  • y-coordinate Such information can be printed on a fault tag which may be applied automatically to the fabric edge or output to hard copy to accompany the roll for further processing.
  • the processed area image may be further analyzed to identify fabric faults and this may ae done by convolving an image region containing a defect indication with a set of masks representative of typicaJ faults for the fabric. Having located the fault centroid and possibly an indication as to whether it is a vertical or horizontal fault and an indication of its area by well-understood routines, a selection of trial masks from a library can be made automatically without having to pick through the entire library and, of course, the masks do not have to be used except in connection with already located faults.
  • This further processing is likely to be time consuming nevertheless and may be relegated to off-line status, fault data being saved in simplified, condensed form.
  • the type of defect can be indicated on the log referred to above alongside the fault coordinates.
  • the methods and apparatus described herein will detect most faults in knitted fabrics as well as woven fabrics and non-wovens.
  • One fault that may be eliminated by the filtering method described is the needle line fault in a knitted fabric which may be at most one or two pixels wide in the unprocessed image and it may be desirable to incorporate a detection routine aimed specifically at this defect.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Textile Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Wood Science & Technology (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Quality & Reliability (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
  • Treatment Of Fiber Materials (AREA)

Abstract

On décrit un procédé et un appareil de vérification d'un tissu à tissage uni afin d'y déceler des défauts, procédé selon lequel le tissu est déplacé par rapport à une caméra à balayage de lignes qui produit une image de pixels de lignes sur toute la surface du tissu de façon à produire des images de lignes successives espacées de manière comparable aux pixels de l'image de ligne. Une succession de telles images de lignes est mémorisée dans une mémoire d'images de façon à composer une image de zone d'une zone rectangulaire du tissu. Ladite image de zone est traitée par un procédé de seuillage pour isoler des zones de déclenchement par rapport aux zones d'arrière-plan, l'image de zone à seuillage est filtrée afin d'éliminer (sous forme de bruit) des zones de déclenchement d'un petit nombre de pixels en ne laissant que des zones de déclenchement représentant des défauts, et un signal indiquant un défaut est émis si au moins une zone de déclenchement représentant un défaut est présente dans l'image filtrée.
PCT/GB1991/002001 1990-11-16 1991-11-13 Procede et appareil de verification de tissus WO1992008967A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP3518207A JPH06502489A (ja) 1990-11-16 1991-11-13 織物検査方法および装置

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB9024936.8 1990-11-16
GB909024936A GB9024936D0 (en) 1990-11-16 1990-11-16 Methods and apparatus for fabric inspection

Publications (1)

Publication Number Publication Date
WO1992008967A1 true WO1992008967A1 (fr) 1992-05-29

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PCT/GB1991/002001 WO1992008967A1 (fr) 1990-11-16 1991-11-13 Procede et appareil de verification de tissus

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EP (1) EP0557331A1 (fr)
JP (1) JPH06502489A (fr)
AU (1) AU8879291A (fr)
GB (1) GB9024936D0 (fr)
WO (1) WO1992008967A1 (fr)

Cited By (20)

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EP0536570A2 (fr) * 1991-09-19 1993-04-14 Futec Incorporated Procédé pour l'indication d'une défaut et dispositif pour cela
EP0574336A1 (fr) * 1992-06-10 1993-12-15 Valinox Dispositif et procédé de détection au défilé de défauts de surface sur des produits longs métalliques
US5283443A (en) * 1990-04-17 1994-02-01 De Montfort University Method for inspecting garments for holes having a contrasting background
EP0626576A1 (fr) * 1993-04-16 1994-11-30 ERHARDT + LEIMER GmbH Méthode et dispositif pour mesure optique sans contact de paramètres de qualité de surface textiles
FR2726299A1 (fr) * 1994-11-02 1996-05-03 Scanera Sc Dispositif d'inspection de textiles
EP0726456A1 (fr) * 1993-10-26 1996-08-14 Asahi Kasei Kogyo Kabushiki Kaisha Procede et appareil de mesure de la non uniformite de la brillance et de l'epaisseur d'une image imprimee
WO1997019217A2 (fr) * 1995-11-21 1997-05-29 Loughborough University Innovations Limited Procedes et equipement de commande
WO1997027471A1 (fr) * 1996-01-26 1997-07-31 Zellweger Luwa Ag Dispositif de controle automatique de structures textiles planes
FR2785626A1 (fr) * 1998-11-05 2000-05-12 Visioreg Synchronisation inspection de tissus
FR2785627A1 (fr) * 1998-11-05 2000-05-12 Visioreg Positionnement inspection de tissus
FR2785628A1 (fr) * 1998-11-05 2000-05-12 Visioreg Detection de defauts de trame en cours de tissage
FR2785625A1 (fr) * 1998-11-05 2000-05-12 Visioreg Systeme d'acquisition inspection tissus
WO2000028120A2 (fr) * 1998-11-05 2000-05-18 Visioreg S.A. Systeme d'acquisition pour l'inspection de tissu
WO2010106306A3 (fr) * 2009-03-20 2010-11-11 Nixtex Limited Mesure de tissus textiles
WO2010139560A2 (fr) * 2009-06-05 2010-12-09 Starlinger & Co Gesellschaft M.B.H. Identification de défauts
WO2016140635A1 (fr) 2015-03-04 2016-09-09 Kirecci Ali Machine d'élimination de noeuds de tissus et procédé d'élimination
WO2020079493A1 (fr) * 2018-10-15 2020-04-23 Smartex Unipessoal Lda Machine et procédé de contrôle de qualité textile
CN113552134A (zh) * 2019-08-07 2021-10-26 浙江大学台州研究院 一种湿法涂胶的合成革卷边检测方法
US20220185619A1 (en) * 2019-04-03 2022-06-16 Fitesa S.A. Device and method for detecting the presence of abnormalities in a reel
WO2024012438A1 (fr) * 2022-07-12 2024-01-18 厦门兴全龙机械有限公司 Dispositif de détection de tissu écru et procédé de détection adapté à une bobineuse de tissu pleine largeur

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EP1043070A1 (fr) * 1999-04-01 2000-10-11 Kraft Jacobs Suchard R & D, Inc. Dispositif de broyage
JP4512852B2 (ja) * 1999-07-16 2010-07-28 グンゼ株式会社 連続染色装置
JP2008032747A (ja) * 2007-10-17 2008-02-14 Hitachi Ltd フィルム状製品
CN108896570B (zh) * 2018-07-06 2020-11-24 湖南工程学院 织物检测控制系统
CN111754470A (zh) * 2020-06-11 2020-10-09 厦门雨程户外运动用品有限公司 自动验布方法、装置、自动验布机和存储介质

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US5283443A (en) * 1990-04-17 1994-02-01 De Montfort University Method for inspecting garments for holes having a contrasting background
EP0536570A3 (en) * 1991-09-19 1993-12-15 Futec Inc Method for displaying defect and apparatus therefor
US5377279A (en) * 1991-09-19 1994-12-27 Futec Inc. Method and apparatus for displaying defect in central area of monitor
EP0536570A2 (fr) * 1991-09-19 1993-04-14 Futec Incorporated Procédé pour l'indication d'une défaut et dispositif pour cela
EP0574336A1 (fr) * 1992-06-10 1993-12-15 Valinox Dispositif et procédé de détection au défilé de défauts de surface sur des produits longs métalliques
FR2692355A1 (fr) * 1992-06-10 1993-12-17 Valinox Dispositif et procédé de détection au défilé de défauts de surface sur des produits longs métalliques.
US5408104A (en) * 1992-06-10 1995-04-18 Valinox Apparatus and process with an annular fluorescent tube for detection in a moving mode of surface defects on long metallic products
EP0626576A1 (fr) * 1993-04-16 1994-11-30 ERHARDT + LEIMER GmbH Méthode et dispositif pour mesure optique sans contact de paramètres de qualité de surface textiles
EP0726456A1 (fr) * 1993-10-26 1996-08-14 Asahi Kasei Kogyo Kabushiki Kaisha Procede et appareil de mesure de la non uniformite de la brillance et de l'epaisseur d'une image imprimee
EP0726456A4 (fr) * 1993-10-26 1998-11-25 Asahi Chemical Ind Procede et appareil de mesure de la non uniformite de la brillance et de l'epaisseur d'une image imprimee
FR2726299A1 (fr) * 1994-11-02 1996-05-03 Scanera Sc Dispositif d'inspection de textiles
WO1996014460A1 (fr) * 1994-11-02 1996-05-17 Scanera S.C. Dispositif d'inspection optique de materiau en deplacement
US5990468A (en) * 1994-11-02 1999-11-23 Cornuejols; Georges Device for the automatic detection and inspection of defects on a running web, such as a textile fabric
WO1997019217A2 (fr) * 1995-11-21 1997-05-29 Loughborough University Innovations Limited Procedes et equipement de commande
WO1997019217A3 (fr) * 1995-11-21 1997-06-26 Univ Loughborough Procedes et equipement de commande
WO1997027471A1 (fr) * 1996-01-26 1997-07-31 Zellweger Luwa Ag Dispositif de controle automatique de structures textiles planes
FR2785626A1 (fr) * 1998-11-05 2000-05-12 Visioreg Synchronisation inspection de tissus
FR2785627A1 (fr) * 1998-11-05 2000-05-12 Visioreg Positionnement inspection de tissus
FR2785628A1 (fr) * 1998-11-05 2000-05-12 Visioreg Detection de defauts de trame en cours de tissage
FR2785625A1 (fr) * 1998-11-05 2000-05-12 Visioreg Systeme d'acquisition inspection tissus
WO2000028120A2 (fr) * 1998-11-05 2000-05-18 Visioreg S.A. Systeme d'acquisition pour l'inspection de tissu
WO2000028120A3 (fr) * 1998-11-05 2000-07-27 Visioreg S A Systeme d'acquisition pour l'inspection de tissu
CN102362181A (zh) * 2009-03-20 2012-02-22 尼克斯特克斯有限公司 织物的测量
WO2010106306A3 (fr) * 2009-03-20 2010-11-11 Nixtex Limited Mesure de tissus textiles
WO2010139560A2 (fr) * 2009-06-05 2010-12-09 Starlinger & Co Gesellschaft M.B.H. Identification de défauts
WO2010139560A3 (fr) * 2009-06-05 2011-04-07 Starlinger & Co Gesellschaft M.B.H. Identification de défauts
WO2016140635A1 (fr) 2015-03-04 2016-09-09 Kirecci Ali Machine d'élimination de noeuds de tissus et procédé d'élimination
WO2020079493A1 (fr) * 2018-10-15 2020-04-23 Smartex Unipessoal Lda Machine et procédé de contrôle de qualité textile
US11798154B2 (en) 2018-10-15 2023-10-24 Smartex Europe, Unipessoal Lda. Circular knitting machine and respective method to control textile quality by use of digital camera
US20220185619A1 (en) * 2019-04-03 2022-06-16 Fitesa S.A. Device and method for detecting the presence of abnormalities in a reel
CN113552134A (zh) * 2019-08-07 2021-10-26 浙江大学台州研究院 一种湿法涂胶的合成革卷边检测方法
CN113567447A (zh) * 2019-08-07 2021-10-29 浙江大学台州研究院 一种合成革卷边在线检测方法
CN113552134B (zh) * 2019-08-07 2024-05-24 浙江大学台州研究院 一种湿法涂胶的合成革卷边检测方法
WO2024012438A1 (fr) * 2022-07-12 2024-01-18 厦门兴全龙机械有限公司 Dispositif de détection de tissu écru et procédé de détection adapté à une bobineuse de tissu pleine largeur

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AU8879291A (en) 1992-06-11
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EP0557331A1 (fr) 1993-09-01

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