EP0661108A2 - Procédé de tri optique de matériau en vrac - Google Patents

Procédé de tri optique de matériau en vrac Download PDF

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Publication number
EP0661108A2
EP0661108A2 EP94250285A EP94250285A EP0661108A2 EP 0661108 A2 EP0661108 A2 EP 0661108A2 EP 94250285 A EP94250285 A EP 94250285A EP 94250285 A EP94250285 A EP 94250285A EP 0661108 A2 EP0661108 A2 EP 0661108A2
Authority
EP
European Patent Office
Prior art keywords
color
test material
rejects
values
areas
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.)
Granted
Application number
EP94250285A
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German (de)
English (en)
Other versions
EP0661108B1 (fr
EP0661108A3 (fr
Inventor
Wolfgang Dr. Graudejus
Eberhard Briem
Wilhelm Dr. Hättich
Heribert Dr. Geisselmann
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Reemtsma Cigarettenfabriken GmbH
Original Assignee
HF and PhF Reemtsma GmbH and Co
Fraunhofer Gesellschaft zur Foerderung der Angewandten Forschung eV
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 HF and PhF Reemtsma GmbH and Co, Fraunhofer Gesellschaft zur Foerderung der Angewandten Forschung eV filed Critical HF and PhF Reemtsma GmbH and Co
Publication of EP0661108A2 publication Critical patent/EP0661108A2/fr
Publication of EP0661108A3 publication Critical patent/EP0661108A3/fr
Application granted granted Critical
Publication of EP0661108B1 publication Critical patent/EP0661108B1/fr
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

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Classifications

    • BPERFORMING OPERATIONS; TRANSPORTING
    • B07SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07CPOSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/36Sorting apparatus characterised by the means used for distribution
    • B07C5/363Sorting apparatus characterised by the means used for distribution by means of air
    • B07C5/365Sorting apparatus characterised by the means used for distribution by means of air using a single separation means
    • B07C5/366Sorting apparatus characterised by the means used for distribution by means of air using a single separation means during free fall of the articles
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B07SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07CPOSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34Sorting according to other particular properties
    • B07C5/342Sorting according to other particular properties according to optical properties, e.g. colour
    • B07C5/3422Sorting according to other particular properties according to optical properties, e.g. colour using video scanning devices, e.g. TV-cameras
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10STECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S209/00Classifying, separating, and assorting solids
    • Y10S209/939Video scanning

Definitions

  • the invention relates to a method for the optical sorting of bulk material according to the preamble of patent claim 1.
  • test material is conveyed on tapes and its image is recorded for testing with a diode line camera or a television camera.
  • the signal is preferably recorded in flight, e.g. the test material is transferred from one belt to another belt.
  • the test material can be examined from several sides with a defined background.
  • the color is also recorded during image acquisition.
  • the color is used to detect conspicuous areas in the image.
  • the image of the test item is evaluated in step-by-step with the image scanning, so that a test item can be classified immediately after it has passed through the measuring station. This makes it possible to eject the parts in flight using flaps or air nozzles.
  • a disadvantage of the known methods is that the detection rate for color-heterogeneous products is low if the detection of conspicuous pixels is limited to the detection of color values that are not contained in the product because there are many different color values in the product. If the detection is expanded to include color values which are also contained in the product, an unbearably high proportion of the error-free product is generally detected as a reject even when extending to colors which rarely occur in the product.
  • the light of each pixel is blocked by color filters in front of the detection elements of a line e.g. broken down into the three color components red (R), green (G) and blue (B). It is thereby achieved that a detection of conspicuous pixels (points with color values which rarely occur in the fault-free product) is possible by evaluating the color values (intensities of the respective color components) measured by the line elements. The geometry is then evaluated in terms of local clusters of conspicuous pixels.
  • the entire range of possible color values in the color space is divided into several sub-areas, the color space being spanned by the different color components that are measured for each pixel.
  • the three color components red, green and blue form a three-dimensional color space.
  • Allow classifiers ie means for evaluating the measured values on the basis of predetermined criteria a classification of the measured color values, with a classifier concentrating on only one sub-area and thereby recognizing detection areas in this sub-area for the color-heterogeneous product, that is, coherent areas of conspicuous pixels.
  • the reject part is detected as a relatively large area of pixels of the color values of the selected sub-area and can be recognized by the classifier by evaluating this detection area.
  • the defect-free product within such a sub-area large areas of conspicuous pixels are generally found only in rare cases, and the number of incorrect detections thus remains small. This improvement in the classification is used in practical application by dividing the committee into typical classes and setting up a classifier for each class, with the classifiers working in parallel during the test.
  • the distribution of their color values is learned in the sub-areas in which reject parts are suspected by showing reject parts.
  • the bulk material preferably moves in flight past an observation head with a light source and a product signal receiver arranged in the vicinity of the light source.
  • the reflected light from each pixel of the test material is transmitted through different color filters of adjacent line elements of a camera line, e.g. a CCD line, the receiver divided into the three colors red (R), green (G) and blue (B).
  • the line elements thus measure the brightness of the pixels, also called color values, in their respective spectral ranges. This results in a three-dimensional distribution of color values, the evaluation of which is discussed below using one-dimensional examples.
  • the test material is measured without rejects in a pre-learning process and the frequency distribution 1 of the color values is determined.
  • the test material is also measured without rejects and in a first step a color value range for good test material is determined by placing a threshold 2 based on experience on the frequency distribution 1 of the color values, the intersection between the threshold 2 and the curve of the frequency distribution being derived from the intersection points 1 result in the limits of the test material color value range.
  • threshold 2 With the selected setting of threshold 2, pixels will also appear in the case of the error-free test material, which are classified as conspicuous. However, these pixels would erroneously serve as a committee if they clustered into large areas classified. Experience has shown that such an agglomeration in turn occurs preferentially in certain color value ranges. In order to measure these color value ranges, a large area detected in the error-free test material is stored in the learning process and the distribution of its color values is measured. This distribution is introduced as threshold 3 after standardization.
  • the color value ranges of the product are divided into sub-ranges.
  • each of the classifiers A, B and C working in parallel concentrates only on one sub-area. If the color components of the color-homogeneous reject part are preferably in the selected sub-area, the reject part is detected as a relatively large area and can be recognized by evaluating the detection areas.
  • the distributions of the color values of these large areas are measured and introduced as thresholds after their normalization. All color values at which these thresholds 4, 5 and 6 exceed the color value distribution 1 of the test material parts are interpreted as rejects and lead to an error detection.
  • the defect-free product large-area detection areas are detected in a color value range covered by a classifier, and the defect-free product is thus classified as a reject.
  • these color values in particular, which lead to large-area detection areas in the fault-free product area are learned and recognized as good test material by changing the thresholds.
  • the threshold 8 shows the Color value distribution of a reject part. Within the color value range determined by threshold 8, error-free test material is classified as a reject.
  • the color value distribution of this large-area detection area is measured in the error-free test material and introduced as a threshold 7 after standardization. All color values at which the threshold 7 exceeds the threshold 8 of the reject part are interpreted as belonging to the test material and thus do not lead to an error detection.
  • the classification system is doubled.
  • One system takes over the test task, while the other system measures the current color value distribution of the product.
  • the measurement of the current color value distribution is monitored by the checking classifier so that no color values of the rejects are recorded during this measurement.
  • the learning classifier with the newly measured distribution is activated for the test task, while the classifier which has been set to test so far takes over the learning task.
  • the test object When the signal is recorded, the test object is illuminated, for example, by two lamps from the direction of the line scan camera.
  • the optical axis of the line scan camera lies between the two lamps.
  • test material is taken up lying on the conveyor belt.
  • the tape is not of a uniform color due to dirt and wear.
  • shadows form on the conveyor belt, which leads to a significant expansion of the color value distribution when measuring the error-free test material. For this reason, the test material is observed in flight.
  • the background has the color of the test material, which has the advantage that the contrast between the background and the test material is low and therefore the color value distribution of the test material is not significantly expanded by edge effects at the transition from the background to the test material.
  • This variant provides the best results in terms of color and spatial resolution.
  • the background as a rotating roller, which immediately throws away deposits.
  • the shadow of the test material on the background becomes diffuse and, depending on the bulk density, harmless if the rotating roller is installed at a suitable distance from the test material.
  • the background can be a cylindrical emitter that emits the color of the test material and is surrounded by a transparent rotating roller that throws away the deposits.
  • the background is a dark hole, which has the advantage that the test material can be segmented from the background and there is no impairment due to dirt and shadows.
  • segmentation of the test material for example, the form for separating good parts and rejects can be used.
  • the line scan camera looks into this container through a slit.
  • the width of the slot is adapted to the aperture and focal length of the camera lens and to the distance to the focus plane.
  • the light of each pixel is broken down into the three colors red (R), green (G) and blue (B).
  • the color components are not ideally measured at the same location, but rather at different locations.
  • the color sensors are even located side by side, so that the color sensors see different spatial areas of the measurement object with respect to a pixel.
  • the color sensors (R, G, B) are arranged horizontally, while the measurement object moves past this horizontal line from top to bottom.

Landscapes

  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Sorting Of Articles (AREA)
  • Spectrometry And Color Measurement (AREA)
  • Epoxy Compounds (AREA)
  • Treatment Of Sludge (AREA)
  • Crystals, And After-Treatments Of Crystals (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)
EP94250285A 1993-12-28 1994-11-25 Procédé de tri optique de matériau en vrac Expired - Lifetime EP0661108B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE4345106A DE4345106C2 (de) 1993-12-28 1993-12-28 Verfahren zum optischen Sortieren von Schüttgut
DE4345106 1993-12-28

Publications (3)

Publication Number Publication Date
EP0661108A2 true EP0661108A2 (fr) 1995-07-05
EP0661108A3 EP0661108A3 (fr) 1997-02-12
EP0661108B1 EP0661108B1 (fr) 1999-11-03

Family

ID=6506597

Family Applications (1)

Application Number Title Priority Date Filing Date
EP94250285A Expired - Lifetime EP0661108B1 (fr) 1993-12-28 1994-11-25 Procédé de tri optique de matériau en vrac

Country Status (7)

Country Link
US (1) US5586663A (fr)
EP (1) EP0661108B1 (fr)
JP (1) JP3517292B2 (fr)
AT (1) ATE186242T1 (fr)
BR (1) BR9405268A (fr)
CA (1) CA2136779C (fr)
DE (2) DE4345106C2 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19511534A1 (de) * 1995-03-29 1996-10-02 Fraunhofer Ges Forschung Verfahren und Vorrichtung zur Erfassung von 3D-Fehlstellen bei der automatischen Inspektion von Oberflächen mit Hilfe farbtüchtiger Bildauswertungssysteme

Families Citing this family (13)

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Publication number Priority date Publication date Assignee Title
US5577733A (en) * 1994-04-08 1996-11-26 Downing; Dennis L. Targeting system
DE19511901A1 (de) * 1995-03-31 1996-10-02 Commodas Gmbh Vorrichtung und Verfahren zum Sortieren von Schüttgut
JPH0943058A (ja) * 1995-05-23 1997-02-14 Olympus Optical Co Ltd 色分類装置及び色むら検査装置
EP0775533A3 (fr) * 1995-11-24 1998-06-17 Elpatronic Ag Méthode de tri
DE19609916A1 (de) * 1996-03-14 1997-09-18 Robert Prof Dr Ing Massen Preisgünstiger spektroskopischer Sensor für die Erkennung von Kunststoffen
AT3418U1 (de) 1999-04-30 2000-03-27 Waagner Biro Binder Aktiengese Verfahren und vorrichtung zum sortieren von altpapier
BR0311359A (pt) * 2002-05-28 2007-04-27 Satake Usa Inc fonte de iluminação para máquina classificadora
MX2008012726A (es) * 2006-04-04 2009-01-16 6511660 Canada Inc Sistema y metodo para identificar y clasificar material.
US20110068051A1 (en) * 2009-05-22 2011-03-24 6358357 Canada Inc. Ballistic separator
WO2013028196A1 (fr) * 2011-08-25 2013-02-28 Alliance For Sustainable Energy, Llc Contrôle continu en ligne de la fabrication de photopiles et de piles à combustible faisant appel à une imagerie par réflectance spectrale
DE102012001868B4 (de) 2012-01-24 2018-03-29 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Verfahren zum Einrichtung einer dem optischen Identifizieren von Objekten dienender Anlage, Laborbildaufnahmesystem zum Durchführen eines solchen Verfahrens und Anordnung umfassend das Laborbildaufnahmesystem sowie die Anlage
US10480935B2 (en) 2016-12-02 2019-11-19 Alliance For Sustainable Energy, Llc Thickness mapping using multispectral imaging
DE102023113725A1 (de) 2023-05-25 2024-11-28 Multivac Sepp Haggenmüller Se & Co. Kg Optimierung der Gut- bzw. Schlechtprodukterkennung für Verpackungsmaschinen

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US3560758A (en) * 1968-01-08 1971-02-02 Conductron Corp Color identification system taking into account the color and reflecting of the base material
DE2544703C3 (de) * 1975-10-07 1978-04-06 Dr.-Ing. Rudolf Hell Gmbh, 2300 Kiel Verfahren und Schaltungsanordnung zum Erkennen von Farben einer farbigen Fläche
US4122951A (en) * 1977-02-28 1978-10-31 Alaminos Jose I L Machine for the automatic detection of blemishes in olives and other fruits
US4246098A (en) * 1978-06-21 1981-01-20 Sunkist Growers, Inc. Method and apparatus for detecting blemishes on the surface of an article
IT1205622B (it) * 1982-12-21 1989-03-23 Illycaffe Spa Procedimento per effettuare una selezione in un materiale granuliforme e macchina per attuare il procedimento
GB2151018B (en) * 1983-12-06 1987-07-22 Gunsons Sortex Ltd Sorting machine and method
JPS63119887A (ja) * 1986-11-06 1988-05-24 カネボウ株式会社 選別装置
DE3802681C2 (de) * 1987-01-30 1997-01-09 Fuji Photo Film Co Ltd Verfahren zur Klassifizierung von Farbvorlagen und Vorrichtung hierfür
US5085325A (en) * 1988-03-08 1992-02-04 Simco/Ramic Corporation Color sorting system and method
EP0342354A3 (fr) * 1988-04-15 1992-01-08 Tecnostral S.A. Industria E Tecnologia Dispositif de trí selon la couleur
JPH0670590B2 (ja) * 1988-09-10 1994-09-07 倉敷紡績株式会社 色順判定方式
NL8803112A (nl) * 1988-12-19 1990-07-16 Elbicon Nv Werkwijze en inrichting voor het sorteren van een stroom voorwerpen in afhankelijkheid van optische eigenschappen van de voorwerpen.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19511534A1 (de) * 1995-03-29 1996-10-02 Fraunhofer Ges Forschung Verfahren und Vorrichtung zur Erfassung von 3D-Fehlstellen bei der automatischen Inspektion von Oberflächen mit Hilfe farbtüchtiger Bildauswertungssysteme
DE19511534C2 (de) * 1995-03-29 1998-01-22 Fraunhofer Ges Forschung Verfahren und Vorrichtung zur Erfassung von 3D-Fehlstellen bei der automatischen Inspektion von Oberflächen mit Hilfe farbtüchtiger Bildauswertungssysteme
US6064478A (en) * 1995-03-29 2000-05-16 Fraunhofer-Gesellschaft Zur Forderung Der Angewandten Forschung E.V. Method of and apparatus for automatic detection of three-dimensional defects in moving surfaces by means of color vision systems

Also Published As

Publication number Publication date
CA2136779C (fr) 2004-04-06
JP3517292B2 (ja) 2004-04-12
HK1013038A1 (en) 1999-08-13
CA2136779A1 (fr) 1995-06-29
DE4345106A1 (de) 1995-06-29
US5586663A (en) 1996-12-24
DE59408885D1 (de) 1999-12-09
JPH08206611A (ja) 1996-08-13
EP0661108B1 (fr) 1999-11-03
BR9405268A (pt) 1995-09-19
DE4345106C2 (de) 1995-11-23
ATE186242T1 (de) 1999-11-15
EP0661108A3 (fr) 1997-02-12

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