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WO2008016309A1 - Contrôle de qualité de produits alimentaires multimodal à vision artificielle - Google Patents

Contrôle de qualité de produits alimentaires multimodal à vision artificielle Download PDF

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Publication number
WO2008016309A1
WO2008016309A1 PCT/NO2007/000278 NO2007000278W WO2008016309A1 WO 2008016309 A1 WO2008016309 A1 WO 2008016309A1 NO 2007000278 W NO2007000278 W NO 2007000278W WO 2008016309 A1 WO2008016309 A1 WO 2008016309A1
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WO
WIPO (PCT)
Prior art keywords
food products
light
light source
fish
imaging
Prior art date
Application number
PCT/NO2007/000278
Other languages
English (en)
Inventor
Stig Jansson
John Reidar Mathiassen
Original Assignee
Sinvent As
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 Sinvent As filed Critical Sinvent As
Publication of WO2008016309A1 publication Critical patent/WO2008016309A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food
    • G01N33/12—Meat; Fish
    • 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
    • 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
    • G01N2021/845—Objects on a conveyor

Definitions

  • the present invention relates to the field of machine vision. More specifi- cally it relates to machine-vision based processing of food products.
  • grading tasks normally consist of removing non-fish and other fish species, checking for over- or undersized fish in the weight class and removing damaged or defective fish.
  • a common primary processor in the Norwegian industry processes in the range of 90 000 to about 300 000 individual fish per hour. It is not possible to inspect these amounts manually with a sufficient accuracy by only a handful of operators.
  • manual processing and grading has several other drawbacks. It is influenced by human factors such as mistakes, occasional omission in processing as well as fatigue (Pau and Olafsson, 1991). To meet the increasingly more stringent demands, the primary processor has the choice between more operators or automation of the grading tasks.
  • the present invention comprises both a method and a system aspect.
  • the method-aspect of the invention to automatically recognize shape and surface defects of food products 11 using computer image analysis is characterized in that optical data is sampled by at least one optical sensor 5 using combinations of different illumination and sampling modes. This is done to enhance detectability of surface differences, subsurface differences, or shape differences between a defective sample and a non-defective model of the food products. It uses a digital camera or an imaging spectrometer as the optical sensor 5.
  • the method applies in one embodiment a first illumination and imaging mode where at least one first light source 4 beams substantially in parallel with the axis of view of the optical sensor to illuminate a light pattern onto the food products 11 , resulting in images that measure and/or enhance a 3D-shape of the food products.
  • the method applies a second illuminating and imaging mode where at least one second light source 6 beams at an substantial angel to the axis of view to illuminate the light pattern onto the food product 11 , resulting in measurement or enhancement of at least one of a UV/VIS/NIR/IR surface, a surface, and internal scattering properties of the food products.
  • a laser is used for one or both of the light sources 4, 6.
  • at least two lasers are used emitting light at different wavelengths onto the same scan line in the optical sensor field of view for one or both of the light sources 4, 6.
  • light with a wavelength in the 600-700 nm range from at least one of the light sources 4, 6 is emitted.
  • the method applies a third illuminating and imaging mode where diffuse light 3 illuminates a light pattern onto the food products 11.
  • the light pattern is at least one of a number of parallel lines.
  • the method at least measures or images surface reflectivity, 3D-shape, and/or diffusivity of the food products 11 or measures surface properties, subsurface properties, or inter- nal scattering properties by using an imaging spectrometer and at least one light source focused into a line.
  • the method detects abnormality types like surface wounds (broken surface), minor surface damage (scratches on surface), missing parts of the food products, and if the food product is out of a predetermined size range.
  • abnormality types like surface wounds (broken surface), minor surface damage (scratches on surface), missing parts of the food products, and if the food product is out of a predetermined size range.
  • at least two of the illuminating and imaging modes are combined to improve the detectability of the abnormality types.
  • a broadband focused line light is used as the light source 4, and a combination of at least one light projector for emitting appropriate illumination patterns and at least one camera for imaging the food products illuminated by the patterns are used to achieve the illumination and imaging modes.
  • the invention is used for food product like shellfish, whole fish, fillets of fish such as Atlantic salmon, rainbow trout, cod fish, herring and mackerel, meat portions, meat lumps, fruit, and vegetables, specifically is whole salmonid fish such as Atlantic salmon and rainbow trout.
  • the food products 11 are conveyed through the field of view of the optical sensor enabling a complete scan of the food products.
  • the optical sensor 5 of the present invention can in one embodiment be sensitive to light in the 400-1050 nm range.
  • Fig 1 shows a perspective view of a system for a conveyor belt
  • Fig 2 shows the system with a more abstract, non-perspective view
  • Figure 1 shows a preferred embodiment of the invention in a perspective view.
  • a conveyor belt 1 moves the food item 11 - a fish in this example, but also shellfish, meat, fruit and vegetables are possible - from right to left through the field of view of an optical sensor 5 (an electronic camera) mounted above the conveyor belt and connected to a computer 7.
  • An optical sensor 5 an electronic camera mounted above the conveyor belt and connected to a computer 7.
  • Three light sources are shown: (1) A laser 4, mounted directly to the camera 5 achieving a direction of the laser beam substantially in parallel/alignment with the axis of view of the camera 5. (2) A laser 6, mounted in a way that the corresponding beam illuminates the food item at a considerable angel to the axis of view of the camera 5. (3) A diffuse light source 3 mounted typically much closer to the conveyor belt.
  • the results from the computer 7 calculations performed on the images taken by the camera 5 can be used to manipulate the food product on the belt by some computer controllable manipulating device 2.
  • Figure 2 shows most of the above described system in a more structural, non-perspective view, also indicating aspects of the illumination itself: (1) a first laser beam 9 projecting on the food item from the laser 4, the beam being substantially in parallel with the axis of view of the camera 5, illuminating a light pattern (f inst one or more parallel lines perpendicular to the conveyor movement direction) onto the food item 11 , resulting in images that enable
  • Multi-modal images are built up from scan lines acquired from the camera 5. These images are transmitted to the computer 7.
  • the software in the computer analyzes these multi-modal images and detects defects in the food products.
  • the processing of images generated by the camera 5 is done in the computer with 20 general off-the-shelf available image processing programs.
  • a signal indicating whether the food product is defect is transmitted from the computer 7 to a manipulation device 2 which in turn acts on the food product item.
  • a first mode, denoted scatter mode, of the multi-modal images of the food product is created by acquiring several scan lines, one directly on the laser line 9 of laser 4 and one or more with a small offset from the laser line 9 of laser 4.
  • a second mode, denoted gloss mode, of the multi-modal images is created by acquiring a scan line 8 viewed through the slit of a diffuse light source 3.
  • a third mode, denoted 3D mode, of the multi-modal images is created by acquiring a 3D range profile by triangulation of the laser line 10 illuminating the food product in an image region in the camera field of view.
  • the food product is whole pelagic fish such as herring and mackerel.
  • the defective fish is characterized by several defect types.
  • defect type is superficial wounds indicated by broken fish skin exposing the underlying muscle.
  • a second similar defect type is scratches or scrapes without exposing the underlying muscle.
  • a third defect type is characterized by the fish missing large parts or being completely or partially split into several parts. These defect types can be detected by the present invention using the 3D mode described above. Sorting of herring is a specific example where all three modes, - 3D, scatter, gloss - is combined with laser and diffuse illumination and a multi-mode line scan camera to enhance the detectability of defects.
  • a light projector e.g. LCD/DLP
  • LCD/DLP can illuminate different patterns onto the food product and a camera takes images of the product with and without these patterns.
  • the pattern-projec- ting-based method is preferable if the food product is not moving in relation to the sensors, while the laser/line based method is better in situations where the food product is moving on - for instance - a conveyor. Based on the pictures - with and without the patterns - the 3D-shape, reflectivity and scattering can be calculated.
  • a fourth defect type is characterized by the fish being outside a predetermined range of weight or size. These defect types can also be detected by the present invention using the 3D mode described above.
  • the camera 5 has a sensor that is sensitive to visible light in the 400-1050 nm range.
  • the lasers and the diffuse illumination emit visible light with a wavelength of 600-700 nm. It is obvious to a skilled person that also sensors with sensitivities in other ranges of the electromagnetic spectrum can be used, such as UV, VIS, NIR, SWIR, MWIR LWIR and FIR.
  • the diffuse illumination and lasers will then emit light within the sensors sensitivity range. It's difficult to get uniformly exposed images of fish.
  • This relates to the shape and nature of the surface of fish, which in nature should be invisible for predators coming above, by having a dark and light absorbing pigments in the skin on the back, and for predators coming below, by having a very light and reflective skin on the belly.
  • the present invention therefore uses both gloss and scattering in combination.
  • the gloss method is particularly effective in the region of the side of the fish and belly but does not work properly on the back of the fish. On the belly and side 5 of the fish without any superficial wounds the light reflection will be even on this part of the fish 11. If a superficial wound or any other abnormal interruption of the reflective surface occurs on the skin, it will create dark regions on the image generated by the camera. The gloss mode is thus effective for generating images that enhance defects on the side and belly of the fish.
  • the image processing io software will detect and evaluate such dark regions and can make the sorting device 2 remove the fish 11.
  • the scattering method is particularly effective to use on the back of the fish because of high light absorption and little scattering on undamaged fish skin on the back.
  • light will be is scattered from the line light stripe on the fish 11 and tissue will be illuminated beside the light line stripe on the fish 11 and measured in the camera as bright pixels in the scan line next to the light line stripe.
  • undamaged skin the light will not be scattered as much and thus the scan line next to the light line stripe will appear dark in this case.
  • scatter mode images will be acquired in whicho defects on the back of the fish 11 appear brighter than the surrounding skin.
  • the image processing software on the computer 7 will detect and evaluate such bright regions and can make the handling device 2 remove the fish 11.
  • 3D mode makes it possible to correctly orient the fish 11 or shellfish for optimal use of gloss and scatter in the detection of superficial wounds.
  • 3D mode is also used to generate 3D images that can be used to detect gross defects, such as missing heads, tails and split fish.
  • the food product is whole salmonid fish such as Atlantic salmon and rainbow trout.
  • the laser 4 is replaced with several lasers emitting light at different wavelengths onto the same scan line in the sensor field of view.
  • the laser 4 is replaced with a broadband focused line light.
  • the camera may be replaced or supplemented with one or more imaging spectrometers.
  • scatter and/or gloss mode of the inspection system will in these embodiments enable detailed quality inspection of food products, including the inspection of properties such as fat content and distribution, water content and distribution, freshness, opaqueness, color and temperature. Further properties, such as presence and distribution of connective tissue, bone, membranes, skin and parasites and can be detected with the inspection system in the present invention.
  • the food product are fillets of fish such Atlantic salmon and rainbow trout, cod fish, herring and mackerel.
  • the food product is meat portions or meat lumps or fruit or vegetables.
  • Ilyukhin SV Haley TA, and Singh RK 2001a, A survey of automation practices in the food industry, Food control, 12(5) 285-296

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  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Food Science & Technology (AREA)
  • Engineering & Computer Science (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Medicinal Chemistry (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

L'invention, destinée à identifier automatiquement les défauts de forme et de surface de produits alimentaires au moyen d'une analyse d'image informatique, est caractérisée par l'échantillonnage de données optiques par au moins un capteur optique au moyen de combinaisons de différents modes d'éclairage et d'échantillonnage. Le but est d'améliorer la capacité de détection des différences de surface, des différences sous-surfaciques, ou des différences de forme entre un échantillon défectueux et un modèle non défectueux des produits alimentaires. Un appareil photo numérique ou un spectromètre d'imagerie est utilisé comme capteur optique.
PCT/NO2007/000278 2006-08-04 2007-08-03 Contrôle de qualité de produits alimentaires multimodal à vision artificielle WO2008016309A1 (fr)

Applications Claiming Priority (2)

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US83546406P 2006-08-04 2006-08-04
US60/835,464 2006-08-04

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Cited By (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2938654A1 (fr) * 2008-11-20 2010-05-21 Sedna Procede et dispositif de controle de la qualite de fraicheur du poisson.
CN102269710A (zh) * 2011-06-17 2011-12-07 中国农业大学 基于多光谱成像的生鲜猪肉有效期的快速无损预测装置
CN102654421A (zh) * 2011-03-02 2012-09-05 中国科学院电子学研究所 一种高空间、高光谱分辨率的高性能成像光谱仪
JP2013518583A (ja) * 2010-02-05 2013-05-23 ベック，エスベン 魚の寄生生物を駆除するための装置及び方法
DE202013103650U1 (de) 2013-08-12 2013-09-23 Aspect Imaging Ltd. Nichtinvasives MRT-System zur Analyse der Qualität von festen Nahrungsmittelprodukten, die von einem flexiblen Aluminiumfolienumschlag umhüllt sind
EP2755018A1 (fr) * 2013-01-15 2014-07-16 Nordischer Maschinenbau Rud. Baader GmbH + Co. KG Dispositif et procédé de reconnaissance sans contact de structures tissulaires rouges ainsi qu'agencement de dissolution d'une bande de structure tissulaire rouge
WO2014121371A1 (fr) * 2013-02-06 2014-08-14 Clearwater Seafoods Limited Partnership Imagerie permettant de déterminer les attributs physiques d'un crustacé
CN104732580A (zh) * 2013-12-23 2015-06-24 富士通株式会社 图像处理装置、图像处理方法和程序
CN107131953A (zh) * 2017-06-29 2017-09-05 中国科学院长春光学精密机械与物理研究所 一种空间光谱辐射测试系统
CN108318487A (zh) * 2018-03-09 2018-07-24 哈尔滨工程北米科技有限公司 一种食品加工视频抽样检测装置
US10345251B2 (en) 2017-02-23 2019-07-09 Aspect Imaging Ltd. Portable NMR device for detecting an oil concentration in water
JP2019124464A (ja) * 2012-12-04 2019-07-25 ゲナント ヴェルスボールグ インゴ シトーク 熱処理監視システム
WO2020007804A1 (fr) * 2018-07-02 2020-01-09 Marel Salmon A/S Détection de caractéristiques de surface d'objets alimentaires
WO2020036620A1 (fr) 2018-08-16 2020-02-20 Thai Union Group Public Company Limited Système d'imagerie à vues multiples et procédés d'inspection non invasive dans le traitement d'aliments
US10664716B2 (en) 2017-07-19 2020-05-26 Vispek Inc. Portable substance analysis based on computer vision, spectroscopy, and artificial intelligence
WO2020106332A1 (fr) * 2018-11-20 2020-05-28 Walmart Apollo, Llc Systèmes et procédés d'évaluation de produits
WO2020122820A1 (fr) * 2018-12-10 2020-06-18 Net Boru Sanayi Ve Dis Ticaret Kollektif Sirketi Bora Saman Ve Ortagi Système de chauffage pour la production d'un tube à double couche
US10869489B2 (en) 2018-08-31 2020-12-22 John Bean Technologies Corporation Portioning accuracy analysis
US11120540B2 (en) 2018-08-16 2021-09-14 Thai Union Group Public Company Limited Multi-view imaging system and methods for non-invasive inspection in food processing
WO2022052480A1 (fr) * 2020-09-11 2022-03-17 广东奥普特科技股份有限公司 Procédé et système de détection et de traitement de défaut dans une plaque d'électrode de batterie au lithium en temps réel
US11300531B2 (en) 2014-06-25 2022-04-12 Aspect Ai Ltd. Accurate water cut measurement
US20220398820A1 (en) * 2021-06-11 2022-12-15 University Of Southern California Multispectral biometrics system
WO2023005321A1 (fr) * 2021-07-30 2023-02-02 江西绿萌科技控股有限公司 Système et procédé de détection, dispositif informatique et support de stockage lisible par ordinateur
US11715059B2 (en) 2018-10-12 2023-08-01 Walmart Apollo, Llc Systems and methods for condition compliance
US11734813B2 (en) 2018-07-26 2023-08-22 Walmart Apollo, Llc System and method for produce detection and classification
US11836674B2 (en) 2017-05-23 2023-12-05 Walmart Apollo, Llc Automated inspection system
US12175476B2 (en) 2022-01-31 2024-12-24 Walmart Apollo, Llc Systems and methods for assessing quality of retail products

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WO1989012397A1 (fr) * 1988-06-23 1989-12-28 Lumetech A/S Procede de localisation de certaines regions dans un morceau de viande et notamment dans un poisson, soumis initialement a un eclairage
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Cited By (55)

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Publication number Priority date Publication date Assignee Title
FR2938654A1 (fr) * 2008-11-20 2010-05-21 Sedna Procede et dispositif de controle de la qualite de fraicheur du poisson.
EP2189789A1 (fr) * 2008-11-20 2010-05-26 Sedna Procédé et dispositif de contrôle de la qualité de fraîcheur du poisson
JP2013518583A (ja) * 2010-02-05 2013-05-23 ベック，エスベン 魚の寄生生物を駆除するための装置及び方法
EP2531022A4 (fr) * 2010-02-05 2013-10-09 Stingray Marine Solutions As Procédé et dispositif pour la destruction de parasites sur un poisson
CN102654421A (zh) * 2011-03-02 2012-09-05 中国科学院电子学研究所 一种高空间、高光谱分辨率的高性能成像光谱仪
CN102269710A (zh) * 2011-06-17 2011-12-07 中国农业大学 基于多光谱成像的生鲜猪肉有效期的快速无损预测装置
CN110235906B (zh) * 2012-12-04 2022-06-21 英戈·施托克格南特韦斯伯格 热处理监控系统
US11013237B2 (en) 2012-12-04 2021-05-25 Ingo Stork Genannt Wersborg Heat treatment monitoring system
JP2019124464A (ja) * 2012-12-04 2019-07-25 ゲナント ヴェルスボールグ インゴ シトーク 熱処理監視システム
CN110235906A (zh) * 2012-12-04 2019-09-17 英戈·施托克格南特韦斯伯格 热处理监控系统
EP3521705A1 (fr) * 2012-12-04 2019-08-07 Stork genannt Wersborg, Ingo Système de surveillance de traitement thermique
EP2755018B2 (fr) † 2013-01-15 2024-04-03 Nordischer Maschinenbau Rud. Baader GmbH + Co. KG Dispositif et procédé de reconnaissance sans contact de structures tissulaires rouges ainsi qu'agencement de dissolution d'une bande de structure tissulaire rouge
EP2755018A1 (fr) * 2013-01-15 2014-07-16 Nordischer Maschinenbau Rud. Baader GmbH + Co. KG Dispositif et procédé de reconnaissance sans contact de structures tissulaires rouges ainsi qu'agencement de dissolution d'une bande de structure tissulaire rouge
WO2014111375A1 (fr) * 2013-01-15 2014-07-24 Nordischer Maschinenbau Rud. Baader Gmbh + Co. Kg Dispositif et procédé de détection sans contact de structures tissulaires rouges ainsi que système pour détacher une bande de structures tissulaires rouges
EP2755018B1 (fr) 2013-01-15 2020-07-22 Nordischer Maschinenbau Rud. Baader GmbH + Co. KG Dispositif et procédé de reconnaissance sans contact de structures tissulaires rouges ainsi qu'agencement de dissolution d'une bande de structure tissulaire rouge
US9351498B2 (en) 2013-01-15 2016-05-31 Nordischer Maschinenbau Rud. Baader Gmbh + Co. Kg Device and method for non-contact identifying of red tissue structures and assembly for removing a strip of red tissue structures
US20150359205A1 (en) * 2013-02-06 2015-12-17 Clearwater Seafoods Limited Partnership Imaging for Determination of Crustacean Physical Attributes
US10638730B2 (en) 2013-02-06 2020-05-05 Clearwater Seafoods Limited Partnership Imaging for determination of crustacean physical attributes
WO2014121371A1 (fr) * 2013-02-06 2014-08-14 Clearwater Seafoods Limited Partnership Imagerie permettant de déterminer les attributs physiques d'un crustacé
US10111411B2 (en) 2013-02-06 2018-10-30 Clearwater Seafoods Limited Partnership Imaging for determination of crustacean physical attributes
CN105164521B (zh) * 2013-02-06 2019-05-07 加拿大北大西洋海鲜渔业公司 用于确定甲壳动物物理属性的成像
CN110057838B (zh) * 2013-02-06 2022-05-10 加拿大北大西洋海鲜渔业公司 用于确定甲壳动物物理属性的成像
JP2017195891A (ja) * 2013-02-06 2017-11-02 クリアウォーター シーフーズ リミテッド パートナーシップ 甲殻類の体格を判断するためのイメージング
CN110057838A (zh) * 2013-02-06 2019-07-26 加拿大北大西洋海鲜渔业公司 用于确定甲壳动物物理属性的成像
AU2013377779B2 (en) * 2013-02-06 2017-01-05 Clearwater Seafoods Limited Partnership Imaging for determination of crustacean physical attributes
JP2016512420A (ja) * 2013-02-06 2016-04-28 クリアウォーター シーフーズ リミテッド パートナーシップ 甲殻類の体格を判断するためのイメージング
CN105164521A (zh) * 2013-02-06 2015-12-16 加拿大北大西洋海鲜渔业公司 用于确定甲壳动物物理属性的成像
DE202013103650U1 (de) 2013-08-12 2013-09-23 Aspect Imaging Ltd. Nichtinvasives MRT-System zur Analyse der Qualität von festen Nahrungsmittelprodukten, die von einem flexiblen Aluminiumfolienumschlag umhüllt sind
CN104732580A (zh) * 2013-12-23 2015-06-24 富士通株式会社 图像处理装置、图像处理方法和程序
CN104732580B (zh) * 2013-12-23 2018-09-25 富士通株式会社 图像处理装置、图像处理方法和程序
US11300531B2 (en) 2014-06-25 2022-04-12 Aspect Ai Ltd. Accurate water cut measurement
US10345251B2 (en) 2017-02-23 2019-07-09 Aspect Imaging Ltd. Portable NMR device for detecting an oil concentration in water
US11836674B2 (en) 2017-05-23 2023-12-05 Walmart Apollo, Llc Automated inspection system
CN107131953A (zh) * 2017-06-29 2017-09-05 中国科学院长春光学精密机械与物理研究所 一种空间光谱辐射测试系统
US10664716B2 (en) 2017-07-19 2020-05-26 Vispek Inc. Portable substance analysis based on computer vision, spectroscopy, and artificial intelligence
CN108318487A (zh) * 2018-03-09 2018-07-24 哈尔滨工程北米科技有限公司 一种食品加工视频抽样检测装置
CN108318487B (zh) * 2018-03-09 2024-05-10 哈尔滨工程北米科技有限公司 一种食品加工视频抽样检测装置
WO2020007804A1 (fr) * 2018-07-02 2020-01-09 Marel Salmon A/S Détection de caractéristiques de surface d'objets alimentaires
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