WO2015155389A1 - Système et procédé automatisé de classement de thons congelés par espèce - Google Patents

Système et procédé automatisé de classement de thons congelés par espèce Download PDF

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Publication number
WO2015155389A1
WO2015155389A1 PCT/ES2015/070188 ES2015070188W WO2015155389A1 WO 2015155389 A1 WO2015155389 A1 WO 2015155389A1 ES 2015070188 W ES2015070188 W ES 2015070188W WO 2015155389 A1 WO2015155389 A1 WO 2015155389A1
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WIPO (PCT)
Prior art keywords
tuna
laser beam
species
conveyor belt
image
Prior art date
Application number
PCT/ES2015/070188
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English (en)
Spanish (es)
Inventor
Joaquín Gracia Salvador
Iñaki MINIÑO ARBILLA
Original Assignee
Tecnologia Marina Ximo, S.L.
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 Tecnologia Marina Ximo, S.L. filed Critical Tecnologia Marina Ximo, S.L.
Publication of WO2015155389A1 publication Critical patent/WO2015155389A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting 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
    • B07C5/34—Sorting according to other particular properties
    • B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting 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
    • B07C5/04—Sorting according to size
    • B07C5/10—Sorting according to size measured by light-responsive means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting 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
    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/0081—Sorting of food items

Definitions

  • the present invention falls within the field of automatic classification systems for frozen tunas through the use of artificial vision.
  • the present invention proposes an automatic system that allows the separation of more important commercially frozen tuna species, depending on their species, size / weight or by specimens that present deformities that prevent its subsequent commercialization or specific processing.
  • patent documents US2008137104-A1, EP2559336-A1, CH701341 -A2, WO2012008843-A1 and US4934537-A disclose the use of artificial vision techniques to automatically identify fish.
  • cameras and lighting means are used to obtain images, which are analyzed and different parameters of the fish are obtained, such as biomass, dimensions of the fish (length, thickness), weight and defects or diseases.
  • the fish are driven by conveyor belts, or they move freely in the water, when the Identification and classification.
  • none of them carries out an identification of the tuna species, only with simpler parameters to identify such as weight, volume and deformities.
  • Patent document CN102749361 -A discloses an automatic method to identify the species of tuna, based on fish chopping, heating of the fish and analysis of the emitted gas, this being a complex, expensive and destructive method, since it requires chopped fish No methods of identifying tuna species by artificial vision techniques are known, given the enormous similarity between the species.
  • the present invention presents a solution that overcomes the limitations described above, being the first artificial vision system capable of identifying the specific species of frozen tunas.
  • it allows a separation by size and deformities.
  • separation by species the current process is carried out manually under the subjective criteria of the operators.
  • the eye and the human brain are able to distinguish, naturally and automatically, the specimens of a species of tuna and differentiate them from those of others.
  • the present invention is able to automatically identify these differences. For this, various studies of the biology of the species in which the different characteristics of shape and color that they may have were analyzed, in addition to studying their evolution throughout their growth, due to the possibility that significant changes occur in any of the morphological parameters.
  • frozen tunas have foreign elements attached, such as brine and frost, or they may have physical damage and deformities that make it difficult to identify the species of tuna.
  • the present invention allows detecting the presence of foreign elements (brine, frost, physical damage, etc.) adhered to the surface of the fish by acquiring and processing 3D images of the separated specimens to prevent these foreign elements from deceiving the system and produce an error in the identification of the species.
  • the separation by sizes currently in some processing plants the specimens are separated manually or by mechanical screening processes only in a few sizes. All those specimens with a weight greater than 20 kilograms are classified as of the same category, due to the impossibility of manipulating them manually due to the weight.
  • the present invention allows an automatic classification with a greater number of categories for sizes due to the differences in performance between them, also allowing the size discrimination to be variable and configurable in real time, depending on the specific needs.
  • the end user can dynamically determine (each time he wishes to configure the system) the size ranges that define each of the categories or sizes in which he wants to separate the received copies. Thanks to the high resolution of the images with which the vision system works, the present invention is able to differentiate tuna specimens by sizes very precisely (> 98%), improving the classification systems by traditional sizes.
  • the present invention allows automatic separation of those specimens that have certain deformities, configurable by the user. Because tunas have large differences between specimens of the same species and size, these deformities must be very evident, such as the absence of a part of tuna or a deformity that affects an important surface of the fish. The present invention allows to automatically remove from the production chain those elements that have previously been considered of insufficient quality (because they are small in size, due to beating or lacking parts).
  • a first aspect of the present invention relates to an automated method of classification of frozen tunas by species, where frozen tunas access a scanning area by a conveyor belt separated some distance from each other.
  • the procedure includes:
  • the laser beam is preferably perpendicular to the conveyor belt, to avoid aberrations in the laser line upon impact on the tuna.
  • the laser beam is red, and preferably with a wavelength of 660 nm.
  • the captured images are preferably filtered to detect only the color range used by the laser beam.
  • the three-dimensional images are taken frontally as the conveyor belt moves in an upper and angled position.
  • the areas that have volume corresponding to the tuna can be identified and the flat areas corresponding to the conveyor belt removed.
  • the geometric parameters can be related to each other and weighted according to their representativeness for the definition of the species.
  • the images are captured in a cabin protected from outside light and without lighting.
  • the capture of three-dimensional images is preferably carried out by means of two 3D cameras, arranged on each side of the conveyor belt, in an upper and angled position.
  • the method also estimates, from the three-dimensional point cloud, the volume and weight of tuna. It can also detect deformities present in tuna caused by friction, blows or crushing.
  • the method detects the passage of a tuna through the scanning area to start the image capture process.
  • a second aspect of the present invention relates to an automated classification system for frozen tunas by species, where frozen tunas access a scanning area by a conveyor belt separated some distance from each other.
  • the system comprises a scanning module with:
  • At least one laser emitter responsible for emitting a laser beam on the tuna as it passes through the scanning area
  • - image capture means configured to capture three-dimensional images of the laser beam projected on the tuna as it passes through the scanning area
  • - data processing means configured to:
  • At least one laser emitter is preferably arranged in such an orientation that the laser beam is perpendicular to the conveyor belt.
  • the scanning module comprises two laser emitters arranged on each side of the conveyor belt and whose laser beam is perpendicular to the conveyor belt and coincident.
  • the image capture means are arranged frontally to the conveyor belt in an upper and angled position.
  • the image capture means preferably comprise two 3D cameras arranged at each side of the conveyor belt, in an upper and angled position.
  • the scanning module may comprise an outer shell and protective bands in the tuna inlet and outlet area that define a cabin protected from outside light and without lighting in which images are captured.
  • the scanning module can comprise presence sensing means responsible for detecting the passage of a tuna through the scanning area and thus activating the image capture means to initiate the image capture process.
  • the present invention has been developed based on artificial vision technology, incorporating laser elements for continuous process at high speeds on conveyor belt.
  • the main advantages of the invention are:
  • Improvement of the business organization Provides a very accurate estimate of the time to devote to each process, which allows informed decision making in real time.
  • Automation of process information management All information generated by the system is recorded in memory, allowing:
  • Figure 1 shows, in side and plan view, the different parts of a manual processing line according to the state of the art.
  • FIG. 2 shows, in side and plan view, the processing line with classification automatic tuna according to the present invention.
  • FIG. 3 shows schematically the different modules distributed in the tuna processing line with automatic classification.
  • Figure 4 shows the passage of a tuna (schematically represented by a rod-shaped solid) through the scanning module.
  • Figure 5 shows a general flow chart of the process carried out in the tuna processing line with automatic classification.
  • Figure 6 shows the obtaining of some specific parameters of tuna used to determine its species.
  • Figure 7 shows the gross tuna profile obtained by the image collection means.
  • Figure 8 shows the three-dimensional point cloud obtained after the tuna scanning process.
  • the present invention relates to an automated frozen tuna classification system. This classification is carried out in an industrial frozen tuna processing plant.
  • Figure 1 shows, in side and plan view, the different parts of a manual processing line, according to the state of the art, where operators located on the distribution belt and responsible for performing the manual classification of frozen fish are seen, distributing it in different containers depending on the size, species and deformities found in tuna.
  • FIG. 2 shows, in side and plan view, the processing line with automatic classification (already without operators) of tuna according to the present invention.
  • the frozen tunas 1 are loaded to a tuna receiving module 10, a tuna accumulator tank with variable capacity, depending on the application, with a hopper bottom that can optionally be coupled to a canyon lift and a flat drum Rotary, and that allows collecting, orienting and distancing the tuna specimens and directing them towards the exit mouth, which flows into the band of the next module, the scanning module 20.
  • an accumulator exit belt is shown 12, an adjustable hatch or hatch 14 and an accelerated and turned tape 16 that allow the separated tunas 1 to be directed at a certain distance towards the scanning module 20, so that it can carry out the individual analysis of the separated tunas 1 from each other.
  • the tuna specimens are automatically oriented and aligned through the adjustable output gate 14 and are deposited one by one on a conveyor belt 22 that directs the tuna to the optical inspection section where the different artificial vision devices that allow 3D scanning of tuna.
  • the scanning module 20 is the core of the invention. It is a complete detection and analysis system using artificial vision elements and microprocessor processing units, capable of capturing images of tunas 1 that move along a scanning conveyor belt 22 and process, in real time, the data obtained , sending the necessary action instructions to the classification system.
  • the scanning module 20 comprises laser liner means and 3-dimensional image capture means. To obtain homogeneous lighting conditions, the scanning module preferably uses an enclosure where image capture occurs to isolate the module from external lighting conditions. Therefore, the inspection area is preferably a closed cabin (not shown in Figure 2, to simplify the figure), a "dark chamber" protected from outside light and possible splashes of water and other elements that may disturb the inspection.
  • the scanning module 20 could also include diffuse lighting means, for example fluorescent lighting where the lighting system is powered by a high frequency electronic ballast to ensure uniformity of the light along the image captured by the cameras.
  • the scanning module 20 allows the species to be determined from among the target species. In addition, it also allows to determine the size and calculate the weight from the dimensions of the specimen and the presence of significant deformations (crushing). After inspecting and analyzing each specimen, it communicates to a classified module 30 the category of the specimen, so that it can direct the tuna to the appropriate container in each case by means of automatic actuation of electro-pneumatic gates.
  • the sorting module 30 comprises a transport system by means of conveyor belts 32 of food rough rubber.
  • the sorting module 30 is based on a set of electro-pneumatic gates or diverters 34 for the diversion of the tunas towards a collection module 40, formed by a plurality of containers 42 located on one side and at the end of the classification section of the transport system.
  • the classification can be carried out in different ways, for example pneumatically, in that case controlled by elements integrated in an electro-pneumatic frame included as part of the sorting module 30.
  • An information module 50 implemented for example by means of a large electronic panel installed on the wall or on a support in the upper intermediate part of the sorting tape, shows in real time all the relevant information corresponding to the content of each of the containers 42. This information will indicate to the truck operator the need for container removal, its contents, process speed, number of lots, alarms, etc. You can also indicate the date, time and time elapsed since the beginning of the inspection of the lot. Said module, which obtains the information of the scanning module 20, can also automatically generate customized reports 52 tailored to the user, allowing the generation of reports remotely via "webserver", whereby reports 52 can be viewed from any connected equipment to your network
  • FIG 3 shows schematically the different modules distributed in the tuna processing line with automatic classification, the core of the invention being the one corresponding to the scanning module 20.
  • Figure 4 shows in detail the elements of the scanning module 20 and the passage of a tuna 1 (schematically represented in the figure as a parallelepiped-shaped solid) through the inspection area. Tuna is detected by presence sensing means (for example, by means of ultrasound detectors arranged on the sides of the conveyor belt 22, not shown in the figure).
  • the laser emitter 24 is arranged so that the laser beam 28 is substantially perpendicular to the conveyor belt 22 (aberrations in the laser line occur on impacting on the tuna when moving away from the perpendicular orientation).
  • the image capture means 26 used are two equal cameras that are arranged frontally to the march of the conveyor belt 22, in an upper and angled position (preferably at an angle of about 45 e with respect to the conveyor belt 22) for the capture of the laser beam 28 on the tuna 1.
  • the system is preferably composed of two laser beam emitters 24 that emit two laser beams perpendicular to the scanning tape, one on each side thereof. Said laser emitters 24 are aligned so that only one laser line can be seen when taking an image. The fact of using two lasers and cameras is to catch as much of the area of the fish that is left against the tape.
  • FIG. 5 shows the general flow chart of the tuna scanning and sorting process
  • the general application of the system starts 100 and the process begins, the tuna conveyor belt is activated, laser lasers and optical sensors are switched on.
  • the tuna passes through an area where presence detecting means (preferably ultrasound) are found that are activated 102 at the passage of the tuna providing a signal that marks the beginning of the process.
  • presence detecting means preferably ultrasound
  • the image collection means 26 begins with the reading or scanning of the tuna 104.
  • the tuna 1 circulates through a laser beam 28 of wavelength 660 nm. It has been proven that the system obtains the best results when working on this wavelength of 660 nm, although other similar or other wavelengths such as 540nm (green color) could be used, although in this case worse results would be obtained.
  • Tuna finishes passing through the area where the ultrasonic sensors are located, which after a programmable "t1" time mark the end of the tuna scanning process 106.
  • the image capture means 26 stops scanning and processing the captured data 1 10 is performed:
  • the algorithm analyzes this cloud of points by breaking it down into a plurality of parameters that define the biometry of a tuna and therefore its species, such as:
  • Length the distance between the beginning of the fish and the end (without considering the tail) is calculated. To have a more robust calculation, polynomial approaches are used to detect the beginning and end of the fish.
  • volume vs. length 3 ratio this parameter provides more information.
  • the application collects that data, calculates the weight through the volume and saves that data in a storage buffer.
  • the fish crosses an area where another ultrasonic sensor is located that indicates that the tuna is in the classified zone and with the activation of said sensor 1 12 the application writes the data previously obtained and calculated in the PLC 1 16.
  • the PLC manages that information and determines in which container it should classify said tuna.
  • the tuna continues on its way through the sorting belt by activating and deactivating the ultrasonic sorting sensors that are in its way and when the PLC detects that the sensor corresponding to the container in which the tuna must be placed is activated, sends a gate opening order 1 18.
  • the gate opens 120 through a pneumatic cylinder and waits for a programmable "t2" time until the tuna has fallen into the container. After that time "t2" the door closes. - With the opening of the door, the PLC writes the data on an information screen 122 indicating that there are "n + 1" tunas in said container with a total net weight of container of "m + x" kilos. Being:
  • n number of tunas already in that container.
  • x the weight of the tuna you just classified.
  • n the sum of the weight of the "n" tunas.
  • Figure 7 shows the gross profile 27 of the tuna obtained by both chambers.
  • the upper image shows a "normal" gross profile (frost-free image) and the lower image shows a 27 'profile with the laser beam more blurred by the uncorrected frost effect (frost image). Although in the lower image the frost effect is not appreciated in a very relevant way, the calculation made with the lower image would be very inaccurate.
  • This figure shows one of the 2000 images per second that the camera can make. From all these raw images, the processing is performed and the 3D point cloud is constructed.
  • Figure 8 shows the three-dimensional point cloud 29 obtained after scanning.
  • the scanning process for tuna 104 is explained in detail below:
  • the scanning process begins with the activation 102 of the presence sensors that detect the passage of tuna 1 through the scanning module 20, in the scanning area.
  • - Tuna 1 passes through a laser beam 28 with a determined wavelength (660 nm, in the preferred embodiment) at an approximate speed of 1 m / s.
  • the image capture means 26 commonly 3D cameras, collect in a region of interest (ROI - Region of Interest) previously defined, the activation or deactivation of a series of pixels that will then be processed by the optical sensor itself generating with that data a three-dimensional point cloud 29.
  • ROI region of interest
  • the pixels are activated when they detect the red beam of the laser 28 and the image collection means 26 are provided with a series of filters that prevent detection outside the range of colors other than the chosen 660 nm.
  • the optical sensor reads or captures a small part of the fish each time defined by the region of interest, which has the width of the laser line (around 1 mm) and the length corresponding to the corresponding width of the conveyor belt.
  • the optical sensor makes the composition of the cloud of points that would determine the biometry of tuna and that will be subsequently processed by the algorithm.
  • the end of data collection by the optical sensor is marked by the end of detection of tuna 106 by the ultrasonic sensors plus a programmable "t1" time delay.
  • the optical sensors activate pixels in function of the reading of the red beam of the laser, therefore they will also activate pixels of the conveyor belt 22 in the area of the region of interest and, therefore, everything that is not tuna must be eliminated.
  • the algorithm preprocess is able to identify the zones that have volume of the flat zones, these being the ones that correspond to the tape. This same preprocess also eliminates possible sources of noise that may interfere with the overall volume.
  • Tuna does not always come perfectly aligned, so you have to establish what its axis is and align the points of the cloud.
  • the system can process the tunas it receives up to an angle of ⁇ 30 e of deviation.
  • the tuna can come both from the front and from the tail and the pre-process will always guide it from the front so that the algorithm is clear that the relationships it is making are consistent.
  • the algorithm process with the previously defined parameters that define tuna biometrics.
  • the algorithm decomposes the cloud of points 29 into measures and relationships (the defined parameters) that, through a decision tree and a comparison with curves and patterns, decide the species and the certainty that it has to hit with said sample. This last point is very important because it allows discarding copies that the machine recognizes with a low degree of success.
  • the definition of what is considered acceptable or unacceptable is programmable by the user.
  • two 3D CMOS cameras are preferably used, to cover almost the entire fish, specially developed for laser triangulation systems with high triangulation rates, with a high sensitivity CMOS sensor and combined with a robust algorithm for the determination of the line.
  • Each camera has a GigE interface that allows the acquisition of profiles in
  • CMOS complementary metal-oxide-semiconductor
  • CMOS complementary metal-oxide-semiconductor
  • the output of the 3D camera is the line position and the qualification information for each line position value.
  • Another feature of the system is that it operates in real time, that is, from the end of the capture of the point cloud until the decision is made. of species, weight and certainty and this is written 1 16 in the PLC can not pass more than 1 second.
  • a wire programming of the different functions has been carried out that take full advantage of each and every processor core by assigning each task or sub-task to a core that is reserved exclusively for this job, always ensuring There is free processing capacity.
  • this programming by threads allows the nuclei to have the capacity to collaborate, that is, when a nucleus has finished its work and finds that There is another core processing information, this free core can help the busy core to perform part of the processes and thus increase the speed of the response.
  • the present invention further corrects additional problems encountered in the tuna scanning phase:
  • the system allows to minimize noise and distortions from sunlight, laser brightness in balderas (structural elements of the conveyor belts made of stainless steel and prevent the fish from falling out of the belt while helping to its focus on the path they travel) and other elements.
  • the system has:
  • a defective part is one that presents significant deformations on its natural form.
  • it may present surface marks produced by the container that contains it or by other adjacent pieces of fish . These marks can be flat (if these are caused by the walls of the container), or bends inwards (caused by other tunas).
  • the defect is considered as a mark if the flat surface or maximum depth is greater than a certain percentage of the total area of the tuna. Normally 10% is considered, so any piece that has more than 10% of its surface affected, adding up all types of defects, will be considered as defective.
  • the system analyzes the number of tuna surfaces that are crushed and the total accumulated area that is crushed per piece scanned, comparing it with the total area of the tuna and if this crushing exceeds 10% (user adjustable parameter ) considers the piece to be deformed and gives the appropriate signal to the selection actuators.

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  • Length Measuring Devices By Optical Means (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

La présente invention concerne un système et un procédé automatisé de classement de thons congelés par espèce, qui accèdent à une zone de balayage par une bande transporteuse (22). Le système comprend: - au moins un émetteur laser (24) chargé d'émettre un faisceau laser (28) sur le thon (1); - des moyens de capture d'images (26) pour capturer des images en trois dimensions du faisceau laser (28) projeté sur le thon (1); - des moyens de traitement de données pour: • détecter le faisceau laser (28) dans chaque image; • obtenir le profil brut (27) du thon à partir du faisceau laser (28) détecté; • générer, à partir des profils bruts (27) obtenus, un nuage de points en trois dimensions (29) du thon; • décomposer le nuage de points en trois dimensions (29) sous forme de paramètres géométriques qui correspondent à des mesures et à des relations définissant la biométrie du thon et de son espèce; • estimer l'espèce du thon au moyen d'un arbre de décision et d'une comparaison des paramètres géométriques du thon à une série de mesures et de relations types.
PCT/ES2015/070188 2014-04-09 2015-03-18 Système et procédé automatisé de classement de thons congelés par espèce WO2015155389A1 (fr)

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Application Number Priority Date Filing Date Title
ESP201430527 2014-04-09
ES201430527A ES2478420B1 (es) 2014-04-09 2014-04-09 Sistema y procedimiento automatizado de clasificación de atunes congelados por especie

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CN107138432A (zh) * 2017-04-05 2017-09-08 杭州迦智科技有限公司 非刚性物体分拣方法和装置
DE102016107687A1 (de) * 2016-04-26 2017-10-26 Nordischer Maschinenbau Rud. Baader Gmbh + Co. Kg Verfahren und Vorrichtung zur Erkennung der Bauch-/Rückenlage von mittels einer Fördereinrichtung geförderten Fischen
CN113070243A (zh) * 2021-03-15 2021-07-06 杭州思看科技有限公司 三维扫描数据的检测方法、装置、系统和电子装置
CN113657842A (zh) * 2021-10-14 2021-11-16 南通天成包装有限公司 一种工业胶带储备管理方法及系统

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NO20220528A1 (en) * 2022-05-09 2023-11-10 Optimar As System and method for estimating weight of biomass

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JPH04323537A (ja) * 1991-04-23 1992-11-12 Hitachi Plant Eng & Constr Co Ltd 魚類の選別方法及びその装置
WO1994009920A1 (fr) * 1992-10-23 1994-05-11 The Minister Of Agriculture Fisheries And Food In Her Britannic Majesty's Government Of The United Kingdom Of Great Britain And Northern Ireland Machine de triage du poisson

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Publication number Priority date Publication date Assignee Title
EP0331390A2 (fr) * 1988-02-29 1989-09-06 Grove Telecommunications Ltd. Machine de tri pour poissons
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JPH04323537A (ja) * 1991-04-23 1992-11-12 Hitachi Plant Eng & Constr Co Ltd 魚類の選別方法及びその装置
WO1994009920A1 (fr) * 1992-10-23 1994-05-11 The Minister Of Agriculture Fisheries And Food In Her Britannic Majesty's Government Of The United Kingdom Of Great Britain And Northern Ireland Machine de triage du poisson

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Publication number Priority date Publication date Assignee Title
DE102016107687A1 (de) * 2016-04-26 2017-10-26 Nordischer Maschinenbau Rud. Baader Gmbh + Co. Kg Verfahren und Vorrichtung zur Erkennung der Bauch-/Rückenlage von mittels einer Fördereinrichtung geförderten Fischen
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CN107138432A (zh) * 2017-04-05 2017-09-08 杭州迦智科技有限公司 非刚性物体分拣方法和装置
CN107138432B (zh) * 2017-04-05 2020-03-13 杭州迦智科技有限公司 非刚性物体分拣方法和装置
CN113070243A (zh) * 2021-03-15 2021-07-06 杭州思看科技有限公司 三维扫描数据的检测方法、装置、系统和电子装置
CN113657842A (zh) * 2021-10-14 2021-11-16 南通天成包装有限公司 一种工业胶带储备管理方法及系统
CN113657842B (zh) * 2021-10-14 2022-03-15 南通天成包装有限公司 一种工业胶带储备管理方法及系统

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