KR20230153371A - Automated Grain Inspection - Google Patents

Abstract

A management and control system is provided for a user to interface with an inspection device having at least one digital optical instrument, the management and control system comprising: receiving images from the at least one optical instrument, analyzing the images, and providing instructions to the inspection device. a processor configured to transmit signals; A display configured to display an analysis of images, wherein a user can interface with and provide commands to an inspection device based on the analysis of the images, the display simultaneously displaying processor-generated histograms and a thumbnail image based on the images. Includes a display. A method for controlling and managing an inspection device for items as well as a UI is disclosed.

Description

Automated Grain Inspection

The presently disclosed subject matter relates to automated inspection devices. More specifically, the disclosed subject matter relates to management and control systems for users to interface with inspection devices.

Automated inspection is the process of inspecting small solid materials, typically hard, as part of controlling the quality of particles on a production line. Optionally, the inspection process may have a classification process for materials. Commercially available inspection machines use optical sensors and image processing to determine impurities, changes in geometry, and color. Typically, inspection machines compare solid particulate objects to user-defined baseline thresholds for qualification or failure of production/shipment of the material.

While traditional manual inspection and/or sorting is subjective, unreliable and inconsistent, optical sorting improves overall product quality, maximizes throughput, increases yield and reduces manual costs.

The inspection machines can be used on food ingredients such as beans, spices, nuts, grains, rice, vegetables and fruits, as well as products such as plastic granules, metal and glass granules.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by a person skilled in the art to which this subject matter pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the disclosed subject matter, suitable methods and materials are now described. In case of conflict, the present specification, including definitions, will control. Additionally, the materials, methods, and examples are illustrative only and are not intended to be limiting.

Accordingly, in accordance with the present subject matter, a management and control system is provided for a user to interface with an inspection device having at least one digital optical instrument, the management and control system comprising:

a processor configured to receive images from at least one optical instrument, analyze the images, and transmit instructions to the inspection device;

A display configured to display an analysis of images, wherein a user can interface with and provide commands to an inspection device based on the analysis of the images, the display simultaneously displaying processor-generated histograms and a thumbnail image based on the images. Includes a display.

According to another preferred embodiment, the inspection and sorting device is configured to inspect items selected from the group consisting of beans, spices, nuts, grains, rice, vegetables, fruits, plastic granules, metal granules, glass granules, pharmaceutical tablets. .

According to another preferred embodiment, the at least one digital optical device comprises an X-ray detector, a magnetic resonance imaging (MRI) device, a computed tomography (CT) scanner, a 3D data scanner, a camera, It is selected from the group of optical devices consisting of optical sensors.

According to another preferred embodiment, the display may include a monitor, a screen, an electroluminescent (ELD) display device, a liquid crystal display (LCD) device, a light-emitting diode (LED) device, a plasma ( PDP) displays, electronic hand-held devices such as tablets, smartphone devices.

According to another preferred embodiment, the commands include sorting items, enabling emission of items, disabling emission of items, generating reports, setting differentiation levels, switching items, setting thresholds for generating alerts, automatic prediction and alerting. It is selected from a group of commands consisting of data set definition, set point definition for production line control.

According to another preferred embodiment, the management system further comprises a memory unit in communication with the processor, the memory unit comprising: images, reference images, a plurality of profiles of items, system settings, system reports, images configured to maintain information selected from a group of information consisting of an analysis, reference profiles including thresholds for different types of items, and statistical analysis associated with the reference profiles.

According to another preferred embodiment, the display graphically displays processor-generated graphs based on the images.

According to another preferred embodiment, the inspection device is integrated into the production line.

Additionally, according to another preferred embodiment, a method is provided for managing and controlling a device for inspecting items, the method comprising:

Capturing images of items being inspected by at least one digital optical instrument of the inspection device;

Receiving images from at least one digital optical device by a processor;

analyzing the images by the processor to analyze the items;

Displaying the analysis on a display, simultaneously displaying histogram representations and thumbnail images;

and receiving commands interfaced by the user by the testing device.

According to another preferred embodiment, analyzing the images includes determining criteria for each item in the image, such as impurities, changes in geometry, color of the items, dark spots, dark gel, It includes steps selected from the group consisting of dark and light contaminants, foreign matter, discoloration, cross-contamination, color measurement and color change, size deviation, shape irregularities, clumping, transparency, and gloss of the items.

According to another preferred embodiment, the method further comprises generating histogram representations of the criteria and dimensions of the items.

According to another preferred embodiment, the method further includes setting thresholds based on histogram representations, thumbnail images, and graphs.

According to another preferred embodiment, the commands interfaced by the user include sorting items, activating items to be ejected, disabling items to be ejected, generating a report, setting a level of discrimination, switching items, setting a threshold to generate an alert, automatically It is selected from a group of commands consisting of defining data sets for predictions and warnings, and defining set points for production line control.

According to another preferred embodiment, an interface is provided that interfaces between a user and an inspection device to enable the user to receive visual and statistical information from the inspection device and simultaneously provide commands to the inspection device, the interface comprising:

a processor configured to receive images from an inspection device, display at least a portion of the images, perform statistical analysis based on the images, and form distribution histograms;

a display configured to simultaneously display at least a portion of the images and distribution histograms;

Includes an input device for a user to provide instructions to the processor and to interface with the inspection and classification device.

According to another preferred embodiment, at least part of the images and the distribution histograms correspond to each other.

Some embodiments of the disclosed subject matter are described with reference to the accompanying drawings by way of example only. Referring now in specific detail to the drawings, the particulars shown are by way of example and solely for the purpose of illustrative discussion of preferred embodiments of the disclosed subject matter and are intended to convey the most useful principles and conceptual aspects of the disclosed subject matter. It should be emphasized that it is provided to provide what is believed to be an easily understandable explanation. In this regard, no attempt is made to show structural details of the disclosed subject matter in more detail than is necessary for a basic understanding of the disclosed subject matter, and the description taken in conjunction with the drawings does not imply that some aspects of the disclosed subject matter will be implemented in practice. The method is made clear to those skilled in the art.
In the drawings:
1 illustrates an automated grains inspection apparatus (AIA) in accordance with some example embodiments of the disclosed subject matter.
2A illustrates a front view of an automated granule inspection device in accordance with some example embodiments of the disclosed subject matter.
FIG. 2B illustrates a front view of the automated granule inspection apparatus of FIG. 1 including a display device in accordance with some example embodiments of the disclosed subject matter.
2C illustrates a front view of another classification system including a display device in accordance with some example embodiments of the disclosed subject matter.
2D illustrates a front view of another classification system including a display device in accordance with some example embodiments of the disclosed subject matter.
3 shows a cross-sectional side view of an automated granule inspection device in accordance with some example embodiments of the disclosed subject matter.
4 shows a top view of an automated granule inspection device in accordance with some example embodiments of the disclosed subject matter.
5 is a screenshot of a video frame showing grains in an inspection process according to some example embodiments of the disclosed subject matter.
6 shows a block diagram of a granule inspection system in accordance with some example embodiments of the disclosed subject matter.
7 shows a flowchart of a method for grain inspection in accordance with some example embodiments of the disclosed subject matter.
8 illustrates a workstation screenshot displaying a results report according to some example embodiments of the disclosed subject matter.
9 illustrates a workstation screenshot displaying another results report according to some example embodiments of the disclosed subject matter.
10 illustrates a workstation screenshot displaying another results report according to some example embodiments of the disclosed subject matter.
11 illustrates a workstation screenshot displaying a results report according to some example embodiments of the disclosed subject matter.
12 illustrates a workstation screenshot displaying a results report in a trend view according to some example embodiments of the disclosed subject matter.
13 illustrates a workstation screenshot displaying a results report in a thumbnail view according to another embodiment of the disclosed subject matter.
14A and 14B illustrate workstation screenshots displaying a results report of a dark defect inspection in a thumbnail view according to some example embodiments of the disclosed subject matter.
15A and 15B illustrate workstation screenshots displaying in a thumbnail view a report of the results of a size monitoring test according to some example embodiments of the disclosed subject matter.
FIG. 16 illustrates a workstation screenshot displaying a results report of a yellowness test in a thumbnail view according to some example embodiments of the disclosed subject matter.

Before describing at least one embodiment of the disclosed subject matter in detail, it should be noted that the disclosed subject matter is not limited in its application to the details of the arrangement and configuration of components illustrated in the drawings or described in the following description. You will understand. The disclosed subject matter can be carried out or practiced in a variety of ways or other embodiments are possible. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. Drawings are generally not to scale. For clarity, non-essential elements have been omitted from some of the drawings.

The terms “consisting of,” “consisting of,” “includes,” “comprising,” “having,” together with their conjugations mean “including but not limited to.” The term “consisting of” has the same meaning as “including but not limited to.”

The term “consisting essentially of” means that a composition, method, or structure is defined only as additional ingredients, steps, and/or parts that do not materially alter the basic and novel characteristics of the claimed composition, method, or structure. means that it may contain additional ingredients, steps, and/or parts.

As used herein, the singular forms (“a”, “a”, and “the”) include plural referents unless the context clearly dictates otherwise. For example, “compound” or “at least one compound” may include a plurality of compounds, including mixtures thereof.

Throughout this application, various embodiments of this disclosed subject matter may be presented in range format. It is to be understood that the description in scope format is solely for convenience and brevity, and should not be construed as a firm limitation on the scope of the disclosed subject matter. Accordingly, a description of a range should be considered to have all possible sub-ranges specifically disclosed as well as the individual numerical values within that range.

It should be understood that certain features of the disclosed subject matter that are described in the context of separate embodiments for clarity may also be provided in combination in a single embodiment. Conversely, various features of the disclosed subject matter, which are described in the context of a single embodiment for the sake of brevity, may also be provided separately or in any suitable sub-combination or as appropriate in any other described embodiments of the disclosed subject matter. . Certain features described in the context of various embodiments should not be considered essential features of such embodiments, except to the extent that the embodiment cannot operate without such elements.

Reference is now made to FIG. 1 which illustrates an automated grains inspection apparatus (AIA) in accordance with some example embodiments of the disclosed subject matter. AIA 100 is a device configured to execute quality control processes that inspect solid materials on a production line. In some example embodiments, AIA 100 may be adapted to inspect and classify materials according to criteria such as color, size, shape, structural properties, and any combination thereof, or the like. Materials classified by AIA (100) include, for example, beans, spices, nuts, grains, rice, vegetables, fruits, plastic granules, metal granules, glass granules, pharmaceutical pellets, and any combinations thereof, or similar These are multiple, separate items.

For simplicity, this disclosure will hereinafter refer to materials classified by AIA 100 as “granules” or “items.”

In some example embodiments, AIA 100 can be used both in-line and in-line; production-line and off-line; in-parallel to the production-line; and any combination thereof. In an exemplary in-line embodiment, all granules to be consumed in production first enter the AIA 100 for inspection, preferably through an inlet funnel 201 and exit 209 from which the granules exit. ) to the production line. In an exemplary off-line embodiment, all or part of the granules may be tested after introduction into the production line. In an exemplary parallel embodiment, a portion of the material to be consumed in production enters the AIA 100 through the inlet funnel 201 for inspection or sorting and proceeds to the production line from the outlet 209.

Reference is now made to FIG. 2A , which illustrates a front view of an automated granule inspection device in accordance with some example embodiments of the disclosed subject matter. AIA 100 includes a housing 200 having an inspection area, an inlet funnel 201, an outlet 209, a second outlet 212 and a sorting mechanism 213. In some example embodiments, inlet funnel 201 interfaces between housing 200 and a supply tube or hopper (not shown) that allows pouring granules into AIA 100. Housing 200 also integrates feeder mechanism 202, slot feeder 204, background surface and preferably first background surface 205, second background surface 206, and camera 207.

In some exemplary embodiments, the slot feeder 204 is adapted to receive granules through the inlet funnel 201 and discharge them in the form of a line into the inspection area of the housing, where the line thickness is desired, but not required. It is roughly and substantially equal to the thickness of a single grain. In this way, the slot feeder 204 acts as a buffer to collect the granules and arrange them in a single line across the housing 200 so that the granules fall like a curtain through the housing and through the inspection area. In the inspection area the granules are imaged. In some example embodiments, feeder mechanism 202 may be used to adjust the line thickness at the outlet (not shown) of slot feeder 204 to the thickness of a single grain or any other suitable thickness. In some example embodiments, first background surface 205 and second background surface 206 may each constitute a different background for images captured by camera 207. It should be noted that the camera 207 located on the camera compartment 210 is pointed towards (looking at) the particles that are emitted like a curtain and the backgrounds 205 and 206 are located behind the particles that fall like a curtain.

It should also be noted that multiple cameras may be used. One or more of the plurality of cameras may be positioned opposite the camera shown in FIG. 2A. In this way, a camera positioned on the opposite side captures images from different sides of the grains. The camera located on the other side can be provided with an independent lighting system and set of backgrounds. This dual function device makes it possible to capture images for full inspection of the grains.

In some other example embodiments, the granules may slide in a curtain-like structure on an inclined surface below the slot feeder, which may be a background surface, for example. This optional structure can reduce the speed of the particles as they pass through the inspection area to improve the quality of the image to be captured by the camera. In general, and in this case especially, the slots in a slotted feeder can be wider, or a feeder without slots can be used and the granules pass through the feeder with a different opening profile.

In some example embodiments, upon detection of granules that fail a quality control check (ineligible), sorting mechanism 213 may be configured to deflect the ineligible granules from outlet 209 to second outlet 212. there is. According to one aspect of the present disclosure, a control system is provided for a user to interface with an inspection device having at least one digital optic, the control system comprising:

a processor configured to receive images from at least one optical instrument, analyze the images, and transmit instructions to the inspection device;

A display configured to display an analysis of images, wherein a user can interface with and provide commands to the inspection device based on the analysis of the images.

According to another aspect of the present disclosure, an interface is also provided that interfaces between a user and an inspection device to enable the user to receive visual and statistical information from the inspection device and simultaneously provide instructions to the inspection device, the interface comprising: :

a processor configured to receive images from an inspection device, display at least a portion of the images, perform statistical analysis based on the images, and form distribution histograms;

a display configured to simultaneously display at least a portion of the images and distribution histograms;

It includes an input device for a user to provide instructions to the processor and to interface with the inspection and sorting device.

Reference is now made to FIG. 2B , which illustrates a front view of the automated granule inspection apparatus of FIG. 2 including a display device in accordance with some example embodiments of the disclosed subject matter. In this embodiment, AIA 100 further includes a display device 220, such as a screen capable of displaying information. Display device 220 may include, for example, an electroluminescent (ELD) display device, a liquid crystal display (LCD) device, a light-emitting diode (LED) device, a plasma (PDP) display, It may be a combination of these, or something similar. The terms “display device” and “screen” are used for substantially the same feature, so these terms are interchangeable. Screen 220 may be connected to AIA 100 by wires or wirelessly. In some embodiments, screen 220 may be a computer monitor. In other embodiments, screen 220 may be on a remote device, such as an electronic hand-held device, eg, a tablet. In another embodiment, screen 220 may be a smartphone device.

Information displayed on screen 220 may be visual or tactile. Preferably, the information can be presented using a graphical user interface (GUI). A GUI allows users to interact with electronic devices through graphical icons and audio indicators, such as primary notation, instead of text-based user interfaces, typed command labels, or text navigation. It is a form of user interface that does. Information is received from camera 207, or from any other digital optical instrument, such as an X-ray detector, magnetic resonance imaging (MRI) device, computed tomography (CT) scanner, 3D data scanner, and the like. Optionally, information may be retrieved from two or more digital optical devices. Screen 220 displays histograms as described below. The user interfaces with the AIA through input device 221. An input device may be a device from a group of devices such as a keyboard, mouse, video, touch screen, etc.

Reference is now made to FIG. 2C , which illustrates a front view of another classification system including a display device in accordance with some example embodiments of the disclosed subject matter. Sorting system 250 is operated to perform a quality control process that inspects solid materials on the production line. Sorting system 250 includes a material supply 251 connected to a conveying system 252 . Transport system 252 is attached to an X-ray inspection component 253 followed by at least one optical inspection component 254. Additional optical inspection components 258 and color cameras 259 may be used to inspect items. The sorting unit 255 sorts the inspected items and separates them into a reject material bin 256 and a clean material bin 257 . Screen 220 is attached to sorting unit 250 to display visual information related to inspection of items as discussed below.

Reference is now made to FIG. 2D , which illustrates a front view of another classification system including a display device in accordance with some example embodiments of the disclosed subject matter. Sorting system 270 is operated to perform a quality control process of inspecting and sorting solid materials on a production line. The sorting system 270 includes a pre-hopper 271 connected to the product supply unit 272. The product supply unit 272 is attached to a transport system 273 that moves items to the inspection area. The inspection area includes a charge-coupled device (CCD) 274 and a fluorescent lamp 275. Discharge nozzle 276 discharges rejected inspected items into storage 278. Items that are not rejected are moved to the second inspection area by the transport system 273. The second inspection area includes a charge-coupled device (CCD) 274 and a fluorescent lamp 275. Discharge nozzle 276 discharges rejected inspected items into storage 278. Items that are not rejected are discharged to storage 278. Screen 220 is attached to sorting unit 270 to display visual information of histograms containing visual information and statistical information related to the examination of items, such as the distribution of items of a particular type or size, as discussed below. do.

Reference is now made to FIG. 3 , which illustrates a cross-sectional side view of an automated grains inspection apparatus (AIA) in accordance with some example embodiments of the disclosed subject matter. Slot feeder 204 includes two panels 204a and 204b primarily facing each other, but each of which is tilted away from the vertical axis of AIA 100. A side cross-sectional view shows that the slot feeder 204 has a trapezoidal shape, where the top base of the trapezoid is wide open in contrast to the narrow base (labeled "S" for the slot) that can be adjusted by the feeder mechanism 202. It can be understood. In some example embodiments, feeder mechanism 202 may adjust slot 211 of slot feeder 204 to a span that corresponds to a typical thickness of the type of grain being inspected.

Granules poured into the inlet funnel 201 enter the slot feeder 204 through the so-called “top base of the trapezoid” and curtain across the field of view (FOV) of the camera 207 into the outlet 209. It will be noted that it exits the slot feeder in a molded form. In some example embodiments, the width of slot 211 may be manually adjusted by feeder mechanism 202. For example, handles, levers, threaded bolts, and any combinations thereof, or any commercially available mechanical means. Additionally, or alternatively, feeder mechanism 202 may be configured to automatically adjust the width of slot 211 by electric/pneumatic motors, actuators, and any combination thereof, or the like. In some example embodiments, automatic adjustment of feeder mechanism 202 may be controlled by a controller of the present disclosure (described in further detail below).

In some example embodiments, classification mechanism 213 may include: bias; flap removal; It may consist of mechanism types such as pressurized-air removal, diverter valves, and any combination thereof, or the like.

Both flap and pressurized-air controls can be used to reject relatively small numbers of granules that fail quality control. In some exemplary embodiments, upon detection of non-conforming granules (described in further detail below), a small number of granules are removed from the production line by flap type or pressurized-air. It should be noted that this removal by means of a flap or by pressurized air, although not essential, can mainly be used in in-line and parallel configurations with the production-line. It should also be noted that this disposal (removal) process can be iterative as long as non-conforming grains are detected.

In some example embodiments, the type of flap removal may be based, for example, on a piece of flat shelf hinged on one side covering the opening. Upon activation, the flap opens to allow a predefined number of granules to be discarded.

In some example embodiments, the pressurized-air control type may be based on a commercially available air nozzle that, when activated, blasts multiple granules. The approximate amount/number of pellets to be discarded can be controlled by adjusting the diameter of the air jet and the blast duration.

In some example embodiments, the bias sorting mechanism may be used primarily, but not necessarily, in an off-line configuration with the production line. The deflection mechanism type may be based on a hinged door that acts as a selector to allow the granules to go to the outlet 209, i.e. to the production line, or to deflect the granules to the second outlet 212. . Typically, the activation bias allows a relatively large amount of grains to be discarded, ie the second outlet 212 is open to allow a predetermined number of grains to be discarded.

In some example embodiments of the disclosed subject matter, sorting mechanism 213, such as the types listed above, may include solenoids, motors, actuators, pneumatic components, etc. to implement any or all sorting mechanism types. Any combination of these, or something similar, can be used.

3 shows a side view of a camera 207, a first background surface 205, a second background surface 206, and at least one background light 214. In some example embodiments, camera 207 points the camera forward and backward, i.e., toward background surfaces 205 and 206, such that the camera's FOV covers an area that includes both backgrounds. It can be positioned on the camera assembly 210 which allows it to slide away from it. Sliding of the camera 207 may be effected by a sliding mechanism 215 to adjust the distance between the focus of the camera and the region covering the background, hereinafter referred to as the region of interest (ROI). In some example embodiments, sliding mechanism 215 may be controlled manually or automatically by a motion control unit (MCU) 604 (described in further detail below).

The camera 207 of the present disclosure is configured to acquire images of granules falling from the slot feeder 204 in a curtain-like fashion, in front of the first and second background surfaces 205 and 206. In some example embodiments, camera 207 may be a video camera, line scan camera, still camera, monochrome camera, color camera, area camera, and any combination thereof, or the like. It is advantageous to use an area camera in current devices because the area camera can capture a significant number of grains on two or more backgrounds. The sensors used in area cameras have a large matrix of image pixels so that a typical two-dimensional image can be created in one exposure cycle, improving its efficiency compared to other options. At least one of the plurality of cameras must be an area camera. Additionally, or alternatively, camera 207 may include optical filters (not shown) of different wavelengths, which may be configured as low-pass, high-pass, band-pass, and any combination thereof, or similar. there is. Filters include color correction; color conversion; color subtraction; Improved contrast; polarization; neutral density; cross screen; Can be used for diffusion and contrast reduction, and any combination thereof, or the like. It should be noted that optical filters can be used to improve the spatial, contrast and color resolution of the grains (described in further detail below). In some example embodiments, camera 207 may be comprised of a plurality of cameras, where each camera of the plurality of cameras may be configured to acquire different image properties. It should be noted that the image may be a video, at least one still photo, or a combination thereof, where the image may be maintained in a digital representation.

In some example embodiments, at least one backlight 214 may be positioned in front of the background, behind the background, or both, i.e., in front and behind the backgrounds. Additionally, or alternatively, at least one of the background lights 214 may have a different wavelength, or may use removal filters intended for color separation. Additionally, or alternatively, one of the surfaces may also serve as an illuminator.

In some example embodiments, first background surface 205 can be (without limitation) white and second background surface 206 can be (without limitation) black. The corresponding ROI acquired by camera 207 is configured to capture falling particles in front of the white and black backgrounds, respectively, i.e. in front of the first and second background surfaces 205 and 206, respectively. In some example embodiments, first and second background surfaces 205 and 206 may each include a grid configured to facilitate image analysis. It will be appreciated that a white background facilitates analysis of grain coloration and/or other color defects, while a black background facilitates analysis of geometric (shape) defects of the grains. In some example embodiments, second background surface 206 (black) can be recessed relative to first background surface 205 (white). The black background is recessed against the white background to avoid reflection of the black background onto the transparent particles while the transparent particles are still in front of the white background. In other words, if the black background is in the same plane as the white background, the black background may cause artifacts on the grains facing the white background. It should be noted that images on a white background are analyzed for color and tonal contamination, so black reflections (artifacts) may be confused as contamination.

It should be noted that the parameters of the background or backgrounds, such as the width of each background, the positioning of one background relative to another, the colors of the backgrounds, etc., can be changed manually or automatically.

Reference is now made to FIG. 4 which illustrates a top view of an automated grains inspection apparatus (AIA) 100 in accordance with some example embodiments of the disclosed subject matter. Slot feeder 204 further includes a plurality of blades 208, also shown in FIGS. 2 and 3. In some example embodiments, a plurality of blades 208 organized vertically along the slot feeder 204 can help distribute the granules evenly across the FOV, i.e., in a curtain-like fashion. Blades 208 also facilitate regulating the flow of granules through the feeder because the buildup of the granules pile can be controlled.

The blades can be moved one by one relative to the other either manually or automatically.

Reference is now made to Figure 5, which is a screenshot of a video frame showing grains in an inspection process in accordance with some example embodiments of the disclosed subject matter. Video frame 500 shows an image of the captured grains 501 of the ROI in front of the white section 505 and black section 506. The white section 505 makes it possible to analyze grain 501 coloration, color and shade qualifications against predetermined thresholds. On the other hand, the black section 506 makes it possible to analyze the grains 501 for geometric size, shape, and structural property qualifications against predetermined thresholds. In the case where the grains are dark, the information retrieved from each of the backgrounds is opposite to the information retrieved for the light colored grains shown in Figure 5.

Reference is now made to FIG. 6 which illustrates a block diagram of a granule inspection system 600 in accordance with some example embodiments of the disclosed subject matter. System 600 is a computerized device adapted to perform methods as shown in FIG. 7.

In some example embodiments, system 600 includes AIA 100 in communication with processor 601. Processor 601 is preferably a central processing unit (CPU), microprocessor, electronic circuit, integrated circuit (IC), or similar. Additionally or alternatively, system 600 may be implemented as firmware written for or ported to a specialized processor, such as a digital signal processor (DSP) or microcontroller, or a field programmable gate array. It may be implemented as hardware or configurable hardware, such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC). Processor 601 may be used to perform any of the calculations required by system 600 or subcomponents thereof.

In some example embodiments of the disclosed subject matter, system 600 may include input/output (I/O) module 602. System 600 includes an I/O module (I/O module) as an interface for sending and/or receiving information and commands between system 600 and external I/O devices using devices such as a mouse, keyboard, or touch screen. 602) can be used. In some example embodiments, processor 601 is included in workstation 605 that also includes memory 603, display adapter 608, communication module 609, or the like. Communication module 609 may be interfacing with network 606.

In some example embodiments, I/O module 602 may output, such as output, visualized results (such as those shown in FIGS. 5, 8, 9, and 10), reports, such as grain size, It may be used to provide an interface to a user of the system by monitoring for improper cutting and/or color/hue defects, or the like, on the display 608 using a UI or GUI. A user may use the workstation 605 to enter information such as pass/fail thresholds, discard batches of grains, perform statistical calculations based on previous tests maintained in a network archive or on the system. . However, it will be appreciated that system 600 may operate without human intervention.

In some example embodiments, network 606 facilitates communication between processor 601 and a cloud computing server (not shown), such as Amazon web services (AWS), with increased and scalability. It can be used to: Additionally or alternatively, network 606 connections may be used to communicate with data storage or other devices in a production facility. Additionally or alternatively, system 600 may use network 606 connectivity to maintain recorded information of AIA 100 in cloud storage (not shown) or any other network storage.

In some example embodiments, system 600 includes controller 604. Controller 604, interfaced with processor 601 via communications 609, controls electro-mechanical and /or configured to drive and sense activities associated with pneumatic components. Controller 604 can communicate with processor 601 and automatically control AIA 100. In some example embodiments, actuation and sensing activities include: inlet funnel 201; feeder mechanism 202; slot feeder 204; video camera (207); classification mechanism (213); background lights (214); sliding mechanism (215); and any combination thereof, or the like.

In some example embodiments, camera 607 within AIA 100 transmits captured images to processor 601 for image analysis and interfaces with processor 601 to convey digital representations of the images. In some example embodiments, the images captured from the at least one camera include video cameras, still cameras, area cameras, line scan cameras, video cameras, monochrome cameras, color cameras, and any combination thereof, Or it may include cameras selected from a group consisting of similar ones.

In some example embodiments, camera 607 may include an array of optical filters (not shown) adapted by controller 604 to engage in front of the lens of at least one camera.

In some example embodiments, system 600 includes memory unit 603. Memory unit 603 may be persistent or volatile. For example, memory unit 603 may include a flash disk, random access memory (RAM), a memory chip, an optical storage device such as a CD, DVD, or laser disk; magnetic storage devices such as tape, hard disk, storage area network (SAN), network attached storage (NAS), or others; It may be a semiconductor storage device such as a flash device, memory stick, or similar. In some example embodiments, memory unit 603 may maintain program code for activating processor 601 to perform actions associated with any of the steps shown in FIG. 7 . The memory unit 603 also stores images captured by the camera 607, a plurality of grain profiles, results (reports) of the system 600, image analysis of each inspection sequence, grains of different types. Reference profiles including thresholds for, statistical analysis associated with the reference profiles; and any combination thereof, or similar.

The components detailed in system 600 may be implemented as one or more sets of interrelated computer instructions, for example, executed by processor 601 or by another process. The components may be arranged as one or more executable files, dynamic libraries, static libraries, methods, functions, services, or the like, under any computing environment and programmed in any programming language.

Reference is now made to FIG. 7 which illustrates a flowchart of a method for grain inspection in accordance with some example embodiments of the disclosed subject matter.

Inspection system 600 actions are based on data generated by image processing performed in-house regarding the appearance of the pellets. Optionally, additional data is collected by system 600 from sensors directly connected to system 600 and/or by data retrieved from other line control devices on the production line. For example, system 600 may receive speed, temperature, and/or pressure readings from a production line and information from these sensors along with information from camera 207 or cameras to infer necessary action. can be used.

Data collected by system 600 may be used to use statistical process control tools (SPC), artificial intelligence (AI) algorithms, data trends to predict upcoming failures or points on existing production lines. analyzed, and processed by specially written algorithms.

In step 701, a grain profile is obtained. In some example embodiments, a grain profile associated with the type of grain being inspected may be obtained from a data store in system 600, such as a storage or memory 603 coupled to network 606. . A grain profile may be one of a plurality of grain profiles maintained in storage, where each grain profile is associated with a different type of grain. In some example embodiments, types of granules may differ from each other with respect to size, color, shape, transparency, weight, and any combinations thereof, or the like. Accordingly, each type of known grain may have a profile that characterizes it for the AIA 100 of the present disclosure.

In some example embodiments, each grain profile of the plurality of grain profiles may include predetermined parameters associated with the AIA 100 setup. Parameters may include camera configuration, lighting and background setup, slot feeder width, and standard thresholds.

At step 702, a slot feeder is set up. In some example embodiments, system 600 adjusts the width 211 of slot feeder 204 to meet grain size requirements according to the parameters of the current grain profile.

In step 703, background lighting is set. In some example embodiments, system 600 may set at least one of the backlights 214 to meet the requirements of grain color, hue, size, and transparency according to the parameters of the current grain profile. It should be remembered that the lights 214 can be set to illuminate each side of the backgrounds as well as to illuminate both sides simultaneously. Additionally or alternatively, system 600 may cause lights 214 to alternate side illumination during the inspection process as well as dimming the illumination during the process, both to improve image resolution of grain inspection. You can.

At step 704, a camera is configured. In some example embodiments, system 600 includes at least one camera 207 to meet the requirements for detecting impurities, changes in geometry, and color of grains according to the parameters of the current grain profile. can be set. Detection requirements include, for example, impurities, dark spots or dark gels, dark and light contaminants, foreign matter, discoloration, cross-contamination, color measurement and color changes, size deviations, shape irregularities, agglomeration, transparency, gloss. You can. It should be remembered that two or more cameras may be used simultaneously as described above. Additionally or alternatively, system 600 may allow one or more cameras 207 to alternate image capture during the sorting process as well as engage optical filters in the image capture process, all according to the current grain profile. there is.

At step 705, granule pouring is enabled. In some example embodiments, granules may be enabled to enter the inlet funnel to initiate a granules monitoring and inspection process.

At step 706, images are captured and analyzed as well as data collected from other sources such as other sensors and/or other line production systems. In some example embodiments, a digital representation of the image may be routed by video front end 207 to processor 601 for image analysis. Image analysis consists of determining the criteria of each grain in the image, where the criteria are: impurities in the grains, changes in geometry, requirements for determining color, e.g. impurities, dark spots or dark spots. selected from the group consisting of gels, dark and light contaminants, foreign matter, discoloration, cross-contamination, color measurement and color change, size deviation, shape irregularities, agglomeration, transparency, and gloss. In some example embodiments, images are maintained in storage with records of 60 seconds each.

In step 707, histograms are generated from all or part of the data collected in step 706. In some example embodiments, system 600 is adapted to generate histogram representations such as those shown in Figures 8-16, for example, for different criteria listed above. The horizontal axis of each histogram represents dimensions, preferably, but not necessarily, given in microns, and the vertical axis represents occurrences scaled in units of 100K grains. Each bar of each histogram contains a representative thumbnail-image per 100K grains. Optionally, additional data may be used, such as data collected by statistical process control tools (SPS), artificial intelligence (AI) algorithms, data trend analysis, and specially written algorithms. SPC and AI may also be based on information from other sensors from the system or from other production lines.

Visual information, such as histograms, as described herein below, may be linked and displayed for any one of the classification systems described above herein as well as other classification and inspection systems.

Inspection systems may be used for the following additional actions - step 708 -:

1. Ejecting the non-conforming pellets using air nozzles or mechanical flaps, this action makes it possible to remove the non-conforming pellets with only zero or a relatively small number of pellets placed close to the non-conforming pellet.

2. Diverting the inspected material flow using a diversion valve or other mechanism that shifts the entire material flow. This action simultaneously removes the non-conforming pellets along with a relatively large number of the non-conforming pellets within the material stream.

3. To set a new set point for one of the production parameters such as speed, temperature, pressure and/or other parameters or to stop production in order to prevent the production of non-conforming pellets or to improve their quality. Transmitting commands to the production line control.

4. Production line to indicate that production is shifting from good or stable production to less good or unstable production, which, if not corrected by the operator, may cause production failure, or to provide an indication of production failure. Generating warnings to the operator.

5. Generating recommendations to line operators on actions to be taken to maintain or achieve good and stable production.

In some example embodiments, sorting or other actions are performed based on predetermined parameters of a given grain profile, including standard thresholds. Thresholds indicate predetermined pass/fail distinction levels for each criterion. System 600 may also generate quality reports.

Reference is now made to Figures 8, 9 and 10. Figure 8 shows a histogram of blackness criteria measured in gray levels; Figure 9 shows a histogram of black size criteria measured in microns; Figure 10 shows a histogram based on grain size measured in microns. In some example embodiments of the disclosed subject matter, system 600 may react to any deviation from the standard in one or two or more of the actions described in step 708. It should be noted that other parameters such as grain size, grain shape, contamination size and shape, color deviation, absolute color of grains or items, etc. can be monitored, inspected and displayed in histograms.

Reference is now made to FIG. 11 , which illustrates a workstation screenshot displaying a results report in accordance with some example embodiments of the disclosed subject matter. The accuracy and reliability of inspection systems depend greatly on the capabilities and quality of work of system operators, accurate management of the set-up and control of the inspection system. In practice, this presents some very serious challenges for users and even experts. Once a user operates an inspection system, at least two types of information representations are important to provide accurate commands to the systems and perform the inspection process effectively: statistical information and image information. According to the disclosed subject matter, a user can interface with the inspection system and provide instructions to the inspection system based on statistical analysis combined with the captured images such that both types of information are presented to the user simultaneously. These features will be further detailed below. The upper portion of screen 220 displays a control indicator 811 showing the status of the results of the current inspection task. According to one embodiment of the subject matter, control indicator 811 includes indications of the number and size of emissions. If the status is normal, the control indicator 811 is green. If the status is not normal, the control indicator 811 will be marked yellow or red. It is possible for any other color to be used without limiting the scope of the subject matter. Optionally, at the operator's request, two or more control indicators 811 may be displayed on screen 220 showing an indication of some parameter, for example, predictions of failure or failed inspection tasks. there is. A central portion 810 of screen 220 displays visual information about the objects being inspected. According to this embodiment, this screenshot shows a display of objects inspected during the processing of pellets using dark contamination inspection. In the Mode tab 812, several display modes are presented and the user can change from one mode to another. In this embodiment, the display mode is in the form of thumbnail pictures. In thumbnail mode, live or reference views of a specific task at a specific point in time are presented. Another display mode tends to display a graphical view of monitored parameters over a selected time interval, as will be further explained in Figure 12. Another display mode is camera view. The state tab 814 has two states - live view and reference view, as further explained in Figure 13. Central portion 810 of screen 220 displays thumbnails of pellets associated with the task. Line 816 operates to set the discrimination level. Each square 818 is a representation of an object being inspected in AIA 100. Screen 220 may display a plurality of squares 818 representing the population being examined. The latest images displayed on the central portion 810 appear on the bottom of the screen 220, pushing the images of previous squares 818 upward in a first-in-first-out stack. The number of squares 818 displayed is associated with the number of specific grains and the height of the central portion 810. On the right-hand side of the central portion 810, only one type of specific grain is detected, and therefore only this particular type of grain is displayed in the thumbnails, while on the left-hand side of the central portion, five or more thumbnails are presented, but these Only 4 of them are displayed. If the height of the center part is higher, more thumbnail pictures will be presented. Lower portion 820 of screen 220 displays quantified data in a histogram view. Optionally, squares 818 may display images according to criteria determined by the operator, for example, best image quality items or most appropriate representation image.

Common task boxes are the tech login box (822), golden reference box (824), histogram box (826), flow box (828), HW box (830), and fleeting box (828). 832). Technician login box 822 includes a login button 8221 and a logout button 8222 to be pressed by users entering and exiting the system. The best reference box 824 is used, during inspection, to establish a snapshot of the highest reference for the current inspection task or generally two or more inspection tasks. Once the save button 8241 is pressed by the user, the current thresholds as well as other decisions and parameter settings that apply will be saved as the highest reference. Once loading button 8242 is pressed by the user, the highest reference graphical interface is displayed on screen 220. Histogram box 826 displays the current task. The user can scroll down the list of options for tasks that appear in the histogram box, such as defect contrast, defect size, pellet size, not in focus, yellowness and similar. When the histogram clear button 8261 is pressed by the user, the histograms displayed in the central portion 810 of the screen 220 are cleared and the central portion 810 of the screen 220 becomes empty. Flow box 828 displays the count of pellets per minute 8281 on the left. The flow box 828 displays the separation percentage of pellets 8282 on the right. Blockage bar 8283 displays a visual indication of the flow of grains being inspected within funnel 201. If flow is good, blockage bar 8283 will be green. If there are obstructions or the flow is not good, blockage bar 8283 will be red. The HW box 830 discharge disable button 8302 can be red or green and changes color when the discharge disable button 8302 is pressed. Once report button 8304 is pressed, a report of the current task is generated and sent to the user. At the bottom of the HW box 830, an indicator portion 8305 indicates whether exhaust is disabled. The pleating box 832 is used when the discharge activation is made using the discharge deactivation button 8302. In the pleating box 832, the user can mark it by checking the box in the name button 8321. Preferably, the name is a description of the task and a counter of the number of emissions generated is displayed. The user can reset the count of flits by checking the counter button 8322.

According to one embodiment of the disclosed subject matter, both types of information previously discussed herein are displayed on screen 220 to allow the user to view a broad view of the results of the inspection process. On the central portion 810, a thumbnail view of images of the items being inspected appears. In the corresponding lower part 820, a histogram view of statistical information about the items being inspected is displayed. A particular square 818 that is part of column A### is a thumbnail image of the inspected item that is part of the group that appears in that column. For example, column A ### is a segment of the inspected items of the task labeled Defect Size as shown in histogram box 826. As explained above, if the height of the center portion 810 had been higher, more thumbnail pictures, all from the same group, could have been presented. Statistical information about the items displayed in column A###, for example the intensity distribution of the parameter, is displayed in the corresponding histogram bar B###. In some embodiments of the subject matter, at a button in each column A###, an overview of statistical information is displayed. Having the ability to simultaneously explore the corresponding two types of information, statistical and visual, provides the user with a better understanding of the specific items being inspected and the inspection task. Based on the image information, the user can determine whether the irregularity is serious and whether a command should be provided to the system. Based on the statistical information, the user can determine decisions related to the entire inspection task as well as a particular corresponding image or images. For example, the highest peak in a histogram indicates the location of the most frequently occurring value in the data set, also referred to as the mode of the data set. This statistical information changes during the inspection process and can affect decisions and commands made by the user. Having a simultaneous view of the image information in column A## with reference to the peaks or modes of the corresponding histogram provides the user with extensive and relevant knowledge for analysis and command generation. For example, threshold setting can be done more efficiently because the operator can examine images considering statistical information. In some cases, for example, the operator may recognize that the defect size threshold is set too stringently so that the defect may be larger before the item is classified as defective.

Reference is now made to FIG. 12 , which illustrates a workstation screenshot displaying a results report in a trend view according to some example embodiments of the disclosed subject matter. In the trend view, information is displayed as a graph of monitored parameters over a selected time interval, for example, hours or days. Duration box 902 includes at least one button indicating a time interval for viewing. By pressing one of the buttons in the duration box 902, relevant information is displayed. For example, when pressing the 24 hour button as shown in Figure 12, information from the last 24 hours is displayed. Parameter checkbox 904 lists parameters that can be monitored, such as black detections, emissions, emissions over the last 10 minutes, grains per minute average, small threshold exceeded, grain size and similar. In the central portion 810 of screen 220, there is a graphical view of information on parameters and changes over time. To the right of central portion 810 of screen 220, a graphical legend 906 appears, indicating a specific graphical representation of each parameter. For example, black detections are marked green, emissions are marked light brown, emissions over the last 10 minutes are marked dark red, and grains per minute average is marked blue. By clicking on a specific dot 908, the interface changes to a thumbnail view as described above. It should be noted that any combination of colors is possible. The trend view allows the user to visually evaluate historical information about previous tasks at a specific point in time and set thresholds according to this information, as described below in FIGS. 14A, 14B, 15A, and 15B. It will be explained.

Reference is now made to FIG. 13 , which illustrates a workstation screenshot displaying a results report in a thumbnail view according to another embodiment of the disclosed subject matter. In thumbnail mode, view button 910 has two modes in which the user must click - live view and reference view. When Live View is pressed, the display is a view of the current inspection run. When a reference view is pressed, the display is a view of another run, such as the highest reference or any other historical reference. In the reference box 912, a description of the reference is displayed and the user presses the option they wish to display - highest, single, range or none. In the lower portion 820 of screen 220, a view of two histograms is displayed - blue for the live view and green for the reference view. Other colors may be selected. Display of histograms of both the current inspection task and general task statistical information allows the user to explore and compare information to make informed decisions. General task statistical information may be, for example, information about similar tasks in a selected time interval or best reference graphical information. For example, having rich information of the current task relative to other tasks in a particular system as well as other devices to compare results in different systems can provide an operator with an indication of the production line. This information can be monitored over time to gain insight into how to improve the production line.

Reference is now made to FIG. 14A , which illustrates a workstation screenshot displaying a results report of a dark defect inspection in a thumbnail view in accordance with some example embodiments of the disclosed subject matter. Setting thresholds or distinction levels is a common activity performed by the user of an inspection system to determine goals related to a particular inspection or analysis task. Users can use the thresholds to set key performance indicators (KPIs) from which an item is good or critical. Green line 920 and green line 922 are set by the user. Green lines 920 and 922 set the distinction level for the allowed number of small contaminants in the range of 200-400 microns. Optionally, a warning is activated if the number exceeds 1000, as shown at line 140 in Figure 14B. Red line 924 sets the distinction level for classifying dark contaminants greater than 700 microns. In the lower portion 820 of screen 220, a corresponding histogram view is displayed. For example, in column 926, a thumbnail display of 3.9 pellets (normalized to 100k pellets) with dark defects measuring 800 microns is displayed. The corresponding histogram display is shown in bar 928.

Reference is now made to FIG. 15A , which illustrates a workstation screenshot displaying in a thumbnail view a results report of a size monitoring test in accordance with some example embodiments of the disclosed subject matter. Distinct ranges or threshold ranges, for example target range and threshold range, result from threshold values. Depending on the critical range the KPI value is in, for example, it is either good or bad. Green line 920 and green line 922 are set by the user. Green lines 920 and 922 set the discrimination level for the allowed number of small particles in the range of 300-1200 microns (normalized to 100k pellets). A warning is activated if the number exceeds 3k, as shown at line 150 in Figure 15B. Red line 924 and red line 926 set the discrimination level for the changes allowed in the mode of the histogram. A warning is activated if the mode change is lower than 2.3 mm or if the mode change exceeds 2.6 mm.

Reference is now made to FIG. 16 , which illustrates a workstation screenshot displaying in a thumbnail view a report of the results of a yellowness test in accordance with some example embodiments of the disclosed subject matter. Green line 920 sets the discrimination level for pellets that are too yellow.

Other parameters may be displayed in the same or similar manner.

Although the invention has been described in relation to specific embodiments of the invention, it will be apparent that numerous alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, this invention is intended to cover all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims. All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. It is hereby incorporated by reference in its entirety into the specification. Additionally, citation or identification of any reference within this application should not be construed as an admission that such reference is available as prior art to the present invention.

Claims (17)

A management and control system for a user to interface with an inspection device having at least one digital optical instrument, comprising:
a processor configured to receive images from the at least one optical instrument, analyze the images, and transmit instructions to the inspection device;
A display configured to display an analysis of the images, wherein the user can interface with and provide instructions to the inspection device based on the analysis of the images, the display configured to display a histogram generated by the processor based on the images. A management and control system comprising a display that simultaneously displays files and thumbnail images.
In claim 1,
The histograms and the thumbnail image correspond to each other.
In claim 1,
The inspection and sorting device is configured to inspect items selected from the group consisting of beans, spices, nuts, grains, rice, vegetables, fruits, plastic granules, metal granules, glass granules, and pharmaceutical pellets.
In claim 1,
The at least one digital optical device is an optical device consisting of an X-ray detector, a magnetic resonance imaging (MRI) device, a computed tomography (CT) scanner, a 3D data scanner, a camera, and an optical sensor. A management and control system selected from a group.
In claim 1,
The displays include monitors, screens, electroluminescent (ELD) display devices, liquid crystal display (LCD) devices, light-emitting diode (LED) devices, plasma (PDP) displays, electronic hand- A management and control system selected from a group of displays consisting of held devices such as tablets and smartphone devices.
In claim 2,
The commands include categorizing items, enabling emission of the items, disabling emission of the items, generating reports, setting differentiation levels, switching the items, setting thresholds for generating alerts, defining data sets for automatic prediction and alerting, A management and control system selected from a group of commands consisting of set point definitions for production line control.
In claim 2,
The management system further includes a memory unit in communication with the processor, the memory unit configured to store the images, reference images, a plurality of profiles of items, system settings, system reports, image analysis, A management and control system, configured to maintain information selected from a group of information consisting of reference profiles including thresholds for items, statistical analysis associated with the reference profiles.
In claim 1,
and the display graphically displays graphs generated by the processor based on the images.
In claim 1,
The inspection device is integrated within the production line, management and control system.
As a method of managing and controlling an inspection device for items,
capturing images of the items being inspected by at least one digital optical instrument of the inspection device;
receiving the images from the at least one digital optical device by a processor;
analyzing the images by the processor to analyze the items;
displaying the analysis on a display, simultaneously displaying histogram representations and thumbnail images;
A method comprising receiving, by the inspection device, commands interfaced by a user.
In claim 10,
Analyzing the images includes determining criteria for each item in the image, including impurities, changes in geometry, color of the items, dark spots, dark gels, dark and light contaminants. , foreign matter, discoloration, cross-contamination, color measurement and color change, size deviation, shape irregularities, agglomeration, transparency, gloss of the above items.
In claim 10,
The method further comprises generating histogram representations of the criteria and dimensions of the items.
In claim 12,
The method further includes setting thresholds based on the histogram representations, thumbnail images, and graphs.
In claim 10,
Commands interfaced by the user include categorizing the items, activating delivery of the items, disabling delivery of the items, generating reports, setting differentiation levels, switching the items, setting thresholds to generate alerts, automatic prediction and alerting. A method selected from a group of instructions consisting of defining a data set for, defining set points for controlling a production line.
In claim 10,
The histograms and the thumbnail image correspond to each other.
An interface that interfaces between a user and the inspection device to enable the user to receive visual and statistical information from the inspection device and simultaneously provide instructions to the inspection device,
a processor configured to receive images from the inspection device, display at least a portion of the images, perform statistical analysis based on the images, and form distribution histograms;
a display configured to simultaneously display at least a portion of the images and the distribution histograms;
An interface comprising an input device for the user to provide instructions to the processor and to interface with the inspection and classification device.
In claim 16,
wherein at least a portion of the images and the distribution histograms correspond to each other.

Images