Recycling coins from scrap metal

Abstract

Materials classification systems utilize a vision system that implements a machine learning system to classify materials and identify or categorize each material. The identification or classification determines that the material has a specified geometric shape, which then places it into a distinct group. Such systems can separate numismatic coins or other valuable metals from other forms of scrap.

Description

[Technical field]

[0001] This application is a continuation-in-part of U.S. Patent Application No. 15 / 963,755, which claims priority to U.S. Provisional Patent Application No. 62 / 490,219, both of which are incorporated herein by reference.

[0002] Government Licensing Rights This disclosure was made with U.S. Government support under Grant No. DE-AR0000422 from the U.S. Department of Energy. The Government may have certain rights in this disclosure.

[0003] Technical Field FIELD OF THE DISCLOSURE This disclosure relates generally to material sorting, and more particularly to the sorting of certain valuable items from scrap. [Background technology]

[0004] This section is intended to introduce various aspects of the art that may be related to exemplary embodiments of the present disclosure. The discussion is believed to be helpful in providing a framework to facilitate a better understanding of certain aspects of the present disclosure. As such, it should be understood that this section should be read in this light, and not necessarily as an admission of prior art.

[0005] Recycling is the process of collecting, treating, and transforming materials that would otherwise be discarded as trash into new products. Recycling benefits local communities and the environment because it reduces the amount of waste sent to landfills and incinerators, conserves natural resources, increases economic security by using domestic materials, prevents pollution by reducing the need to collect new raw materials, and saves energy. After collection, recyclables are typically sent to materials recovery facilities where they are sorted, cleaned, and processed into materials that can be used in manufacturing. Summary of the Invention [Problem to be solved by the invention]

[0006] Many automobiles designated for shredding and the subsequent recycling process have been found to have relatively significant numbers of numismatic coins located within them, between the seats, under the floor mats, etc. At least one study has estimated that there may be approximately US$10-15 in coins per vehicle. Similarly, such vehicles may contain lost jewelry.

[0007] Automobiles also contain printed circuit boards ("PCBs") that contain valuable metals (eg, copper, gold, silver, etc.) that can be recycled.

[0008] Given the large number of vehicles recycled each year, the recycling industry is seeking techniques to recover valuable scrap pieces as a beneficial by-product of the normal vehicle recycling process. Additionally, federal legislation was recently passed that will require the U.S. government to pay the face value of any coins, even those that are damaged. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 shows a schematic diagram of a sorting system constructed in accordance with an embodiment of the present disclosure. [Diagram 2] 4 illustrates a flow chart of the operation of a sorting device configured in accordance with an embodiment of the present disclosure. [Figure 3A] 1 shows visual images of various exemplary monetary coins. [Figure 3B] 1 shows a visual image of an exemplary numismatic coin mixed with other scrap pieces. [Figure 3C] 1 shows visual images of various exemplary portions of a gemstone. [Figure 3D] 1 shows a visual image of an exemplary jewelry piece mixed with other scrap pieces. [Figure 4] 1 shows a flow chart diagram arranged in accordance with an embodiment of the present disclosure. [Diagram 5] 1 illustrates a block diagram of a data processing system configured in accordance with an embodiment of the present disclosure. [Figure 6]1 illustrates a flowchart of an exemplary configuration of a machine learning system according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Various detailed embodiments of the present disclosure are disclosed herein. However, it should be understood that the disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various alternative forms. The figures are not necessarily to scale, and some features may be exaggerated or minimized to show the details of certain components. Therefore, specific structural and functional details disclosed herein should not be interpreted as limiting, but merely as a representative basis for teaching those skilled in the art to use various embodiments of the present disclosure.

[0011] Embodiments of the present disclosure utilize the machine learning based vision system described herein to effectively recycle certain valuable scrap pieces (e.g., monetary coins, jewelry, PCBs, copper, brass, etc.) from shredded scrap (e.g., automobile scrap).

[0012] As used herein, "materials" may include any physical item, including but not limited to scrap pieces. Classes or types of materials may include metals (ferrous and non-ferrous), alloys, monetary coins, jewelry (e.g., rings, earrings, necklaces, bracelets, etc.), gold, silver, buttons, electrical box knockouts, washers, plastics (including but not limited to PCBs, HDPE, UHMWPE, and various colored plastics), rubber, foam, glass (including but not limited to borosilicate or soda-lime glass, and various colored glasses), ceramics, paper, cardboard, Teflon, PE, bundled wire, insulation covered wire, rare earth elements, and the like. As used herein, the terms "scrap" and "scrap pieces" refer to pieces of material in a solid state. In this disclosure, the terms "scrap," "scrap pieces," "materials," and "pieces of material" may be used interchangeably.

[0013] As used herein, a heterogeneous mixture of materials means a collection of individual materials of different classes or types. As used herein, a homogeneous set of materials means a collection of individual materials of the same or substantially similar class or type.

[0014] As defined in the Non-Ferrous Scrap Guidelines promulgated by the Institute of Scrap Recycling Industries, Inc, the term "Zorba" is a collective term for shredded non-ferrous metals, including but not limited to those derived from End-of-Life Vehicles ("ELV") or Waste Electronic and Electrical Equipment ("WEEE"). The Institute of Scrap Recycling Industries, Inc ("ISRI") established the Zorba specifications in the United States. In Zorba, each scrap piece may be composed of a combination of non-ferrous metals (e.g., aluminum, copper, lead, magnesium, stainless steel, nickel, tin, zinc, in elemental or alloyed (solid) form). Additionally, the term "Twitch" refers to fragmented aluminum scrap. Twitch may be produced by a float process in which heavier metal scrap sinks, causing aluminum scrap to float to the top (e.g., some processes may include sand to change the density of the water in which the scrap is immersed).

[0015] As used herein, the terms "identify" and "classify," as well as "identification" and "classification," may be used interchangeably. For example, in certain embodiments of the present disclosure, a vision system (as described further herein) may be configured to collect (e.g., using a machine learning system) any type of information available within the sorting system to selectively classify materials (e.g., scrap pieces) as a function of a (user-defined) set of one or more physical characteristics, including, but not limited to, the material's color, size, shape, texture, appearance, uniformity, hue, and / or production type.

[0016] It should be noted that at least some of the material being sorted may have irregular sizes and shapes (see, e.g., FIGS. 3B and 3D). For example, such material (e.g., a zorba and / or a twitch) may have been through some sort of shredding mechanism that shreds the material into pieces of irregular shapes and sizes (producing scrap pieces) that can be fed into a conveyor system.

[0017] An embodiment of the present disclosure is described herein as separating materials (e.g., scrap pieces) into separate groups by physically depositing (e.g., discharging) the materials (e.g., scrap pieces) into separate receptacles or containers as a function of user-defined classification. As an example, in certain embodiments of the present disclosure, materials (e.g., scrap pieces) can be sorted into separate containers to separate scrap pieces of designated value from other scrap materials. Such scrap pieces of designated value (by a user of the system 100) can be monetary coins, jewelry (e.g., rings, earrings, necklaces, bracelets, etc.), precious metals (e.g., gold, silver, platinum, copper, brass, etc.), or PCBs (which can include valuable metals, e.g., gold, silver, copper).

[0018] FIG. 1 illustrates an example of an automated material sorting system 100 configured in accordance with various embodiments of the present disclosure to sort materials automatically (i.e., without the need for manual human intervention). Hereinafter, embodiments of the present disclosure are described as sorting scrap pieces, but such embodiments are applicable to sorting any type of material. A conveyor system 103 is embodied to transport one or more streams of individual scrap pieces 101 through the sorting system 100, where each of the individual scrap pieces 101 is tracked, classified, and sorted into a predetermined desired group. Such a conveyor system 103 may be implemented using one or more conveyor belts along which the scrap pieces 101 typically move at a predetermined constant speed. However, certain embodiments of the present disclosure may be embodied with other types of conveyor systems, including vibratory and mechanical conveyors, such as systems in which the scrap pieces free-fall over various components of the sorting system. Hereinafter, the conveyor system 103 is referred to simply as the conveyor belt 103.

[0019] Additionally, while FIG. 1 depicts a single stream of scrap pieces 101 on the conveyor belt 103, embodiments of the present disclosure may be implemented such that multiple such streams of scrap pieces pass through various components of the sorting system 100 in parallel with one another. Or, a collection of scrap pieces randomly deposited on the conveyor belt 103 may be passed by various components of the sorting system 100. Thus, certain embodiments of the present disclosure may simultaneously track, classify, and sort multiple such parallel moving streams of scrap pieces, or scrap pieces randomly deposited on a conveyor belt. In accordance with embodiments of the present disclosure, singulation of the scrap pieces 101 is not required for a vision system that tracks, classifies, and sorts the scrap pieces.

[0020] According to certain embodiments of the present disclosure, some suitable feeder mechanism may be utilized to feed the scrap pieces 101 to the conveyor belt 103, which may then transport the scrap pieces 101 through various components within the sorting system 100. Within certain embodiments of the present disclosure, the conveyor belt 103 is operated to move at a predetermined speed by a conveyor belt motor 104. This predetermined speed may be programmable and / or adjustable by an operator in any known manner. Monitoring of the predetermined speed of the conveyor belt 103 may instead be performed using a belt speed detector 105. Within certain embodiments of the present disclosure, control of the conveyor belt motor 104 and / or the belt speed detector 105 may be performed by an automated control system 108. Such an automated control system 108 may be operated under the control of a computer system 107, and / or functionality for performing the automated control may be embodied in software within the computer system 107.

[0021] The conveyor belt 103 may be a conventional endless belt conveyor that uses a conventional drive motor 104 suitable for moving the conveyor belt 103 at a predetermined speed. A belt speed detector 105, which may be a conventional encoder, may be coupled to the conveyor belt 103 and the automated control system 108 to provide information corresponding to the operation (e.g., speed) of the conveyor belt 103. Thus, as described further herein, by utilizing control over the conveyor belt drive motor 104 and / or the automated control system 108 (or alternatively including the belt speed detector 105), each of the scrap pieces 101 moving on the conveyor belt 103 may be identified and tracked by location and time (with respect to various components of the system 100) such that various components of the sorting system 100 may be activated / deactivated when each scrap piece 101 passes within their vicinity. As a result, the automated control system 108 may track the location of each of the scrap pieces 101 as they move along the conveyor belt 103.

[0022] According to certain embodiments of the present disclosure, after the scrap pieces 101 are received by the conveyor belt 103, a tumbler and / or vibrator (not shown) may be utilized to separate individual scrap pieces from the collection of scrap pieces. According to another embodiment of the present disclosure, the scrap pieces may be arranged into one or more singulated (i.e., single file) streams, which may be performed by an optional active or passive singulator 106. As previously mentioned, it is not necessary to incorporate or use a singulator. Instead, the conveyor system (e.g., conveyor belt 103) may simply transport a collection of scrap pieces that are randomly arranged on the conveyor belt 103.

[0023] 1 , embodiments of the present disclosure may utilize a vision or optical recognition system 110 as a means to initiate tracking of each of the scrap pieces 101 as they move on the conveyor belt 103. The vision system 110 may utilize one or more still or real-life cameras 109 (which may include one or more three-dimensional cameras) to record the position (i.e., location and time) of each of the scrap pieces 101 on the moving conveyor belt 103. The vision system 110 may be further configured to perform a particular type of identification (e.g., classification) of all or a portion of the scrap pieces 101. For example, such a vision system 110 may be utilized to obtain information about each of the scrap pieces 101. For example, the vision system 110 can be configured (e.g., using a machine learning system) to collect any type of information available within the system 100 to selectively sort the scrap pieces 101 as a function of a (user-defined) set of one or more physical characteristics, including, but not limited to, the color, size, shape, texture, appearance, uniformity, composition, and / or manufacturing type of the scrap pieces. The vision system 110 captures images of each of the scrap pieces 101, for example, by using an optical sensor such as those utilized in typical digital cameras and video equipment. Such images captured by the optical sensor can be stored in a memory device as image data. According to embodiments of the present disclosure, such image data represents images captured within wavelengths of light (i.e., wavelengths of light observed by a typical human eye). However, alternative embodiments of the present disclosure can utilize optical sensors configured to capture images of materials at wavelengths of light outside the visual wavelengths of a typical human eye.

[0024] Further, such a vision system 110 may be configured to identify which scrap pieces 101 are not of a type that should be sorted by the sorting system 100 (e.g., scrap pieces classified as other than certain valuable scrap pieces) and send a signal to reject such scrap pieces. Scrap pieces 101 so identified may be ejected utilizing one of the mechanisms described herein for physically moving classified scrap pieces to individual bins.

[0025] 2, a system and process 200 for actuating each of the automated sorting devices (e.g., sorting devices 126, 127, 128, 129) to discharge the classified scrap pieces into a sorting bin is shown. Such a system and process 200 may be embodied within the automated control system 108 described above with respect to FIG. 1, or may be embodied within an overall computer system (e.g., computer system 107) that controls the sorting system. At process block 201, a signal is received from the automated control system 108 that a designated and tracked scrap piece is in position for sorting. At process block 202, a determination is made whether the timing associated with this signal is equal to the current time. The system and process 200 determines whether the timing associated with the classified scrap piece corresponds to an expected time that the classified scrap piece will pass near a particular sorting device (e.g., air jet, pneumatic plunger, paintbrush type plunger, etc.) associated with the classification associated with the classified scrap piece. If the timing signals do not correspond, a determination is made at process block 203 whether the signal is greater than the current time. If YES, the system may return an error signal 204. In such a case, the system may not be able to discharge the pieces into the appropriate bin. Once the system and process 200 determines that the classified scrap pieces are passing near a sorting device associated with that classification, the sorting device is actuated at process block 205 to discharge the classified scrap pieces into the sorting bin associated with that classification. This may be accomplished by actuating a pneumatic plunger, a paintbrush type plunger, an air jet, or the like. At process block 206, the selected sorting device is stopped.

[0026] As previously mentioned, the sorting device may include any known mechanism for redirecting selected scrap pieces to a desired location, including, but not limited to, ejecting scrap pieces from the conveyor belt system into multiple sorting bins. For example, the sorting device may utilize air jets, with each air jet assigned to one or more classifications. When one of the air jets (e.g., 127) receives a signal from the automated control system 108, the air jet emits a stream of air to eject scrap pieces 101 from the conveyor belt 103 into the sorting bin (e.g., 137) corresponding to the air jet. A high speed air valve (e.g., available from Mac Industries) may be used to provide the air jet with the appropriate air pressure configured to eject scrap pieces 101 from the conveyor belt 103, for example.

[0027] Although the example shown in FIG. 1 uses air jets to eject the scrap pieces, other mechanisms can be used to eject the scrap pieces, such as robotically removing the scrap pieces from the conveyor belt, pushing the scrap pieces off the conveyor belt (e.g., with a paintbrush-type plunger), creating an opening in the conveyor belt (such as a trap door) through which the scrap pieces can fall, using one or more air jets to separate the scrap pieces into separate bins as they fall off the end of the conveyor belt, or using a robotic arm to pick up designated scrap pieces from the conveyor belt 103.

[0028] In addition to the N sorting bins 136, 137, 138, 139 into which the scrap pieces 101 are discharged, the system 100 may also include a receptacle or container 140 that receives scrap pieces 101 that are not discharged from the conveyor belt 103 into any of the aforementioned sorting bins 136, 137, 138, 139. For example, if the classification of the scrap piece 101 cannot be determined (or if the sorting device simply fails to properly discharge the piece), the scrap piece 101 may not be discharged from the conveyor belt 103 into one of the N sorting bins 136, 137, 138, 139. Thus, the container 140 may function as a default container into which unclassified scrap pieces are dumped. Alternatively, the container 140 may be used to receive one or more classifications of scrap pieces that are not purposefully assigned to any of the N sorting bins 136, 137, 138, 139. For example, according to an embodiment of the present disclosure, scrap pieces that are not classified as specific valuable scrap pieces may pass through bin 140 .

[0029] According to certain embodiments of the present disclosure, a set of one or more air jets may be configured to direct scrap pieces classified as designated valuable scrap pieces into a first receptacle as they fall off the end of the conveyor belt 103, while scrap pieces not classified as designated valuable scrap pieces are simply allowed to fall off the end of the conveyor belt 103 into a separate second receptacle (e.g., container 140). Alternatively, the reverse may be performed where scrap pieces classified as designated valuable scrap pieces are simply allowed to fall off the end of the conveyor belt 103.

[0030] According to certain embodiments of the present disclosure, monetary coins may be sorted separately based on their different denominations and therefore sorted into separate bins accordingly.

[0031] Depending on the variety of scrap piece sorting required, multiple classifications (e.g., coins of specific different denominations) can be mapped to a single sorting device and associated sorting bins. In other words, there need not be a one-to-one correlation between classifications and sorting bins. For example, it may be desired by a user to sort a particular class or type of material into the same sorting bin (e.g., one or more different denominations of monetary coins, or both monetary coins and copper and / or brass, etc.). To achieve such sorting, if scrap pieces 101 are sorted to fall into a group of predefined classifications (e.g., one or more different denominations of monetary coins, or both monetary coins and copper and / or brass), the same sorting device can be operated to sort these into the same sorting bin. Such combination sorting can be applied to generate any desired combination of sorted scrap pieces. To generate such desired combinations, the mapping of classifications may be programmed by a user (e.g., using a sorting algorithm operated by computer system 107 (see, e.g., FIG. 4)). Furthermore, the classification of the scrap pieces is user definable and is not limited to specific known classifications of scrap pieces. As non-limiting examples of the foregoing, the machine learning system of the present disclosure may be configured to separately classify two or more denominations of monetary coins for sorting into the same bin (e.g., one or more of bins 136, 137, 138, 139), or may be configured to classify a particular denomination (e.g., pennies) into the same bin as scrap pieces that are not classified as monetary coins.

[0032] In another non-limiting example of the foregoing, the machine learning system of the present disclosure may be configured to classify and separate both monetary coins and another class or type of valuable into a common container. The other class or type of valuable may be jewelry (e.g., rings, earrings, bracelet components, necklace components, etc., as shown in FIG. 3C), a particular class or type of metal piece (e.g., gold, silver, copper, brass, etc.), and / or scrap pieces identified by the machine learning system as containing a particular metal (e.g., PCBs containing copper, gold, or silver). Such scrap pieces collected in the common container may then be passed through the system 100 again (or such scrap pieces may be conveyed to a second similar system such as the system 100) to separate the collected valuable scrap pieces (e.g., between monetary coins and copper and / or brass).

[0033] Conveyor system 103 may include a carousel (not shown) that returns unsorted scrap pieces (or scrap pieces of two or more classes or types of material for resorting) to the beginning of sorting system 100 to pass through system 100 again. System 100 may also specifically track each scrap piece 101 as it travels through conveyor system 103, such that certain sorting devices (e.g., sorting device 129) may eject scrap pieces 101 that system 100 was unable to classify (e.g., currency coins, jewelry, PCBs, PCBs and jewelry, etc.) after a predetermined number of cycles through sorting system 100.

[0034] Within certain embodiments of the present disclosure, the conveyor belt 103 may be divided into multiple belts configured in series, such as, for example, two belts, where a first belt transports scrap pieces past a vision system and a second belt transports scrap pieces from the vision system to a sorting device. Furthermore, such a second conveyor belt may be at a lower height than the first conveyor belt, such that scrap pieces fall from the first belt onto the second belt.

[0035] As previously mentioned, embodiments of the present disclosure may implement one or more vision systems (e.g., vision system 110) to identify, track, and / or classify scrap pieces. Such a vision system is comprised of one or more devices for capturing or acquiring images of the scrap pieces passing on a conveyor system. The devices may be configured to capture or acquire any desired range of wavelengths reflected by the scrap pieces, including, but not limited to, visible, infrared (IR), and ultraviolet (UV) light. For example, the vision system may be comprised of one or more cameras (still and / or video cameras that may be configured to capture two-dimensional images, three-dimensional phase, and / or holographic images) positioned near (e.g., on) the conveyor system such that visual images of the scrap pieces passing through the conveyor system are captured by the vision system.

[0036] Regardless of the type of image captured of the scrap pieces, the image may then be transmitted to a computer system (e.g., computer system 107) and processed by a machine learning system to subsequently identify and / or classify each scrap piece in order to sort the scrap pieces in a desired manner. Such a machine learning system may implement one or more well-known machine learning algorithms, including those that implement the following: Neural networks (e.g., artificial neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, reinforcement learning), fuzzy logic, artificial intelligence (AI), deep learning algorithms, deep structured learning hierarchical learning algorithms, support vector machines (SVMs) (linear SVM, nonlinear SVM, SVM regression, etc.), decision tree learning (classification and regression trees (CART) etc.), ensemble methods (ensemble learning, random forests, bagging & pasting, patching & subspace, boosting, stacking etc.), dimensionality reduction (projection, manifold learning, principal component analysis etc.), and / or deep machine algorithms as described and published at deeplearning.net (including all software, publications and hyperlinks to available software referenced within this website), which are incorporated herein by reference.Non-limiting examples of publicly available machine learning algorithms, software, and libraries that can be utilized within embodiments of the present disclosure include Python, OpenCV, Inception, Theano, Torch, PyTorch, Pylearn2, Numpy, Blocks, TensorFlow, MXNet, Caffe, Lasagne, Keras, chainer, Matlab Deep Learning, CNTK, MatConvNet (a toolbox for MATLAB that implements convolutional neural networks for computer vision applications), DeepLearnToolbox (a Matlab toolbox for deep learning (Rasmus Berg Palm)), BigDL, Cuda-Convnet (a fast C++ / CUDA implementation of convolutional (or more generally feedforward) neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Includes Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factored RBM and mcRBM, mPoT (a Python code using CUDAMat and Gnumpy to train a model on natural images), ConvNet, Elektronn, OpenNN, NeuralDesigner, Theano Generalized Hebbian Learning, Apache Singa, Lightnet, and SimpleDNN.

[0037] Machine learning often occurs in two stages, or phases. For example, initially, training is performed offline and the sorting system 100 is not utilized to perform actual sorting of scrap pieces. According to certain embodiments of the present disclosure, a portion of the system 100 is utilized to train the machine learning system where one or more homogenous sets of scrap pieces (i.e., representative sets of one or more denominations of monetary coins (e.g., see FIG. 3A), rings, bracelets, necklaces, and / or earrings, etc., representative scrap pieces of PCBs, or representative scrap pieces of a particular type of precious metal (e.g., gold, silver, copper, brass)) are passed through the vision system 110 using the conveyor system 103 (each set of homogenous scrap pieces may be collected in a common unsorted receptacle (e.g., receptacle 140)). Alternatively, training may be performed at another location remote from the system 100, including using other mechanisms to collect images of homogenous sets of particular valuable scrap pieces.

[0038] It should be noted that, according to certain embodiments of the present disclosure, a set of homogeneous monetary coins may be a collection of monetary coins of the same denomination (and thus having the same shape, size, color, hue, etc.) or may be a collection of monetary coins of different denominations (and thus having different shapes, sizes, colors, hues, etc.), but sharing at least one physical characteristic (shape, etc., e.g., circular, polygonal) that is the same or substantially the same. Furthermore, according to certain embodiments of the present disclosure, since most monetary coins are approximately circular, this may be the specific physical characteristic utilized by the machine learning system to classify and sort materials. Since there are some foreign coins that are not circular (e.g., polygonal), such specific non-circular shapes (but substantially polygonal (e.g., octagonal) shapes) may also be utilized by the machine learning system to classify and sort materials. For purposes of describing various embodiments of the present disclosure, it will be understood that most monetary coins have closed geometric shapes (e.g., circular, polygonal).

[0039] During this training phase, a machine learning algorithm extracts features from the captured images using image processing techniques well known in the art. Non-limiting examples of training algorithms include, but are not limited to, linear regression, steepest descent, feed forward, polynomial regression, learning curve, regularized learning model, and logistic regression. During this training phase, the machine learning algorithm can be configured to learn the relationship between designated valuable scrap pieces (e.g., monetary coins (which may include different denominations), rings, bracelets, necklaces, earrings, PCBs, etc.) and their features (e.g., color, texture, hue, shape (e.g., round, polygonal), brightness, etc. captured by the images), forming a knowledge base of classifications of heterogeneous mixtures of scrap pieces received by the sorting system 100 for sorting monetary coins from heterogeneous mixtures of scrap pieces. According to certain embodiments of the present disclosure, such a knowledge base may include the requirement that scrap pieces to be classified as monetary coins have a substantially circular and / or polygonal shape (e.g., coins are within a predetermined threshold of being substantially circular and / or polygonal because they may be somewhat damaged in a vehicle or by an automobile shredder, as illustrated by some of the coins in FIG. 3A). Such a knowledge base may include rejection of circular scrap pieces with holes formed therein, in order not to classify metal washers as monetary coins. Such a knowledge base may further include rejection of circular scrap pieces having a particular color or hue (e.g., in order not to classify U.S. pennies among other monetary coins).

[0040] Such a knowledge base may include one or more libraries, each library containing parameters for utilization by the vision system 110 in classifying and sorting the scrap pieces during the second stage or phase. For example, a particular library may contain parameters configured during the training stage to recognize and classify coins of a particular denomination. According to certain embodiments of the present disclosure, such libraries may be input to the vision system, and a user of the system 100 may then adjust certain ones of the parameters to tune the operation of the system 100 (e.g., adjusting the effectiveness of a threshold for how well the vision system recognizes a particular denomination of monetary coins from a heterogeneous mixture of materials (see, e.g., FIG. 3B )).

[0041] For example, FIG. 3A shows a captured or acquired image of a homogenous set of exemplary monetary coins that may be used during the training phase described above. During the training phase, a control sample of such monetary coins (e.g., a homogenous set of exemplary monetary coins of one or more specified denominations) is delivered through the vision system (e.g., by conveyor system 103) and the machine learning system detects, extracts, and learns what features visually represent such exemplary monetary coins. In other words, images of monetary coins such as those shown in FIG. 3A may be first passed through such a training phase to allow the machine learning system to "learn" how to detect, recognize, and classify monetary coins among a heterogeneous mixture of scrap pieces (e.g., as shown in FIGS. 3B and 3D). This creates a library of parameters specific to the specified monetary coins.

[0042] A point to mention here is that the detected / extracted features do not necessarily have to be mere color, brightness, or circular or polygonal shape. They can be abstract formulations that can only be expressed mathematically, or not at all. Nevertheless, the machine learning system analyzes all the data looking for patterns that can classify control samples (e.g., actual monetary coins) during the training phase. The machine learning system may take subsections of the captured images of the scrap pieces and try to find correlations between predefined classifications (e.g., one or more different monetary coin denominations).

[0043] According to certain embodiments of the present disclosure, a machine learning system can be configured to classify scrap pieces that are approximately circular, but not exactly circular, as monetary coins. For example, when monetary coins contained within a scrap yard vehicle are processed (e.g., run through a shredder), they may be damaged (e.g., slightly bent, notched, etc.). FIG. 3A shows examples of some such damaged coins. The machine learning system can adjust the tolerance parameters to classify such scrap pieces as monetary coins. For example, a scrap piece is classified as a monetary coin if it is not perfectly circular or does not have a completely closed circular shape, but its overall size (e.g., diameter) and / or color, hue, texture, etc. match a particular denomination of monetary coins (e.g., U.S. quarter, nickel, dime, etc.). The machine learning system can also be trained to classify scrap pieces as monetary coins by including representative samples of damaged (e.g., notched, bent, etc.) coins within the aforementioned control sample (see FIG. 3A).

[0044] Additionally, because there are non-U.S. monetary coins that are not circular in shape and may have other polygonal shapes, the machine learning system of the present disclosure may be configured to classify such objects in the scrap piece stream as monetary coins.

[0045] Some scrap being sorted may be manufactured from materials that include metal electrical boxes, such that the scrap pieces may include circular knockouts that resemble monetary coins. However, a machine learning system configured in accordance with an embodiment of the present disclosure may be configured to not classify such knockouts as monetary coins. This may be accomplished by running a homogenous set of such knockouts through the machine learning system during a training phase. The machine learning system may "learn" not to classify the knockouts as monetary coins due to how different the knockouts appear from monetary coins (e.g., texture, color, lack of a pattern imprinted on the surface, etc.).

[0046] According to some embodiments of the present disclosure, the machine learning system may be configured to not classify as a monetary coin any circular scrap piece that does not have a diameter equivalent to one or more specific monetary coins, including, but not limited to, scrap pieces with diameters larger and / or smaller than a predetermined diameter (e.g., U.S. quarters, nickels, dimes, etc.). This would ensure, for example, that clothing buttons would not be classified as monetary coins. Such diameter specifications can be utilized to classify monetary coins by denomination.

[0047] According to certain embodiments of the present disclosure, training a machine learning system to identify numismatic coins for sorting using a homogenous set of exemplary coins (e.g., see FIG. 3A ) enables the machine learning system 100 to sort specific numismatic coins from a heterogeneous mixture of scrap pieces (e.g., see FIG. 3B ).

[0048] According to certain embodiments of the present disclosure, to enable the machine learning system 100 to classify specific gemstone scrap pieces from a heterogeneous mixture of scrap pieces (e.g., see FIG. 3D), the machine learning system may be trained to identify specific types of gemstones by running exemplary samples of gemstone pieces (e.g., see FIG. 3C) through the machine learning system previously disclosed. As previously disclosed, the machine learning system may identify and classify gemstone scrap pieces from a heterogeneous mixture of such scrap pieces by learning certain physical characteristics of such specific gemstone scrap pieces. FIG. 3D provides a non-limiting example of how such gemstone scrap pieces may be visually distinguished from other scrap pieces.

[0049] Although not shown in the figures, the exemplary pieces of PCB may be run through the machine learning system as a homogenous set to enable the machine learning system 100 to identify and classify such PCB scrap pieces from a heterogeneous mixture of scrap pieces. For example, the machine learning system might do this by looking for scrap pieces that are green or look like green plastic sheeting.

[0050] 6 depicts at an abstract level examples of various possible embodiments of the present disclosure. A machine learning algorithm may essentially embody one or more aspects of the system and process 600, but not necessarily exactly as outlined in the flow diagram of FIG.

[0051] In block 601, the vision system 110 acquires an image of the scrap piece 101 as described herein. Block 602 represents that the machine learning system may be configured to identify scrap pieces that resemble certain valuable scrap pieces (e.g., whether monetary coins are round or polygonal in shape). Other physical characteristics (e.g., color, shade, tone, texture, stamped features, diameter, etc.) can be utilized to identify certain features (e.g., coin related) in the scrap piece 101.

[0052] Optional block 603 abstractly represents how a machine learning system may be configured to exclude scrap pieces 101 that are similar to (i.e., have similar physical characteristics) electrical box knockouts from the monetary coin sorting.

[0053] Optional block 604 represents how the learning machine of the abstract system can be further configured to not classify scrap pieces that are not of a desired denomination (e.g., having the color of a U.S. penny, smaller than a dime, larger than a quarter) as monetary coins. Block 604 abstractly represents how the machine learning system may separately sort coins of various denominations.

[0054] After the machine learning algorithms are established and the machine learning system has fully learned the differences in material classifications, a library of different classes or types of materials (e.g., coins of one or more denominations, rings, bracelets, necklaces, earrings, PCBs, etc.) is implemented in the material classification system (e.g., system 100) and used to identify and / or classify and sort specific scrap pieces from a heterogeneous mixture of scrap pieces.

[0055] FIG. 4 illustrates a flow chart diagram illustrating an example embodiment of a process 400 for sorting scrap pieces utilizing a vision system, according to certain embodiments of the present disclosure. Aspects of the process 400 may be configured to operate within any of the embodiments of the present disclosure described herein, including the sorting system 100 of FIG. 1. Operations of the process 400 may be performed by hardware and / or software, including within a computer system (e.g., computer system 3400 of FIG. 5) that controls the sorting system (e.g., computer system 107 and / or vision system 110 of FIG. 1). In optional process block 401, the scrap pieces may be passed through some type of well-known sieve (not shown), which may be configured to allow scrap pieces smaller than a predetermined size to pass through the sieve. For example, slots formed in the sieve may be configured to allow objects of a size similar to monetary coins to pass through. However, any of the devices described herein or another sorting system may be utilized to initially separate the smaller scrap pieces from the larger scrap pieces.

[0056] At process block 402, scrap pieces may be deposited on a conveyor belt. FIG. 3B shows a digital photograph of an exemplary heterogeneous collection of such scrap pieces, including various numismatic coins, deposited on a conveyor belt. FIG. 3A shows a digital photograph of an exemplary heterogeneous collection of scrap pieces, including various jewelry scrap pieces, deposited on a conveyor belt. In a non-limiting example, a sieve may be positioned to deposit scrap pieces that pass through onto the belt conveyor. For example, with reference to FIG. 1, such a sieve may be positioned between the ramp or chute 102 and the conveyor belt 103. The position of each scrap piece 101 on the conveyor belt 103 is detected to track each scrap piece as it passes through the sorting system. This is performed by the vision system 110 (e.g., by distinguishing scrap pieces from the underlying conveyor belt material while communicating with a conveyor belt speed detector (e.g., belt speed detector 105)), and this information is collected and monitored by the automation control system 108. Alternatively, a linear sheet laser beam may be used to identify the location of the scrap pieces (or any system capable of producing a light source (including but not limited to visible light, UV, VIS, and IR) and with detectors that can be used to identify the location of the parts). In process block 403, one or more images of the scrap pieces are captured / obtained as the scrap pieces move near the vision system 110. In process block 404, the machine learning system may perform image pre-processing as previously disclosed, which may be utilized to detect or identify (extract) each of the scrap pieces from the background (e.g., the conveyor belt 103). In other words, image pre-processing may be utilized to identify the difference between the scrap pieces and the background. Well-known image processing techniques such as dilation, thresholding, contour processing, etc. may be utilized to identify the scrap pieces as distinct from the background. In process block 405, image segmentation may be performed. For example, the one or more images captured by the camera of the vision system may include one or more images of the scrap pieces.Additionally, a particular scrap piece may be located at a seam of the conveyor belt when its image is captured. Thus, in such cases, it may be desirable to separate the image of the individual scrap piece from the image background. In the exemplary technique of process block 405, the first step is to apply a high contrast of the image. In this way, the background pixels are reduced to substantially all black pixels, and at least some pixels associated with the scrap piece are brightened to substantially all white pixels. Next, the image pixels of the white scrap piece are expanded to cover the entire size of the scrap piece. After this step, the location of the scrap piece becomes a high contrast image of all white pixels on a black background. Next, utilizing a contour algorithm, the boundaries of the scrap piece are detected. The boundary information is saved and the boundary location is transferred to the original image. Segmentation is performed on the original image for areas larger than the previously defined boundaries. In this way, each scrap piece is identified and separated from the background. In process block 406, the size and shape of each scrap piece can be determined.

[0057] Image post-processing may be performed at process block 407. Image post-processing may include resizing the images for use with the neural network. It may also include modifying certain image properties (e.g., increasing image contrast, changing the image background, applying filters) in such a way that the machine learning system's ability to classify scrap pieces is enhanced. Following image post-processing, normalization of the various images may be performed at process block 408 so that images of various different scrap pieces can be more easily compared to one another. At process block 409, data representing each image may be resized. Image resizing may be necessary under certain circumstances to match the data input requirements of certain machine learning systems, such as neural networks. Neural networks require image sizes (e.g., 225X255 pixels or 299X299 pixels) that are much smaller than the size of images captured by a typical digital camera. Additionally, smaller image sizes require less processing time for classification. Thus, reducing image size ultimately increases the throughput of the sorting system, increasing its value.

[0058] In process blocks 410 and 411, each scrap piece is identified / classified based on the detected features. For example, process block 410 can be configured with a neural network using one or more machine learning algorithms that compare the extracted features (e.g., circular / polygonal, non-porous, color, etc.) with those stored in a knowledge base generated in a training phase and assign each scrap piece a best matching classification based on such comparison. The machine learning algorithms can process the captured images hierarchically using automatically trained filters. The filter responses are then successfully incorporated into the next level of the algorithm until a probability is obtained in the final step. In process block 411, these probabilities are calculated using a number of iterations N(N >1) may be used for each of the classifications to determine which of N sorting bins a respective scrap piece should be sorted into. For example, each of the N classifications may be assigned to a respective sorting bin, and the scrap piece under consideration is sorted into the bin corresponding to the classification that returns the highest probability greater than a predetermined threshold. Within embodiments of the present disclosure, such predetermined threshold may be pre-set by a user. If none of the probabilities are greater than the predetermined threshold (e.g., the scrap piece is not classified as a monetary coin), the particular scrap piece may be sorted into the outlier bin (e.g., sorting bin 140).

[0059] At process block 412, a sorting device corresponding to one or more classifications of the scrap piece is activated (see, e.g., FIG. 2). Between the time an image of the scrap piece 101 is captured by the vision system 110 and the time the sorting device is activated, the scrap piece 101 moves from near the vision system 110 to a position downstream of the conveyor belt 103 at the conveying speed of the conveyor belt 103. In an embodiment of the disclosure, the operation of the sorting devices (e.g., 126, 127, 128, 129) is timed such that when the scrap piece 101 passes a sorting device mapped to the classification of the scrap piece, the sorting device is activated and the scrap piece is sent to the associated sorting bin (e.g., 136, 137, 138, 139). In an embodiment of the present disclosure, activation of the sorter can be timed by an automatic control system in communication with the belt speed detector 105 which detects the time that a scrap piece passes in front of the sorter and sends a signal to activate the sorter. At process block 413, the sorting bin corresponding to the activated sorter receives the indicated scrap piece.

[0060] According to certain embodiments of the present disclosure, at least a portion of the systems 100 may be linked together in series to perform multiple iterations or layers of sorting. For example, when two or more systems 100 are so linked, the conveyor system may be implemented with a single conveyor belt or multiple conveyor belts to transport scrap pieces of a first set of heterogeneous materials past a first vision system configured to sort scrap pieces of a first set of heterogeneous materials by a sorter (e.g., first automated control system 108 and associated one or more sorting devices 126, 127, 128, 129) into a first set of one or more receptacles (e.g., sorting bins 136, 137, 138, 139) and then transport scrap pieces of a second set of heterogeneous materials by a second sorter into a second set of one or more sorting bins.

[0061] Such a series of systems 100 may include any number of such systems linked together in this manner. According to certain embodiments of the present disclosure, each successive vision system may be configured to sort different materials than the previous vision system (e.g., first sorting coins and copper / brass from scrap, then sorting coins and copper / brass pieces).

[0062] As described herein, embodiments of the present disclosure may be implemented to perform various functions described for identifying, tracking, classifying, and sorting materials, such as scrap pieces. Such functions may be implemented in hardware and / or software, such as in one or more data processing systems (e.g., data processing system 3400 of FIG. 5), the computer system 107, vision system 110, and / or automation control system 108 described above. However, the functions described herein are not limited to implementation on any particular hardware / software platform.

[0063] As will be appreciated by those skilled in the art, aspects of the present disclosure may be embodied as a system, a process, a method, and / or a program product. Thus, various aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, referred to herein as a "circuit," "module," or "system." Furthermore, aspects of the present disclosure may take the form of a program product embodied in one or more computer-readable storage media having computer-readable program code embodied therein. (However, any combination of one or more computer-readable media may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.)

[0064] A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, biological, atomic, or semiconductor system, apparatus, controller, or device, or any suitable combination of the foregoing, where the computer-readable storage medium is not itself a transitory signal. More specific examples of computer-readable storage media (a non-exhaustive list) may include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM) (e.g., RAM 3420 in FIG. 5), a read-only memory (ROM) (e.g., ROM 3435 in FIG. 5), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device (e.g., hard drive 3431 in FIG. 5), or any suitable combination of the foregoing. As used herein, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, controller, or device. The program code embodied in a computer readable signal medium may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, RF, or any suitable combination of the foregoing.

[0065] A computer-readable signal medium may include a propagated data signal having computer-readable program code embodied therein, for example in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including but not limited to electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium is not a computer-readable storage medium, but may be any computer-readable medium that can communicate, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, controller, or device.

[0066] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, processes, and program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code, including one or more executable program instructions for performing a specified logical function. It should be noted that in some implementations, the functions noted in the blocks may differ from the order noted in the figures. For example, two blocks shown in succession may in fact be executed substantially simultaneously. Or, the blocks may be executed in the reverse order, depending on the functionality involved.

[0067] Modules implemented in software for execution by various types of processors (e.g., GPU 3401, CPU 3415) may, for example, comprise one or more physical or logical blocks of computer instructions and may be organized as, for example, an object, procedure, or function. Executable identified modules need not be physically located together, but may comprise different instructions stored in different locations, which when logically combined comprise the module and achieve the module's designated purpose. In practice, a module of executable code may be a single instruction or multiple instructions, and may be distributed across multiple different code segments, different programs, and multiple memory devices. Similarly, operational data (e.g., a material classification library described herein) may be identified and illustrated within modules herein, may be embodied in any suitable format, and may be organized within any suitable type of data structure. The operational data may be collected as a single data set or may be distributed across various locations, including various storage devices. The data may provide electronic signals over a system or network.

[0068] These program instructions are provided to one or more processors and / or controllers of a general purpose computer, special purpose computer, or other programmable data processing apparatus (e.g., a controller) capable of producing a machine, and the instructions, which execute via the processor (e.g., GPU 3401, CPU 3415) of the computer or other programmable data processing apparatus, create circuits or means for implementing the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams.

[0069] It should also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by a special purpose hardware-based system (which may include, for example, one or more graphics processing units (e.g., GPU 3401, CPU 3415)) that performs a particular function or act, or a combination of special purpose hardware and computer instructions. For example, the modules can be implemented as hardware circuits including custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, controllers, or other discrete components. The modules can also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, etc.

[0070] Computer program code, i.e., instructions, for carrying out operations of aspects of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smolltalk, Python, C++, traditional procedural programming languages ​​such as the "C" programming language or similar programming languages, or any of the machine learning software disclosed herein. The program code can be executed entirely on the user's computer system, partially on the user's computer system as a standalone software package, partially on the user's computer system (e.g., a computer system utilized for sorting), partially on a remote computer system (e.g., a computer system utilized to train a machine learning system), or entirely on a remote computer system or server. In the latter scenario, the remote computer system can be connected to the user's computer system via any type of network, including a local area network (LAN) or wide area network (WAN), or can be connected to an external computer system (e.g., via the Internet using an Internet Service Provider). As an example of the foregoing, various aspects of the present disclosure can be configured to run on one or more of the computer system 107, the automation control system 108, and the vision system 110.

[0071] These program instructions may also be stored on a computer-readable storage medium that can direct a computer system, other programmable data processing apparatus, controller, or other device to function in a particular manner, such that the instructions stored on the computer-readable medium create an article of manufacture that includes instructions that implement the functions / acts specified in the flowchart and / or block diagram blocks.

[0072] The program instructions may also be loaded into a computer, other programmable data processing apparatus, controller, or other device to cause the computer, other programmable apparatus, or other device to perform a series of operational steps to generate a computer-implemented process, where the instructions executing on the computer or other programmable apparatus provide a process for implementing the functions / acts specified in the flowchart and / or block diagram blocks.

[0073] One or more databases may be included in the host to store and provide access to data for various implementations. Those skilled in the art will also appreciate that for security reasons, any database, system, or component of the present disclosure may include any combination of databases or components in a single location or multiple locations, where each database or system may include any of a variety of suitable security features such as firewalls, access codes, encryption, decryption, etc. The databases may be any type of database, such as relational, hierarchical, object-oriented, etc. Common database products that may be used to implement the databases include IBM's DB2, database products available from Oracle Corporation, Microsoft Access from Microsoft Corporation, or other database products. The databases may be organized in any suitable manner, such as data tables and lookup tables.

[0074] Association of specific data (e.g., for each scrap piece processed by the sorting system described herein) can be accomplished by any data association technique known and practiced in the art. For example, association can be performed manually or automatically. Automatic association techniques can include, for example, database lookup, database merge, GREP, AGREP, SQL, and the like. The association step can be performed, for example, by a database merge function using key fields in each of the manufacturer and retailer data tables. The key fields divide the database according to high-level classes of objects defined in the key fields. For example, a particular class can be specified as a key field in both the first and second data tables, and the two data tables can be merged based on the class data in the key fields. In these embodiments, the data corresponding to the key fields in each of the merged data tables is preferably the same. However, data tables with similar but not identical data in the key fields can also be merged using, for example, AGREP.

[0075] As used herein, reference is made to "configuring" a device, or a device that is "configured" to perform some function. It should be understood that this may involve selecting predefined logic blocks and logically associating them to provide a particular logical function, including monitoring or control functions. This may also involve programming the computer software-based logic of a retrofit control device, wiring discrete hardware components, or a combination of any or all of the above. A device so configured is physically designed to perform a specified function.

[0076] In the description herein, numerous specific details of examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, controllers, etc. are provided to provide a thorough understanding of embodiments of the present disclosure. However, one of ordinary skill in the art will recognize that the disclosure may be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations may not be shown or described in detail to avoid obscuring aspects of the disclosure.

[0077] 5, a block diagram is shown illustrating a data processing (computer) system 3400 in which aspects of an embodiment of the present disclosure may be implemented. (The terms "computer," "system," "computer system," and "data processing system" may be used interchangeably herein.) The computer system 107, the automation control system 108, and / or the vision system 110 may be configured similarly to the computer system 3400. The computer system 3400 may employ a local bus 3405 (e.g., a Peripheral Component Interconnect ("PCI") local bus architecture). Any suitable bus architecture may be utilized, such as Accelerated Graphics Port ("AGP") or Industry Standard Architecture ("ISA"). One or more processors 3415, volatile memory 3420, and non-volatile memory 3435 may be connected to the local bus 3405 (e.g., via a PCI bridge (not shown)). An integrated memory controller and cache memory may be coupled to the one or more processors 3415. The one or more processors 3415 may include one or more central processor units and / or one or more graphic processor units and / or one or more tensor processing units. In certain embodiments of the present disclosure, one or more GPUs 3401 (e.g., GPGPUs, or general purpose computing for graphics processing units) are implemented within the computer system 107 to operate any one or more of the disclosed machine learning systems. Additional connections to the local bus 3405 may be made via direct component interconnections or add-in boards. In the illustrated example, a communications (e.g., network (LAN)) adapter 3425, an I / O (e.g., small computer system interface ("SCSI") host bus) adapter 3430, and an expansion bus interface (not shown) are connected to the local bus 3405 by direct component connections. An audio adapter (not shown), a graphics adapter (not shown), and a display (connected to a display 3440) are also connected to the local bus 3405 by direct component connections. A display adapter 3416 (integrated with a display device) may be connected to the local bus 3405 (eg, by a board inserted into an add-in expansion slot).

[0078] User interface adapter 3412 may provide connections for a keyboard 3413 and mouse 3414, a modem (not shown), and additional memory (not shown). I / O adapter 3430 may provide connections for a hard disk drive 3431, a tape drive 3432, and a CD-ROM drive (not shown).

[0079] An operating system may run on one or more processors 3415 and may be used to coordinate and provide control of various components within the computer system 3400. In FIG. 5, the operating system may be a commercially available operating system. An object oriented programming system (e.g., Java, Python, etc.) may run in conjunction with the operating system and provide calls to the operating system from one or more programs (e.g., Java, Python, etc.) executing on the system 3400. The operating system, instructions for the object oriented operating system, and programs may be located on a non-volatile memory 3435 storage device, such as a hard disk drive 3431, and may be loaded into the volatile memory 3420 for execution by the processor 3415.

[0080] Those skilled in the art will appreciate that the hardware in FIG. 5 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash ROM (or equivalent non-volatile memory) or optical disk drives, may be used in addition to or in place of the hardware shown in FIG. 5. Also, any of the processes of this disclosure may be applied to a multi-processor computer system or performed by multiple such systems 3400. For example, training of the vision system 110 may be performed by a first computer system 3400, while operation of the vision system 110 for sorting may be performed by a second computer system 3400.

[0081] As another example, computer system 3400 may be a stand-alone system configured to be bootable independent of some type of network communication interface, regardless of whether computer system 3400 includes any type of network communication interface. As a further example, computer system 3400 may be an embedded controller configured with ROM and / or flash ROM that provides non-volatile memory for storing operating system files or user-generated data.

[0082] 5 and above are not meant to imply architectural limitations, and the computer program form of the disclosed aspects may reside on any computer readable storage medium (i.e., floppy disk, compact disk, hard disk, tape, ROM, RAM, etc.) for use by a computer system.

[0083] Reference to "embodiments" or similar language throughout this specification means that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the terms "in one embodiment," "in an embodiment," "embodiments," "particular embodiments," "various embodiments," and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment. Furthermore, the described features, structures, aspects, and / or features of the present disclosure may be combined in any suitable manner in one or more embodiments. Correspondingly, even if features are initially described as acting in a particular combination, in some cases one or more features from the described combination may be deleted from the combination, and the described combination may lead to a subcombination or a variation of the subcombination.

[0084] Benefits, advantages, and solutions to problems have been described above with respect to specific embodiments. However, the benefits, advantages, solutions to problems, and elements that may cause or make the benefits, advantages, or solutions more noticeable may not be construed as critical, essential, or critical features or elements in any or all of the claims. Moreover, no element described herein is required for the practice of the disclosure unless expressly described as essential or critical.

[0085] Those skilled in the art who have read this disclosure will recognize that changes and modifications can be made to the embodiments without departing from the scope of the disclosure. It is understood that the specific implementations shown and described herein may be illustrative of the disclosure and its best mode, and are not intended to otherwise limit the scope of the disclosure. Other variations may be within the scope of the following claims.

[0086] While this specification contains many details, these should not be construed as limitations on the scope of the disclosure or what may be claimed, but rather as descriptions of features inherent to particular implementations of the disclosure. The headings herein may not be intended to limit the disclosure, the embodiments of the disclosure, or other matters disclosed under the heading.

[0087] As used herein, the term "or" can be intended to be inclusive, such that "A or B" includes A or B, and also includes both A and B. As used herein, the term "and / or," when used in the context of a list of components, refers to the components present either singly or in combination. Thus, for example, the phrase "A, B, C, and / or D" includes A, B, C, and D individually, as well as any and all combinations and subcombinations of A, B, C, and D.

[0088] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" can be intended to include the plural forms as well, unless the context clearly dictates otherwise.

[0089] Corresponding structure, materials, acts, and equivalents of all means- or step-plus-function elements in the following claims may be intended to include any structure, material, or act for performing a function in combination with other claimed elements as specifically claimed.

[0090] As used herein with respect to an identified characteristic or condition, "substantially" and "approximately" refer to a degree of deviation that is small enough that it does not measurably impair the identified characteristic or condition. The exact degree of deviation that is permitted may vary depending on the particular situation.

[0091] As used herein, a plurality of items, structural elements, components, and / or materials may be presented in a common list for convenience. However, these lists should be construed as though each member of the list were individually identified as a separate and unique member. Thus, the individual members of such lists should not be construed as being de facto equivalents to other members of the same list solely based on their presentation in a common grouping, absent indication to the contrary.

[0092] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the presently disclosed subject matter belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the presently disclosed subject matter, representative methods, devices, and materials are described below.

[0093] Unless otherwise indicated, all numbers expressing amounts of ingredients, reaction conditions, and the like used in the specification and claims should be understood in all cases as being modified by the term "about". Thus, unless otherwise indicated, the numerical parameters described in the specification and the appended claims are approximations that may vary depending on the desired properties sought to be obtained by the subject matter disclosed herein. As used herein, the term "about" when referring to a value or mass, weight, time, volume, concentration, or percentage is meant to encompass variations of ±20% in some embodiments, ±10 in some embodiments, ±5% in some embodiments, ±1% in some embodiments, ±0.5% in some embodiments, and ±0.1% in some embodiments, such variations being appropriate for carrying out the disclosed methods. [Explanation of symbols]

[0094] 100 Machine Learning Systems 101 Scrap Piece 102 SHOTS 103 Conveyor System 104 Drive motor 105 Belt Speed ​​Detector 106 Passive Singulator 107 Computer Systems 108 Automation Control Systems 110 Vision System 126, 127, 128, 129 Sorting device 136, 137, 138, 139 Sorting containers

Claims

1. capturing image data for each piece of a heterogeneous mixture of materials moving in a stream past the camera, the materials of the heterogeneous mixture of materials being a collection of different classes or types of materials; classifying the material having one or more designated classes or types of material into a first classification; classifying the materials that do not have the one or more specified classes or types of material into a second classification; separating, by an automated sorting device, the materials classified in the first category from the materials classified in the second category; Equipped with wherein the classification of the materials is performed by a neural network employing one or more machine learning algorithms that compare features detected in the image data captured from each piece of a heterogeneous mixture of materials with features stored in a knowledge base generated in a prior training phase; The method, wherein the materials classified into the first category are selected from the list consisting of at least one of: monetary coins, jewels, precious metals, printed circuit boards, and combinations thereof.

2. 10. The method of claim 1, wherein during the training phase, the one or more machine learning algorithms learn relationships between the one or more specified classes or types of materials and their features extracted from captured image data creating a knowledge base.

3. 10. The method of claim 1, wherein during the training phase, control samples are delivered through the camera and the one or more machine learning algorithms detect, extract, and learn features that are visually representative of one or more specified classes or types of material that are classified into the first classification.

4. The method of claim 1 , wherein materials having a circular shape and also having holes formed therein are classified in said second category.

5. The method of claim 1 , further comprising the step of passing a bulk of material through a sieve prior to capturing the image data to generate a heterogeneous mixture of the material having a size less than a predetermined size.

6. a camera configured to capture image data of each piece of a heterogeneous mixture of materials moving in a stream past the camera, the materials of the heterogeneous mixture of materials being a collection of different classes or types of materials; a circuit configured to classify the material having a specified class or type of material into a first classification; a circuit configured to classify the material not having the specified class or type into a second classification; an automated sorting device configured to separate the materials classified in the first category from the materials classified in the second category; Equipped with wherein the classification of the materials is performed by a neural network employing one or more machine learning algorithms that compare features detected in the image data captured from each piece of the heterogeneous mixture of materials with features stored in a knowledge base generated in a prior training phase; The system, wherein the first classification comprises at least one of monetary coins, jewelry, precious metals, printed circuit boards, and combinations thereof.

7. The system of claim 6, wherein during the training phase, the one or more machine learning algorithms learn relationships between the one or more specified classes or types of materials and their features extracted from captured image data creating a knowledge base.

8. 8. The system of claim 7, further comprising a circuit configured to classify a material having a circular shape and having holes formed therein into the second classification.

9. The system of claim 6 , further comprising a sieve for separating out heterogeneous mixtures of said materials having a size below a predetermined size prior to capturing said image data.

10. 7. The system of claim 6, wherein during the training phase, control samples are delivered through the camera and the one or more machine learning algorithms detect, extract, and learn features that are visually representative of one or more specified classes or types of material that are classified into the first classification.

11. The system of claim 6 , wherein the heterogeneous mixture of materials comprises zorba or the heterogeneous mixture of materials comprises scrap from end-of-life automobiles.

12. 1. A computer program product stored on a computer readable storage medium that, when executed, performs a method for classifying materials for sorting, comprising: receiving image data for each piece of a heterogeneous mixture of materials moving in a stream past a vision system camera, the materials of the heterogeneous mixture of materials being a collection of different classes or types of materials; classifying said material having a designated class or type of material into a first classification designated as a monetary coin; classifying the materials not having the specified class or type of material into a second classification; sending information regarding the classification to an automated sorting device such that the automated sorting device is capable of separating the materials classified in the first classification from the materials classified in the second classification; Including, wherein the classification of the materials is performed by a neural network employing one or more machine learning algorithms that compare features detected in the image data captured from each piece of the heterogeneous mixture of materials with features stored in a knowledge base generated in a prior training phase.

13. During the training phase, the one or more machine learning algorithms learn relationships between the one or more specified classes or types of materials and their features extracted from the captured image data creating a knowledge base; During the training phase, control samples are delivered through the vision system camera and the one or more machine learning algorithms detect, extract, and learn features that are visually representative of one or more specified classes or types of material that are classified into the first classification; 13. The computer program product of claim 12, wherein the materials classified in the first category are selected from the list consisting of at least one of: monetary coins, gemstones, precious metals, printed circuit boards, and combinations thereof.

Images