Sorting apparatus, selection device, and sorting method

Abstract

This sorting apparatus sorts plastic wastes. The sorting apparatus comprises a reception unit and a processing device. The reception unit receives an image obtained by imaging the plastic wastes in an imaging area. The processing device: extracts an individual image for each of the plastic wastes from the image received by the reception unit; sets, as an analysis object, the surface shape or the entire shape of the plastic waste appearing in the extracted individual image; and sorts the plastic wastes according to results obtained by analyzing the analysis object by using an analysis model constructed by machine learning or a rule-based analysis program.

Description

Classification device, selection device, and classification method

[0001] The present application primarily relates to a sorting device for sorting plastic waste.

[0002] Patent Document 1 discloses a oscillating sorter for sorting industrial waste such as waste plastics. The oscillating sorter includes a oscillating body. The industrial waste is placed on the oscillating body. As the oscillating body oscillates, a force is exerted on the industrial waste to move it forward. Furthermore, because the oscillating body is tilted upward at the front, a force is exerted on the industrial waste to move it backward due to its own weight. If the industrial waste is light, the force due to its own weight is weaker, so the industrial waste moves forward and falls from the light waste discharge port. On the other hand, if the industrial waste is heavy, the force due to its own weight is stronger, so the industrial waste moves backward and falls from the heavy waste discharge port. Furthermore, holes are formed in the oscillating body, and small-diameter waste falls through the holes and is collected. This allows the industrial waste to be sorted into light, heavy, and small-diameter waste.

[0003] Japanese Patent Application Laid-Open No. 2017-213489

[0004] The sorting device in Patent Document 1 sorts industrial waste materials by weight, specific gravity, or size. However, when sorting plastic waste, there are cases where it is preferable to sort the waste materials by criteria other than weight, specific gravity, or size. However, the sorting device in Patent Document 1 cannot sort the plastic waste materials in a manner that satisfies this requirement.

[0005] The present application has been made in view of the above circumstances, and its main object is to provide a sorting device that can sort plastic waste based on criteria other than weight, specific gravity, or size.

[0006] The problem to be solved by the present application is as described above. Next, the means for solving this problem and the effects thereof will be explained.

[0007] According to a first aspect of the present application, there is provided a classification device having the following configuration. That is, the classification device classifies plastic waste. The classification device includes a receiving unit and a processing unit. The receiving unit receives images obtained by photographing the plastic waste in a photographing area. The processing unit extracts individual images for each of the plastic waste from the images received by the receiving unit, analyzes the surface shape or overall shape of the plastic waste that appears in the extracted individual images, and classifies the plastic waste according to the results of analyzing the analysis target using an analytical model constructed by machine learning or a rule-based analytical program.

[0008] According to a second aspect of the present application, the following classification method is provided. That is, in the classification method, plastic waste is classified. In the classification method, the plastic waste in a photographing area is photographed to generate images. In the classification method, individual images for each of the plastic waste are extracted from the generated images. In the classification method, the surface shape or overall shape of the plastic waste that appears in the individual images is used as an analysis object, and the plastic waste is classified according to the results of analyzing the analysis object using an analytical model constructed by machine learning or a rule-based analytical program.

[0009] According to the present application, plastic waste can be sorted on criteria other than weight, specific gravity, or size.

[0010] The present invention relates to a sorting device for sorting plastic waste, a method for sorting plastic waste, and a method for sorting plastic waste.

[0011] Next, an embodiment of the present application will be described with reference to the drawings.

[0012] The sorting device 1 shown in FIG. 1 is a device that classifies and separates plastic waste, which is the target material, based on various criteria. "Classification" means distinguishing according to predetermined criteria. "Sorting" means moving plastic waste according to the classification results. Plastic waste is waste plastic products that contain plastic as a primary component. Plastic waste may be supplied to the sorting device 1 after being subjected to processes such as compression and crushing, or may be supplied to the sorting device 1 without being subjected to processes such as compression and crushing. In either case, there is a high possibility that at least a partial shape of the plastic product remains in the plastic waste.

[0013] As shown in FIG. 1 , the sorting device 1 includes a conveyor 10 , a camera 12 , a lighting device 13 , a receiving unit 14 , a processing device 15 , and a sorting unit 16 .

[0014] The conveyor 10 transports plastic waste supplied from the outside downstream. The conveyor 10 is, for example, a belt conveyor or a roller conveyor. Other transport devices capable of transporting plastic waste may be used instead of the conveyor 10. The other transport devices may be, for example, arm robots that grab and move plastic waste. Note that transport devices such as the conveyor 10 are not essential components and may be omitted.

[0015] The camera 12 is disposed above the conveyor 10. The camera 12's imaging area is the upper surface of the conveyor 10. The camera 12 captures an image of one or more plastic waste objects in the imaging area to generate an image. The camera 12 in this embodiment is a line camera. The camera 12 acquires line images of the plastic waste objects placed on the upper surface of the conveyor 10. The camera 12 or the processing device 15 generates an image showing one or more plastic waste objects based on the multiple line images. Note that the camera 12 is not limited to a line image camera, and may be a camera with a rectangular or circular imaging area. The image generated by the camera 12 may be an image in which a color is assigned to each pixel, or may be a distance image in which a distance from the imaging position is assigned to each pixel. When generating a distance image, it is preferable to use a stereo camera or a three-dimensional sensor instead of a visible light camera.

[0016] The lighting device 13 is disposed above the conveyor 10. The lighting device 13 irradiates the photographing area of ​​the camera 12 with light. This allows the darkened parts of the plastic waste to be clearly imaged. This makes it easier for the surface shape of the plastic waste to appear clearly in the image. Note that since images of the plastic waste can be generated without lighting, the lighting device 13 is not an essential component and can be omitted.

[0017] The receiving unit 14 is a communication module compatible with wired or wireless communication. The receiving unit 14 receives images generated by the camera 12. The images received by the receiving unit 14 are output to the processing device 15 for processing. The receiving unit 14 and the processing device 15 may be separate devices, or may be a single control device built into a single housing.

[0018] The processing device 15 includes an arithmetic device such as an FPGA, an ASIC, or a CPU, and a storage device such as an SSD or a flash memory. The processing device 15 reads out programs stored in the storage device and executes them with the arithmetic device, thereby performing various processes related to the sorting device 1. For example, the processing device 15 controls the conveyor 10 and the sorting unit 16, processes images generated by the camera 12, and analyzes these images. Details of the processes performed by the processing device 15 will be described later.

[0019] The sorting device 1 of this embodiment separates and separates plastic waste into soft plastics and hard plastics. When recycling plastic waste, it may be necessary to separate the plastic waste into soft plastics and hard plastics. This is because the companies that recycle soft plastics may differ from the companies that recycle hard plastics. Another reason is that the temperatures and heating times required to melt soft plastics and hard plastics differ, making melting difficult or inefficient when soft and hard plastics are mixed together.

[0020] Soft plastics are products that are very thin and flexible, such as plastic bags, films, and picnic sheets. Hard plastics are products that are relatively thick and inflexible, such as plastic toys, plastic cases, and hangers. However, classification standards for soft and hard plastics may differ depending on the recycler. Furthermore, recycling applications do not require strict standards or accurate classification.

[0021] The sorting unit 16 has a storage unit that stores compressed air and an injection unit that injects the compressed air. Multiple injection units are arranged in the width direction of the conveyor 10. The position where the injection unit is installed is called the sorting position. The sorting unit 16 injects compressed air at the plastic waste at the sorting position, blowing the plastic waste to the soft plastic recovery unit 17. On the other hand, if the sorting unit 16 does not inject compressed air, the plastic waste falls into the hard plastic recovery unit 18. In this way, the plastic waste can be sorted into soft plastics and hard plastics. Note that the sorting unit 16 is not an essential component and can be omitted. A device without the sorting unit 16 is used, for example, to detect the content ratio of soft plastics and hard plastics.

[0022] The sorting unit 16 may sort hard plastics by spraying compressed air. The sorting unit 16 is not limited to a machine that sprays compressed air. For example, the sorting unit 16 may be an arm robot. The arm robot picks up plastic waste and transports it to the soft plastic recovery unit 17 or the hard plastic recovery unit 18.

[0023] 2 and 3, the details of the processing by the receiving unit 14 and the processing device 15 will be described. The receiving unit 14 and the processing device 15 classify plastic waste based on images, and therefore correspond to the "classification device 20."

[0024] First, the receiving unit 14 receives an image of the photographing area from the camera 12 (S101). If the image received by the receiving unit 14 is a line image, the processing device 15 generates an image by combining a plurality of line images.

[0025] Next, the processing device 15 pre-adjusts the image of the photographed area (S102). Pre-adjustment is a process for making it easier to extract an image of plastic waste from the image of the photographed area. For example, plastic waste may contain black parts, which may be difficult to distinguish from shaded parts. Furthermore, if the conveyor surface is black, it may be difficult to identify the boundary between the black parts of the plastic waste and the conveyor surface. Therefore, the processing device 15 adjusts the parameters for image rendering so that the black parts of the plastic waste are emphasized. Furthermore, plastic waste may contain parts that strongly reflect light, and to reduce the influence of these parts, the processing device 15 may perform adjustments such as median processing. Note that pre-adjustment is not a required process and can be omitted.

[0026] Next, the processing device 15 extracts individual images of each piece of plastic waste from the image of the photographed area (S103). Specifically, the processing device 15 identifies the contours of the plastic waste through image processing. Because the conveyor surface and the plastic waste are basically different in color, individual images of the plastic waste can be extracted from the image of the photographed area based on the color differences. Note that if the colors of the conveyor surface and the plastic waste are similar, there is a possibility that an image of one piece of plastic waste will be extracted in separate parts. Therefore, if the pixel proximity of the extracted images is below a threshold, the processing device 15 may combine those images and treat them as a single individual image of plastic waste.

[0027] Next, the processing device 15 selects one individual image from the multiple individual images extracted in step S103 (S104). The selected individual image is the target of analysis. Since the processing device 15 analyzes all individual images, the order of analysis is not particularly limited. Alternatively, plastic waste that is sorted early, in other words, plastic waste located downstream of the conveyor 10, may be analyzed early on. Furthermore, instead of analyzing individual images one by one, multiple individual images may be analyzed in parallel.

[0028] The processing device 15 then uses the individual images to perform material analysis and product analysis of the plastic waste.

[0029] Material analysis is a process of estimating the constituent materials of plastic waste. In this embodiment, for example, the material analysis estimates whether the plastic waste is soft plastic or hard plastic. Note that the classification of material analysis in this embodiment is an example.

[0030] The target of material analysis is the surface shape of plastic waste that appears in individual images. For example, soft plastics are flexible, so their surfaces are wavy or have irregular curves. On the other hand, hard plastics are not flexible, so their surfaces are flat or have relatively large flat surfaces.

[0031] The analytical method for material analysis is an analytical model or an analytical program. The analytical model is constructed by machine learning training data. The training data is, for example, data that associates images of plastic waste with information indicating whether the waste is soft plastic or hard plastic. The machine learning is supervised learning. The analytical model constructed can be of a type commonly used in image processing, such as logistic analysis or SVM. The output of the analytical model is a score for each constituent material that is an analysis candidate. This score is a numerical representation of the probability of belonging to each analysis candidate. Since the method of outputting the probability of belonging by using a model such as logistic analysis is well known, a detailed explanation will be omitted.

[0032] Another analytical method for material analysis is an analysis program. The analysis program is constructed using a rule-based algorithm rather than AI. For example, when using the analysis program, the processing device 15 first identifies areas where surface waviness appears from individual images through image processing. Next, the processing device 15 calculates a score such that the greater the proportion of areas where waviness appears, the higher the probability that the plastic is soft, and the smaller the proportion of areas where waviness appears, the higher the probability that the plastic is hard. Alternatively, a score corresponding to the probability of soft or hard plastic may be calculated based on the proportion of areas where flat surfaces form in the plastic waste.

[0033] The product analysis estimates the type of product made from the plastic waste. The product analysis of this embodiment estimates whether the plastic waste falls into one of the following categories: plastic bags, films, hangers, plastic toys, etc. Note that the classification of the product analysis of this embodiment is an example, and it may also be possible to estimate whether the plastic waste falls into a container or a non-container category, for example.

[0034] The target of product analysis is the overall shape of plastic waste that appears in individual images. For example, the overall shapes of plastic bags, films, hangers, and plastic toys have distinctive shapes that can be distinguished from others.

[0035] The analytical method for product analysis is an analytical model or an analytical program. The same parts of the analytical method for product analysis as those for material analysis will not be explained here. The learning data for the analytical model for product analysis is, for example, data that associates images of plastic waste with information indicating the type of product shown in the image. The output of the analytical model is a score for each type of product that is a candidate for analysis.

[0036] Another analytical method for product analysis is the use of an analysis program. Before using the analysis program, a product database of plastic waste is created. The product database is a database that associates types of plastic products with corresponding images. It is preferable to register multiple images. When using the analysis program, the processing device 15 performs image matching between the individual images of plastic waste and the product database, and identifies images that are similar to the individual images of plastic waste. This allows the type of product of the plastic waste in the individual images to be inferred. The degree of match in the matching process corresponds to the score. A higher degree of match in the matching process indicates a higher probability of matching the product type.

[0037] Furthermore, the results of product analysis can be used to improve the accuracy of material analysis. In other words, there is a correlation between the type of product and the product's constituent material. For example, plastic bags are likely to be made of soft plastic, hangers are likely to be made of hard plastic, and plastic toys are likely to be made of hard plastic. Therefore, if the product analysis results indicate a plastic bag, the plastic waste in the individual image is likely to be soft plastic. Specifically, the results of product analysis are used in the following manner. In this case, the product database described above additionally registers whether the product's constituent material is soft or hard plastic in association with the product. By using the analysis program described above, it is possible to estimate whether the plastic waste in the individual image is soft or hard plastic based on the product analysis results and the information added to the product database. In this case, the value obtained by multiplying the score corresponding to the product analysis results by a predetermined multiplier is used as a correction value. Then, the final score, which is the sum of the score calculated by material analysis and the correction value, is used to estimate whether the plastic waste is soft or hard plastic. The predetermined multiplier is a value for adjusting the magnitude of the score calculated by the product analysis to the magnitude of the score calculated by the material analysis. Furthermore, the predetermined multiplier also has the meaning of weighting, which adjusts the extent to which the analysis results of the product analysis are reflected in the results.

[0038] In this embodiment, the results of the product analysis are used to assist the material analysis. That is, the processing device 15 calculates a score based on the above-described material analysis (S105), calculates a correction value based on the above-described product analysis (S106), and calculates a final score by adding the score and the correction value (S107). Next, the processing device 15 classifies the plastic waste into soft plastics or hard plastics based on the magnitude of the final score (S108). This completes the processing for one individual image. Thereafter, the processing device 15 processes another individual image in the same way and classifies it as either soft plastics or hard plastics.

[0039] The processing device 15 stores the classification results in association with identification information for identifying the plastic waste. Thereafter, when the plastic waste reaches the sorting position, if the plastic waste is soft plastic, the sorting unit 16 is operated to blow it off to the soft plastic recovery unit 17, and if the plastic waste is hard plastic, the sorting unit 16 is not operated and the plastic waste falls into the hard plastic recovery unit 18.

[0040] As described above, plastic waste can be classified and separated into soft plastics and hard plastics. The method of Patent Document 1 does not allow for classification and separation according to the type of constituent material or product. A method of separating materials using wind power is also known, but even when using wind power, separation is based on the weight or specific gravity of the objects, so classification and separation according to the type of constituent material or product cannot be performed.

[0041] In this embodiment, the score calculated in the material analysis is calculated using a correction value calculated in the product analysis. Specifically, the score calculated in the material analysis using the analytical model is corrected by the score calculated in the product analysis using the analytical program. This allows flexible analysis using machine learning to be utilized while being supplemented by rule-based analysis.

[0042] However, the correction value calculated in the product analysis is not required, and the material analysis may be performed using only the score calculated in the material analysis. In this case, the score may be calculated using an analytical model or an analytical program. Also, the sorting device 1 may be used to classify and sort products according to the type of product rather than the constituent materials. In this case, the score may be calculated using an analytical model or an analytical program.

[0043] (Feature 1) As described above, the classification device 20 of this embodiment classifies plastic waste. The classification device 20 includes a receiving unit 14 and a processing device 15. The receiving unit 14 receives images obtained by photographing plastic waste in an imaging area. The processing device 15 extracts individual images for each plastic waste from the images received by the receiving unit 14, analyzes the surface shape or overall shape of the plastic waste that appears in the extracted individual images, and classifies the plastic waste according to the results of analyzing the analysis target using an analytical model constructed by machine learning or a rule-based analytical program.

[0044] This allows plastic waste to be classified according to the shape that appears in the individual images of the plastic waste, and therefore allows classification based on criteria other than weight, specific gravity, or size.

[0045] (Feature 2) In the classification device 20 of this embodiment, the processing device 15 classifies the plastic waste according to its constituent materials based on the results of analyzing the surface shape of the plastic waste that appears in the individual images using an analytical model or an analytical program.

[0046] It can meet the needs of processing plastic waste for each constituent material.

[0047] (Feature 3) In the sorting device 20 of this embodiment, the processing device 15 sorts plastic waste into items made of soft plastics and items made of hard plastics.

[0048] It can meet the needs of plastic waste disposal by separating it into soft and hard plastics.

[0049] (Feature 4) In the classification device 20 of this embodiment, the processing device 15 classifies the plastic waste according to the type of product based on the results of analyzing the overall shape of the plastic waste appearing in the individual images using an analytical model or an analytical program.

[0050] We can meet the needs of plastic waste disposal depending on the product and use.

[0051] (Feature 5) In the sorting device 20 of this embodiment, the processing device 15 sorts plastic waste into items whose product type corresponds to containers and other items.

[0052] It can meet the needs of treating plastic waste by separating it into containers and non-containers.

[0053] (Feature 6) In the classification device 20 of this embodiment, the processing device 15 classifies the plastic waste according to the results of analyzing the surface shape or overall shape of the plastic waste that appears in the individual images using an analytical model.

[0054] This allows the use of machine learning to classify plastic waste, taking into account aspects that cannot be determined by rule-based methods.

[0055] (Feature 7) In the classification device 20 of this embodiment, the processing device 15 classifies the plastic waste according to the results of analyzing the surface shape or overall shape of the plastic waste that appears in the individual images using an analysis program.

[0056] This allows plastic waste to be classified based on rules, allowing it to be classified according to pre-determined criteria.

[0057] (Feature 8) In the classification device 20 of this embodiment, the processing device 15 calculates a score corresponding to a classification candidate based on the results of analyzing the surface shape or overall shape of the plastic waste appearing in the individual images using an analytical model. The processing device 15 calculates a correction value corresponding to the classification candidate based on the results of analyzing the surface shape or overall shape of the plastic waste appearing in the individual images using an analysis program. The processing device 15 calculates a final score based on the sum of the score and the correction value. The processing device 15 classifies the plastic waste based on the final score.

[0058] Because correction values ​​are used from the analysis program, it is possible to accurately classify plastic waste that cannot be adequately classified using the analysis model alone.

[0059] (Feature 9) In the classification device 20 of this embodiment, the processing device 15 calculates a score that quantifies whether the plastic waste is likely to be soft plastic or hard plastic based on the results of analyzing the surface shape of the plastic waste appearing in the individual image using an analytical model. The processing device 15 calculates a correction value that quantifies whether the plastic waste is likely to be soft plastic or hard plastic based on the type of product obtained by analyzing the overall shape of the plastic waste appearing in the individual image using an analytical program. The processing device 15 calculates a final score based on the sum of the score and the correction value. The processing device 15 classifies the plastic waste as soft plastic or hard plastic based on the final score.

[0060] Because correction values ​​from the analysis program are used, plastic waste that cannot be adequately classified using the analysis model alone can be accurately classified as either soft plastic or hard plastic. (Feature 10) The sorting device 1 of this embodiment includes a sorting device 20, a camera 12, and a sorting unit 16. The camera 12 captures images. The sorting unit 16 moves the plastic waste to a collection position according to the classification results from the sorting device 20.

[0061] This allows plastic waste to be moved and collected at locations according to classification criteria other than weight, specific gravity, or size.

[0062] The classification device 20 or the sorting device 1 can be realized by combining the above-described features 1 to 10, for example, as follows. The same applies to the classification method or sorting method. [Configuration 1] Classification device 20 of feature 1. [Configuration 2] Classification device 20 having feature 2 in addition to configuration 1. [Configuration 3] Classification device 20 having feature 3 in addition to configuration 2. [Configuration 4] Classification device 20 having feature 4 in addition to any one of configurations 1 to 3. [Configuration 5] Classification device 20 having feature 5 in addition to configuration 4. [Configuration 6] Classification device 20 having feature 6 in addition to any one of configurations 1 to 5. [Configuration 7] Classification device 20 having feature 7 in addition to any one of configurations 1 to 6. [Configuration 8] Classification device 20 having feature 8 in addition to configuration 1. [Configuration 9] Classification device 20 having feature 9 in addition to configuration 1.

[0063] The preferred embodiment of the present application has been described above, but the above configuration can be modified, for example, as follows. Each modification may be made alone, or multiple modifications may be made in any combination.

[0064] The flowcharts shown in the above embodiments are merely examples, and some processes may be omitted, the contents of some processes may be changed, or new processes may be added.

[0065] In the above embodiment, plastic waste is classified and sorted into two types. Alternatively, plastic waste may be classified and sorted into three or more types. For example, plastic waste may be classified into container plastics, non-container and soft plastics, and non-container and hard plastics. In this case, both material analysis and product analysis are performed on the individual images.

[0066] The functions of the elements disclosed herein can be performed using circuits or processing circuits, including general-purpose processors, special-purpose processors, integrated circuits, ASICs (Application Specific Integrated Circuits), conventional circuits, and / or combinations thereof, configured or programmed to perform the disclosed functions. A processor is considered a processing circuit or circuit because it includes transistors and other circuitry. In this disclosure, a circuit, unit, or means is hardware that performs the recited functions or hardware that is programmed to perform the recited functions. The hardware may be hardware disclosed herein or other known hardware that is programmed or configured to perform the recited functions. Where the hardware is a processor, which is considered a type of circuit, the circuit, means, or unit is a combination of hardware and software, and the software is used to configure the hardware and / or processor.

[0067] 1 Sorting device 12 Camera 13 Lighting device 14 Receiving unit 15 Processing device 16 Sorting unit

Claims

1. A classification device for classifying plastic waste, comprising: a receiving unit that receives images obtained by photographing the plastic waste in a photographing area; and a processing device that extracts individual images for each piece of plastic waste from the images received by the receiving unit, analyzes the surface shape or overall shape of the plastic waste that appears in the extracted individual images, and classifies the plastic waste according to the results of analyzing the analysis target using an analytical model constructed by machine learning or a rule-based analytical program.

2. A classification device as described in claim 1, wherein the processing device classifies the plastic waste according to its constituent materials based on the results of analyzing the surface shape of the plastic waste appearing in the individual images using the analytical model or the analytical program.

3. A sorting device according to claim 2, wherein the processing device sorts the plastic waste into items made of soft plastics and items made of hard plastics.

4. A classification device as described in claim 1, wherein the processing device classifies the plastic waste according to the type of product based on the results of analyzing the overall shape of the plastic waste appearing in the individual images using the analytical model or the analytical program.

5. A sorting device according to claim 4, wherein the processing device sorts the plastic waste into items whose product type corresponds to containers and other items.

6. A classification device as described in claim 1, wherein the processing device classifies the plastic waste according to the results of analyzing the surface shape or overall shape of the plastic waste appearing in the individual images using the analytical model.

7. A classification device as described in claim 1, wherein the processing device classifies the plastic waste according to the results of analyzing the surface shape or overall shape of the plastic waste that appears in the individual images using the analysis program.

8. A classification device as described in claim 1, wherein the processing device calculates a score corresponding to a classification candidate based on the results of analyzing the surface shape or overall shape of the plastic waste appearing in the individual images using the analytical model, the processing device calculates a correction value corresponding to the classification candidate based on the results of analyzing the surface shape or overall shape of the plastic waste appearing in the individual images using the analytical program, the processing device calculates a final score based on the sum of the score and the correction value, and the processing device classifies the plastic waste based on the final score.

9. A classification device as described in claim 1, wherein the processing device calculates a score that quantifies whether the plastic waste is likely to be soft plastic or hard plastic based on the results of analyzing the surface shape of the plastic waste appearing in the individual image using the analytical model, the processing device calculates a correction value that quantifies whether the plastic waste is likely to be soft plastic or hard plastic based on the type of product as a result of analyzing the overall shape of the plastic waste appearing in the individual image using the analytical program, the processing device calculates a final score based on the sum of the score and the correction value, and the processing device classifies the plastic waste as soft plastic or hard plastic based on the final score.

10. A sorting device comprising: the sorting device according to claim 1; a camera for taking images; and a sorting unit for moving the plastic waste to a collection position according to the classification results obtained by the sorting device.

11. A classification method for classifying plastic waste, comprising: photographing the plastic waste in a photographing area to generate images; extracting individual images for each of the plastic waste from the generated images; analyzing the surface shape or overall shape of the plastic waste that appears in the individual images; and classifying the plastic waste according to the results of analyzing the analysis object using an analytical model constructed by machine learning or a rule-based analytical program.

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