System for counting quantity of game tokens

Abstract

To provide a recognition system of a chip having a setup capable of recognizing the color of a chip according to an image and counting the quantity of chips under a different illumination environment.SOLUTION: In a chip recognition system 10, a chip W is configured to at least partially have a specific color 121 showing the value of the chip W. The chip recognition system 10 includes: a recording device 11 for recording a state of the chip W as an image by using a camera 212; an image analysis device 14 for recognizing at least two colors of the specific color 121, and a reference color 4a differing from the specific color 121 existing in the recorded image by analyzing the image; and a recognition device 12 at least including an artificial intelligence device 12a for identifying the specific color 121 of the chip W by using an image analysis result by the image analysis device 14. Multiple images of the chip W irradiated with light having different illuminance, and the reference color 121 are taught to an artificial intelligence device 12a of the chip recognition device 12 as teacher data.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a system that recognizes a large number of chips used in a gaming house by image for each type and measures the number of chips for each type of chip.

Background Art

[0002] Conventionally, in gaming houses such as casinos, gaming tokens (hereinafter referred to as "chips") are used, and games are bet and paid back with chips. In order to accurately recognize the chips stacked on the table, a method of recognizing the chips as an image using a camera has been adopted. Patent Document 1 discloses an example of a system that recognizes the movement of chips during a game with a camera.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When recognizing chips with a camera based on an image, it is required to recognize not only the outline of the chips but also the difference in the color of the chips, and to grasp the number of stacked chips for each type of chip. However, even if the chips recognized by the camera are of the same type, depending on the lighting environment where the chips are placed, they are imaged or recognized as different colors and are not recognized as the same color. Therefore, conventionally, it has not been possible to count the number for each type from the image.

[0005] The present invention has been made in view of such problems, and provides a chip recognition system having a mechanism that can recognize the color of chips by an image and measure the number of chips even under different lighting environments.

Means for Solving the Problems

[0006] A chip recognition system according to one aspect of the present invention is a chip recognition system for recognizing chips used on a gaming table in a casino, wherein the chip has a configuration having at least partially a specific color indicating the value of the chip, a recording device that records the state of the chip as an image using a camera, an image analysis device that performs image analysis on the recorded image to recognize at least two colors including the specific color and a reference color different from the specific color present in the image, a recognition device including at least an artificial intelligence device that specifies the specific color of the chip using the image analysis result by the image analysis device, The artificial intelligence device of the chip recognition device is an artificial intelligence device taught with a plurality of images of the chip and the reference color irradiated with different illuminances as teaching data.

[0007] Furthermore, the chip has at least a specific color indicating the value of the chip at a predetermined position or in a predetermined shape.

[0008] Furthermore, the recognition device specifies the number of the chips by specifying the specific color of the chips. Also, the number of chips for each specific color may be specified by specifying the specific color of a plurality of the chips for each chip.

[0009] Furthermore, the artificial intelligence device of the recognition device is taught with a plurality of images of the reference color and the chip irradiated with different lighting environments as teaching data. Also, the specific color of the chip may be determined using the relative relationship with the reference color.

[0010] And the recognition device determines the specific color of a plurality of stacked chips, and may have a structure capable of determining the specific color or the number of chips even when a part is hidden due to a blind spot of the camera.

[0011] The recognition system for recognizing an article according to one aspect of the present invention includes The article is configured to at least partially have a specific color that can identify the article or its packaging on the article itself or the packaging, A recording device that records the state of the article as an image using a camera, An image analysis device that performs image analysis on the recorded image to recognize at least two colors, namely the specific color and a reference color different from the specific color present in the image, A recognition device that includes at least an artificial intelligence device that identifies the specific color of the article itself or the packaging using the image analysis result by the image analysis device. The artificial intelligence device of the article recognition device is an artificial intelligence device taught with a plurality of images of the reference color and the specific color of the article itself or the packaging irradiated with different illuminances as teacher data.

[0012] Furthermore, the article itself or the packaging at least partially has a specific color that can identify the article or the packaging at a predetermined position or in a predetermined shape.

[0013] Furthermore, the recognition device identifies the number of the articles by identifying the specific color of the article itself or the packaging. Also, the number of articles for each specific color may be identified by identifying the specific colors of a plurality of the articles themselves or the packaging for each article.

[0014] Moreover, the artificial intelligence device of the recognition device is taught with a plurality of images of the reference color and the specific color of the article itself or the packaging irradiated with different lighting environments as teacher data. Also, it may be configured to determine the specific color of the article itself or the packaging using the relative relationship with the reference color.

[0015] The recognition device determines the specific color of a plurality of stacked articles themselves or their packaging, and is configured to be able to determine the specific color even when a part is hidden due to the blind spot of the camera. Also, it determines the specific color of a plurality of stacked articles themselves or their packaging, and is configured to be able to determine the total number of the articles or the number of articles for each specific color even when a part is hidden due to the blind spot of the camera.

[0016] A chip recognition system according to an aspect of the present invention is a chip recognition system for recognizing chips used in a gaming table in a playground, wherein the chip is configured to have at least partially a specific color indicating the value of the chip, a recording device that records the state of the chip as an image using a camera, an image analysis device that performs image analysis on the recorded image to recognize at least two colors including the specific color and a reference color different from the specific color present in the image, and a recognition device including at least an artificial intelligence device that specifies the specific color of the chip using the image analysis result by the image analysis device. The artificial intelligence device of the recognition device extracts a center line from the image of the chip, and by performing image analysis on the peripheral image within a predetermined range centered on the center line, recognizes at least two colors including the specific color and the reference color different from the specific color in the peripheral image. It is an artificial intelligence device taught with a plurality of peripheral images of the chip and the reference color irradiated with different illuminations as teacher data.

[0017] A recognition system for recognizing an article according to an aspect of the present invention is a recognition system for recognizing an article, wherein the article is configured to have at least partially a specific color capable of specifying the article or its packaging on the article itself or its packaging, a recording device that records the state of the article as an image using a camera, An image analysis device that performs image analysis on the recorded image to recognize at least two colors including the specific color and a reference color different from the specific color present in the image, A recognition device including at least an artificial intelligence device that specifies a specific color of the article itself or the packaging using the image analysis result by the image analysis device. The artificial intelligence device of the recognition device is configured to recognize a specific color from an image of the article itself or the packaging, extract an image portion of the specific color, and perform image analysis on a peripheral image of the specific color, thereby recognizing at least two colors including the specific color and the reference color in the peripheral image. It is an artificial intelligence device taught using a plurality of the peripheral images of the specific color and the reference color of the article itself or the packaging irradiated with different illuminances as teacher data.

Advantages of the Invention

[0018] According to the present invention, chips used on a gaming table can be recognized by type and counted from an image.

Brief Description of the Drawings

[0019]

Figure 1

Figure 2

Figure 3

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Figure 6

Embodiments for Carrying Out the Invention

[0020] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In each figure, components having the same function are denoted by the same reference numerals, and detailed descriptions of components denoted by the same reference numerals will not be repeated.

[0021] FIG. 1 is a diagram schematically showing a chip recognition system 10 according to an embodiment of the present invention. As shown in FIG. 1, in the present embodiment, a camera 212 for imaging the state of chips W stacked on a gaming table 4 is provided outside the gaming table 4. The chip W has a configuration having at least partially a specific color 121 indicating the value of the chip W as shown in FIG. 2, a recording device 11 for recording the state of the chip W as an image using the camera 212, an image analysis device 14 for performing image analysis on the recorded image to recognize at least two colors including the specific color 121 and a reference color R different from the specific color 121 present in the image, and a recognition device 12 including at least an artificial intelligence device 12a for specifying the specific color 121 of the chip W using the image analysis result by the image analysis device 14. The artificial intelligence device 12a of the chip recognition device 12 is an artificial intelligence device taught with a plurality of images of the chip W and the reference color 121 irradiated with different illuminances as teaching data. Note that the chip recognition system 10 according to the present embodiment is communicably connected to the camera 212.

[0022] FIG. 3 is a block diagram showing a schematic configuration of the chip recognition system 10 according to the present embodiment. As shown in FIG. 3, the chip recognition system 10 includes a recording device 11, a recognition device 12, a learning machine 13, and an image analysis device 14. Note that at least a part of the chip recognition system 10 is realized by a computer.

[0023] The recording device 11 includes a fixed-type data storage such as a hard disk, for example. The recording device 11 records the state of the chips W stacked on the gaming table 4 as an image captured by the camera 212. Note that the image may be a moving image or a series of still images. The recording device 11 may assign an index or a time to the image acquired from the camera 212 so that the captured recording can be analyzed later by a recognition device described later.

[0024] The image analysis device 14 performs image analysis on the image recorded by the recording device 11, and recognizes at least two colors of a reference color R different from the specific color 121 that is at least partially applied to the chip W and present in the image. Note that the specific color 121 is at least partially applied in a predetermined position or a predetermined shape of the chip W. For example, it may be applied in the circumferential direction on the side surface of the chip W, or may be applied as a predetermined mark on the surface of the chip W. Also, the reference color R may be, for example, the color of a specific area of the gaming table 4, or a color applied at a location different from the specific color 121 in the chip W.

[0025] The recognition device 12 includes an artificial intelligence device 12a that identifies a specific color by, for example, deep learning technology, using the image analysis result by the image analysis device 14, and determines the number and types of the chips W arranged on the gaming table 4. The recognition device 12 may further determine the position of the chip W on the gaming table 4. As shown in FIG. 3, the recognition device 12 outputs the determination result to the output device 15. The output device 15 may output the determination result of the recognition device 12 as character information to a monitor or the like on the gaming table 4.

[0026] In this embodiment, the learning machine 13 acquires, through the image analysis device 14, a plurality of images of the chip W and the reference color R irradiated with different illuminances recorded by the recording device 11, and learns by having a person teach, as teacher data, the acquired images and the correct colors of the specific color 121 of the chip W and the reference color R in each image, thereby creating a learning model 13a (recognition program). Note that, for images of the chip W and the reference color R irradiated with the same illuminance condition, since the specific color 121 and the reference color R are irradiated with the same illuminance, the relative relationship between the specific color 121 and the reference color R can be obtained. For example, this relative relationship may be used for the recognition of the specific color 121.

[0027] By repeatedly performing the teaching operation of having a person input the above teacher data into the learning machine 13 for learning, the identification accuracy of the specific color 121 of the chip W by the learning model 13a possessed by the learning machine 13 can be improved. Even when a part of the plurality of chips W placed on the gaming table 4 is hidden due to the blind spot of the camera 212, the learning machine 13 repeatedly learns such images, thereby making it possible to create a learning model 13a capable of determining the specific color 121 of the chip W on the gaming table 4.

[0028] The created learning model 13a can be input into the artificial intelligence device 12a via an external medium such as a USB memory or an HDD, or a communication network.

[0029] Also, as shown in FIG. 3, the images of the chip W and the reference color R and the determination result of the image recognition device 12 may be used as teacher data input to the learning machine 13.

[0030] It should be noted that various modifications can be made based on the above-described embodiment. Hereinafter, with reference to the drawings, an example of a modification will be described. In the following description and the drawings used in the following description, for parts that can be configured in the same manner as in the above-described embodiment, the same reference numerals as those used for the corresponding parts in the above-described embodiment are used, and redundant descriptions are omitted.

[0031] Figure 4 is a diagram schematically showing an article recognition system 20 according to another embodiment of the present invention. As shown in Figure 4, in this embodiment, a camera 212 for imaging the state of an article B arranged on an article display shelf 5 is provided outside the article display shelf 5. The article B is configured to at least partially have a specific color 121 capable of identifying the article or its packaging on the article itself or its packaging, a recording device 11 for recording the state of the article B as an image using the camera 212, an image analysis device 14 for performing image analysis on the recorded image to recognize at least two colors of a reference color R different from the specific color 121 present in the image, and a recognition device 12a including at least an artificial intelligence device 12a for specifying the specific color 121 of the article B using the image analysis result by the image analysis device 14. The artificial intelligence device 12a of the article recognition device 12 is an artificial intelligence device taught with a plurality of images of the reference color R and the specific color 121 of the article B itself or its packaging irradiated with different illuminances as teaching data. The article recognition system 20 according to this embodiment is communicably connected to the camera 212.

[0032] The article recognition system 20 has a recording device 11, a recognition device 12, a teaching device 13, and an image analysis device 14. Note that at least a part of the article recognition system 10 is realized by a computer. The recording device 11 includes a fixed-type data storage such as a hard disk, for example. The recording device 11 records the state of the article B arranged on the article display stand 5 as an image captured by the camera 212. Note that the image may be a moving image or a series of still images. The recording device 11 may assign an index or a time to the image acquired from the camera 212 so that the imaging record can be analyzed later by a recognition device described later.

[0033] The image analysis device 14 analyzes the image recorded by the recording device 11, and recognizes at least two colors of a reference color R different from the specific color 121 that is at least partially applied to the article B and present in the image. The specific color 121 applied to the article B itself or the packaging is at least partially present at a predetermined position or in a predetermined shape on the article B itself or the packaging, and may be applied to any position on the article B itself or the packaging, and its shape may be diverse. Also, the reference color R may be, for example, the color of a part of the frame of the article display shelf 5, or the color of the wall in the background.

[0034] The recognition device 12 includes an artificial intelligence device 12a that uses the image analysis result by the image analysis device 14 to identify a specific color, for example, by deep learning technology, and determines the number and types of articles B arranged on the article display shelf 5. The recognition device 12 may further determine the position of the article B arranged on the article display shelf 5.

[0035] In this embodiment, the learning machine 13 acquires, through the image analysis device 14, a plurality of images of the article B itself or the packaging irradiated with different illuminances and the reference color R recorded by the recording device 11, and learns by a person teaching, as teacher data, the acquired images and the correct colors of the specific colors applied to the article B itself or the packaging of each image, and creates a learning model 13a (recognition program). Note that for images of the article B and the reference color R irradiated with the same illuminance condition, since the specific color 121 and the reference color R are illuminated with the same illuminance, the relative relationship between the specific color 121 and the reference color R can be obtained. For example, this relative relationship may be used for the recognition of the specific color 121.

[0036] By repeatedly performing the teacher operation of having a person input the above teacher data into the learning machine 13 for learning, the specific accuracy of the specific color attached to the article B itself or the packaging by the learning model 13a possessed by the learning machine 13 can be enhanced. Even when a part of the plurality of articles B placed on the article display shelf 5 is hidden due to the blind spot of the camera 212, the learning machine 13 can create a learning model 13a capable of determining the specific color of the article B on the article display shelf 5 by repeatedly learning such images.

[0037] The created learning model 13a can be input into the artificial intelligence device 12a via an external medium such as a USB memory or an HDD, or a communication network.

[0038] Also, as shown in FIG. 3, the image of the specific color 121 of the article B itself or the packaging and the reference color R and the determination result of the image recognition device 12 may be used as teacher data to be input into the learning machine 13.

[0039] FIG. 5 is a diagram schematically showing another example when determining the color of the chip. In the present embodiment, the artificial intelligence device 12a of the chip recognition device 12 extracts the center line C of the chip W from the image of the chip W using artificial intelligence.

[0040] Specifically, as shown in FIG. 3, the learning machine 13 acquires a plurality of images of the center line C of the chip W irradiated with different illuminances recorded by the recording device 11 through the image analysis device 14, and learns by a person teaching the acquired images and the correct positions of the center line C of the chip W in each image as teacher data, thereby creating a learning model 13a (recognition program).

[0041] By repeatedly performing the teacher operation of having a person input the above teacher data into the learning machine 13 for learning, the identification accuracy of the center line C of the chip W by the learning model 13a possessed by the learning machine 13 can be improved. Even when a plurality of chips W placed on the gaming table 4 are partially hidden due to the blind spot of the camera 212, the learning machine 13 can create a learning model 13a capable of determining the center line C of the chip W on the gaming table 4 by repeatedly learning such images.

[0042] The created learning model 13a can be input into the artificial intelligence device 12a via an external medium such as a USB memory or an HDD, or a communication network, etc., so that the artificial intelligence device 12a can extract the center line C of the chip W from the image of the chip W using artificial intelligence.

[0043] When performing image analysis on the center line C from the image, the image may be analyzed as it is, or it may be analyzed after performing image processing such as color enhancement and noise removal so that the recognition of the center line C becomes easy.

[0044] Also, the chip recognition device 12 may extract the center line C of the chip W by a method of measuring features on the image such as shape, brightness, saturation, and color tone, using the image captured by the camera 212 and recorded as an image and further using the result of the image analysis, without using artificial intelligence.

[0045] As shown in FIG. 6, the artificial intelligence device 12a further analyzes a peripheral image of a predetermined range around the extracted center line C (for example, a range of 8 pixels orthogonal to the center line centered on the center line), and is configured to recognize at least two colors of the reference color R different from the specific color 121 in the peripheral image. When performing image analysis on the peripheral image of a predetermined range around the extracted center line C, the image may be analyzed as it is, or it may be analyzed after performing image processing such as color enhancement and noise removal so that the recognition of the specific color 121 becomes easy.

[0046] The artificial intelligence device 12a is an artificial intelligence device taught with a plurality of images of the chip W and the reference color 121 irradiated with different illuminances as teaching data. Note that for the peripheral image around the center line C of the chip W irradiated with the same illuminance, since the specific color 121 and the reference color R are illuminated with the same illuminance, the relative relationship between the specific color 121 and the reference color R can be obtained. For example, this relative relationship may be used for the recognition of the specific color 121.

[0047] Further, the chip recognition device 12 may recognize the specific color 121 by using a method of measuring features on the image such as shape, brightness, saturation, and color tone, using the result of image analysis of an image captured by the camera 212 and recorded without using artificial intelligence.

[0048] In summary, the artificial intelligence device 12a of the recognition device 12 extracts the center line C from the image of the chip W, and by performing image analysis on the peripheral image within a predetermined range centered on the center line C, it has a configuration for recognizing at least two colors including the specific color 121 in the peripheral image and a reference color R different from the specific color 121. It is an artificial intelligence device taught with a plurality of the peripheral images of the chip W and the reference color R irradiated with different illuminances as teaching data.

[0049] In another embodiment when determining an article, the artificial intelligence device 12a of the article recognition device 12 extracts the specific color 121 of the article B itself or the package from the image of the article B itself or the package using artificial intelligence.

[0050] Specifically, as shown in FIG. 3, the learning machine 13 obtains, through the image analysis device 14, a plurality of images of the article B itself or the package and the reference color R irradiated with different illuminances recorded by the recording device 11, and the obtained images and the correct positions of the specific color 121 attached to the article B itself or the package of each image are taught by a person as teaching data to perform learning and create a learning model 13a (recognition program).

[0051] By repeatedly performing the teacher operation of having a person input the above teacher data into the learning machine 13 for learning, it is possible to improve the specific accuracy of the specific color attached to the article B itself or the packaging by the learning model 13a possessed by the learning machine 13. Even when a part of the plurality of articles B placed on the article display shelf 5 is hidden due to the blind spot of the camera 212, the learning machine 13 can create a learning model 13a that can determine the specific color of the article B on the article display shelf 5 by repeatedly learning such images.

[0052] The created learning model 13a can be input into the artificial intelligence device 12a via an external medium such as a USB memory or an HDD, or a communication network. As a result, the artificial intelligence device 12a can extract the specific color 121 part attached to the article B itself or the packaging from the image of the article B using artificial intelligence.

[0053] When analyzing the specific color 121 part from the image, the image may be analyzed as it is, or it may be analyzed after performing image processing such as color enhancement and noise removal so that the recognition of the specific color 121 part becomes easier.

[0054] Also, the article recognition device 12 may extract the specific color 121 part of the article B itself or the packaging by a method of measuring features on the image such as shape, brightness, saturation, and color tone without using artificial intelligence.

[0055] The artificial intelligence device 12a further includes a configuration for recognizing at least two colors different from the specific color 121, i.e., the reference color R, in the peripheral image by analyzing a peripheral image of a predetermined range around the extracted specific color 121 part (for example, a range of 8 pixels around the specific color part). When analyzing the peripheral image of a predetermined range around the extracted specific color 121 part, the image may be analyzed as it is, or it may be analyzed after performing image processing such as color enhancement and noise removal so that the recognition of the specific color 121 part becomes easier.

[0056] The artificial intelligence device 12a is an artificial intelligence device taught with a plurality of images of the specific color 121 portion of the article B itself or its packaging irradiated with different illuminances and a reference color 121 as teaching data. Note that for the peripheral image of the specific color 121 portion of the article B itself or its packaging irradiated with the same illuminance condition, since the specific color 121 and the reference color R are illuminated with the same illuminance, the relative relationship between the specific color 121 and the reference color R can be obtained. For example, this relative relationship may be used for the recognition of the specific color 121.

[0057] Also, the article recognition device 12 may recognize the specific color 121 by using a method of measuring features on the image such as shape, brightness, saturation, and hue, using the result of image analysis of an image captured by the camera 212 and recorded without using artificial intelligence.

[0058] In summary, the artificial intelligence device 12a of the recognition device 12 recognizes the specific color 121 from the image of the article B itself or its packaging, extracts the image portion of the specific color 121, and by performing image analysis on the peripheral image of the specific color 121, it is configured to recognize at least two colors, namely the specific color 121 and the reference color R, in the peripheral image, and it is an artificial intelligence device taught with a plurality of the peripheral images of the specific color 121 and the reference color R of the article B itself or its packaging irradiated with different illuminances as teaching data.

[0059] The above-described embodiments are described for the purpose of enabling those with ordinary knowledge in the technical field to which the present invention pertains to implement the present invention. Various modifications of the above embodiments are naturally possible for those skilled in the art, and the technical idea of the present invention can also be applied to other embodiments. Therefore, the present invention should not be limited to the described embodiments, but should be within the broadest scope in accordance with the technical idea defined by the claims.

Explanation of Reference Numerals

[0060] 4 Game table 5 Article display shelf 10 Chip recognition system 11 Recording device 12 Recognition device 12a Artificial intelligence device 13 Learning machine 13a Learning model 14 Image analysis device 15 Output device 20 Item recognition system 121 Specific color 212 Camera W Chip B Item R Reference color C Center line

Claims

1. A chip recognition system for use in a gaming table in a playground, wherein the chip has a configuration having at least partially a specific color indicating the value of the chip, a recording device for recording the state of the chip as an image using a camera, an image analysis device for performing image analysis on the recorded image to recognize at least two colors including the specific color and a reference color different from the specific color present in the image, a recognition device including at least an artificial intelligence device for specifying the specific color of the chip using the image analysis result by the image analysis device, The artificial intelligence device of the chip recognition device is an artificial intelligence device taught with a plurality of images of the chip and the reference color irradiated with different illuminances as teaching data.

2. The recognition system according to claim 1, wherein the chip has at least a specific color indicating the value of the chip at a predetermined position or in a predetermined shape.

3. The recognition system according to claim 1 or 2, wherein the recognition device specifies the number of the chips by specifying the specific color of the chips.

4. The recognition system according to claims 1 to 3, wherein the recognition device specifies the number of chips for each specific color by specifying the specific color of a plurality of the chips for each chip.

5. The recognition system according to any one of claims 1 to 4, wherein the artificial intelligence device of the recognition device is an artificial intelligence device taught with a plurality of images of the reference color and the chip irradiated with different lighting environments as teaching data.

6. The recognition system according to any one of claims 1 to 5, wherein the artificial intelligence device of the recognition device is an artificial intelligence device for determining the specific color of the chip using the relative relationship with the reference color.

7. The recognition system according to any one of claims 1 to 6, wherein the recognition device determines the specific color of a plurality of stacked chips, and has a structure capable of determining the specific color or the number of chips even when a part is hidden due to a blind spot of the camera.

8. A recognition system for recognizing an article, wherein the article has a configuration having at least partially a specific color capable of specifying the article or the package on the article itself or the package. A recording device that records the state of the article as an image using a camera, An image analysis device that performs image analysis on the recorded image and recognizes at least two colors, namely the specific color and a reference color different from the specific color present in the image, A recognition device including at least an artificial intelligence device that specifies a specific color of the article itself or the packaging using the image analysis result by the image analysis device, The artificial intelligence device of the article recognition device is an artificial intelligence device taught with a plurality of images of the reference color and the specific color of the article itself or the packaging irradiated with different illuminances as teacher data. Recognition system.

9. The recognition system according to claim 8, The article itself or the packaging has at least partially a specific color capable of specifying the article or the packaging at a predetermined position or in a predetermined shape. Chip recognition system.

10. The recognition system according to claim 8 or 9, The recognition device specifies the number of articles by specifying the specific color of the article itself or the packaging. Chip recognition system.

11. The recognition system according to claim 10, The recognition device specifies the number of articles for each specific color by specifying the specific colors of a plurality of the articles themselves or the packaging for each article. Chip recognition system.

12. The recognition system according to any one of claims 8 to 11, The artificial intelligence device of the recognition device is an artificial intelligence device taught with a plurality of images of the reference color and the specific color of the article itself or the packaging irradiated with different lighting environments as teacher data. Recognition system.

13. The recognition system according to any one of claims 8 to 12, The artificial intelligence device of the recognition device is an artificial intelligence device that determines the specific color of the article itself or the packaging using the relative relationship with the reference color. Recognition system.

14. The recognition system according to any one of claims 8 to 13, The recognition device determines the specific colors of a plurality of stacked articles themselves or the packaging, and is configured to be able to determine the specific color even when a part is hidden due to a blind spot of the camera. Recognition system.

15. The recognition system according to any one of claims 8 to 14, The recognition device determines the specific colors of the stacked plurality of the articles themselves or the packages, and has a configuration capable of determining the total number of the articles or the number of articles for each of the specific colors even when a part is hidden due to a blind spot of the camera. Recognition system.

Images