Product appearance imaging system, information processing device control method and program

Abstract

To provide a product appearance photography system, information processing device, and program that can reduce the physical and time burden on those involved in the distribution of scaffolding. [Solution] The product appearance photography system 100 includes a product image receiving unit 31 that receives a product image taken by a photographer, an appearance area identification unit 34 that identifies the appearance area of ​​the product included in the received product image, an appearance area information provision control unit 35 that transmits information regarding the appearance area of ​​the product identified by the appearance area identification unit 34 to the photographer, and a confirmed appearance area information receiving unit 36 ​​that receives confirmed appearance area information, which is information regarding the appearance area of ​​the target product after it has been confirmed by the photographer.

Description

Technical Field

[0001] The present invention relates to a product appearance photographing system, an information processing apparatus control method, and a program.

Background Art

[0002] Conventionally, a technique is known that acquires product information by performing image analysis on a product image that includes a product to be analyzed as a subject. For example, Patent Document 1 describes this type of technique.

[0003] Patent Document 1 discloses an information acquisition unit that captures appearance information related to the appearance of a product to be inspected via a user terminal and acquires product name information related to the product name of the captured product, and a trained model in which three or more levels of correlation between reference appearance information obtained by capturing images of product appearances in the past and product quality are defined, with the reference appearance information as input and the product quality as output. The system further includes an estimation unit that estimates the product quality by prioritizing items with higher correlation based on the reference appearance information corresponding to the appearance information acquired by the information acquisition unit. The estimation unit pre-stores three or more levels of correlation between the reference appearance information and product quality for each product name, reads the correlation level for the product name corresponding to the product name information acquired by the information acquisition unit, and estimates the product quality, describing a product quality inspection system. In addition, paragraph 0021 of Patent Document 1 states that products include those from all fields, and may be steel, materials, electrical products, machinery, building members, circuit components, semiconductor chips, and the like. Paragraph 0022 describes that quality includes not only the high performance of a so-called product, but also the purity and yield in the case of materials, and for products embodied as articles, matters related to appearance factors such as the presence or absence and degree of scratches, dirt, and impurities are also included.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

[0005] The product quality inspection system described in Patent Document 1 aims to provide a product quality inspection system that can automatically and accurately estimate the quality of products produced in a factory without relying on human intervention, and is characterized by utilizing a pre-trained model with all aspects of quality of all products in mind. Indeed, analysis using pre-trained models has yielded significant results, particularly in the field of image recognition combined with deep learning, so the era in which a versatile product quality inspection system like the one described in the prior art document will be put into practical use may eventually arrive.

[0006] However, the problems the applicant currently faces are more realistic and serious. One of the co-applicants deals in construction and demolition-related products, particularly scaffolding used at construction and demolition sites. They purchase scaffolding from manufacturers throughout Japan, ship it by truck to construction and demolition sites nationwide, and also collect used scaffolding and resell it on their website. Scaffolding is (A) manufactured and shipped from a factory, (B) purchased scaffolding is temporarily stored outdoors in open piles, (C) temporarily stored scaffolding is eventually loaded onto trucks, (D) unloaded at construction and demolition sites, (E) used scaffolding is collected by truck, (F) temporarily stored outdoors in open piles, and (G) reshipped. In order to properly distribute scaffolding, it is necessary to manage the quality and quantity of the scaffolding at all stages from (A) to (G) above. However, because scaffolding is heavy and long, even counting it, let alone quality inspection, is hard work and takes a long time. The significant amount of manpower and time required for scaffolding management results in delays in loading trucks and other issues, leading to frequent losses of valuable sales opportunities and hindering the smooth distribution of scaffolding. It was desired to achieve at least one, preferably all, of the following: (1) reducing the physical and time burden on those involved in the distribution of scaffolding (both the shipping and receiving sides), (2) sharing information about the status of scaffolding at various stages of distribution, and (3) achieving the greatest cost performance based on the current level of technology.

[0007] The present invention aims to provide a product appearance photography system, information processing device, and program that can reduce the physical and time burden on those involved in the distribution of scaffolding. [Means for solving the problem]

[0008] One aspect of the present invention is a product appearance photography system, comprising: product image receiving means for receiving a product image taken by a photographer; appearance region identifying means for identifying an appearance region of the product included in the received product image; appearance region information providing control means for transmitting information regarding the appearance region of the product identified by the appearance region identifying means to the photographer; and confirmed appearance region information receiving means for receiving confirmed appearance region information, which is information regarding the appearance region of the product after it has been confirmed by the photographer.

[0009] Furthermore, one aspect of the present invention is a control method for an information processing device, comprising: a product image receiving step of receiving a product image taken by a photographer; an appearance area identification step of identifying an appearance area of ​​the product included in the received product image; an appearance area information provision control step of transmitting information regarding the appearance area of ​​the product identified by the appearance area identification step to the photographer; and a confirmed appearance area information receiving step of receiving confirmed appearance area information, which is information regarding the appearance area of ​​the product after it has been confirmed by the photographer.

[0010] Furthermore, one aspect of the present invention is a program for causing a computer to execute the following steps: a product image receiving step of receiving a product image taken by a photographer; an appearance area identification step of identifying an appearance area of ​​the product included in the received product image; an appearance area information provision control step of transmitting information regarding the appearance area of ​​the product identified by the appearance area identification step to the photographer; and a confirmed appearance area information receiving step of receiving confirmed appearance area information, which is information regarding the appearance area of ​​the product after it has been confirmed by the photographer. [Effects of the Invention]

[0011] According to the present invention, it is possible to provide a product appearance photography system, information processing device, and program that can reduce the physical and time burden on those involved in the distribution of scaffolding. [Brief explanation of the drawing]

[0012] [Figure 1] This is a schematic diagram showing the configuration of a product appearance imaging system according to one embodiment of the present invention. [Figure 2] This is a flowchart showing the product distribution cycle. [Figure 3] This is a block diagram showing the hardware configuration of an information processing device. [Figure 4] A block diagram showing the hardware configuration of the user terminal. [Figure 5] This is a functional block diagram showing an example of the functional configuration of a product appearance imaging system. [Figure 6] This figure shows an example of the display of the shooting screen on the user's terminal. [Figure 7] This figure shows the first example of an error screen displayed on the user's terminal. [Figure 8] This figure shows a second example of the error screen displayed on the user's terminal. [Figure 9] This figure shows an example of the analysis screen displayed on the user's terminal. [Figure 10] This figure shows an example of the display of the analysis screen after image enhancement processing has been applied, as shown on the user's terminal. [Figure 11] This is a flowchart showing the processing flow of the product exterior photography system. [Modes for carrying out the invention]

[0013] One embodiment of the present invention will be described below with reference to the drawings.

[0014] <System Overview> First, let's describe the overall system configuration. Figure 1 is a schematic diagram showing the configuration of a product appearance imaging system 100 according to one embodiment of the present invention. The product appearance imaging system 100 is implemented by an information processing device 1 that sends and receives various information to and from a user terminal 2 via a communication network such as the Internet.

[0015] The information processing device 1 is a computer that operates according to a control program such as a computer program, and is an overall control device that provides various information for managing products based on images obtained by photographing the products to the user via the user terminal 2. The products are, for example, materials such as scaffolds. The user terminal 2 is a computer that operates according to a control program such as a computer program, and is used by a user who photographs product images and is provided with various information related to the products from the information processing device 1.

[0016] In the following description, the user terminal 2 used by the shipper who is a user that ships products is referred to as a user terminal 2a. The shipper is, for example, a rental trader that provides a product rental service. In addition, the user terminal 2 used by the receiver who is a user that receives products is referred to as a user terminal 2b. The receiver is, for example, a user of the rental service and a trader that rents the products. Note that for matters common to the user terminal 2a and the user terminal 2b, description may be made simply as the user terminal 2 without distinguishing between the shipper and the receiver. The shipper may be the same person as the photographer, or may be a different person. Similarly, the receiver may be the same person as the photographer, or may be a different person.

[0017] Note that the photographing of the product is preferably performed indoors in order to make the photographing conditions favorable. Even for the same scaffold, the product may differ in color tone, gloss and the like depending on the manufacturing company or model number. Furthermore, the scaffolds to be imaged are often used items, and the appearance of the product itself is diverse due to dirt, scratches and the like. For this reason, the background is often different, and it is preferable to perform the photographing indoors where the photographing conditions are better than outdoors, where photographing may be performed under backlight, at dusk, or in the rain.

[0018] A product distribution cycle will be described with reference to FIG. 2. FIG. 2 is a flowchart showing the product distribution cycle.

[0019] In step S1, the products are temporarily stored in a temporary storage location after procurement. In step S2, the temporarily stored products are loaded onto a transport means such as a truck and transported to the destination after being photographed by the shipper's user terminal 2a. In step S3, the products are unloaded at the destination (e.g., construction site, demolition site), photographed by the recipient's user terminal 2b, and then used at the destination. The product appearance imaging system 100 can eliminate redundant work and manual work in step S2 or step S3 by sharing information obtained based on the appearance of the products (e.g., number of products) obtained by the shipper using the user terminal 2a to photograph the products in steps S1 and S2 with relevant parties such as the recipient via the user terminal 2b in step S2 or step S3.

[0020] Furthermore, in step S4, the products used at the destination are collected by means of transport such as trucks. In step S5, the collected products are temporarily stored in a temporary storage location. In step S6, the products that were temporarily stored after collection are reshipped. After step S6, the process returns to step S3, and the cycle of product use at the destination, collection, temporary storage, and reshipment is repeated. The product appearance imaging system 100 can also eliminate redundant work and manual labor in step S4 or step S6 by sharing information obtained based on the appearance of the product (e.g., the number of products) obtained by imaging the product in step S5 with the relevant parties in step S4 or step S6.

[0021] The information obtained in steps S1 and S5 based on the appearance of the products (e.g., the number of products) is understood as information regarding the inventory of the products (for both new and used items). Therefore, based on the information obtained in steps S1 and S5 based on the appearance of the products (e.g., the number of products), it becomes possible to adjust the quantity of purchases, adjust the inventory levels at temporary storage locations, and sell excess inventory to third parties.

[0022] Furthermore, the product appearance imaging system 100 of this embodiment is not intended to be used only by a select few in the distribution of materials such as scaffolding. It is a system intended to be used by at least several, and preferably all, of the manufacturers, distributors, and users of the product. With this product appearance imaging system 100 of this embodiment, the results of product-related tasks (e.g., counting the number of scaffolding units) that were previously performed separately by the manufacturers, distributors, and users can be shared and utilized, thereby reducing the workload.

[0023] <Hardware Configuration> Next, an example of the hardware of the information processing device 1 and user terminal 2 that constitute the product appearance imaging system 100 will be described.

[0024] First, let's describe the hardware configuration of the information processing device 1. The information processing device 1 is a computer that provides various information related to information processing to the user terminal 2, and functions as a server.

[0025] Figure 3 is a block diagram showing the hardware configuration of the information processing device 1. The information processing device 1 includes a CPU (Central Processing Unit) 11 as a processor, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an output unit 16, an input unit 17, a storage unit 18, a communication unit 19, and a drive 20.

[0026] The CPU 11 executes various processes according to the program stored in the ROM 12 or the program loaded from the storage unit 18 into the RAM 13. The RAM 13 also stores data necessary for the CPU 11 to execute various processes. The CPU 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output interface 15 is also connected to this bus 14.

[0027] The input / output interface 15 is connected to an output unit 16, an input unit 17, a storage unit 18, a communication unit 19, and a drive 20. The output unit 16 consists of a display, speakers, etc., and outputs various information as images and sounds. The input unit 17 consists of a keyboard, mouse, etc., and inputs various information. The storage unit 18 consists of a hard disk, DRAM (Dynamic Random Access Memory), etc., and stores various data. The communication unit 19 communicates with other devices via a network, including the Internet.

[0028] The drive 20 is appropriately equipped with removable media 21, which may consist of a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory. Programs read from the removable media 21 by the drive 20 are installed in the storage unit 18 as needed. The removable media 21 can also store various types of data stored in the storage unit 18, just like the storage unit 18.

[0029] Next, we will describe user terminal 2. User terminal 2 consists of a smartphone, tablet, head-mounted display, mobile personal computer, etc. User terminal 2 may exchange various types of information with information processing device 1 using a pre-installed program, or it may exchange various types of information through a web browser.

[0030] Figure 4 is a block diagram showing the hardware configuration of user terminal 2. As shown in Figure 4, user terminal 2 includes a CPU 111, ROM 112, RAM 113, bus 114, input / output interface 115, display unit 116, input unit 117, storage unit 118, communication unit 119, drive 120, and imaging unit 122.

[0031] The CPU 111 executes various processes according to the program stored in the ROM 112 or the program loaded into the RAM 113 from the storage unit 118. The RAM 113 also stores data necessary for the CPU 111 to execute these processes. The CPU 111, ROM 112, and RAM 113 are interconnected via a bus 114. An input / output interface 115 is also connected to this bus 114.

[0032] The input / output interface 115 is connected to a display unit 116, an input unit 117, a storage unit 118, a communication unit 119, and a drive 120. The display unit 116 consists of display means such as a display and projection means such as a projector, and outputs various information as images and sounds. The input unit 117 consists of a touch panel display, buttons, a keyboard, etc., and accepts input of various information. The storage unit 118 consists of a hard disk, DRAM (Dynamic Random Access Memory), etc., and stores various data. The communication unit 119 communicates with other devices via a network including the Internet.

[0033] The drive 120 is appropriately equipped with removable media 121, which may consist of a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory. Programs read from the removable media 121 by the drive 120 are installed in the storage unit 118 as needed. The removable media 121 can also store various data stored in the storage unit 118, just like the storage unit 18.

[0034] The imaging unit 122 is an external imaging means that photographs the product and acquires an image. The imaging unit 122 may be a camera built into the user terminal 2, or it may be an external camera that communicates with the user terminal 2.

[0035] The hardware configuration described here is merely an example. The computer described in this embodiment, including the information processing device 1 and the user terminal 2, may have the same configuration as those in Figures 3 and 4, or it may have a different configuration. Furthermore, the computer may consist of two or more computers.

[0036] <Functional configuration> Next, the functional configuration of the information processing device 1 will be described. Figure 5 is a functional block diagram showing an example of the functional configuration of the product appearance imaging system 100 according to this embodiment.

[0037] As shown in Figure 5, the information processing device 1 includes a product image receiving unit 31, a product type determination unit 32, a machine learning model selection unit 33, an appearance area identification unit 34, an appearance area information provision control unit 35, a confirmed appearance area information receiving unit 36, an information provision unit 37, and a matching result presentation unit 38, all of which are functional units implemented on the processor (CPU 11).

[0038] Furthermore, a database 90 is constructed in one area of ​​the storage unit 18 of the information processing device 1. The database 90 stores, for example, photographic image data of products, management information, etc. The management information may include, for example, the date and time the product was photographed, the location of the photograph, the photographer, the shooting conditions (e.g., weather), the shooting results (e.g., quantity), the type of product (e.g., scaffolding support), the attributes of the product (e.g., new, used), the condition of the product (e.g., at the time of shipment), and information such as the learning model used for analysis. In addition, training image data may be stored in the area of ​​the database 90 where the photographic image data is stored. Thus, the database 90 functions as a means for storing photographic image data and a means for storing management information. It should be noted that the photographic image data and management information do not necessarily have to be stored in a database format. Means for storing photographic image data and management information in other ways may be configured.

[0039] The database 90 may be located outside the storage unit 18 of the information processing device 1, and various types of information may be read via a communication network.

[0040] The machine learning model 50 is constructed using supervised learning with a neural network and is used to identify the external appearance region of a product from a product image. There may be one machine learning model 50 or multiple types. In this embodiment, multiple types of machine learning models 50a1 to 50an (where n is a natural number) are pre-constructed, and the appropriate machine learning model 50a1 to 50an is selected depending on the type of product or image, the shooting conditions, etc. Note that in the following description, descriptions common to each machine learning model 50a1 to 50an may simply be described as machine learning model 50.

[0041] Next, we will describe the various functional units that perform various processes on the CPU 11 of the information processing device 1. First, we will describe the product image receiving unit 31.

[0042] The product image receiving unit 31 is a product image receiving means that receives product images taken by the photographer that include the product as the subject, and it also serves as a terminal interface unit. While the product image receiving unit 31 is receiving product images, it may indicate to the display unit 116 of the user terminal 2, which is the source, that it is uploading.

[0043] Furthermore, the product image receiving unit 31 may also receive analysis-related information used for analysis along with the product image. This analysis-related information may include, for example, the date and time the product was photographed, the location of the photograph, the photographer, the shooting conditions (e.g., weather), the shooting results (e.g., the quantity of products), the type of product (e.g., scaffolding support), the attributes of the product (e.g., new, used), the condition of the product (e.g., at the time of shipment), and information such as the learning model to be used for analysis. The analysis-related information acquired by the product image receiving unit 31 is stored as management information in the database 90, which also serves as a management information storage means. As will be described later, the analysis-related information stored in the database 90 can be shared between the shipper who ships the product and the recipient who receives the product.

[0044] The product type determination unit 32 is a product type determination means that determines the type of product based on the product image received by the product image receiving unit 31. The product type determination unit 32 automatically identifies the products included in the image by performing image analysis on the product image. At this determination stage by the product type determination unit 32, it is not necessary to determine the number of products. For example, for an image containing multiple products, it determines the type of a specific product included in the image.

[0045] The product type determination unit 32 may determine the type of product by comparing pre-set matching image information with the product image received by the product image receiving unit 31, or it may determine the type based on a machine learning model constructed using machine learning or the like. The machine learning model can be constructed, for example, by training a dataset containing images of multiple products as raw data and product types as labels using supervised learning with neural network technology or the like.

[0046] Furthermore, the product type determination unit 32 may determine the product type based on text information or the like added to the product image received by the product image receiving unit 31. The text information is, for example, information entered or selected by the photographer when they take a picture of the product.

[0047] The machine learning model selection unit 33 is a machine learning model selection means that selects a machine learning model 50 corresponding to the type of product determined by the product type determination unit 32. The machine learning model 50 is constructed by performing machine learning using a dataset that includes, for example, an image containing the product as raw data and the appearance region of the product as a label identified from the image. There may be one machine learning model 50 or multiple types. In this embodiment, multiple types of machine learning models 50a1 to 50an (where n is a natural number) are constructed in advance, and the appropriate machine learning model 50a1 to 50an is selected by the information processing device 1 according to the type of product or image, the shooting conditions, etc. In the following description, a common description of each machine learning model 50a1 to 50an may be simply described as machine learning model 50.

[0048] In this embodiment, multiple machine learning models 50 are registered. For example, machine learning model 50a1 is used to identify the external appearance area of ​​scaffolding (e.g., support columns) from a product image. Machine learning model 50a2 is used to identify the external appearance area of ​​safety steel plates from a product image. The machine learning model selection unit 33 selects machine learning model 50a1 if the product type determined by the product type determination unit 32 is scaffolding, and selects machine learning model 50a2 if the product type is safety steel plates.

[0049] Furthermore, even for the same product, there may be multiple machine learning models 50. Also, the machine learning model 50 may be selected according to the shooting conditions of the product image data (e.g., backlighting, twilight, rain). Additionally, the machine learning model selection unit 33 may display a selection screen on the user terminal 2 to select the target product, and the machine learning model 50 to be used may be set according to the selection result. Target products may be distinguished by displaying a photograph of the target product.

[0050] The appearance region identification unit 34 is an appearance region identification means that analyzes product image data and identifies the appearance region in the product image that includes the appearance of the product. By identifying the appearance region of the product in the product image, the number of products can be counted.

[0051] The appearance region identification unit 34 may transmit information to the user terminal 2 to indicate that analysis is in progress while it is identifying the appearance region, causing the user terminal 2 to display that analysis is in progress. In addition, if the analysis fails, the appearance region identification unit 34 may transmit information to the user terminal 2 to indicate that the analysis has failed, causing the user terminal 2 to display error information indicating that the analysis failed.

[0052] In this embodiment, the appearance region identification unit 34 identifies the appearance region by processing using the machine learning models 50a1 to 50an selected by the machine learning model selection unit 33.

[0053] The appearance area information provision control unit 35 is an appearance area information provision control means that performs control to provide the photographer with information regarding the appearance area of ​​a product. The appearance area information provision control unit 35 counts the number of products in the product image based on the identified appearance area and calculates the number of products as information regarding the appearance area of ​​the product. Information regarding the appearance area can also be said to be information that shows the result of identifying the appearance area of ​​the target product.

[0054] The appearance area information provision control unit 35 transmits information regarding the appearance area of ​​the product (number of products) to the user terminal 2 used by the user who is taking the photograph. Based on the information transmitted from the appearance area information provision control unit 35, the display unit 116 of the user terminal 2 displays information regarding the appearance area of ​​the product.

[0055] Information regarding the product's external appearance is displayed on user terminal 2, for example, in the form of an analysis screen. The analysis screen includes, for example, an image of the target product taken by the user, and the results of identifying the target product's external appearance.

[0056] The verified appearance area information receiving unit 36 ​​is an appearance area information receiving means that receives verified appearance area information confirmed by the photographer. The verified appearance area information includes the product image and information about the product's appearance area confirmed by the photographer. For example, the information about the product's appearance area is the confirmation result of the product analysis screen provided to the user by the appearance area information provision control unit 35. The confirmation result may be information indicating that the analysis image provided by the appearance area information provision control unit 35 was approved as is, or it may be an analysis image modified by the photographer. The verified appearance area information receiving unit 36 ​​receives the verified appearance area information for the product analysis screen provided to the user by the appearance area information provision control unit 35 from the user terminal 2.

[0057] Furthermore, the verified appearance area information receiving unit 36 ​​may store the product image in the database 90, which is a means for storing captured image data, after receiving the verified appearance area information. The product image to be stored may be an analysis image, which is a product image that includes the analysis results, or an analysis image that has been added or modified by the user, such as markings.

[0058] Furthermore, the verified appearance area information receiving unit 36 ​​can also store the analyzed image after receiving the verified appearance area information as training data in the database 90. In this way, the product appearance imaging system 100 may learn the type of target product based on the information about the target product selected at the user terminal 2 and the target product image. This can improve the overall performance of the system. This method is called "Human In The Loop" (HITL).

[0059] The information provision unit 37 is an information provision means that controls the sharing of product images and analysis-related information to both the product shipper and the product recipient. The information provision unit 37 may, for example, enable the shipper and recipient to access the database 90, which is a management information storage means, thereby enabling the sharing of product images and analysis-related information, or it may enable the sharing of product images and analysis-related information by distributing copies of the analysis-related information to the shipper and recipient. As a result, product images and analysis-related information (e.g., product quantity) acquired at the time of shipment of a confirmed product can be shared with the product recipient as product shipment-related information. Similarly, product images and analysis-related information (e.g., product quantity) acquired at the time of product receipt can be shared with the product shipper as product receipt-related information.

[0060] The matching result presentation unit 38 performs a matching process to match product shipment-related information and product receipt-related information. For example, the matching result presentation unit 38 can perform the matching process by presenting product shipment-related information, product receipt-related information, and a predetermined input interface to the user terminal 2. Specifically, for example, the matching result presentation unit 38 can present the product shipment-related information and product receipt-related information along with an input interface that includes content such as "Do you accept this information?" (including acceptance after confirmation of no discrepancies, and acceptance after confirmation of discrepancies), and "Do you want to reshoot?", and receive a response from the recipient. In addition, the matching result presentation unit 38 can pre-match the product shipment-related information and product receipt-related information, present the presence or absence of discrepancies to the user terminal 2, and allow the user to confirm whether to proceed with acceptance or not (for example, input indicating acceptance or rejection and reshooting. Specifically, input such as confirmation of no discrepancies, confirmation of discrepancies, and confirmation of whether reshooting is necessary). Furthermore, the matching result display unit 38 pre-matches product shipment-related information with product receipt-related information, and if there are discrepancies in the information, it can display a message to that effect on the user terminal 2, or a message prompting the user to retake the product photograph.

[0061] In the matching process, the analysis-related information of the product shipment information and the analysis-related information of the product receipt information are compared. The matching result presentation unit 38 determines that there is a discrepancy in the information, for example, if the quantity of products at the time of shipment differs from the quantity of products at the time of receipt, or if the type of product identified differs. In addition, the matching process may also compare the product image of the product shipment information with the product image of the product receipt information. The matching result presentation unit 38 determines that there is a discrepancy if the differences in color, shape, etc., on the images of the product image of the product shipment information and the product image of the product receipt information exceed a certain threshold. The image comparison may use a machine learning model 50 constructed using machine learning.

[0062] Furthermore, when the matching result presentation unit 38 registers the analyzed images as training data in the database 90, it may match the product shipment-related information and the product receipt-related information and exclude any product image data with discrepancies from the training data. For example, the matching result presentation unit 38 may match the product shipment-related information and the product receipt-related information and store the product image data with the correct result as training data in the database 90, while excluding the product image data with the incorrect result from the training data.

[0063] Next, the functional configuration of user terminal 2 will be described. User terminal 2 is equipped with a management processing unit 61, an image capture processing unit 62, and an analysis result display processing unit 63 as functional units implemented on the processor (CPU 111). In the following description, the product image capture function of user terminal 2 will be described without distinguishing between the shipper and the recipient.

[0064] The management processing unit 61 manages information about the user taking product images. For example, when the management processing unit 61 detects that the user has performed an input operation to log in to the product appearance imaging system 100 on the login screen displayed on the display unit 116 of the user terminal 2, it switches the user terminal 2 to product imaging mode. The login screen may be displayed by an application installed on the user terminal 2, or it may be displayed based on information obtained from the information provision unit 37 via a communication network. The login screen may be, for example, a screen where an ID and password can be entered, and the ID may be the user's email address. A graphical user interface (hereinafter abbreviated as "GUI") that allows the user to remain logged in may be displayed on the login screen.

[0065] The management processing unit 61 performs a process to display a menu on the display unit 116 of the user terminal 2 after logging into the product exterior photography system 100. Menu options include, for example, "Management Screen," "Product Selection," "Product Photography," and "Logout." On the management screen, information such as the user's name, the user's organization, the number of photos taken, and whether or not the user is an administrator is displayed on the display unit 116. The management screen may display the shooting record by year and month, or it may display the shooting year and month in a way that the user can select. The management screen may also allow the administrator to designate other users as administrators, or if there are two or more administrators, it may be possible to display the user's own administrator attribute so that it can be excluded. The management screen may also display a user registration screen or a user deletion screen.

[0066] Furthermore, the management processing unit 61 may display error messages on the display unit 116 of the user terminal 2, including messages indicating that the user terminal 2 is not connected to the internet, that the product's appearance imaging system 100 is unavailable, that an error of unknown cause has occurred, that the application needs to be restarted, or that the analysis has failed.

[0067] The imaging processing unit 62 activates the imaging unit 122, switches to product image acquisition mode, and performs processing to image the product. For example, by selecting "Product Photography" displayed as a menu on the display unit 116, the photography screen is displayed on the display unit 116. The photography screen is a screen that allows the user terminal 2 to take a picture of the product.

[0068] Figure 6 shows an example of the display of the shooting screen. As shown in the example in Figure 6, the shooting screen may display a GUI for performing various operations. In this example, the shooting screen displays a camera image 201 for capturing product images, a shooting button 202, a learning model selection unit 203 for displaying and changing machine learning models 50a1 to 50an, an information display unit 204 for displaying various information such as the number of shots taken, etc. The shooting screen may be activated in response to the user's selection of the target product. In this way, the product exterior shooting system 100 can be configured to acquire images of the target product in response to the user's selection of the shooting GUI.

[0069] In the case of long pipes used for scaffolding, it is preferable that the product image be taken so that the cross-sectional shape is included. As shown in the example of camera image 201 in Figure 6, if the product image is acquired so that the end face of the pipe is clearly visible, the identification process of the appearance region identification unit 34 can be performed accurately. Conversely, even if a product image includes the product, if the cross-sectional shape (end face shape) is not included, the appearance region may not be able to be properly identified.

[0070] If the identification process of the external area identification unit 34 is not performed correctly, an error screen may be displayed on the user terminal 2. The external area identification unit 34 may also send an error message to the user terminal 2 along with information to display a GUI that allows the user to retake the image.

[0071] Figure 7 shows a first example of the error screen displayed on the user terminal 2. The error screen in Figure 7 displays a product image display unit 205 indicating that the external appearance area could not be identified, a text display unit 206 indicating that the external appearance area could not be identified, and a reshoot button 207 instructing reshooting. In this example, the product image display unit 205 shows that the circumferential surface of the scaffolding pipe, which is the product, is within the field of view, but the cross-sectional shape (end face of the pipe) is not captured within the field of view, and there is a possibility that the pipe is hidden outside the area visible in the product image display unit 205. Therefore, even if the product image receiving unit 31 receives the product image from the user terminal 2 as in the example in Figure 7, the external appearance area identification unit 34 determines that the external appearance area cannot be accurately identified. When the reshoot button 207 is pressed, the user terminal 2 returns to the shooting mode shown in Figure 6.

[0072] Figure 8 shows a second example of the error screen displayed on the user terminal 2. The error screen in Figure 8 displays a product image display unit 208 indicating that the external appearance area could not be identified, a text display unit 206 indicating that the external appearance area could not be identified, and a reshoot button 207 instructing reshooting. In this example, the product image display unit 208 shows that although the end face of the scaffolding pipe, which is the product, is within the field of view, an obstacle is visible in the image, and it is possible that the pipe is hidden outside the area visible in the product image display unit 208. Therefore, even if the product image receiving unit 31 receives the product image from the user terminal 2 as in the example in Figure 8, the external appearance area identification unit 34 determines that the external appearance area cannot be accurately identified. In this example as well, when the reshoot button 207 is pressed, the user terminal 2 returns to the shooting mode shown in Figure 6.

[0073] Next, the analysis result display processing unit 63 will be described. The analysis result display processing unit 63 performs the process of displaying the analysis results on the display unit 116 based on the information transmitted from the appearance area information provision control unit 35. Figure 9 is a diagram showing an example of the analysis screen displayed on the user terminal 2. The analysis screen in Figure 9 displays the product image display unit 210 that was the target of analysis, as well as the analysis result display unit 211 that displays the analysis results, the zoom in / out display unit 212, the learning model change unit 213, and the shooting start instruction unit 214. In this example, the analysis result display unit 211 shows the type of product, information about the appearance area (product count), and the name of the machine learning model 50 used for identification. By checking the analysis screen, the user can understand the type of product, the number of scaffolding pipes (for example, 70), and the machine learning model 50 used. The zoom in / out display unit 212 is an operation unit for operating the zoom in or out of the image displayed on the analysis result display unit 211. The learning model change unit 213 is an operation unit for operating the machine learning model 50 used for analysis. When the learning model change unit 213 is operated, a screen is displayed that allows the user to change the machine learning model 50, and information about the specified machine learning model 50 is sent to the information processing device 1. The shooting start instruction unit 214 is an operation unit for starting to photograph another product. When the shooting start instruction unit 214 is operated, the user terminal 2 returns to shooting mode.

[0074] The results of the identification of the target product's appearance region on the analysis screen displayed on the user terminal 2, processed by the appearance region information provision control unit 35, may be enhanced through image processing to make it stand out compared to other parts of the product image. This enhancement may involve, for example, marking the target product's appearance region (e.g., filling it with a conspicuous color such as red).

[0075] Figure 10 shows an example of the display of the analysis screen after enhanced image processing is performed, as shown on the user terminal 2. The analysis screen in Figure 10 displays the product image display unit 220, the analysis result display unit 211, the zoom display unit 212, the learning model change unit 213, and the shooting start instruction unit 214, in addition to the marker display setting unit 215. The product image display unit 220 highlights the end face of the pipe, which is the target product. The highlighted area may be the entire pipe, not just the end face. The analysis result display unit 211, the zoom display unit 212, the learning model change unit 213, and the shooting start instruction unit 214 are the same as those described in Figure 9. The marker display setting unit 215 is an operation unit for the user to add or delete marking displays.

[0076] Thus, on the analysis screen displayed on the user terminal 2, markings may be displayed in a way that allows the user to add or delete them, and GUIs for zooming in on the analysis image and GUIs for zooming out on the analysis image may be displayed. In addition, GUIs for changing the machine learning model 50, for proceeding to the next product's image capture, and for saving the analysis image may be displayed on the analysis screen.

[0077] Furthermore, the enhanced image processing may also be a process that makes areas other than the exterior area of ​​the target product less noticeable (e.g., displaying them relatively darker). In addition, the results of identifying the exterior area of ​​the product may be displayed in a way that makes the exterior area of ​​the target product with quality problems stand out on the analysis screen. Also, in the analysis screen, the exterior areas of target products that are not arranged regularly may be displayed in a way that makes them stand out. Target products that are not arranged regularly may be bent products.

[0078] The analysis result display unit 211 transmits the modified information to the verified appearance area information receiving unit 36 ​​when the user modifies the information regarding the appearance area (for example, by modifying the count). The verified appearance area information receiving unit 36 ​​registers the modified information as verified appearance area information. In this way, the information regarding the appearance area of ​​the product can be modified by the photographer.

[0079] Furthermore, the management processing unit 61, the shooting processing unit 62, and the analysis result display processing unit 63 may be located on the information processing device 1 side and configured to perform login processing for the user terminal 2 via a communication network such as the Internet, or to send commands to the target user terminal 2 to switch the user terminal 2 to shooting mode.

[0080] <Processing flow> Referring to Figure 11, the overall processing flow of the product appearance imaging system 100, which shares information between the shipper and the recipient, will be described. Figure 11 is a flowchart of the processing flow of the product appearance imaging system 100.

[0081] In step S11, the product image receiving unit 31 of the information processing device 1 receives product image data from the shipper's user terminal 2a.

[0082] In step S12, the appearance region identification unit 34 of the information processing device 1 identifies the appearance region from the product image data of the shipper received by the product image receiving unit 31. The appearance region identification can be performed, for example, by a machine learning model 50 selected by the machine learning model selection unit 33 based on the determination result of the product type determination unit 32.

[0083] In step S13, the appearance area information provision control unit 35 of the information processing device 1 transmits information regarding the appearance area (first information) to the shipper's user terminal 2a. The information regarding the appearance area includes, for example, a count obtained by counting the appearance area of ​​the product identified from the product image data.

[0084] In step S14, the verified appearance area information receiving unit 36 ​​of the information processing device 1 receives verified appearance area information (shipper's second information) from the shipper's user terminal 2a, which has received information regarding the appearance area, indicating that verification has been performed. If the shipper has made corrections, the verified appearance area information will reflect the results of those corrections.

[0085] In step S15, the information provision unit 37 registers the verified appearance area information, along with other related information such as the shipper's username, as product shipment-related information in the database 90, and manages access so that it can be used by both the shipper and the recipient.

[0086] In step S16, the product image receiving unit 31 of the information processing device 1 receives product image data from the recipient's user terminal 2b.

[0087] In step S17, the appearance region identification unit 34 of the information processing device 1 identifies the appearance region from the product image data of the recipient received by the product image receiving unit 31. The identification of the appearance region is performed by the same process as in step S12.

[0088] In step S18, the appearance area information provision control unit 35 of the information processing device 1 transmits information regarding the appearance area (second information) to the recipient's user terminal 2b. The information regarding the appearance area is the same as the information transmitted in step S13.

[0089] In step S19, the verified appearance area information receiving unit 36 ​​of the information processing device 1 receives verified appearance area information (recipient's second information) from the recipient's user terminal 2b, which has received information regarding the appearance area, indicating that verification has been performed. If the recipient has made corrections, the verified appearance area information will reflect the results of those corrections.

[0090] In step S20, the matching result presentation unit 38 receives information of the recipient's confirmation result, which is obtained by matching the information regarding the appearance area included in the shipper's product receipt-related information (shipper's second information) with the information regarding the appearance area included in the recipient's confirmed appearance information (recipient's second information) to determine whether the information regarding the appearance area matches. Specifically, for example, the matching result presentation unit 38 presents the recipient with at least one of the information used to determine whether the information regarding the product shipment-related information and the product receipt-related information (such as the quantity as a result of the analysis of the appearance area and appearance information) matches, and the information of the matching result, and receives the confirmation result information. Specifically, for example, the matching result presentation unit 38 presents the user with information used to determine whether the count of the product as information regarding the appearance area matches at the time of shipment and at the time of receipt, and receives confirmation from the recipient that they match, confirmation that they do not match but will be managed as received, or confirmation that they do not match and will be re-photographed. If the recipient confirms that they will proceed as if the item has been received, the matching result presentation unit 38 proceeds to step S21 (step S20; OK). If the recipient does not confirm that they will proceed as if the item has been received, the matching result presentation unit 38 proceeds to step S23 (step S20; NG).

[0091] In step S21, the matching result presentation unit 38 transmits information (confirmation information) to the recipient's user terminal 2b notifying that the recipient has confirmed that they will proceed as if the item has been received. The matching result presentation unit 38 may also transmit information to the shipper's user terminal 2a notifying that the recipient has confirmed that they will proceed as if the item has been received.

[0092] In step S22, the information provision unit 37 registers the verified appearance area information, along with other relevant information such as the shipper's user name, as product receipt-related information in the database 90, and manages access so that it can be used by both the shipper and the recipient. After the processing in step S22, the process ends.

[0093] In step S23, the matching result presentation unit 38, having not received confirmation from the recipient that it would proceed as received, sends information to the recipient's user terminal 2b notifying them to retake the image. The recipient sets the user terminal 2b to shooting mode, takes a picture of the product, and sends it to the information processing device 1. This returns the process to step S16, and the process from step S16 onwards is repeated.

[0094] As described above, the product appearance imaging system 100 of this embodiment includes a product image receiving unit 31 that receives a product image taken by a photographer, an appearance area identification unit 34 that identifies the appearance area of ​​the product included in the received product image, an appearance area information provision control unit 35 that transmits information regarding the appearance area of ​​the product identified by the appearance area identification unit 34 to the photographer, and a confirmed appearance area information receiving unit 36 ​​that receives confirmed appearance area information, which is information regarding the appearance area of ​​the target product after it has been confirmed by the photographer. Furthermore, the control method for the information processing device 1 of this embodiment includes a product image receiving step of receiving a product image taken by a photographer; an appearance area identification step of identifying an appearance area of ​​the product included in the received product image; an appearance area information provision control step of transmitting information regarding the appearance area of ​​the product identified by the appearance area identification step to the photographer; and a confirmed appearance area information receiving step of receiving confirmed appearance area information, which is information regarding the appearance area of ​​the target product after it has been confirmed by the photographer. Furthermore, the program of this embodiment causes a computer to execute the following steps: a product image receiving step, which receives a product image taken by a photographer; an appearance area identification step, which identifies the appearance area of ​​the product included in the received product image; an appearance area information provision control step, which transmits information regarding the appearance area of ​​the product identified by the appearance area identification step to the photographer; and a confirmed appearance area information receiving step, which receives confirmed appearance area information, which is information regarding the appearance area of ​​the target product after it has been confirmed by the photographer.

[0095] This allows for the identification of the external appearance area from product images of the product, and the registration of information related to the external appearance area, such as the number of products. Since this information about the external appearance area is verified by the photographer, its accuracy is also guaranteed. This reduces the physical and time burden on those involved in the distribution of the product (both the shipping and receiving sides), and allows for the greatest possible cost-effectiveness given the current level of technology. It is preferable that the product images be acquired under stable imaging conditions.

[0096] Furthermore, in this embodiment, the appearance region identification unit 34 includes machine learning models 50a1 to an. This allows for the precise identification of external appearance areas from product images. In particular, for external appearance images of specific surfaces (e.g., end faces) for each type of product, such as scaffolding, accurate detection is possible using machine learning models 50a1 to 50an, even if the shooting conditions are not ideal, such as being taken outdoors, being from different manufacturers, or being used products. The product external appearance shooting system 100 of this embodiment can also be described as one aspect of the invention that proposes a system specifically designed to capture images of the external appearance of specific surfaces for each type of product and to perform machine learning on it.

[0097] Furthermore, in this embodiment, the verified appearance area information includes the product image and information regarding the appearance area of ​​the product. This allows product images and information regarding the product's appearance (product count) to be shared with relevant parties.

[0098] Furthermore, in this embodiment, the verified appearance area information is stored in a way that allows both the product shipper and the product recipient to access it. This allows information about the status of scaffolding at various stages in the distribution of scaffolding and other products to be shared between the shipper and the recipient. It also enables the shipper (e.g., the lender) and the recipient (e.g., the borrower) in the distribution of materials such as scaffolding to share information obtained from imaging results of specific surfaces of the product (e.g., the number of products), thereby significantly improving operational efficiency by eliminating redundant work (e.g., not having to count again).

[0099] Furthermore, in this embodiment, if the verified appearance area information obtained upon receipt of the product differs from the verified appearance area information obtained upon shipment of the product, the system prompts the photographer to retake the photograph of the product. This means that even if there are discrepancies in information regarding the product's external appearance due to how the product image is taken, the external appearance can be identified based on the new product image obtained through re-shooting.

[0100] Furthermore, in this embodiment, the product appearance imaging system 100 includes a product type determination unit 32 that determines the type of product based on the received product image, and a machine learning model selection unit 33 that selects machine learning models 50a1 to 50an corresponding to the type of product based on the product type determination unit 32. The applicant has discovered that by using machine learning to recognize product images as primary information, and by adding features corresponding to the type of material as secondary information to the primary information, it is possible to identify materials such as scaffolding with a higher accuracy than that achievable by machine learning alone. With the configuration of this embodiment, the appearance area can be more accurately identified by appropriate machine learning models 50a1 to 50an according to the type of product. The product appearance imaging system 100 of this embodiment can also be described as one aspect of the present invention, which proposes a system specifically designed for simple and accurate material identification by combining machine learning functions for obtaining information from the appearance of a product with human know-how regarding the shape of materials.

[0101] Furthermore, in this embodiment, the information regarding the external appearance area of ​​the product is the number of products obtained by counting the external appearance area of ​​each product. Products such as scaffolding used at construction sites can be used even if they have some scratches or dirt. Also, since the types of scaffolding are standardized, workers can distinguish them at a glance. On the other hand, counting the number of scaffolding units (e.g., the number of support columns) is prone to errors, even for skilled workers. However, since scaffolding is often heavy and long, two workers are needed to move and count each one individually. In this respect, the product appearance imaging system 100 of this embodiment counts the number of products based on the product image, thus reducing the time and burden on those involved in product distribution, such as confirming the number of products shipped or received.

[0102] Furthermore, in this embodiment, information regarding the product's appearance can be modified by the photographer. As a result, even if an error occurs in the identification process of the external area identification unit 34, it can be corrected by the photographer, thus enabling accurate information to be shared with those involved in the distribution of the product.

[0103] Although one embodiment of the present invention has been described above, the present invention is not limited to the embodiments described above, and any modifications, improvements, etc. that can achieve the objectives of the present invention are included in the present invention.

[0104] For example, the product type determination unit 32, machine learning model selection unit 33, information provision unit 37, and matching result presentation unit 38 can be omitted from the functional unit of the information processing device 1 of the above embodiment.

[0105] Furthermore, the series of processes described above can be executed by hardware or by software. In other words, the functional configuration described above is merely illustrative and not particularly limiting. That is, it is sufficient that the information processing device 1 is equipped with a function that can execute the series of processes described above as a whole, and the type of functional block used to realize this function is not particularly limited to the example above. Also, the location of the functional block is not particularly limited and can be arbitrary. For example, the functional block of the information processing device 1 may be transferred to another device such as a user terminal 2. Conversely, the functional block of another device such as a user terminal 2 may be transferred to the information processing device 1. In addition, a single functional block may be composed of hardware alone, software alone, or a combination of both.

[0106] When a series of processes are executed by software, the programs that make up that software are installed on a computer or other device from a network or storage medium. The computer may be a computer built into dedicated hardware. Alternatively, the computer may be a computer capable of performing various functions by installing various programs, such as a server, a general-purpose smartphone, or a personal computer.

[0107] Such recording media containing programs may consist not only of removable media (not shown) distributed separately from the main device to provide the programs, but also of recording media provided pre-installed in the main device. Since programs can be distributed via a network, the recording media may be installed on or accessible from a computer connected to or capable of connecting to a network.

[0108] In this specification, the step of describing a program to be recorded on a recording medium includes not only processes that are performed chronologically in that order, but also processes that are not necessarily performed chronologically, but are executed in parallel or individually. Furthermore, in this specification, the term "system" refers to an overall system composed of multiple devices, means, etc.

[0109] The following additional information is disclosed regarding the above embodiments and modifications.

[0110] [Note 1] An information processing system that assists in imaging the appearance of a product (for example, a product appearance imaging system 100), A product image data receiving means (for example, a product image receiving unit 31) that receives data of a product image containing the product taken by the photographer, An appearance region identification means (for example, an appearance region identification unit 34) identifies an appearance region in the product image that includes the appearance of the product, based on the data of the product image. An appearance region information provision control means (for example, an appearance region information provision control unit 35) that performs control to provide the photographer with first information relating to the aforementioned appearance region, A confirmed appearance area information receiving means (for example, a confirmed appearance area information receiving unit 36) that receives second information regarding the appearance area of ​​the product confirmed by the photographer, An information processing system equipped with the following features.

[0111] [Note 2] The aforementioned appearance region identification means identifies the appearance region by processing using a machine learning model. The information processing system described in Appendix 1.

[0112] [Note 3] The appearance region information receiving means receives the second information, which includes the product image data and information indicating the appearance region of the product. The information processing system described in Appendix 1.

[0113] [Note 4] Information providing means (e.g., information providing unit 37) that performs control to provide the second information to the shipper of the product and the recipient of the product, The information processing system described in Appendix 1 further comprises the following:

[0114] [Note 5] The seller of the aforementioned product (for example, the shipper, including the sender) is designated as the photographer. The receiving means receives the product image data, The aforementioned external appearance region identification means identifies the external appearance region, The aforementioned external area information provision control means provides the first information to the photographer, The confirmed appearance area information receiving means receives the appearance of the product confirmed by the photographer. The aforementioned second information regarding the area is received as the second exhibitor information. The recipient of the aforementioned product is the photographer, The receiving means receives the product image data, The aforementioned external appearance region identification means identifies the external appearance region, The aforementioned external area information provision control means provides the first information to the photographer, The confirmed appearance area information receiving means receives the appearance of the product confirmed by the photographer. The aforementioned second information concerning the domain is received as the recipient's second information, If the second seller information and the second recipient information differ, a means for presenting a message to the recipient prompting them to retake the product photographs, The information processing system described in Appendix 1 further comprises the following:

[0115] [Note 6] The information relating to the appearance region in the first and second information includes the number of products obtained by counting the appearance region of the products. The information processing system described in Appendix 1.

[0116] [Note 7] The confirmed appearance area information receiving means receives, as the second information confirmed by the photographer, information regarding the appearance area of ​​the product that has been modified by the photographer. The information processing system described in Appendix 1.

[0117] [Note 8] A product appearance imaging system, Product image receiving means (e.g., product image receiving unit 31) that receives images of products including multiple or more products, which are captured outdoors by a photographer. An appearance region identification means (e.g., an appearance region identification unit 34) for identifying the appearance region of a product included in the received image of the product. An appearance area information transmission means (e.g., an appearance area information provision control unit 35) transmits information regarding the appearance area of ​​the product, identified by the appearance area identification means, to the photographer. A means for receiving verified appearance area information (e.g., a verified appearance area information receiving unit 36) that receives information about the appearance area of ​​the target product after it has been confirmed by the photographer. It has, The external appearance information is transmitted to the photographer in an editable state and is provided to the photographer along with the number of products. Product appearance imaging system. [Explanation of symbols]

[0118] 1. Information Processing Device 2 User terminals 31 Product Image Receiving Unit 32 Product type determination section 33 Machine Learning Model Selection Section 34. Exterior area identification section 35. Appearance Area Information Provision Control Unit 36 Confirmed Appearance Area Information Receiving Unit 37 Information provision department 50 Learning Models 100 Exterior Photography System

Claims

1. A product appearance photography system, Product image receiving means for receiving product images taken by a photographer, Appearance region identification means for identifying the appearance region of a product included in the received product image, An appearance region information provision control means that transmits information regarding the appearance region of the product, identified by the appearance region identification means, to the photographer. Confirmed appearance area information receiving means, which receives confirmed appearance area information, which is information regarding the appearance area of ​​the product after it has been confirmed by the photographer. A product appearance photography system, including the following.

2. The aforementioned appearance region identification means includes a machine learning model, A product appearance imaging system according to claim 1.

3. The confirmed appearance region information includes the product image and the product, and the information regarding the appearance region. A product appearance imaging system according to claim 1.

4. The verified appearance area information is stored in a manner that allows the shipper and recipient of the product to access it. A product appearance imaging system according to claim 1.

5. If the verified appearance area information obtained upon receipt of the product differs from the verified appearance area information obtained upon shipment of the product, the system will prompt the photographer to re-photograph the product. A product appearance imaging system according to claim 1.

6. Product type determination means for determining the type of product based on the received product image, A machine learning model selection means that selects a machine learning model corresponding to the type of product based on the product type determination means. Having, The product appearance imaging system according to claim 2.

7. The information relating to the external region of the product is the number of the product obtained by counting the external region of the product. A product appearance imaging system according to claim 1.

8. The information relating to the external appearance region of the product can be modified by the photographer. A product appearance imaging system according to claim 1.

9. A method for controlling an information processing device, A product image receiving step, which involves receiving product images taken by the photographer, A step of identifying the appearance region of the product included in the received product image, An appearance region information provision control step that transmits information regarding the appearance region of the product identified by the appearance region identification step to the photographer, A confirmed appearance area information receiving step, which receives confirmed appearance area information, which is information about the appearance area of ​​the product after it has been confirmed by the photographer, A control method for an information processing device, including the device itself.

10. A product image receiving step, which involves receiving product images taken by the photographer, A step of identifying the appearance region of the product included in the received product image, An appearance region information provision control step that transmits information regarding the appearance region of the product identified by the appearance region identification step to the photographer, A confirmed appearance area information receiving step, which receives confirmed appearance area information, which is information about the appearance area of ​​the product after it has been confirmed by the photographer, A program that causes a computer to execute something.

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