WO2023177105A1 - Capture box for capturing electronic device, and unmanned purchasing device comprising same - Google Patents

Abstract

Disclosed is a capture box for capturing an electronic device. One embodiment may comprise: an input unit which may be opened/closed so as to enable an electronic device to be inputted into the capture box; a seating unit in which the electronic device is seated; an alignment unit which aligns the seated electronic device to a predetermined position; an app linking unit which is linked to an application installed on the electronic device so as to enable a monochrome screen to be displayed on the electronic device; a capture unit which, in a given capturing environment, captures the electronic device which has been aligned to the predetermined position; and a control unit which controls the alignment unit so that the seated electronic device is aligned to the predetermined position, controls the capture unit so that, when the seated electronic device is aligned to the predetermined position, the exterior of the aligned electronic device is captured, controls the app linking unit so that the monochrome screen is displayed on the aligned electronic device in order for a screen inspection of the aligned electronic device, and controls the capture unit so that the monochrome screen is captured.

Description

A shooting box for shooting electronic devices and an unmanned acquisition device containing the same

The following embodiments relate to a photography box that photographs electronic devices and an unmanned purchase device including the same.

Among the many ways to purchase used electronic devices these days, there is one that uses automated devices. Artificial intelligence exterior analysis technology is being introduced as a more intelligent method for non-face-to-face operations, reduction of operating labor costs, and stable and standardized analysis.

The above-mentioned background technology is possessed or acquired by the inventor in the process of deriving the disclosure of the present application, and cannot necessarily be said to be known technology disclosed to the general public before the present application.

In order to learn the artificial intelligence appearance analysis algorithm, learning data is needed, and this learning data can be obtained by using a shooting box to photograph electronic devices. If the shooting box of the actually operated automated device (or unmanned purchase device) is different from the shooting box used to secure the learning data, the learned artificial intelligence appearance analysis algorithm may not properly evaluate the appearance of the electronic device. The artificial intelligence appearance analysis algorithm needs to be learned through learning data obtained in the same environment as the shooting environment of the shooting box when it is actually operated.

A photographing box for photographing an electronic device according to an embodiment includes an input unit that allows the electronic device to be introduced into the photographing box; A seating portion on which the electronic device is seated; an alignment unit that aligns the mounted electronic device to a predetermined position; An app linkage unit that displays a single-color screen on the electronic device in conjunction with an application installed on the electronic device; a photographing unit that photographs electronic devices aligned at the predetermined location in a given photographing environment; and controlling the alignment unit to align the mounted electronic device at the predetermined position, and controlling the photographing unit to capture an appearance of the aligned electronic device when the mounted electronic device is aligned at the predetermined position, wherein the alignment It may include a control unit that controls the app linkage unit to display the monochromatic screen on the aligned electronic device in order to inspect the screen of the aligned electronic device, and controls the photographing unit to capture the monochromatic screen.

The photographing unit includes a first camera that photographs the front of the aligned electronic devices; a second camera that photographs the rear of the aligned electronic devices; a plurality of third cameras that photograph sides of the aligned electronic devices; and one or more light sources providing illumination.

The alignment unit may include a movable bar.

The control unit may control the moving bar so that the mounted electronic device moves to the determined position, and may return the moving bar when the mounted electronic device moves to the determined position.

The control unit may calculate the distance that the mounted electronic device must move to the determined position using the size of the mounted electronic device, and may control the moving bar based on the calculated distance.

The photographing unit includes a plurality of light sources; and light output by a first light source among the light sources is formed as a first line illumination on one side of the front of the aligned electronic device, and light output by a second light source among the light sources is directed to another side of the front. It may include a first structure configured to be formed as a second line light on one side.

The control unit turns on the first light source and turns off the second light source to control the photographing unit to capture the front surface when the first line lighting is formed on one side of the front surface, and the first light source By turning off and turning on the second light source, the photographing unit can be controlled to capture the front surface when the second line lighting is formed on the other side of the front surface.

An unmanned acquisition device according to an embodiment includes a photographing box for photographing an electronic device; a system control unit that transmits images obtained by photographing the electronic device and evaluation results of the internal state of the electronic device to a server, and receives a value of the electronic device determined through the images and the evaluation results from the server; and a display displaying the received value.

A method of operating a photographing box for photographing an electronic device according to an embodiment includes the steps of aligning an electronic device seated in the photographing box to a predetermined position; Photographing the exterior of the electronic device aligned at the predetermined location in a given photographing environment; Displaying a single-color screen on the electronic device in conjunction with an application installed on the electronic device; and photographing the electronic device displaying the monochromatic screen.

Software executing the above operating method may be stored in a computer-readable recording medium.

In one embodiment, the shooting environment of the shooting box used to secure learning data and the shooting environment of the shooting box when actually operated may be the same, allowing the appearance evaluation of the electronic device to be performed more accurately.

1A to 1B are diagrams explaining an unmanned purchase device and a server according to an embodiment.

Figure 2 is a block diagram explaining an unmanned purchase device according to an embodiment.

Figure 3 is a block diagram explaining a shooting box according to an embodiment.

FIGS. 4 to 5C are diagrams illustrating a seating portion and an alignment portion of a photographing box according to an embodiment.

Figure 6 is a diagram explaining a photographing unit of a photographing box according to an embodiment.

7 to 9 are diagrams illustrating line lighting of a photographing unit of a photographing box according to an embodiment.

FIG. 10 is a diagram illustrating camera focus depth according to the position of an electronic device within a shooting box according to an embodiment.

FIG. 11 is a diagram illustrating a status bar of an electronic device according to an embodiment.

12 to 15 are diagrams explaining the operation of an electronic device value evaluation device according to an embodiment.

Figure 16 is a block diagram illustrating the configuration of a computing device for training a deep learning model according to an embodiment.

17A to 17C are diagrams explaining a target mask and a prediction mask according to an embodiment.

Figure 18 is a flowchart explaining a deep learning model training method of a computing device according to an embodiment.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be changed and implemented in various forms. Accordingly, the actual implementation form is not limited to the specific disclosed embodiments, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical idea described in the embodiments.

Terms such as first or second may be used to describe various components, but these terms should be interpreted only for the purpose of distinguishing one component from another component. For example, a first component may be named a second component, and similarly, the second component may also be named a first component.

When a component is referred to as being “connected” to another component, it should be understood that it may be directly connected or connected to the other component, but that other components may exist in between.

Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “comprise” or “have” are intended to designate the presence of the described features, numbers, steps, operations, components, parts, or combinations thereof, and are intended to indicate the presence of one or more other features or numbers, It should be understood that this does not exclude in advance the possibility of the presence or addition of steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art. Terms as defined in commonly used dictionaries should be interpreted as having meanings consistent with the meanings they have in the context of the related technology, and unless clearly defined in this specification, should not be interpreted in an idealized or overly formal sense. No.

Hereinafter, embodiments will be described in detail with reference to the attached drawings. In the description with reference to the accompanying drawings, identical components will be assigned the same reference numerals regardless of the reference numerals, and overlapping descriptions thereof will be omitted.

1A to 1B are diagrams explaining an unmanned purchase device and a server according to an embodiment.

1A and 1B, an unmanned purchase device 110 and a server 120 are shown.

The unmanned purchase device 110 may purchase electronic devices (or used electronic devices) (e.g., smartphones, tablet PCs, wearable devices, etc.) from users and/or sell electronic devices (or used electronic devices) to users. . For example, the type of electronic device may be classified into a bar type, rollable type, or foldable type depending on its shape.

The unmanned purchase device 110 may be, for example, in the form of a kiosk, but is not limited thereto.

The unmanned embedded device 110 may include a shooting box, a system control unit, and a display.

When the door of the shooting box of the unmanned embedding device 110 is opened, the user can connect the cable (e.g. USB Type C cable, Lightning cable, etc.) of the unmanned embedding device 110 and the electronic device, and the electronic device can be placed in the shooting box. Electronic devices may be connected to the system control unit of the unmanned embedding device 110 through a cable. Depending on the embodiment, the electronic device may be connected to the system control unit of the unmanned embedded device 110 wirelessly (e.g., Bluetooth, Bluetooth Low Energy (BLE), etc.).

The system control unit of the unmanned purchase device 110 may install a first application on the electronic device to inspect the internal state of the electronic device and collect information about the electronic device (e.g., model name, serial number, operating system version, etc.). It is not limited to this, and the user may pre-install the first application on the electronic device before inserting the electronic device into the unmanned purchase device 110.

By running on the electronic device, the first application can collect information about the electronic device and evaluate (or analyze) the internal state (e.g., hardware operating state, etc.) of the electronic device. For example, the hardware operation state may indicate whether the hardware (eg, sensor, camera, etc.) of the electronic device is operating normally. The first application can evaluate (or determine) whether the hardware of the electronic device operates normally.

A plurality of cameras and a plurality of light sources (eg, light emitting diodes (LEDs)) may be located in the shooting box. The first camera in the photographing box may acquire one or more front images of the electronic device by photographing the front of the electronic device. The second camera in the shooting box may acquire one or more rear images of the electronic device by photographing the rear of the electronic device. Each of the plurality of third cameras in the shooting box may acquire one or more side images (or corner images) by photographing each side (or corner) of the electronic device.

The first camera may acquire one or more images (hereinafter referred to as “screen images”) by photographing the screen of the electronic device. Depending on the embodiment, the first application may display a single-color (e.g., white, black, red, blue, green, etc.) screen on the electronic device. When a monochromatic screen is displayed on the electronic device, the first camera may acquire an image (hereinafter referred to as a “monochromatic screen image”) by photographing the monochromatic screen of the electronic device. For example, when a white screen is displayed on the electronic device, the first camera may acquire a first monochromatic screen image by photographing the white screen of the electronic device. While a black screen is displayed on the electronic device, the first camera may acquire a second monochromatic screen image by photographing the black screen of the electronic device. While the electronic device displays a screen of a single color other than white and black (e.g., red, blue, green, etc.), the first camera may acquire a third single-color screen image by photographing the other single-color screen of the electronic device. .

The electronic device value evaluation device 130 is based on images acquired by photographing electronic devices (e.g., one or more front images, one or more rear images, one or more side images, and one or more monochromatic screen images) and deep learning evaluation models. Appearance condition evaluation of electronic devices can be performed.

In the example shown in FIG. 1A , the electronic device valuation device 130 may be included in the server 120 . In the example shown in FIG. 1A, the server 120 may receive images obtained by photographing an electronic device from the unmanned purchase device 110, and may transmit the received images to the electronic device value evaluation device 130. . As described above, the first application in the electronic device may perform an evaluation of the internal state of the electronic device and transmit the result of the evaluation of the internal state of the electronic device to the server 120 through the unmanned embedded device 110. As another example, the first application may allow the electronic device to connect to the server 120 and transmit the result of evaluating the internal state of the electronic device to the server 120 through the electronic device. The electronic device value evaluation device 130 evaluates the electronic device based on the result of the external state evaluation of the electronic device and the result of the internal state evaluation of the electronic device (e.g., the result of the first application performing the internal state evaluation of the electronic device). Value (e.g. price) can be determined. The electronic device value evaluation device 130 can transmit the value of the electronic device to the unmanned purchasing device 110, and the unmanned purchasing device 110 can convey the value of the electronic device to the user by displaying the value of the electronic device on the display. there is. The user can accept the value (e.g., price) of the electronic device and convey to the unmanned purchasing device 110 that the electronic device will be sold, and the unmanned purchasing device 110 is placed in the shooting box when the user decides to sell the electronic device. Electronic devices can be moved to a recovery box (or storage box). Depending on the embodiment, the recovery box may be located inside or outside the unmanned burial device 110.

In the example shown in FIG. 1B , the electronic device valuation device 130 may be included in the unmanned purchase device 110 . In one embodiment, the system control unit of the unmanned purchase device 110 may perform the operation of the electronic device value evaluation device 130. In contrast, the electronic device value evaluation device 130 may operate independently from the system control unit of the unmanned purchase device 110. In the example shown in FIG. 1B , the electronic device value evaluation device 130 may receive images obtained by photographing the electronic device from cameras in a photographing box. The electronic device value evaluation device 130 may receive the result of evaluating the internal state of the electronic device from the first application. The electronic device value evaluation device 130 may determine the value (e.g., price) of the electronic device based on the result of evaluating the external condition of the electronic device and the result of evaluating the internal state of the electronic device. The electronic device value evaluation device 130 can convey the value of the electronic device to the user by displaying the value of the electronic device on the display. The user can accept the value (e.g., price) of the electronic device and convey to the unmanned purchasing device 110 that the electronic device will be sold, and the unmanned purchasing device 110 is placed in the shooting box when the user decides to sell the electronic device. Electronic devices can be moved to a recovery box (or storage box).

According to one embodiment, deep learning models must be trained based on learning images acquired in the same environment as the shooting box in the unmanned purchase device 110 to improve the accuracy of the appearance evaluation results of the electronic device. As will be described later, trained deep learning models can be installed in an electronic device value evaluation device as deep learning evaluation models.

There are several important points regarding the structure and operation method of the shooting box.

First, in order to secure learning data used for training a deep learning model, there must be no or reduced shooting variability regardless of the shooting target (e.g. electronic device). Whenever shooting various electronic devices, the shooting box must provide a shooting environment that aligns the various electronic devices in a given position and allows shooting the various electronic devices at the same angle.

Second, when the screens of various electronic devices are controlled, the same environment must be provided.

The shooting box (or system control unit) can display a single-color screen on the electronic device through the first application installed on the electronic device. At this time, the brightness and/or color of the monochromatic screen of the electronic device may vary depending on the type and manufacturer of the electronic device. For example, the white screen of electronic device A may have a blue color, and the white screen of electronic device B may have a red color. The shooting box (or system control unit) can cause various electronic devices to display a white screen with the same brightness and color through the first application.

Third, an indirect lighting environment must be provided regardless of the size of the shooting box.

Indirect lighting can make microscopic defects or microscratches on the exterior of electronic devices more detectable. It may not be easy to provide indirect lighting in a shooting box of a certain size. As will be explained below, the shooting box can provide indirect lighting in the form of a line through a line-type structure and light source.

Figure 2 is a block diagram explaining an unmanned purchase device according to an embodiment. Figure 3 is a block diagram explaining a shooting box according to an embodiment.

Referring to FIG. 2, the unmanned purchase device 110 according to an embodiment may include a photographing box 210, a system control unit 220, and a display 230. Referring to Figure 3, the photography box 210 according to one embodiment includes an input unit 310, a seating unit 320, an alignment unit 330, an app linkage unit 340, and a photography unit 350. , and a control unit 360.

In one embodiment, the system control unit 220 of FIG. 2 and the control unit 360 of FIG. 3 may be in a master-slave relationship. The system control unit 220 may correspond to the master and the control unit 360 may correspond to the slave.

In one embodiment, the control unit 360 may be omitted from the shooting box 210, and the system control unit 220 may perform the operation of the control unit 360.

In one embodiment, the app linking unit 340 may be omitted from the shooting box 210, and the control unit 360 may perform the operation of the app linking unit 340.

The system control unit 220 can control the overall operation of the unmanned embedding device 110.

The system control unit 220 may display guidelines and/or precautions for selling electronic devices on the display 230.

The system control unit 220 may display a user interface for receiving various information from the user (eg, operating system (OS) information of an electronic device to be sold by the user) on the display 230.

In the process of preparing for sale, a first application installed on the electronic device may display a serial number on the display of the electronic device. The user can enter the serial number into display 230. The system control unit 220 may verify the serial number entered through the display 230 or request verification from the server 120. The system control unit 220 can open the door when the entered serial number is verified.

When the door is opened, the input portion 310 (or input port) of the photographing box 210 and the internal space of the photographing box 210 may be revealed. The user may place the electronic device on the seating unit 320 within the photographing box 210. Depending on the embodiment, the shooting box 210 may include a wired cable that can be connected to an electronic device. The user can connect the wired cable and the electronic device, and place the electronic device connected to the wired cable on the seating unit 320.

The alignment unit 330 can align the mounted electronic device to a predetermined position. If the electronic device is not aligned in a designated position, the photographing unit 350 may not be able to properly photograph the exterior of the electronic device. If the electronic device is not aligned in a designated position, the image acquired by the photographing unit 350 may be out of focus.

In one embodiment, the alignment unit 330 may include a movable movement bar. The control unit 360 can control the moving bar so that the mounted electronic device moves to a predetermined position, and can return the moving bar when the mounted electronic device moves to the predetermined position.

The app linkage unit 340 may display a single-color screen (e.g., a white screen, a black screen, etc.) on the electronic device by linking with an application installed on the electronic device (e.g., the first application described above).

The app linking unit 340 may receive an evaluation result of the internal state of the electronic device from the first application installed on the electronic device. The app linkage unit 340 can deliver the evaluation result of the internal state of the electronic device to the system control unit 220 through the control unit 360 or directly deliver the evaluation result of the internal state of the electronic device to the system control unit 220. there is.

The photographing unit 350 can photograph electronic devices aligned at a predetermined location. The photographing unit 350 includes a first camera for photographing the front of the aligned electronic devices, a second camera for photographing the rear of the aligned electronic devices, and a plurality of cameras for photographing the sides (or corners) of the aligned electronic devices. It may include third cameras and a plurality of light sources that provide illumination.

The control unit 360 can control at least one of the input unit 310, the alignment unit 330, the app linking unit 340, and the capturing unit 350.

When the mounted electronic device is aligned at a predetermined position, the control unit 360 may control the photographing unit 350 to photograph the exterior of the aligned electronic device.

The control unit 360 may control the app linking unit 340 to display a single-color screen on the aligned electronic devices in order to inspect the screens of the aligned electronic devices, and may control the capturing unit 350 to capture a monochromatic screen of the electronic device. You can control it.

The system control unit 220 receives images obtained by photographing an electronic device (e.g., one or more front images, one or more rear images, one or more side (or corner) images, and one or more screen images) from the shooting box 210. can do.

The system control unit 220 may transmit the received images and evaluation results of the internal state of the electronic device to the server 120. The server 120 may determine the value of the electronic device through the received images and an evaluation result of the internal state of the electronic device. For example, the server 120 may evaluate the external state of the electronic device through received images and a plurality of deep learning evaluation models, and use the evaluation result of the external state and the internal state of the electronic device to evaluate the external state of the electronic device. The value can be determined. The server 120 may transmit the determined value to the unmanned purchasing device 110 (or the system control unit 220).

Display 230 may display the received value.

In one embodiment, the system control unit 220, the app linking unit 340, and the control unit 360 may be implemented by one processor. Without being limited thereto, the system control unit 220, the app linking unit 340, and the control unit 360 may each be implemented by separate processors. Alternatively, the system control unit 220 may be implemented by one or more processors and the app linking unit 340 and the control unit 360 may be implemented by one or more processors.

FIGS. 4 to 5C are diagrams illustrating a seating portion and an alignment portion of a photographing box according to an embodiment.

Referring to FIG. 4 , a first camera 430, an electronic device 420, and a plate 410 are shown. In the example shown in Figure 4, the first camera 430 may be fixed. When the door of the unmanned embedding device 110 is opened, the user can insert an electronic device into the interior of the shooting box 210 through the input unit 310 and seat the electronic device 420 on the seating unit 320. You can. At this time, the user can seat the electronic device on the seating unit 320 while connecting the electronic device 420 and the wired cable. Alternatively, when the user places the electronic device 420 on the seating unit 320, the control unit 360 (or system control unit 220) detects the seating of the electronic device 420 and connects the electronic device 420 and the wireless device. A communication link (e.g. Wi-Fi, Bluetooth link, etc.) can be formed.

When the electronic device 420 is seated in the imaging box 210, the alignment unit 330 can align the electronic device 420 to a predetermined position. The seating unit 320 may include a plate 410, and the alignment unit 330 moves the seated electronic device 420 so that the center of the seated electronic device 420 is located at the center of the plate 410. You can do it.

In the example shown in FIGS. 5A to 5C , the alignment unit 330 may include a moving bar 510 .

In the example shown in FIG. 5A, when the electronic device 420 is seated, as in the example shown in FIG. 5B, the control unit 360 sets the seated electronic device 420 to a determined position (e.g., the center position on the plate 410). The movement bar 510 can be controlled to move to . As the moving bar 510 moves, the electronic device 420 can move to the center position on the plate 410. Depending on the embodiment, the control unit 360 may know the size of the mounted electronic device 420. For example, the control unit 360 can know the model name of the mounted electronic device 420 and can know the size of the mounted electronic device 420 through the model name. The control unit 360 may use the size of the mounted electronic device 420 to calculate the distance that the mounted electronic device 420 must move to a given location. The control unit 360 may control the moving bar 510 so that the moving bar 510 moves by the calculated distance.

When the electronic device 420 is at a predetermined position on the plate 410 (in other words, when the electronic device 420 is aligned), the control unit 360 returns the moving bar 510 to the original position, as shown in the example shown in FIG. 5C. It can be restored, and the photographing unit 340 can be controlled so that the photographing unit 340 photographs the electronic device 420. In the example shown in FIG. 4, the first camera 430 can photograph the front of the electronic device 420, and when a monochromatic screen is displayed on the electronic device 420, the first camera 430 can photograph a monochromatic screen of the electronic device 420. . Although not shown in FIG. 4 , the plate 410 may be transparent, and the second camera may photograph the rear of the electronic device 420 . Each of the plurality of third cameras may photograph each side (or corner) of the electronic device 420.

Figure 6 is a diagram explaining a photographing unit of a photographing box according to an embodiment.

Referring to FIG. 6, the photographing unit 350 includes a plurality of cameras (610-1 to 610-n), a plurality of light sources (620-1 to 620-m), a camera control unit 630, and a lighting control unit 640. ), and may include a shooting control unit 650.

According to one embodiment, the camera control unit 630 and the lighting control unit 640 may be omitted from the shooting unit 350, and the shooting control unit 650 may perform the respective operations of the camera control unit 630 and the lighting control unit 640. It can be done.

According to one embodiment, the camera control unit 630, lighting control unit 640, and shooting control unit 650 may be omitted from the shooting unit 350, and the control unit 330 (or system control unit 220) may be used as the camera control unit 350. Each operation of the control unit 630, lighting control unit 640, and photography control unit 650 can be performed.

The shooting control unit 650 may transmit a control signal to the camera control unit 630 and the lighting control unit 640, respectively.

The camera control unit 630 may transmit a control signal to camera 1 (or first camera) 610-1 to allow camera 1 (610-1) to photograph the front of the electronic device. The lighting control unit 640 may transmit a power on signal to light source 1 (620-1) corresponding to camera 1 (610-1) so that light source 1 (620-1) is turned on. Turned on, light source 1 (620-1) can perform direct lighting or indirect lighting. Previously, it was explained that one light source 1 (620-1) corresponds to camera 1 (610-1), but this is only an example, and two or more light sources may correspond to camera 1 (610-1). A plurality of light sources corresponding to Camera 1 (610-1) may be turned on to provide lighting for Camera 1 (610-1) to photograph the front of the electronic device. The camera control unit 630 may receive shooting data (e.g., one or more front images) of camera 1 (610-1) from camera 1 (610-1), and send the shooting data of camera 1 (610-1) to the control unit. It can be transmitted to 360 (or system control unit 220).

The camera control unit 630 may transmit a control signal to camera 2 (or second camera) 610-2 to allow camera 2 (610-2) to photograph the rear of the electronic device. The lighting control unit 640 may transmit a power-on signal to light source 2 (620-2) corresponding to camera 2 (610-2) so that light source 2 (620-2) is turned on. The turned-on light source 2 (620-2) can perform direct lighting or indirect lighting. Previously, it was explained that one light source 2 (620-2) corresponds to camera 2 (610-2), but this is only an example, and two or more light sources may correspond to camera 2 (610-2). The light sources corresponding to Camera 2 (610-1) may be turned on to provide lighting for Camera 2 (610-1) to photograph the rear of the electronic device. The camera control unit 630 may receive shooting data (e.g., one or more rear images) of camera 2 (610-2) from camera 2 (610-2), and may receive shooting data of camera 2 (610-2) from the control unit. It can be transmitted to 360 (or system control unit 220).

The camera control unit 630 transmits information to cameras (or third cameras) for photographing the sides (or corners) of the electronic device so that each of the cameras photographs each of the sides (or corners) of the electronic device. can do. The lighting control unit 640 may transmit a power-on signal to the light sources corresponding to the cameras so that the light sources are turned on. Turned on light sources may perform direct illumination or indirect illumination. The camera control unit 630 may receive shooting data (e.g., side (corner) images of the electronic device) from cameras that photograph the sides (or corners) of the electronic device, and transmit the shooting data to the control unit 360. (Or it can be transmitted to the system control unit 220).

Photographing the front, back, and sides (or corners) of the electronic device can be performed simultaneously. Without being limited thereto, filming of the front, back, and sides (or corners) of the electronic device may be performed sequentially. For example, when attempting to photograph the front of an electronic device, the shooting control unit 650 may transmit a control signal to the camera control unit 630 and the lighting control unit 640. The camera control unit 630 may transmit a control signal to Camera 1 (610-1) to allow Camera 1 (610-1) to photograph the front of the electronic device. The lighting control unit 640 may transmit a power-on signal to one or more light sources corresponding to Camera 1 (610-1) so that the one or more light sources corresponding to Camera 1 (610-1) are turned on. When the front of the electronic device is photographed, the photography control unit 650 may transmit a control signal for photographing the rear of the electronic device to the camera control unit 630 and the lighting control unit 640. The camera control unit 630 may transmit a control signal to Camera 2 (610-2) to allow Camera 2 (610-2) to photograph the rear of the electronic device. The lighting control unit 640 may transmit a power-on signal to one or more light sources corresponding to Camera 2 (610-2) so that the one or more light sources corresponding to Camera 2 (610-2) are turned on. The lighting control unit 640 may transmit a power-off signal to one or more light sources that were turned on to photograph the front of the electronic device. When the rear of the electronic device is photographed, the photography control unit 650 may transmit a control signal for photographing the sides (or corners) of the electronic device to the camera control unit 630 and the lighting control unit 640. The camera control unit 630 may transmit a control signal to cameras for side shooting (or corner shooting) to allow the cameras to shoot the sides (or corners) of the electronic device. The lighting control unit 640 may transmit a power-on signal to light sources corresponding to cameras for side imaging to turn on the light sources. The lighting control unit 640 may transmit a power-off signal to one or more light sources that were turned on to photograph the rear of the electronic device.

The app linking unit 340 may display a single-color screen on the screen of the electronic device by linking with the first application installed on the electronic device. The app linkage unit 340 may transmit a screen capture request to the capture control unit 650. The shooting control unit 650 may transmit a control signal to the camera control unit 630 and the lighting control unit 640 in response to a screen shooting request from the app linking unit 340. Camera 630 may transmit a control signal to Camera 1 (610-1) to allow Camera 1 (610-1) to capture a monochromatic screen of the electronic device. The lighting control unit 640 may transmit a control signal to light source 1 (620-1) corresponding to camera 1 (610-1) so that light source 1 (620-1) is turned on. The camera control unit 630 may receive shooting data (e.g., one or more screen images) of camera 1 (610-1) from camera 1 (610-1), and may send the shooting data of camera 1 (610-1) to the control unit. It can be transmitted to 360 (or system control unit 220).

7 to 9 are diagrams illustrating line lighting of a photographing unit of a photographing box according to an embodiment.

Referring to FIG. 7 , the imaging unit 350 may include light sources 710-1 and 710-2, a first camera 720, and a first structure 730. The first structure 730 can cause the light output by the first light source 710-1 to be formed as first line lighting on one side of the front of the electronic device 740, and the second light source 710-2 ) can be formed as second line lighting on the other side of the front of the electronic device 740.

Images 801, 803, and 805 shown in FIG. 8 are examples of front images of electronic devices. Referring to the image 801, line lighting is not formed on the front of the electronic device, so it may be difficult to determine whether there is a defect on the front of the electronic device in the image 801. In other words, it may be difficult for the server to determine whether there is a defect on the front of the electronic device from the image 801. Referring to the image 803, the light output by the second light source 710-2 may be formed as a second line light 810 on the other side of the front of the electronic device by the first structure 730. there is. Referring to the image 805, the light output by the first light source 710-1 may be formed as a first line light 820 on one side of the front of the electronic device by the first structure 730. . By using the first line lighting 820 and the second line lighting 810, it can be more clearly confirmed that there are no defects on the front of the electronic device. In other words, the server can determine that there are no defects on the front of the electronic device from the image 803 and the image 805.

Images 901, 903, and 905 shown in FIG. 9 are examples of front images of electronic devices. Referring to the image 901, since line lighting is not formed on the front of the electronic device, it may be difficult for the server to determine whether a defect exists on the front of the electronic device from the image 901. Referring to the image 903, the light output by the second light source 710-2 may be formed as a second line light 910 on the other side of the front of the electronic device by the first structure 730. there is. Compared to image 901 , defects 930 - 1 and 930 - 2 in image 903 can be observed by second line illumination 910 . The server may detect defects 930-1 and 930-2 present on the front of the electronic device from the image 903. Referring to the image 905, the light output by the first light source 710-1 may be formed as a first line light 920 on one side of the front of the electronic device by the first structure 730. . Compared to image 901 , defects 940 - 1 to 940 - 4 in image 905 may be observed by first line illumination 910 . The server may detect defects 940-1 and 940-2 present on the front of the electronic device from the image 905.

FIG. 10 is a diagram illustrating camera focus depth according to the position of an electronic device within a shooting box according to an embodiment.

10, the focal depth point (a) of camera 1020 is shown. The evaluation area (e.g., side, etc.) of the electronic device 1010 must be located within the range of a ± a' (focus range in Figure 10) (hereinafter referred to as "camera focus range") to obtain an image that is not out of focus. You can. Here, a' may represent the tolerance. For example, in the example shown in FIG. 10 , if electronic device 1010 is at location x, a side of electronic device 1010 may be located within the camera focal range, such that the camera may be Images taken from the side may not be out of focus. When the electronic device 1010 is at position y, the side of the electronic device 1010 may not be located within the camera focus range, and accordingly, the image taken by the camera of the side of the electronic device 1010 may be out of focus. there is.

There may be various ways to prevent out-of-focus images from being acquired. For example, as described above, the alignment unit 330 may move the electronic device 1010 to a designated position if the electronic device 1010 is not in a designated position. As another example, if the electronic device 1010 is not in a designated location, the photographing unit 350 may perform auto-focusing of the camera. As another example, the camera may be capable of a pan operation and/or a tilt operation, and the photographing unit 350 may allow the camera to perform a pan operation and/or a tilt operation. As another example, the camera may not be fixed within the shooting box 210 and may move in the x-axis, y-axis, and z-axis directions within the shooting box 210. The photographing unit 350 can determine the appropriate position of the camera according to the seating position of the electronic device 1010 and move the camera 1020 to the determined position.

Depending on the embodiment, the shooting box 210 may secure learning data for a deep learning evaluation model, which will be described later. In configuring the shooting box environment to secure learning data, the location and setting values of lighting and cameras (e.g., lighting intensity, lighting wavelength, shooting angle, etc.) may be important, and the location where the electronic device 1010 is physically seated. and tolerance (a') may also be important. If the electronic device 1010 deviates from the allowable space (or a predetermined position, camera focus range), the shooting box 210 may move the electronic device 1010 to the allowable space. Alternatively, if the electronic device 1010 deviates from the allowable space, the shooting box 210 can capture the exterior of the electronic device by adjusting the camera and lighting to the seating position of the electronic device.

FIG. 11 is a diagram illustrating a status bar of an electronic device according to an embodiment.

Referring to FIG. 11 , the screen of the electronic device 1110 may include a first status bar 1120 and a second status bar 1130. The first status bar 1120 may be located at the top of the screen, and the second status bar 1130 may be located at the bottom of the screen.

When the electronic device 1110 displays a white screen, an afterimage of the icon of the first status bar 1120 and/or an afterimage of the icon of the second status bar 1130 may exist. These afterimages may correspond to screen defects.

The brightness and color tone of the white screen of an electronic device may vary depending on the electronic device. For example, the default RGB values may differ depending on the manufacturer of the display module of the electronic device, and the brightness and/or color of the white screen of the electronic device may vary accordingly. The app linkage unit 340 (or control unit 360) displays a white screen with consistent brightness and color for each electronic device through at least one of the manufacturer of the electronic device, the type of display module of the electronic device, and the RCB value set as default. You can display it.

12 to 15 are diagrams explaining the operation of an electronic device value evaluation device according to an embodiment.

Referring to FIG. 12 , the electronic device evaluation device 130 may include a memory 1210, an appearance condition evaluation module 1220, and a value determination module 1230.

In one embodiment, the appearance condition evaluation module 1220 and the value determination module 1230 may be implemented with one processor.

In one embodiment, the appearance condition evaluation module 1220 and the value determination module 340 may each be implemented with separate processors. For example, a first processor may implement the appearance condition evaluation module 1220 and a second processor may implement the value determination module 340.

The memory 1210 may store a plurality of deep learning evaluation models. For example, the memory 1210 may detect a defect in a first evaluation area (e.g., the front) of the electronic device and use a first deep learning evaluation model to determine the grade of the detected defect (or the first evaluation area), the electronic device. A second deep learning evaluation model that detects defects in a second evaluation area (e.g., back side) and determines the grade of the detected defect (or second evaluation area), a third evaluation area (e.g., side (or a third deep learning evaluation model that detects defects in the corner) and determines the grade of the detected defect (or third evaluation area), and detects defects in the fourth evaluation area (e.g., screen) of the electronic device and determines the grade of the detected defect (or third evaluation area) It may include a fourth deep learning evaluation model that determines the grade of the defect (or fourth evaluation area). Table 1 below shows examples of defect types and grades for each of the evaluation areas (e.g. screen, front, side (or corner), rear).

Rating	screen (display)		Front	side (or corner)	back side	model output
D	3 or more white flowers	Screen lines and stains	-	-	-	7
	black spot	bullet damage
DL	LCD-level afterimage, LCD level whitening		-	-	-	6
C	-		Damage, liquid crystal lifting (e.g. tempered glass lifting)	-	break, rear lifting, Broken camera glass (or lens)	5
CL	Strong afterimage, 2 or less white flowers		-	-	-	4
B	Afterimage		Front level damage, scratches on the front	-	-	3
B+	-		-	body scratches	-	2
A	clean		clean	clean	clean	One

In Table 1 above, medium afterimage is, for example, an electronic device that displays a white screen, but the user sees certain areas of the screen (e.g., the status display area at the top of the screen) as non-white and icons in certain areas. It can represent the visible phenomenon. Strong afterimage, for example, may indicate a phenomenon in which an electronic device displays a white screen, but the user sees a color other than white overall on the screen. LCD-level afterimages are a condition in which the degree of afterimages is more severe than strong afterimages. For example, an electronic device displays a white screen, but the user may see a color other than white overall on the screen and an icon may appear on the screen. .

Each of the first to fourth deep learning evaluation models can perform image segmentation on a given input image.

Figure 13 shows the schematic structure of a deep neural network that is the basis for each deep learning evaluation model. Hereinafter, for convenience of explanation, the structure of a deep neural network will be described as an example, but it is not necessarily limited to this and neural networks of various structures can be used in deep learning evaluation models.

A deep neural network is a method of implementing a neural network and includes multiple layers. A deep neural network includes, for example, an input layer 1310 to which input data is applied, and an output layer 1340 to output a result value derived through prediction based on input data based on training. ), and may include multiple hidden layers 1320 and 1330 between the input layer and the output layer.

Deep neural networks can be classified into convolutional neural networks, recurrent neural networks, etc., depending on the algorithm used to process information. Hereinafter, according to general practice in the field of neural networks, the input layer is called the lowest layer and the output layer is called the highest layer, and the layers can be named by sequentially ranking them from the output layer, which is the highest layer, to the input layer, which is the lowest layer. In Figure 13, hidden layer 2 is a layer higher than hidden layer 1 and the input layer, and may correspond to a lower layer than the output layer.

In a deep neural network, among adjacent layers, a relatively higher layer can output a predetermined calculation result by multiplying the output value of the relatively lower layer by a weight and receiving a biased value. At this time, the output operation result can be applied to the upper layer adjacent to the corresponding layer in a similar manner.

For example, a method of training a neural network is called deep learning, and as described above, various algorithms such as convolutional neural networks and recurrent neural networks can be used in deep learning.

“Training a neural network” means determining and updating the weight(s) and bias(s) between layers, and/or weight(s) between a plurality of neurons belonging to different layers among adjacent layers, and It can be understood as encompassing both determining and updating bias(es).

The plurality of layers, the hierarchical structure between the plurality of layers, and the weights and biases between neurons can all be collectively expressed as the “connectivity” of a neural network. Accordingly, “training a neural network” can also be understood as building and training connectivity.

In a neural network, each of a plurality of layers may include a plurality of nodes. Nodes may correspond to neurons in a neural network. The term “neuron” may be used interchangeably with the term “node.”

In the deep neural network of FIG. 13, it can be seen that connection relationships are formed between a plurality of nodes included in one layer and combinations of a plurality of nodes included in an adjacent layer. In this way, a combination of all nodes included in adjacent layers of a neural network can be called “fully-connected.” Node 3-1 of hidden layer 2 (1330) shown in FIG. 13 is connected to all nodes of hidden layer 1 (1320), that is, all nodes 2-1 to 2-4, and is connected to the output value of each node. You can input a value multiplied by a predetermined weight.

Data input to the input layer 1310 is processed through a plurality of hidden layers 1230 and 1330, so that an output value may be output through the output layer 1340. At this time, a larger weight multiplied by the output value of each node may mean that the connectivity between the two corresponding nodes is strengthened, and a smaller weight may mean that the connectivity between the two nodes is weakened. If the weight is 0, it may mean that there is no connectivity between the two nodes.

Returning to FIG. 12 , the appearance condition evaluation module 1220 may perform an appearance condition evaluation on the electronic device based on a plurality of images obtained by photographing the electronic device and deep learning evaluation models. For example, the appearance condition evaluation module 1220 may generate a mask predicting the defect state of each of the first to fourth evaluation areas of the electronic device from images through deep learning evaluation models. The appearance condition evaluation module 1220 may determine the grade of defects in each of the first to fourth evaluation areas based on each generated mask. The exterior condition evaluation module 1220 may determine the final grade of the exterior condition of the electronic device through each determined grade.

In the example shown in FIG. 14, the first deep learning evaluation model 1410 may receive a front image as input. The first deep learning evaluation model 1410 can generate a first mask that predicts the defect state of the front of the electronic device (e.g., at least one of the location of the defect, the type of the defect, and the degree of the defect) through the front image. there is. Here, the degree of defect may be related to the defect type. For example, the first deep learning evaluation model 1410 may perform image segmentation on the front image to classify each pixel of the front image into one of the first classes, and generate a first mask according to this classification. can do. Table 2 below shows examples of first classes.

Class 1-1 (e.g. frontal scratches, frontal major scratches, etc.)

Class 1-2 (e.g. front breakage, floating liquid crystal, etc.)

Classes 1-3 (e.g. non-electronic devices)

Classes 1-4 (e.g. front of electronic devices)

Previously, the first camera in the shooting box 210 can photograph not only the front of the electronic device but also the surroundings of the front, so the front image may include parts that are not the electronic device.

The first deep learning evaluation model 1410 may classify some pixels of the front image as class 1-1, and classify each of the remaining pixels as class 1-2, class 1-3, or class 1-4. It can be classified as: Through this classification, the first deep learning evaluation model 1410 can generate a first mask.

An example of an image visually representing the first mask is shown in FIG. 15.

In the example shown in FIG. 15, the black areas 1510-1, 1510-2, 1510-3, and 1510-4 are the first deep learning evaluation model 1410 that selects some pixels of the front image as 1-3. It may represent a result of classification into a class (or a result of the first deep learning evaluation model 1410 predicting that some pixels in the front image do not correspond to electronic devices). The area 1520 is a result of the first deep learning evaluation model 1410 classifying some pixels of the front image into first and second classes (or the first deep learning evaluation model 1410 is located on the front of the electronic device from the front image). (results predicted to be damaged) can be displayed. The area 1530 is a result of the first deep learning evaluation model 1410 classifying some pixels of the front image into the 1-1 class (or the first deep learning evaluation model 1410 is located on the front of the electronic device from the front image). (result predicted to have a flaw) can be displayed. The area 1540 is a result of the first deep learning evaluation model 1410 classifying some pixels of the front image into classes 1 to 4 (or the first deep learning evaluation model 1410 classifies the front of the electronic device in the front image). predicted results).

The first deep learning evaluation model 1410 may determine a grade for a defect on the front surface based on the first mask. For example, when the first deep learning evaluation model 1410 predicts that there is at least one of damage and floating liquid crystal on the front of the electronic device through the front image, the grade of the defect on the front of the electronic device is graded as C (e.g. : It can be determined as Grade C in Table 1 above). The first deep learning evaluation model 1410 may output a score of 5 corresponding to a C grade. When the first deep learning evaluation model 1410 predicts that there are damages and scratches on the front of the electronic device through the front image, the grade of the defect on the front of the electronic device is graded as C (e.g. grade C in Table 1 above). can be decided. The first deep learning evaluation model 1410 may output a score of 5 corresponding to a C grade. If the first deep learning evaluation model 1410 predicts that there is at least one of a scratch and a front damage level scratch on the front of the electronic device through the front image, the grade of the defect on the front of the electronic device is graded as B (e.g., above). It can be determined by grade B in Table 1). The first deep learning evaluation model 1410 may output a score of 3 corresponding to grade B. If the first deep learning evaluation model 1410 predicts that the front of the electronic device is clean (or has no defects on the front) through the front image, the grade of the defect on the front of the electronic device is graded as A (e.g., above). It can be determined by grade A in Table 1). The first deep learning evaluation model 1410 may output a score of 1 corresponding to grade A.

The second deep learning evaluation model 1420 can receive a rear image as input. The second deep learning evaluation model 1420 may generate a second mask predicting the defect state (e.g., at least one of defect location, defect rectification, and defect degree) of the back of the electronic device through the back image. there is. For example, the second deep learning evaluation model 1420 may perform image segmentation on the back image, and classify each pixel of the back image into one of the second classes. Through this classification, the second deep learning evaluation model 1420 may perform image segmentation on the back image. You can create a mask. Table 3 below shows examples of second classes.

Class 2-1 (e.g. breakage, back lifting, camera retention (or lens) breakage, etc.)

Class 2-2 (e.g. non-electronic devices)

Classes 2-3 (e.g. rear of electronic devices)

The second deep learning evaluation model 1420 may determine the grade of the defect on the back side based on the second mask. For example, if the second deep learning evaluation model 1420 predicts that there is at least one of damage, back lifting, and camera lens damage on the back of the electronic device through the back image, the grade of the defect on the back of the electronic device can be determined as C grade (e.g., C grade in Table 1 above). The second deep learning evaluation model 1420 may output a score of 5 corresponding to a C grade. If the second deep learning evaluation model 1420 predicts that the back of the electronic device is clean through the back image, the grade of the defect on the back of the electronic device may be determined as Grade A (e.g., Grade A in Table 1 above). . The second deep learning evaluation model 1420 may output a score of 1 corresponding to grade A.

The third deep learning evaluation model 1430 may receive side images (or corner images) as input. The third deep learning evaluation model 1430 determines the defect status (e.g., location of defect, type of defect, and degree of defect) of the sides (or corners) of the electronic device through side images (or corner images). A third mask predicting (at least one) can be generated. For example, the third deep learning evaluation model 1430 may perform image segmentation on side images (or corner images) and classify each pixel of each side image into one of the third classes. And through this classification, a third mask can be created. Table 4 below shows examples of third classes.

Class 3-1 (e.g. scratches)

Class 3-2 (e.g. non-electronic devices)

Class 3-3 (e.g. the side (or corner) of an electronic device)

The third deep learning evaluation model 1430 may determine the grade of defects in the sides (or corners) based on the third mask. For example, when the third deep learning evaluation model 1430 predicts that there is a scratch on the first side (or first corner) of the electronic device through side images (or corner images), the side of the electronic device The grade for defects in corners (or corners) can be determined as a B+ grade (e.g., B+ grade in Table 1 above). The third deep learning evaluation model 1430 may output a score of 2 corresponding to a B+ grade. If the third deep learning evaluation model 1430 predicts that the sides (or corners) of the electronic device are clean through the side images (or corner images), the sides (or corners) of the electronic device are defective. The grade for can be determined as grade A (e.g. grade A in Table 1 above). The third deep learning evaluation model 1430 may output a score of 1 corresponding to grade A.

The fourth deep learning evaluation model 1440 can receive a screen image (e.g., a single-color screen image) as input to an electronic device. The fourth deep learning evaluation model 1440 can generate a fourth mask that predicts the defect state (e.g., at least one of the location of the defect, the type of the defect, and the degree of the defect) of the screen of the electronic device through the screen image. there is. For example, the fourth deep learning evaluation model 1440 can perform image segmentation on the screen image and classify each pixel of the screen image into one of the fourth classes, and through this classification, the fourth class You can create a mask. Table 5 below shows examples of the fourth classes.

Class 4-1 (e.g. 3 or more white spots, screen lines, stains, black spots, bullet damage, etc.)

Class 4-2 (e.g. LCD-class afterimage, LCD-class whitening, etc.)

Class 4-3 (e.g. strong afterimage, 2 or less white flowers, etc.)

Class 4-4 (e.g. medium afterimage, etc.)

Classes 4-5 (e.g. non-electronic devices)

Class 4-6 (e.g. screens of electronic devices)

The fourth deep learning evaluation model 1440 may determine a grade for a defect on the screen of an electronic device based on the fourth mask. For example, if the fourth deep learning evaluation model 1440 predicts that the screen of the electronic device has at least three white spots, screen lines, black spots, or bullet damage through the screen image, the screen of the electronic device The grade of the defect can be determined as grade D (e.g. grade D in Table 1 above). The fourth deep learning evaluation model 1440 can output a score of 7 corresponding to a D grade. When the fourth deep learning evaluation model 1440 predicts that there is at least one of LCD-class afterimage and LCD-class whitening on the screen of the electronic device through the screen image, the grade of defect on the screen of the electronic device is DL grade (e.g. It can be determined by the DL grade in Table 1 above). The fourth deep learning evaluation model 1440 can output a score of 6 corresponding to the DL grade. When the fourth deep learning evaluation model 1440 predicts that there is at least one of two or less strong afterimages and whitening on the screen of the electronic device through the screen image, the grade for the defect on the screen of the electronic device is a CL grade (e.g. : It can be determined by the CL grade in Table 1 above). The fourth deep learning evaluation model 1440 can output a score of 4 corresponding to the CL grade. If the fourth deep learning evaluation model 1440 predicts that there is a medium afterimage on the screen of the electronic device through the screen image, the defect grade on the screen of the electronic device is graded as B (e.g., grade B in Table 1 above). You can decide. The fourth deep learning evaluation model 1440 can output a score of 3 corresponding to grade B. If the fourth deep learning evaluation model 1440 predicts that the screen of the electronic device is clean through the screen image, the defect grade of the screen of the electronic device may be determined as grade A (e.g. grade A in Table 1 above). . The fourth deep learning evaluation model 1440 may output a score of 1 corresponding to grade A.

Returning to FIG. 12 , the value determination module 1230 may determine the value of the electronic device based on the result of evaluating the external condition of the electronic device and/or the result of evaluating the internal state of the electronic device.

In one embodiment, the value determination module 1230 may determine the minimum grade among the grades determined by each of the first to fourth deep learning evaluation models 1410 to 1440 as the final grade for the external condition of the electronic device. Grade A is the highest, Grade B+ can be lower than Grade A and higher than Grade B. Grade CL may be lower than Grade B and higher than Grade C. Grade D may be the lowest. For example, the grade determined by the first deep learning evaluation model 1410 is a grade C, the grade determined by the second deep learning evaluation model 1420 is a grade B+, and the grade determined by the third deep learning evaluation model 1430 is a grade B+. The determined grade may be a C grade, and the grade determined by the fourth deep learning evaluation model 1440 may be a CL grade. Among the grades determined by each of the first to fourth deep learning evaluation models 1410 to 1440, the C grade determined by the first deep learning evaluation model 1410 may be the minimum grade, so the value determination module 1230 is an electronic device. The final grade for the appearance condition can be determined as grade C. Depending on the embodiment, the lower the grade, the higher the score output by each of the first to fourth deep learning evaluation models 1410 to 1440. The value determination module 1230 may determine the maximum score among the scores output by each of the first to fourth deep learning evaluation models 1410 to 1440 as the final score for the appearance evaluation of the electronic device.

In one embodiment, the value determination module 1230 may apply weights to the grades (or scores) determined by each of the first to fourth deep learning evaluation models 1410 to 1440, and the grade to which each weight is applied ( or score) can be used to determine the final grade (or final score) for the external condition of the electronic device. For example, the value determination module 1230 may apply a first weight to the grade (or score) determined by the first deep learning evaluation model 1410, and the grade determined by the second deep learning evaluation model 1420. A second weight may be applied to the (or score), and a third weight may be applied to the grade (or score) determined by the third deep learning evaluation model 1430, and the fourth deep learning evaluation model 1440 A fourth weight can be applied to the grade determined by . Here, each of the first to fourth weights may be greater than 0 and less than 1. The value determination module 1230 may determine the final grade (or final score) for the external condition of the electronic device by adding up the grades (or scores) to which each of the first to fourth weights are applied.

The value determination module 1230 may determine the first amount based on a result of evaluating the external condition of the electronic device (e.g., a final rating (or final score) for the external condition of the electronic device) and evaluating the internal condition of the electronic device. The second amount can be determined based on the results of . The value determination module 1230 may calculate the price of the electronic device by subtracting the first amount and the second amount from the reference price of the electronic device (e.g., the highest used price of the same type of electronic device). For example, the value determination module 1230 may obtain a standard price of an electronic device by linking it with a used market price database. The value determination module 1230 may obtain the final grade of the external condition of the electronic device and the mapped first amount of money from the first table in which the grade of the external condition and the amount are mapped to each other. The value determination module 1230 may obtain a second amount of money mapped to the result of evaluating the internal state of the electronic device from a second table in which the level of the internal state and the amount are mapped to each other. The value determination module 1230 may calculate the price of the electronic device by subtracting the first amount and the second amount from the reference amount.

In the example shown in FIG. 1A, the value determination module 1230 may transmit the value (eg, price) of the electronic device to the unmanned purchase device 110. The unmanned purchase device 110 may show the value (eg, price) of the electronic device to the user through the display 230.

In the example shown in FIG. 1B, the value determination module 1230 may display the value (eg, price) of the electronic device on the display 230 of the unmanned purchase device 110.

In one embodiment, the electronic device value evaluation device 130 may include a preprocessing module. The preprocessing module may determine whether each of the images (e.g., front image, back image, side images, screen image) includes one or more objects that would be mistaken for a defect. Here, the object may include at least one of an object corresponding to a floating icon on the screen of the electronic device, an object corresponding to a sticker attached to the electronic device, and an object corresponding to a foreign substance on the electronic device. The object corresponding to the floating icon may represent the object included in the image by photographing the floating icon on the screen of the electronic device. The object corresponding to the sticker attached to the electronic device may represent the object included in the image when the sticker attached to the electronic device is photographed. The object corresponding to the foreign matter on the electronic device can represent the object included in the image by photographing the foreign matter on the electronic device. Floating icons may include, but are not limited to, for example, a floating icon for assistive touch, a floating icon for triggering a specific task, etc.

If there is an image containing an object that would be mistaken for a defect, the preprocessing module may perform processing on the object. For example, the preprocessing module may perform masking processing on an object, but is not limited thereto. The appearance state evaluation module 1220 may perform appearance state evaluation based on the image in which the object has been processed, the remaining images not including the object, and the first to fourth deep learning evaluation models 1410 to 1440. . The appearance state evaluation module 1220 determines the defect state of each of the evaluation areas of the electronic device from the images in which the object has been processed and the remaining images that do not include the object through the first to fourth deep learning evaluation models 1410 to 1440. A mask that predicts can be generated, a grade for a defect in each of the evaluation areas can be determined based on each generated mask, and the final grade for the external state of the electronic device can be determined through each determined grade.

The preprocessing module may determine that there is no image containing the above-described object among images obtained by photographing an electronic device. In this case, as described above, the appearance condition evaluation module 1220 may perform appearance condition evaluation based on the images and the first to fourth deep learning evaluation models 1410 to 1440.

The preprocessing module selects images that cannot be analyzed by one or more of the first to fourth deep learning evaluation models (1410 to 1440) among images obtained by shooting an electronic device (hereinafter referred to as “model-analyzable images”). You can determine whether or not there is. For example, the preprocessing module may determine that among images obtained by photographing an electronic device, images with light reflection above a certain level, images with the camera out of focus, etc., are images that cannot be analyzed by the model. If there are images that cannot be analyzed by the model, the preprocessing module can request the operator to evaluate the external condition of the electronic device.

In one embodiment, the electronic device value evaluation device 130 may evaluate the value of a bar-type electronic device. In this case, as described above, the electronic device value evaluation device 130 (or the appearance condition evaluation module 1220) uses a plurality of images obtained by photographing a bar-type electronic device and the first to fourth deep learning evaluations. Based on the models 1410 to 1440, the external condition of the bar-type electronic device can be evaluated.

The electronic device value evaluation device 130 can evaluate the value of electronic devices whose shape can be changed (eg, foldable, rollable, etc.). An electronic device that can change its shape may have a first shape (e.g., an unfolded shape or a contracted shape) and a second shape (e.g., a folded shape or expansion) by manipulation. form) can be changed. For example, a foldable electronic device may be in an unfolded form, and its shape may be changed to a folded form through manipulation. The rollable electronic device may be in a collapsed form, and the shape may be changed to an expanded form by manipulation. The collapsed form may represent a state in which the rollable display is rolled in into the device, and the expanded form may represent a state in which the rollable display is rolled out from the device.

For example, the electronic device value evaluation device 130 (or the appearance condition evaluation module 1220) uses a plurality of images obtained by photographing a foldable electronic device in an unfolded form and the first to fourth deep learning evaluations. Based on the models 1410 to 1440, the level of defects in each evaluation area of the foldable electronic device in the unfolded form can be determined.

The unmanned purchase device 110 can change the foldable electronic device in the shooting box from the unfolded form to the folded form. Alternatively, the unmanned purchase device 110 may request the user to change the foldable electronic device from the unfolded form to the folded form and then reinsert the electronic device in the folded form into the unmanned purchase device 110. When a foldable electronic device changes from an unfolded form to a folded form, the folded portion may form a side surface, and the sub-screen of the foldable electronic device may be activated. The unmanned embedding device 110 may obtain an image (hereinafter referred to as an image of the folded side) by photographing the side surface corresponding to the folded portion of the foldable electronic device through one or more of the plurality of third cameras in the shooting box. The unmanned embedding device 110 may acquire an image (hereinafter referred to as a sub-screen image) by photographing a sub-screen of the foldable electronic device through the first camera in the capturing box.

The electronic device value evaluation device 130 (or the appearance condition evaluation module 1220) is based on the image of the folded side and the fifth deep learning evaluation model to determine the fifth evaluation area (e.g., corresponding to the folded portion) of the foldable electronic device. side) can be evaluated. Here, the fifth deep learning evaluation model may be a deep learning evaluation model that detects defects in the fifth evaluation area of the foldable electronic device and determines the grade of the detected defect (or fifth evaluation area). For example, the electronic device value evaluation device 130 (or the external condition evaluation module 1220) may input an image of the folded side into the fifth deep learning evaluation model. The fifth deep learning evaluation model is a fifth mask that predicts the defect state (e.g., at least one of the location of the defect, the type of the defect, and the degree of the defect) of the fifth evaluation area of the foldable electronic device through the image of the folded side. can be created. The fifth deep learning evaluation model can determine the grade for defects in the fifth evaluation area of the foldable electronic device based on the fifth mask.

The electronic device value evaluation device 130 (or the appearance condition evaluation module 1220) can evaluate the sixth evaluation area (e.g., sub-screen) of the foldable electronic device based on the sub-screen image and the sixth deep learning evaluation model. there is. Here, the sixth deep learning evaluation model may be a deep learning evaluation model that detects defects in the sixth evaluation area of the foldable electronic device and determines the grade of the detected defect (or sixth evaluation area). For example, the electronic device value evaluation device 130 (or the external condition evaluation module 1220) may input a sub-screen image into the sixth deep learning evaluation model. The sixth deep learning evaluation model uses a sixth mask that predicts the defect state (e.g., at least one of the defect location, defect type, and defect degree) of the sixth evaluation area of the foldable electronic device through the sub-screen image. can be created. The sixth deep learning evaluation model can determine the grade for defects in the sixth evaluation area of the foldable electronic device based on the sixth mask. Depending on the embodiment, the electronic device value evaluation device 130 (or the appearance condition evaluation module 1220) is based on the sub-screen image and the fourth deep learning evaluation model 1440 described above. The sixth evaluation area ( For example, the grade of the defect (sub screen) can be determined.

The electronic device value evaluation device 130 (or value determination module 1230) provides a result of evaluating the appearance condition of the foldable electronic device (e.g., a grade determined by each of the first to sixth deep learning evaluation models) and/or a folder. The value of a foldable electronic device can be determined based on the results of evaluating the internal state of the foldable electronic device.

For another example, the electronic device value evaluation device 130 (or the appearance condition evaluation module 1220) uses a plurality of images obtained by photographing a miniature rollable electronic device and first to fourth deep learning evaluation models. Based on the fields 1410 to 1440, the level of defects in each evaluation area of the miniature rollable electronic device can be determined.

The unmanned embedding device 110 can change the rollable electronic device in the shooting box from a reduced form to an expanded form. Alternatively, the unmanned embedding device 110 may request the user to change the rollable electronic device from a reduced form to an expanded form and then reinsert the electronic device in the expanded form into the unmanned embedding device 110. When a rollable electronic device changes from a reduced form to an expanded form, the screen and sides can be expanded. The unmanned embedding device 110 may acquire an image (hereinafter referred to as an image of the expanded side) by photographing the expanded side through one or more of the plurality of third cameras in the shooting box. The unmanned embedding device 110 may acquire an image (hereinafter referred to as an image of the expanded screen) by photographing the expanded screen of the electronic device through the first camera in the shooting box.

The electronic device value evaluation device 130 (or the appearance condition evaluation module 1220) is configured to determine the seventh evaluation area (e.g., the expanded side) of the rollable electronic device based on the image of the expanded side and the seventh deep learning evaluation model. can be evaluated. Here, the seventh deep learning evaluation model may be a deep learning evaluation model that detects defects in the seventh evaluation area of the rollable electronic device and determines the grade of the detected defect (or seventh evaluation area). For example, the electronic device value evaluation device 130 (or the external condition evaluation module 1220) may input an image of the expanded side surface into the seventh deep learning evaluation model. The seventh deep learning evaluation model predicts the defect state (e.g., at least one of the location of the defect, the type of the defect, and the degree of the defect) of the seventh evaluation area of the rollable electronic device through the extended side image. You can create a mask. The seventh deep learning evaluation model can determine the grade of a defect in the seventh evaluation area of the rollable electronic device based on the seventh mask. Depending on the embodiment, the electronic device value evaluation device 130 (or the external condition evaluation module 1220) performs a seventh evaluation of the rollable electronic device based on the expanded side image and the third deep learning evaluation model 1430. Areas (e.g. extended aspects) can be assessed.

The electronic device value evaluation device 130 (or the appearance condition evaluation module 1220) is based on the image of the expanded screen and the fourth deep learning evaluation model 1440 to determine the eighth evaluation area (e.g., expansion) of the rollable electronic device. screen) can be evaluated. For example, the electronic device value evaluation device 130 (or the external condition evaluation module 1220) may input an image of the expanded screen into the fourth deep learning evaluation model 1440. As described above, the fourth deep learning evaluation model 1440 may be a deep learning evaluation model that generates a mask predicting the defect state of the screen from a given screen image and determines the grade of the screen defect based on the generated mask. there is. The fourth deep learning evaluation model 1440 predicts the defect state (e.g., at least one of the location of the defect, the type of the defect, and the degree of the defect) of the eighth evaluation area of the rollable electronic device through the image of the expanded screen. An eighth mask can be created. The fourth deep learning evaluation model 1440 may determine the grade of a defect in the eighth evaluation area of the rollable electronic device based on the eighth mask.

The electronic device value evaluation device 130 (or value determination module 1230) determines the results of the external state evaluation of the rollable electronic device (e.g., by each of the first to fourth deep learning evaluation models and the seventh deep learning evaluation model). The value of the rollable electronic device may be determined based on the determined rating) and/or the results of an evaluation of the internal condition of the rollable electronic device.

In one embodiment, the unmanned purchase device 110 may receive a wearable device (eg, a smart watch) from a user. The electronic device value evaluation device 130 may store deep learning evaluation models that can evaluate the appearance (e.g., front, back, side, screen) of the wearable device. The electronic device value evaluation device 130 may perform an external condition evaluation of the wearable device based on images obtained by photographing the wearable device and deep learning evaluation models. The electronic device value evaluation device 130 may determine the value of the wearable device based on the result of evaluating the external state of the wearable device and the result of evaluating the internal state of the wearable device.

Figure 16 is a block diagram illustrating the configuration of a computing device for training a deep learning model according to an embodiment.

Referring to FIG. 16, a computing device 1600 that trains a deep learning model may include a memory 1610 and a processor 1620.

Memory 1610 may store one or more deep learning models. The deep learning model can be based on the deep neural network described above. Deep learning models can perform image segmentation on given input images.

The processor 1620 can train a deep learning model.

The processor 1620 can input a learning image for a defect into a deep learning model and generate a mask predicting the state of the defect from the learning image through the deep learning model. The learning image may include, for example, an image acquired by the photographing box 210 photographing a defective electronic device.

The processor 1620 may calculate the similarity between the generated mask and the labeled mask for the defect.

17A to 17C show examples of the generated mask and label mask, respectively. In FIG. 9A, the target mask may represent a label mask and the prediction mask may represent a mask generated by a deep learning model.

The processor 1620 may update at least one parameter in the deep learning model when the calculated similarity is less than a threshold. If the calculated similarity is greater than or equal to a threshold, the processor 1620 may end training for the deep learning model.

Depending on the embodiment, when the processor 1620 inputs the first learning image (e.g., the front image in which the front surface with the first defect is photographed) into the first deep learning model, the processor 1620 uses the first deep learning model to A first mask that predicts the defect state of the front of the electronic device can be generated from the learning image. An image obtained by photographing an electronic device with a first defect on the front of the photographing box 210 using a first camera may correspond to the first learning image. The first deep learning model may perform image segmentation on the first learning image to generate a first mask that predicts the defect state of the front of the electronic device from the first learning image. The processor 1620 may calculate a first similarity between the first mask and the label mask for the first defect. The processor 1620 may update at least one parameter in the first deep learning model when the calculated first similarity is less than a threshold. If the calculated first similarity is greater than or equal to the threshold, the processor 1620 may end training for the first deep learning model. The first deep learning model for which training has been completed can be mounted on the electronic device value evaluation device 130 as the first deep learning evaluation model 1410.

When the processor 1620 inputs a second learning image (e.g., a rear image of a rear surface with a second defect) into a second deep learning model, the processor 1620 uses the second deep learning model to select an electronic device from the second learning image. A second mask predicting the defect state of the rear surface can be generated. An image obtained by photographing an electronic device with a second defect on the back of the photographing box 210 using a second camera may correspond to the second learning image. The second deep learning model may perform image segmentation on the second learning image to generate a second mask that predicts the defect state of the back of the electronic device from the second learning image. The processor 1620 may calculate a second similarity between the second mask and the label mask for the second defect. The processor 1620 may update at least one parameter in the second deep learning model when the calculated second similarity is less than the threshold. If the calculated second similarity is greater than or equal to the threshold, the processor 1620 may end training for the second deep learning model. The second deep learning model for which training has been completed can be mounted on the electronic device value evaluation device 130 as the second deep learning evaluation model 1420.

When a third learning image (e.g., a side (or corner) image in which a third defective side (or corner) is photographed) is input to the third deep learning model, the processor 1620 uses the third deep learning model. Thus, a third mask that predicts the defect state of the side (or corner) of the electronic device can be generated from the third learning image. An image obtained by photographing an electronic device with a third defect on a side (or corner) of the shooting box 210 using a third camera may correspond to the third learning image. The third deep learning model may perform image segmentation on the third learning image to generate a third mask that predicts the defect state of the side (or corner) of the electronic device from the third learning image. The processor 1620 may calculate a third similarity between the third mask and the label mask for the third defect. The processor 1620 may update at least one parameter in the third deep learning model when the calculated third similarity is less than the threshold. If the calculated third similarity is greater than or equal to the threshold, the processor 1620 may end training for the third deep learning model. The third deep learning model for which training has been completed can be mounted on the electronic device value evaluation device 130 as the third deep learning evaluation model 1430.

When a fourth learning image (e.g., a screen image in which a screen with a fourth defect is captured) is input to the fourth deep learning model, the processor 1620 uses the fourth deep learning model to select an electronic device from the fourth learning image. A fourth mask predicting the defect state of the screen can be generated. An image obtained when the shooting box 210 captures the screen with the fourth defect using the first camera may correspond to the fourth learning image. The fourth deep learning model may perform image segmentation on the fourth learning image to generate a fourth mask that predicts the defect state of the screen of the electronic device from the fourth learning image. The processor 1620 may calculate a fourth similarity between the fourth mask and the label mask for the fourth defect. The processor 1620 may update at least one parameter in the fourth deep learning model when the calculated fourth similarity is less than the threshold. If the calculated fourth similarity is greater than or equal to the threshold, the processor 1620 may end training for the fourth deep learning model. The fourth deep learning model for which training has been completed can be mounted on the electronic device value evaluation device 130 as the fourth deep learning evaluation model 1440.

In one embodiment, when the processor 1620 inputs the fifth learning image (e.g., an image in which the folded side with the fifth defect is photographed) to the fifth deep learning model, the processor 1620 uses the fifth deep learning model to 5 From the learning image, a fifth mask that predicts the defect state of the side corresponding to the folded portion of the foldable electronic device can be generated. An image obtained by photographing an electronic device with a fifth defect on the side where the photographing box 210 is folded using a third camera may correspond to the fifth learning image. The processor 1620 may calculate a fifth similarity between the fifth mask and the label mask for the fifth defect. The processor 1620 may update at least one parameter in the fifth deep learning model when the calculated fifth similarity is less than the threshold. If the calculated fifth similarity is greater than or equal to the threshold, the processor 1620 may end training for the fifth deep learning model. The fifth deep learning model for which training has been completed can be mounted on the electronic device value evaluation device 130 as a fifth deep learning evaluation model.

In one embodiment, when the sixth learning image (e.g., an image in which a sixth defective sub-screen is captured) is input to the sixth deep learning model, the processor 1620 uses the sixth deep learning model to 6 A sixth mask that predicts the defect state of the sub-screen of the foldable electronic device can be generated from the learning image. An image obtained when the shooting box 210 captures the sub-screen with the sixth defect using the first camera may correspond to the sixth learning image. The processor 1620 may calculate a sixth similarity between the sixth mask and the label mask for the sixth defect. The processor 1620 may update at least one parameter in the sixth deep learning model when the calculated sixth similarity is less than the threshold. If the calculated sixth similarity is greater than or equal to the threshold, the processor 1620 may end training for the sixth deep learning model. The sixth deep learning model for which training has been completed can be mounted on the electronic device value evaluation device 130 as a sixth deep learning evaluation model.

In one embodiment, when the processor 1620 inputs the seventh learning image (e.g., an image of the expanded side with the seventh defect) into the seventh deep learning model, the processor 1620 uses the seventh deep learning model to A seventh mask predicting the defect state of the extended side of the rollable electronic device can be generated from the seventh learning image. An image obtained by photographing the expanded side of the photographing box 210 where the seventh defect is located through a third camera may correspond to the seventh learning image. The processor 1620 may calculate a seventh similarity between the seventh mask and the label mask for the seventh defect. The processor 1620 may update at least one parameter in the seventh deep learning model when the calculated seventh similarity is less than the threshold. If the calculated seventh similarity is greater than or equal to the threshold, the processor 1620 may end training for the seventh deep learning model. The seventh deep learning model for which training has been completed can be mounted on the electronic device value evaluation device 130 as the seventh deep learning evaluation model. Depending on implementation, the processor 1620 may train the third deep learning model using the seventh learning image and allow the third deep learning model to generate the seventh mask.

In one embodiment, similar to the training method described above, the processor 1620 trains each of the deep learning models based on learning images taken of a wearable device with defects in the exterior to determine the exterior (e.g., front) of the wearable device. , back, side, and screen) can be created to create deep learning evaluation models.

Figure 18 is a flowchart explaining a deep learning model training method of a computing device according to an embodiment.

Referring to FIG. 18, in step 1810, the computing device 1600 may input a training image for a defect into a deep learning model.

In step 1820, the computing device 1600 may generate a mask predicting the state of a defect from a learning image through a deep learning model.

In step 1830, the computing device 1600 may calculate the similarity between the generated mask and the label mask for the defect.

In step 1840, the computing device 1600 may determine whether the calculated similarity is less than a threshold.

If the calculated similarity is less than the threshold, the computing device 1600 may update at least one parameter in the deep learning model in step 1050. The computing device 1600 may repeatedly perform steps 1810 to 1840.

If the calculated similarity is greater than or equal to the threshold, the computing device 1600 may end training for the deep learning model in step 1860.

The embodiment described with reference to FIG. 16 can be applied to the deep learning model training method of the computing device 1600 of FIG. 18, so detailed description is omitted.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, and a field programmable gate (FPGA). It may be implemented using a general-purpose computer or a special-purpose computer, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and software applications running on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on a computer-readable recording medium.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. A computer-readable medium may store program instructions, data files, data structures, etc., singly or in combination, and the program instructions recorded on the medium may be specially designed and constructed for the embodiment or may be known and available to those skilled in the art of computer software. there is. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc.

The hardware devices described above may be configured to operate as one or multiple software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with limited drawings, those skilled in the art can apply various technical modifications and variations based on this. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments and equivalents of the claims also fall within the scope of the following claims.

Claims (14)

1. In a photography box for photographing electronic devices,

   an input unit that allows the electronic device to be input into the shooting box;

   A seating portion on which the electronic device is seated;

   an alignment unit that aligns the mounted electronic device to a predetermined position;

   An app linkage unit that displays a single-color screen on the electronic device in conjunction with an application installed on the electronic device;

   a photographing unit that photographs electronic devices aligned at the predetermined location in a given photographing environment; and

   Controlling the alignment unit to align the mounted electronic device at the predetermined position, controlling the photographing unit to capture an appearance of the aligned electronic device when the mounted electronic device is aligned at the predetermined position, and A control unit that controls the app linkage unit to display the monochromatic screen on the aligned electronic device for screen inspection of the electronic device, and controls the photographing unit to capture the monochromatic screen.

   Including,

   Shooting box.
2. According to paragraph 1,

   The filming department,

   a first camera that photographs the front of the aligned electronic devices;

   a second camera that photographs the rear of the aligned electronic devices;

   a plurality of third cameras that photograph sides of the aligned electronic devices; and

   One or more light sources providing illumination

   Including,

   Shooting box.
3. According to paragraph 1,

   The sorting unit,

   Includes a movable bar,

   The control unit,

   Controlling the moving bar so that the mounted electronic device moves to the determined position, and returning the moving bar when the mounted electronic device moves to the determined position,

   Shooting box.
4. According to paragraph 3,

   The control unit,

   Calculating the distance that the mounted electronic device must move to the determined position using the size of the mounted electronic device, and controlling the moving bar based on the calculated distance,

   Shooting box.
5. According to paragraph 1,

   The filming department,

   multiple light sources; and

   The light output by the first light source among the light sources is formed as a first line illumination on one side of the front of the aligned electronic device, and the light output by the second light source among the light sources is formed on the other side of the front. A first structure to be formed with a second line lighting on the side

   Including,

   Shooting box.
6. According to clause 5,

   The control unit,

   Turning on the first light source and turning off the second light source to control the photographing unit to capture the front surface when the first line lighting is formed on one side of the front surface, and turning off the first light source Turning on the second light source to control the photographing unit to photograph the front surface when the second line lighting is formed on the other side of the front surface,

   Shooting box.
7. In the unmanned acquisition device,

   Shooting box for shooting electronic devices;

   a system control unit that transmits images obtained by photographing the electronic device and evaluation results of the internal state of the electronic device to a server, and receives a value of the electronic device determined through the images and the evaluation results from the server; and

   Display indicating the received value

   Including,

   The shooting box is,

   an input unit that allows the electronic device to be input into the shooting box;

   A seating portion on which the electronic device is seated;

   an alignment unit that aligns the mounted electronic device to a predetermined position;

   An app linkage unit that displays a single-color screen on the electronic device in conjunction with an application installed on the electronic device;

   a photographing unit that photographs electronic devices aligned at the predetermined location in a given photographing environment; and

   Controlling the alignment unit to align the mounted electronic device at the predetermined position, controlling the photographing unit to capture an appearance of the aligned electronic device when the mounted electronic device is aligned at the predetermined position, and A control unit that controls the app linkage unit to display the monochromatic screen on the aligned electronic device for screen inspection of the electronic device, and controls the photographing unit to capture the monochromatic screen.

   Including,

   Unmanned acquisition device.
8. In clause 7,

   The filming department,

   a first camera that photographs the front of the mounted electronic device;

   a second camera that photographs the rear of the mounted electronic device;

   a plurality of third cameras that photograph side surfaces of the mounted electronic device; and

   one or more light sources

   Including,

   Unmanned acquisition device.
9. In clause 7,

   The sorting unit,

   Includes a movable bar,

   The control unit,

   Controlling the moving bar so that the mounted electronic device moves to the determined position, and returning the moving bar when the mounted electronic device moves to the determined position,

   Unmanned acquisition device.
10. According to clause 9,

    The control unit,

    Calculating the distance that the mounted electronic device must move to the determined position using the size of the mounted electronic device, and controlling the moving bar based on the calculated distance,

    Unmanned acquisition device.
11. According to clause 9,

    The filming department,

    multiple light sources; and

    The light output by the first light source among the light sources is formed as a first line illumination on one side of the front of the aligned electronic device, and the light output by the second light source among the light sources is formed on the other side of the front. A first structure to be formed with a second line lighting on the side

    Including,

    Unmanned acquisition device.
12. According to clause 11,

    The control unit,

    Turning on the first light source and turning off the second light source to control the photographing unit to capture the front surface when the first line lighting is formed on one side of the front surface, and turning off the first light source Turning on the second light source to control the photographing unit to photograph the front surface when the second line lighting is formed on the other side of the front surface,

    Unmanned acquisition device.
13. In a method of operating a photography box for photographing an electronic device,

    Aligning the electronic device mounted in the photographing box to a predetermined position;

    Photographing the exterior of the electronic device aligned at the predetermined location in a given photographing environment;

    Displaying a single-color screen on the electronic device in conjunction with an application installed on the electronic device;

    Photographing an electronic device displaying the monochromatic screen

    Including,

    How the shooting box works.
14. Software stored in a computer-readable recording medium, which executes the operating method of claim 13.

Images