PROGRAM, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING SYSTEM

Abstract

To provide a program or the like capable of determining whether or not to permit entry of a vehicle to a garbage disposal facility, according to a type of the vehicle.SOLUTION: A program according to the invention causes execution of a process comprising: acquiring vehicle image data including at least one of first image data and second image data, the first image data obtained by imaging a vehicle 2 being entering a garbage disposal facility 1 where a plurality of types of vehicles including a garbage vehicle for discharging garbage to a hopper directly and other vehicles for discharging garbage to the hopper through a garbage throwing device enter, and the second image data obtained by imaging the vehicle 2 inside the garbage disposal facility 1; acquiring a type of the vehicle 2 by inputting the obtained vehicle image data to a learning model having learned so as to output the type of the vehicle 2 upon input of the vehicle image data; and determining whether or not to permit the vehicle 2 to enter the garbage disposal facility 1, based on an availability status of in-facility stopping spaces including a stopping space near the hopper and other stopping spaces near the garbage throwing device adjacent to the hopper, and the obtained type of the vehicle 2.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a program, an information processing method, and an information processing system. [Background technology]

[0002] A waste treatment plant management system is known that guides refuse trucks, so-called refuse collection trucks, to a waste treatment facility. In the waste treatment plant management system described in Patent Document 1, the waste treatment facility is provided with a device such as a hopper for receiving refuse and a parking space for the refuse truck. The refuse truck loaded with refuse enters the waste treatment facility and stops at the parking space. The refuse loaded in the refuse truck is received by the hopper. The waste treatment facility has a maximum number of refuse trucks that can enter, for example, the same number as the number of parking spaces. When the number of refuse trucks parked in the parking spaces is less than the maximum number, the management system allows the refuse truck to enter the waste treatment facility. When the number of refuse trucks parked in the parking spaces is the maximum number, the management system does not allow the refuse truck to enter the waste treatment facility. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-167963 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the above waste treatment plant management system, it is possible to prevent more than the upper limit of refuse trucks from entering the waste treatment facility. However, it is assumed that a person may bring refuse to the waste treatment facility in a vehicle other than a refuse truck, such as a general vehicle, i.e., a vehicle other than a refuse truck may enter the waste treatment facility. Since the method of discharging refuse may differ depending on the type of vehicle, for example, a refuse truck and a general vehicle, the parking space may be provided depending on the type of vehicle to be parked. When the parking space differs depending on the type of vehicle, for example, when the parking space where a refuse truck is parked is different from the parking space where a general vehicle is parked, it is necessary to take the type of vehicle into consideration when determining whether to allow the vehicle to enter the waste treatment facility.

[0005] The present disclosure aims to provide a program etc. that can determine whether or not to permit a vehicle to enter a waste treatment facility depending on the type of vehicle. [Means for solving the problem]

[0006] A program according to one embodiment of the present disclosure acquires vehicle image data including at least one of first image data of a vehicle entering a waste treatment facility where multiple types of vehicles enter, including garbage trucks that discharge garbage directly into a hopper and other vehicles that discharge garbage into the hopper through a garbage dump device, and second image data of the vehicle inside the waste treatment facility, and inputs the acquired vehicle image data into a learning model trained to output the type of vehicle when vehicle image data is input to acquire the type of vehicle, and causes a computer to execute a process of determining whether to allow the vehicle to enter the waste treatment facility based on the availability of parking spaces within the facility, including parking spaces near the hopper and other parking spaces near the garbage dump device adjacent to the hopper, and the acquired type of vehicle.

[0007] An information processing method according to one embodiment of the present disclosure acquires vehicle image data including at least one of first image data of a vehicle entering a waste treatment facility where multiple types of vehicles enter, including garbage trucks that discharge garbage directly into a hopper and other vehicles that discharge garbage into the hopper through a garbage dump device, and second image data of the vehicle inside the waste treatment facility, acquires the type of vehicle by inputting the acquired vehicle image data into a learning model trained to output the type of vehicle when vehicle image data is input, and causes a computer to execute a process of determining whether or not to allow the vehicle to enter the waste treatment facility based on the availability of parking spaces within the facility, including parking spaces near the hopper and other parking spaces near the garbage dump device adjacent to the hopper, and the acquired type of vehicle.

[0008] An information processing system according to one embodiment of the present disclosure includes an image data acquisition unit that acquires vehicle image data including at least one of first image data in which a vehicle entering a waste treatment facility where multiple types of vehicles enter, including a garbage truck that discharges garbage directly into a hopper and other vehicles that discharge garbage into the hopper through a garbage dump device, and second image data in which the vehicle within the waste treatment facility is imaged; a type acquisition unit that acquires the type of vehicle by inputting the acquired vehicle image data into a learning model that has been trained to output the type of vehicle when vehicle image data is input; and a determination unit that determines whether to allow the vehicle to enter the waste treatment facility based on the availability of parking spaces within the facility, including parking spaces near the hopper and other parking spaces near the garbage dump device adjacent to the hopper, and the acquired type of vehicle. Effect of the Invention

[0009] According to the present disclosure, it is possible to determine whether or not to permit a vehicle to enter a waste treatment facility depending on the type of vehicle. [Brief description of the drawings]

[0010] [Figure 1] 1 is a schematic diagram of a waste treatment plant having waste treatment facilities. [Diagram 2] 1 is a block diagram illustrating an example of the configuration of an information processing system. [Diagram 3] FIG. 4 is a schematic diagram showing an example of vehicle image data. [Figure 4] FIG. 2 is an explanatory diagram illustrating the configuration of a learning model. [Diagram 5] 10 is a flowchart illustrating a determination process performed by a control unit to determine whether or not to permit entry of a vehicle. [Figure 6] 10 is a flowchart illustrating a determination process performed by a control unit of the second embodiment. [Figure 7] FIG. 11 is a schematic diagram showing a waste treatment plant according to a third embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of the contents of a detection unit DB. [Figure 9] 13 is a flowchart illustrating a determination process performed by a control unit of the third embodiment. [Figure 10] FIG. 11 is a schematic diagram showing a waste treatment plant according to a fourth embodiment. [Figure 11] 13 is a flowchart illustrating a determination process performed by a control unit of the fourth embodiment. [Figure 12] FIG. 13 is a block diagram showing an example of the configuration of an information processing system according to a fifth embodiment. [Figure 13] FIG. 2 is a schematic diagram showing an example of in-facility image data. [Figure 14] 13 is a flowchart illustrating a notification process performed by a control unit of the fifth embodiment for notifying the approach of a person to a vehicle or a specific device. [Figure 15] FIG. 13 is a block diagram showing an example of the configuration of an information processing system according to a modified example of the fifth embodiment. [Figure 16] FIG. 13 is a block diagram showing an example of the configuration of an information processing system according to a sixth embodiment. [Figure 17] FIG. 2 is a conceptual diagram showing an example of the contents of a garbage type information DB. [Figure 18] 13 is a flowchart illustrating a notification process performed by a control unit in embodiment 6 for notifying the type of waste loaded in a vehicle scheduled to enter. [Figure 19]FIG. 13 is a schematic diagram of a waste treatment plant according to embodiment 7. [Figure 20] 23 is a flowchart illustrating a determination process performed by a control unit of the seventh embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] (Embodiment 1) Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a schematic diagram of a waste treatment plant 100 having a waste treatment facility 1. The ceiling of the waste treatment facility 1 is omitted. The waste treatment facility 1 accepts, for example, household waste, non-burnable waste, or plastic waste. Multiple types of vehicles 2 run inside the waste treatment plant 100. The multiple types of vehicles 2 include refuse trucks that collect waste, general vehicles, forklifts, wheel loaders, and hand trucks. In this embodiment, an example using two types of vehicles 2, refuse trucks and general vehicles, will be described. Note that the types of vehicles 2 may be three or more. For example, the types of vehicles 2 may be refuse trucks, general vehicles, and other vehicles. The other vehicles are vehicles 2 other than refuse trucks and general vehicles, such as forklifts, wheel loaders, and hand trucks.

[0012] The vehicle 2 is loaded with the above-mentioned waste. The waste treatment plant 100 has an information processing device 3 that determines whether or not to permit entry of the vehicle 2 into the waste treatment facility 1. The method of determining whether or not to permit entry of the vehicle 2 will be described later. When entry of the vehicle 2 into the waste treatment facility 1 is permitted, the vehicle 2 enters the waste treatment facility 1 to bring in waste.

[0013] In the waste treatment plant 100, a stop line 11 is provided outside the waste treatment facility 1 and close to the entrance 10 of the waste treatment facility 1. In FIG. 1, one vehicle 2 stops at the stop line 11 and waits to enter the waste treatment facility 1. Behind the vehicle 2 waiting at the stop line 11, another vehicle 2 is waiting. In other words, two vehicles 2 are waiting in a line near the entrance 10. In the example of FIG. 1, the vehicle 2 waiting at the stop line 11 is a refuse truck. The vehicle 2 behind the vehicle 2 is a general vehicle.

[0014] The waste treatment facility 100 is provided with a first camera 5 that captures an image of a vehicle 2 waiting at a stop line 11. The vehicle 2 waiting at the stop line 11 is a vehicle 2 entering the waste treatment facility 1. For example, the first camera 5 is a surveillance camera that captures images continuously. In FIG. 1, the first camera 5 is attached to the outer wall of the waste treatment facility 1. The first camera 5 may be attached to a location other than the outer wall of the waste treatment facility 1 as long as it can capture an image of the vehicle 2 entering the waste treatment facility 1. The orientation of the first camera 5 is fixed, and the range captured by the first camera 5 is also fixed. Hereinafter, image data captured by the first camera 5 of the vehicle 2 entering the waste treatment facility 1 is also referred to as first image data. The first image data may be a still image of one frame, or a video of several frames. Note that the first camera 5 may be configured to capture an image of the vehicle 2 entering the waste treatment facility 1, and the orientation of the first camera 5 and the range captured by the first camera 5 may not be fixed. For example, the orientation of the first camera 5 may be changeable.

[0015] At the entrance 10 of the waste treatment facility 1, a restricting device is provided that restricts the entry of the vehicle 2 into the waste treatment facility 1. In the example of FIG. 1, the restricting device is a display unit 4 that displays whether or not the vehicle 2 is permitted to enter the waste treatment facility 1 based on the result of a determination as to whether or not the vehicle 2 is permitted to enter the waste treatment facility 1, which will be described later. The display unit 4 is provided in a place visible from the position where the vehicle 2 entering the waste treatment facility 1 is waiting, for example, on the outer wall of the waste treatment facility 1 near the entrance 10. For example, the display unit 4 is a signal light that can be lit in blue and red. The display unit 4 lit in blue indicates that the entry of the vehicle 2 is permitted. The display unit 4 lit in red indicates that the entry of the vehicle 2 is not permitted. When the display unit 4 is lit in red, the entry of the vehicle 2 is restricted. Note that the colors of the signal light are not limited to blue and red. The display unit 4 may display whether or not the entry is permitted by turning the signal light on and off. For example, the display unit 4 may be configured to light up when indicating that the entry of the vehicle 2 is not permitted, and to turn off when indicating that the entry of the vehicle 2 is permitted.

[0016] The driver of vehicle 2 entering the waste treatment facility 1 checks the display unit 4 to confirm whether or not vehicle 2 is permitted to enter. If entry of vehicle 2 is permitted, the driver allows vehicle 2 to enter the waste treatment facility 1. If entry of vehicle 2 is not permitted, the driver makes vehicle 2 wait until entry is permitted. Note that display unit 4 is not limited to signal lights and may be, for example, a display. The display shows symbols such as "o" and "x", or letters such as "permitted" and "not permitted", depending on whether entry is permitted. The restricting device is not limited to display unit 4 and may be, for example, a barrier or shutter. The barrier or shutter opens and closes depending on whether entry is permitted. When the barrier or shutter is closed, entry of vehicle 2 is restricted.

[0017] The waste treatment facility 1 has an exit 12 provided at a position opposite the entrance 10. In the waste treatment facility 1, a plurality of types of parking spaces 6 are provided according to the type of vehicle 2. In this embodiment, the number of parking spaces 6 is four. Of the four parking spaces 6, three parking spaces 6 are used for garbage trucks. The remaining one parking space 6 is used for general vehicles. Therefore, the upper limit of the number of garbage trucks that can enter the waste treatment facility 1 is three. The upper limit of the number of general vehicles that can enter the waste treatment facility 1 is one. For example, the upper limit of the number of vehicles 2 that can enter these waste treatment facilities is stored in the information processing device 3. The number of parking spaces 6 is not limited to four. A plurality of parking spaces 6 for general vehicles may be provided.

[0018] The four parking spaces 6 are arranged in a line in the direction in which the entrance 10 and the exit 12 face each other. Hereinafter, the parking space 6 closest to the entrance 10 in the above direction among the four parking spaces 6 is also referred to as the first parking space 6a, the parking space 6 second closest to the entrance 10 is also referred to as the second parking space 6b, and the parking space 6 third closest to the entrance 10 is also referred to as the third parking space 6c. Among the four parking spaces 6, the parking space 6 farthest from the entrance 10 in the above direction is also referred to as the fourth parking space 6d. In FIG. 1, the first parking space 6a, the second parking space 6b, and the third parking space 6c are parking spaces 6 where garbage trucks are parked. The fourth parking space 6d is a parking space 6 where general vehicles are parked. In FIG. 1, vehicles 2 are parked in the third parking space 6c and the fourth parking space 6d. The vehicle 2 parked in the third parking space 6c is a garbage truck. The vehicle 2 parked in the fourth parking space 6d is a general vehicle. The parking spaces 6 do not have to be arranged side by side.

[0019] The waste treatment facility 1 is provided with a hopper 13 that receives waste. In FIG. 1, the hopper 13 is disposed near the first parking space 6a, the second parking space 6b, and the third parking space 6c. If the vehicle 2 is a refuse truck, after entering the waste treatment facility 1, it stops at any one of the first parking space 6a, the second parking space 6b, and the third parking space 6c. The waste loaded on the vehicle 2 is received by the hopper 13. After the waste is received by the hopper 13, the vehicle 2 exits the waste treatment facility 1 through the exit 12.

[0020] The waste treatment facility 1 is provided with a dumping box 14 for dumping waste into the hopper 13. In FIG. 1, the dumping box 14 is located near the hopper 13 and close to the fourth parking space 6d. If the vehicle 2 is a general vehicle, it enters the waste treatment facility 1 and then stops at the fourth parking space 6d. The driver of the vehicle 2 places the waste loaded on the vehicle 2 in the dumping box 14. The dumping box 14 is driven by an operator of the waste treatment facility 1, and dumps the placed waste into the hopper 13. After the driver places the waste in the dumping box 14, the vehicle 2 exits the waste treatment facility 1 through the exit 12. Note that a passenger of the vehicle 2 other than the driver may place the waste in the dumping box 14.

[0021] For example, the waste treatment facility 1 may be provided with a container for storing waste instead of or in addition to the dumping box 14. For example, the container is placed near the fourth parking space 6d. Drivers of general vehicles throw waste into the container.

[0022] Although the entrance 10 and the exit 12 are provided separately, one of the entrance 10 and the exit 12 may also serve as the other. For example, the vehicle 2 may enter and exit the waste treatment facility 1 through the entrance 10. In this case, the exit 12 does not need to be provided.

[0023] The waste treatment facility 1 is provided with a second camera 15 that captures images of the vehicles 2 within the waste treatment facility 1. For example, the second camera 15 is a surveillance camera that captures images continuously. For example, the second camera 15 is attached to an inner wall of the waste treatment facility 1. Hereinafter, image data captured by the second camera 15 of the vehicles 2 within the waste treatment facility 1 is also referred to as second image data. Like the first image data, the second image data may be a still image of one frame or a video of several frames.

[0024] The second camera 15 is fixed in a position and orientation that allows the vehicle 2 parked in the four parking spaces 6 to be imaged at once. Therefore, the range imaged by the second camera 15 is fixed. The second image data shows at least one of the first parking space 6a and the vehicle 2 parked in the first parking space 6a, and at least one of the second parking space 6b and the vehicle 2 parked in the second parking space 6b. Furthermore, the second image data shows at least one of the third parking space 6c and the vehicle 2 parked in the third parking space 6c, and at least one of the vehicle 2 parked in the fourth parking space 6d and the vehicle 2 parked in the fourth parking space 6d. Note that the second camera 15 only needs to be able to image the vehicle 2 in the waste treatment facility 1, and the position and orientation of the second camera 15 and the range imaged by the second camera 15 may not be fixed. For example, the orientation of the second camera 15 may be changeable.

[0025] The type of vehicle 2 captured in the second image data may be only a garbage truck or only a general vehicle among garbage trucks and general vehicles. The type of vehicle 2 captured in the second image data may be both a garbage truck and a general vehicle.

[0026] In Fig. 1, the information processing device 3 is provided within the waste treatment facility 1. The information processing device 3 may be provided outside the waste treatment facility 1. Fig. 2 is a block diagram showing an example of the configuration of an information processing system S. The information processing system S includes the information processing device 3, and determines whether or not to permit entry of a vehicle 2 into the waste treatment facility 1.

[0027] The information processing device 3 is connected to the first camera 5, the second camera 15, the display unit 4, and a speaker 16 provided in the waste treatment facility 1. Note that the speaker 16 is not shown in FIG. 1. In FIG. 1, connection lines connecting the information processing device 3 to the first camera 5, the second camera 15, and the display unit 4 are not shown. The information processing device 3 acquires first image data from the first camera 5, and acquires second image data from the second camera 15. The information processing device 3 has a learning model M1, described later, that has been trained to output the type of vehicle 2 when vehicle image data including at least one of the first image data and the second image data is input.

[0028] The information processing device 3 inputs the acquired first image data into the learning model M1 to acquire the type of vehicle 2 scheduled to enter the waste treatment facility 1. The vehicle 2 scheduled to enter the waste treatment facility 1 is a vehicle 2 waiting at the stop line 11. The information processing device 3 inputs the acquired second image data into the learning model M1 to acquire the type of vehicle 2 in the waste treatment facility 1.

[0029] In this embodiment, the information processing device 3 has the learning model M1, but a device other than the information processing device 3 may have the learning model M1. For example, the first camera 5 and the second camera 15 may each have the learning model M1. In this case, the first camera 5 inputs the captured first image data to the learning model M1 to acquire the type of the vehicle 2 scheduled to enter, and outputs the acquired type of the vehicle 2 scheduled to enter to the information processing device 3. The second camera 15 inputs the captured second image data to the learning model M1 to acquire the type of the vehicle 2 in the waste treatment facility 1, and outputs the acquired type of the vehicle 2 in the waste treatment facility 1 to the information processing device 3. The information processing device 3 acquires the type of the vehicle 2 scheduled to enter from the first camera 5, and acquires the type of the vehicle 2 in the waste treatment facility 1 from the second camera 15.

[0030] For example, a cloud server (not shown) capable of communicating with the information processing device 3 may have the learning model M1. In this case, the information processing device 3 outputs the acquired first image data and second image data to the cloud server. The cloud server acquires the outputted first image data and second image data. The cloud server inputs the acquired first image data into the learning model M1 to acquire the type of the vehicle 2 scheduled to enter, and outputs the acquired type of the vehicle 2 scheduled to enter to the information processing device 3. The cloud server also inputs the acquired second image data into the learning model M1 to acquire the type of the vehicle 2 in the waste treatment facility 1, and outputs the acquired type of the vehicle 2 in the waste treatment facility 1 to the information processing device 3. The information processing device 3 acquires the type of the vehicle 2 scheduled to enter and the type of the vehicle 2 in the waste treatment facility 1 from the cloud server.

[0031] Although details will be described later, the information processing device 3 identifies the type of stopping space 6 in which a vehicle 2 is not parked among the four stopping spaces 6 based on the acquired type of vehicle 2 in the waste treatment facility 1. Hereinafter, a stopping space 6 in which a vehicle 2 is not parked is also referred to as an empty stopping space. The information processing device 3 determines whether or not to permit entry based on the identified type of empty stopping space and the acquired type of vehicle 2 scheduled to enter the waste treatment facility 1.

[0032] When the information processing device 3 permits the vehicle 2 to enter the waste treatment facility 1, it outputs information indicating that the entry is permitted to the display unit 4. Hereinafter, information indicating that the entry is permitted is also referred to as entry permission information. The display unit 4 receives the entry permission information and lights up in blue. It is possible to notify the driver of the vehicle 2 that is scheduled to enter that entry is permitted.

[0033] When permitting the vehicle 2 to enter the waste treatment facility 1, the information processing device 3 may output the entry permission information including voice data of a voice such as "The vehicle is entering" to the speaker 16, and cause the speaker 16 to output the voice. The fact that the entry of the vehicle 2 to be entered is permitted can be notified to a worker at the waste treatment facility 1. For example, the above voice may be output from the speaker 16 at a volume that can be heard by the driver of the vehicle 2 to be entered. The fact that entry is permitted can be notified to the driver by voice. For example, the waste treatment facility may be provided with two speakers 16, one for notifying the worker and the other for notifying the driver. The information processing device 3 may output the entry permission information to both the display unit 4 and the speaker 16.

[0034] The information processing device 3 may output the entry permission information to a terminal (computer) (not shown) carried by a worker at the waste treatment facility 1 and capable of communicating with the information processing device 3. The terminal carried by the worker is, for example, a terminal dedicated to the waste treatment facility 1. For example, a worker at the waste treatment facility 1 carries the terminal when performing work. Hereinafter, the terminal carried by the worker is also referred to as a facility terminal. The facility terminal acquires the output entry permission information and displays a message such as "Vehicle will enter" on a display unit such as a display provided on the facility terminal.

[0035] The information processing device 3 may output the entry permission information including the above-mentioned voice data to the facility terminal. For example, the facility terminal has a speaker. When the above-mentioned entry permission information is output from the information processing device 3 to the facility terminal, the facility terminal acquires the output entry permission information and outputs the voice of the voice data included in the acquired entry permission information to the speaker of the facility terminal. The entry of the vehicle 2 can be notified to the worker. For example, the facility terminal may be connected to earphones worn by the worker. In this case, the facility terminal outputs the voice of the voice data to the earphones.

[0036] When the information processing device 3 does not permit entry of the vehicle 2 into the waste treatment facility 1, it outputs information indicating that entry is not permitted to the display unit 4. Hereinafter, information indicating that entry is not permitted is also referred to as entry denial information. The display unit 4 receives the entry denial information and lights up in red. It is possible to notify the driver of the vehicle 2 that is scheduled to enter that entry is not permitted.

[0037] When the information processing device 3 does not permit the vehicle 2 to enter the waste treatment facility 1, the information processing device 3 may output entry denial information including voice data of a voice such as "Vehicle is not permitted to enter" to the speaker 16, and cause the voice to be output to the speaker 16. The information processing device 3 may also output the entry denial information to the facility terminal.

[0038] The display unit 4 (regulating device) and the speaker 16 are included in a result notification unit that notifies the driver of the vehicle 2, or the driver of the vehicle 2 and a worker, of the result of the determination as to whether or not entry is permitted. When the entry permission information or the entry denial information is output to the facility terminal as described above, the facility terminal is included in the result notification unit. The speaker 16 corresponds to the audio output unit. Sounds such as "Vehicle entering" and "Vehicle not allowed to enter" correspond to sounds related to the determination result. As described above, a method that can be perceived by humans through sight and hearing is used to notify the determination result, but the method of notifying the determination result is not limited to the above example. The method of notifying the determination result may be, for example, a method that can be perceived by humans using the five senses other than sight and hearing.

[0039] The information processing device 3 is a dedicated or general-purpose computer. The information processing device 3 may be a server computer. The information processing device 3 includes a control unit 30, a storage unit 31, and an input / output unit 32. The control unit 30, the storage unit 31, and the input / output unit 32 are connected to each other. The input / output unit 32 is an input / output interface connected to the first camera 5, the second camera 15, the display unit 4, and the speaker 16.

[0040] The control unit 30 has one or more arithmetic processing devices such as a central processing unit (CPU) or a graphics processing unit (GPU). The control unit 30 also has a memory such as a read only memory (ROM) or a random access memory (RAM). The control unit 30 reads out a program P described below and executes various processes. The control unit 30 communicates with the first camera 5, the second camera 15, the display unit 4, and the speaker 16 via the input / output unit 32. For example, the information processing device 3 may be configured to include a wireless communication unit (not shown) for wireless communication, and the control unit 30 may communicate with at least one of the first camera 5, the second camera 15, the display unit 4, and the speaker 16 via the wireless communication unit.

[0041] The storage unit 31 includes a non-volatile memory such as a hard disk or a solid state drive (SSD). The storage unit 31 stores programs and data, including the program P, referenced by the control unit 30, and the learning model M1. The storage unit 31 may be configured with a plurality of storage devices, or may be an external storage device connected to the information processing device 3. The program P and learning model M1 stored in the storage unit 31 may be recorded in a recording medium A so that the computer can read them. The storage unit 31 stores the program P and learning model M1 read from the recording medium A by a reading device (not shown). The program P and learning model M1 stored in the storage unit 31 may be downloaded from an external computer (not shown) connected to a communication network (not shown) and stored in the storage unit 31. The information processing device 3 is not limited to one device. Processing may be distributed among a plurality of devices.

[0042] The control unit 30 executes the program P stored in the memory unit 31 to function as a first image data acquisition unit that acquires first image data from the first camera 5, and functions as a second image data acquisition unit that acquires second image data from the second camera 15. The control unit 30 functions as an image data acquisition unit that acquires vehicle image data. The control unit 30 acquires the first image data from the first camera 5 and the second image data from the second camera 15 via the input / output unit 32.

[0043] The control unit 30 executes the program P stored in the memory unit 31, thereby functioning as a first type acquisition unit that inputs the first image data into the learning model M1 and acquires the type of the vehicle 2 scheduled to enter the waste treatment facility 1. The control unit 30 also functions as a second type acquisition unit that inputs the second image data into the learning model M1 and acquires the type of the vehicle 2 in the waste treatment facility 1. The control unit 30 functions as a type acquisition unit that inputs the vehicle image data into the learning model M1 and acquires the type of the vehicle 2. The control unit 30 executes the program P stored in the memory unit 31, thereby functioning as a determination unit that determines whether or not to permit the vehicle 2 to enter the waste treatment facility 1 based on the relationship between the acquired type of vehicle 2 and the parking space 6.

[0044] The following describes how the control unit 30 acquires the type of vehicle 2. In acquiring the type of vehicle 2, the control unit 30 acquires vehicle image data and inputs the acquired vehicle image data to the learning model M1. The learning model M1 detects a detection target in the vehicle image data based on the vehicle image data. In this embodiment, the detection target is the vehicle 2. The detected type of vehicle 2 is output from the learning model M1. The control unit 30 acquires the type of vehicle 2 output from the learning model M1. Note that the detection target is not limited to the vehicle 2.

[0045] FIG. 3 is a schematic diagram showing an example of vehicle image data. FIG. 4 is an explanatory diagram explaining the configuration of learning model M1. FIG. 3 shows an example of second image data as vehicle image data. Two vehicles 2 are shown in the vehicle image data of FIG. 3. Therefore, the number of detection targets in the vehicle image data of FIG. 3 is two. In FIG. 3, the vehicle 2 on the right side is a garbage truck. The vehicle 2 on the left side is a general vehicle.

[0046] In the learning model M1, the relationship between the vehicle image data, the area of ​​the detection target in the vehicle image data, and the type of the detection target is learned in advance. The area of ​​the detection target is the area in which the detection target is captured in the vehicle image data.

[0047] The learning model M1 is a model generated by machine learning including deep learning, and is an identifier that outputs the type of detection target in response to input of vehicle image data. The learning model M1 is configured with a neural network such as a convolutional neural network (CNN). For example, RCNN (Regions with Convolutional Neural Network), which is a type of CNN, is used for the learning model M1.

[0048] When vehicle image data is input, the learning model M1 outputs the area of ​​the detection target and the type of the detection target. In detail, when vehicle image data is input, the learning model M1 outputs the area of ​​the vehicle 2 and the type of the vehicle 2. For example, in outputting the type of the vehicle 2, the learning model M1 outputs the probability that the detected vehicle 2 is a garbage truck and the probability that the detected vehicle 2 is a general vehicle. If the probability that the vehicle 2 is a garbage truck is greater than the probability that the vehicle 2 is a general vehicle, the type of the vehicle 2 is a garbage truck. If the probability that the vehicle 2 is a general vehicle is greater than the probability that the vehicle 2 is a garbage truck, the type of the vehicle 2 is a general vehicle. If the detection target is not detected based on the vehicle image data, the learning model M1 outputs information indicating that the detection target has not been detected. For example, the learning model M1 may output only the type of the detection target out of the area and type of the detection target.

[0049] The learning model M1 is generated in advance in the information processing device 3 or an external device (not shown) and trained. As described above, the learning model M1 is configured by a neural network. The learning model M1 includes an input layer that receives input of vehicle image data, more specifically, pixel values ​​constituting the vehicle image data, an intermediate layer that extracts image features of the vehicle image data, and an output layer that outputs the area of ​​the detection target and the type of the detection target. For example, when the learning model M1 is a CNN, the intermediate layer includes a convolution layer and a pooling layer. The input layer, intermediate layer, and output layer each include one or more neurons.

[0050] The learning model M1 is not limited to RCNN. The learning model M1 can be implemented using YOLO (You Only Any object detection algorithm may be used, such as Fast Look Once (FAST RCNN), Faster RCNN, Single Shot Multibox Detector (SSD), or a segmentation network. In this embodiment, the learning model M1 is described as being a neural network, but the learning model M1 is not limited to a neural network, and may be a learning model M1 constructed by another learning algorithm, such as a Support Vector Machine (SVM), a Bayesian network, a regression tree, or a U-net.

[0051] The learning model M1 is generated using a large amount of training data. In the training data, vehicle image data is associated with the area and type of the detection target captured in the vehicle image data. For example, the training data includes vehicle image data, area data indicating the area of ​​the detection target captured in the vehicle image data, and type data indicating the type of the detection target captured in the vehicle image data. When there are two or more detection targets, a plurality of types indicated by the type data are associated with a plurality of areas indicated by the area data.

[0052] In generating the learning model M1, the information processing device 3 or the external device inputs vehicle image data included in the training data to the learning model M1 and acquires the area and type of the detection target output from the learning model M1. The information processing device 3 or the external device evaluates each of the acquired areas and types of the detection target based on the area indicated by the area data and the type indicated by the type data. The information processing device 3 or the external device updates parameter values ​​such as weight coefficients and biases of the learning model M1 based on the evaluation results. For example, the information processing device 3 or the external device calculates the amount of adjustment of the parameters using the backpropagation method and updates the parameter values ​​based on the calculated amount of adjustment.

[0053] The following describes the determination by the control unit 30 as to whether or not to permit the vehicle 2 to enter the waste treatment facility 1. The control unit 30 acquires second image data from the second camera. The control unit 30 inputs the acquired second image data to the learning model M1. The learning model M1 outputs the area of ​​the vehicle 2 in the waste treatment facility 1 and the type of the vehicle 2. The control unit 30 acquires the type of the vehicle 2 in the waste treatment facility 1 output from the learning model M1. For example, when the second image data of FIG. 3 is input to the learning model M1, two vehicles 2 are detected. The types of the two detected vehicles 2 are output from the learning model M1. The control unit 30 acquires the types of the two vehicles 2 output.

[0054] The control unit 30 identifies the number of refuse trucks and the number of general vehicles in the waste treatment facility 1 based on the number of detection targets and the type of vehicles 2 in the waste treatment facility 1. In the example of Fig. 3, the number of refuse trucks and the number of general vehicles in the waste treatment facility 1 are one each.

[0055] Furthermore, the control unit 30 identifies the type of vacant parking space. As described above, the waste treatment facility 1 is provided with three parking spaces 6 for refuse trucks to park and one parking space 6 for general vehicles to park. It is considered that the vehicles 2 in the waste treatment facility 1 park in the parking spaces 6.

[0056] In the above example, the number of refuse trucks in the waste treatment facility 1 is one, and there are three parking spaces 6 for refuse trucks, so there are two vacant parking spaces for refuse trucks in the waste treatment facility 1. The number of general vehicles in the waste treatment facility 1 is one, and there is one parking space 6 for general vehicles, so there is no vacant parking space for general vehicles in the waste treatment facility 1. Therefore, the control unit 30 specifies that the type of vacant parking space is a vacant parking space for refuse trucks.

[0057] For example, when both of the two vehicles 2 are identified as refuse vehicles, the control unit 30 identifies the type of the vacant parking space as a vacant parking space for refuse vehicles and a vacant parking space for general vehicles. When three vehicles 2 are detected and all of the three vehicles 2 are identified as refuse vehicles, the control unit 30 identifies the type of the vacant parking space as a vacant parking space for general vehicles. When four vehicles 2 are detected and one of the four vehicles 2 is identified as a general vehicle and the remaining three vehicles 2 are identified as refuse vehicles, the control unit 30 identifies that there is no vacant parking space.

[0058] The control unit 30 identifies the type of vehicle 2 that is permitted to enter (hereinafter, the permitted vehicle type) based on the type of the identified vacant parking space. If the type of vacant parking space is a vacant parking space for refuse trucks, the control unit 30 identifies the permitted vehicle type as a refuse truck. If the type of vacant parking space is a vacant parking space for general vehicles, the control unit 30 identifies the permitted vehicle type as a general vehicle. If the type of vacant parking space is a vacant parking space for refuse trucks and a vacant parking space for general vehicles, the control unit 30 identifies the permitted vehicle types as a refuse truck and a general vehicle. If there is no vacant parking space, the control unit 30 identifies that there is no permitted vehicle type.

[0059] The control unit 30 acquires the first image data from the first camera 5. As described above, the range captured by the first camera 5 is fixed. For example, the first camera 5 is provided at a position that does not capture the vehicle 2 behind the vehicle 2 that is scheduled to enter the waste treatment facility 1 even if a vehicle 2 exists behind the vehicle 2 that is scheduled to enter when capturing an image of the vehicle 2. Therefore, even if a plurality of vehicles 2 are waiting near the entrance 10, only the vehicle 2 that is scheduled to enter among the plurality of vehicles 2 is captured in the first image data.

[0060] The control unit 30 inputs the acquired first image data to the learning model M1. The learning model M1 outputs the area of ​​the vehicle 2 scheduled to enter and the type of the vehicle 2. The control unit 30 acquires the type of the vehicle 2 scheduled to enter output from the learning model M1.

[0061] In this embodiment, an example will be described in which, even if a plurality of vehicles 2 are waiting near the entrance 10, only the vehicle 2 that is scheduled to enter is imaged in the first image data, but a plurality of vehicles 2 may be imaged in the first image data. Therefore, the type of vehicle 2 imaged in the first image data may be both a garbage truck and a general vehicle. Furthermore, the type of vehicle 2 imaged in the first image data may be only a garbage truck, or only a general vehicle, among garbage trucks and general vehicles. For example, when a plurality of vehicles 2 are imaged in the first image data, the control unit 30 determines the vehicle 2 that is scheduled to enter among the plurality of vehicles 2 in the first image data, and acquires the type of the vehicle 2 that is scheduled to enter using the first image data and the learning model M1.

[0062] For example, the first image data shows a plurality of vehicles 2 waiting in a line near the entrance 10. In this first image data, the vehicle 2 at the front of the line is the vehicle 2 scheduled to enter. For example, the first camera 5 captures the plurality of vehicles 2 waiting in a line so that the vehicle 2 at the front of the line appears at the forefront. By identifying the vehicle 2 that appears at the forefront among the plurality of vehicles 2 shown in the first image data, the vehicle 2 scheduled to enter can be identified. By identifying the vehicle 2 that appears at the largest among the plurality of vehicles 2 shown in the first image data, the vehicle 2 scheduled to enter may be identified. The vehicle 2 that appears at the largest is, for example, the vehicle 2 that appears at the largest (widest) area among the plurality of areas output when the first image data is input to the learning model M1.

[0063] The control unit 30 determines whether or not to permit the vehicle 2 to enter the waste treatment facility 1 based on the permitted vehicle type identified based on the type of vacant parking space as described above and the type of vehicle 2 scheduled to enter acquired as described above. In more detail, the control unit 30 determines whether or not the type of vehicle 2 scheduled to enter is a permitted vehicle type. If the type of vehicle 2 scheduled to enter is a permitted vehicle type, the control unit 30 permits the vehicle 2 to enter. If the type of vehicle 2 scheduled to enter is not a permitted vehicle type, the control unit 30 does not permit the vehicle 2 to enter.

[0064] When the control unit 30 permits the entry of the vehicle 2, it outputs entry permission information to the display unit 4 via the input / output unit 32. When the control unit 30 does not permit the entry of the vehicle 2, it outputs entry denial information to the display unit 4 via the input / output unit 32.

[0065] 5 is a flowchart illustrating a determination process performed by the control unit 30 to determine whether or not to permit entry of the vehicle 2. For example, the control unit 30 starts the following process based on an operation of an operation unit (not shown), such as a keyboard or a touch panel, provided on the information processing device 3 by a worker at the waste treatment facility 1.

[0066] The control unit 30 acquires second image data from the second camera 15 (S11). As described above, the control unit 30 inputs the acquired second image data to the learning model M1 (S12), and acquires the type of vehicle 2 in the waste treatment facility 1 output from the learning model M1 (S13).

[0067] As described above, the control unit 30 identifies the number of refuse trucks and the number of general vehicles in the waste treatment facility 1 based on the acquired types of vehicles 2 in the waste treatment facility 1. Furthermore, the control unit 30 identifies the type of vacant parking space based on the identified number of refuse trucks and the number of general vehicles in the waste treatment facility 1. The control unit 30 identifies the type of vehicle that is allowed to enter based on the type of vacant parking space (S14).

[0068] The control unit 30 acquires the first image data from the first camera 5 (S15). As described above, the control unit 30 inputs the acquired first image data to the learning model M1 (S16), and acquires the type of the vehicle 2 scheduled to enter output from the learning model M1 (S17).

[0069] The control unit 30 determines whether the type of the vehicle 2 scheduled to enter is an allowed vehicle type (S18). If the type of the vehicle 2 scheduled to enter is an allowed vehicle type (S18: YES), the control unit 30 outputs the entry permission information to the display unit 4 (S19) and ends the determination process. The display unit 4 receives the entry permission information and lights up in blue to indicate that entry is permitted.

[0070] If the type of vehicle 2 scheduled to enter is not an allowed vehicle type (S18: NO), the control unit 30 outputs entry denial information to the display unit 4 (S181) and ends the determination process. The display unit 4 receives the entry denial information and turns on in red to indicate that entry is not permitted.

[0071] For example, the control unit 30 may return to the process of S11 after outputting the entry permission information or the entry denial information. Also, when the learning model M1 to which the first image data is input outputs information indicating that the detection target has not been detected, the control unit 30 may end the determination process or return to the process of S11.

[0072] For example, the control unit 30 may perform the process of identifying the vehicle type permitted to enter, including the processes of S11, S12, S13, and S14, and the process of acquiring the type of vehicle 2 scheduled to enter, including the processes of S15, S16, and S17, in parallel. Furthermore, the control unit 30 may perform the process of identifying the vehicle type permitted to enter after the process of acquiring the type of vehicle 2 scheduled to enter.

[0073] The information processing device 3 acquires the type of the vehicle 2 scheduled to enter using the first image data and the learning model M1, and acquires the type of the vehicle 2 in the waste treatment facility 1 using the second image data and the learning model M1. The information processing device 3 determines whether or not to permit the entry of the vehicle 2 scheduled to enter based on the relationship between the acquired type of the vehicle 2 and the parking space 6. In determining whether or not to permit the entry, the information processing device 3 specifies the vehicle type that is permitted to enter based on the type of the vacant parking space specified based on the type of the vehicle 2 in the waste treatment facility 1. The information processing device 3 determines whether or not the acquired type of the vehicle 2 scheduled to enter is a vehicle type that is permitted to enter. Therefore, it is possible to determine whether or not to permit the entry of the vehicle 2 taking the type of the vehicle 2 into consideration. Since the entry of the vehicle 2 is permitted based on the type of the vehicle 2 scheduled to enter and the type of the vacant parking space, an excessive number of vehicles 2 do not enter the waste treatment facility 1. Therefore, it is possible to suppress the occurrence of contact and collision between the vehicles 2.

[0074] For example, when the types of vehicles 2 are refuse compactors, general vehicles, and other vehicles, the number of other vehicles does not need to be specified. For example, no determination is made as to whether or not to permit entry of other vehicles. Other vehicles can enter the waste treatment facility 1 at their discretion.

[0075] The information processing device 3 outputs information on whether entry is permitted or not, including entry permission information and entry denial information, to the display unit 4 depending on the result of the determination on whether entry is permitted or not. The display unit 4 receives the information on whether entry is permitted or not and displays whether entry is permitted or not. Since the display unit 4 is visible from the position where the vehicle 2 entering the waste treatment facility 1 is waiting, it is possible to notify the driver of the vehicle 2 waiting at the above-mentioned position of whether entry is permitted or not.

[0076] For example, the information processing device 3 may be configured to output only one of the entry permission information and the entry denial information. In this case, the information processing device 3 outputs the entry permission information when entry is permitted, and outputs neither the entry permission information nor the entry denial information when entry is not permitted. Alternatively, the information processing device 3 outputs neither the entry permission information nor the entry denial information when entry is permitted, and outputs the entry denial information when entry is not permitted.

[0077] For example, sensors that detect the passage of vehicles 2 may be provided at the entrance 10 and the exit 12. The sensors are capable of communicating with the information processing device 3 and output the detection results to the information processing device 3. Since vehicles 2 passing through the entrance 10 and the exit 12 are detected, the information processing device 3 can detect the total number of vehicles 2 within the waste treatment facility 1. For example, when the total number of vehicles 2 within the waste treatment facility 1 is four, i.e., when the facility is full, the information processing device 3 performs a determination process after detecting the exit of one vehicle.

[0078] The detection target may include an empty parking space. For example, when vehicle image data is input, the learning model M1 outputs the probability that the detection target is a garbage truck, the probability that the detection target is a general vehicle, the probability that the detection target is an empty space related to a garbage truck, and the probability that the detection target is an empty space related to a general vehicle. The control unit 30 can input the second image data to the learning model M1 and obtain the type of the empty parking space from the learning model M1.

[0079] Since the range imaged by the second camera 15 is fixed as described above, it is possible to specify in advance image coordinates indicating the area in the second image data in which the vehicle 2 parked in each parking space 6 is imaged. The image coordinates indicating the above area are specified for each parking space 6. For example, the image coordinates indicating the above area are stored in advance in the storage unit 31. The information processing device 3 may acquire the area of ​​the vehicle 2 in the waste treatment facility 1 from the learning model M1 and determine whether the acquired area of ​​the vehicle 2 in the waste treatment facility 1 matches the above area. If the area of ​​the vehicle 2 in the waste treatment facility 1 matches the above area, it is presumed that the vehicle 2 is parked in the parking space 6. If the area of ​​the vehicle 2 in the waste treatment facility 1 does not match the above area, it is presumed that the vehicle 2 is traveling in the waste treatment facility 1.

[0080] For example, the information processing device 3 acquires the second image data from one second camera 15, but may acquire the second image data from multiple second cameras 15. That is, the number of the second cameras 15 is not limited, and may be one or multiple. Hereinafter, an example in which two second cameras 15 are provided will be described. For example, the second image data captured by one second camera 15 captures at least one of the first parking space 6a and the vehicle 2 parked in the first parking space 6a, and at least one of the second parking space 6b and the vehicle 2 parked in the second parking space 6b. The second image data captured by the other second camera 15 captures at least one of the third parking space 6c and the vehicle 2 parked in the third parking space 6c, and at least one of the fourth parking space 6d and the vehicle 2 parked in the fourth parking space 6d. The information processing device 3 acquires two sets of second image data, that is, the second image data captured by one second camera 15 and the second image data captured by the other second camera 15. The information processing device 3 uses the two acquired second image data and the learning model M1 to acquire the type of the vehicle 2 in the waste treatment facility 1. Even if one second camera cannot capture an image of the entire interior of the waste treatment facility 1, the entire interior of the waste treatment facility 1 can be captured by using multiple second cameras.

[0081] For example, the learning model M1 may be trained to output the type of vehicle 2 parked in the stopping space 6 related to the above area when image data of the above area in the second image data is input. The types of vehicle 2 parked in the stopping space 6 related to the above area are, for example, a garbage truck, a general vehicle, and no vehicle. The information processing device 3 can input the image data of the above area in the second image data to the learning model M1 to acquire the type of vehicle 2 in the stopping space 6 related to the above area.

[0082] For example, the storage unit 31 may store two learning models M1. One learning model M1 is trained to output the area of ​​the vehicle 2 in the waste treatment facility 1 and the type of the vehicle 2 when the second image data is input. Training data including the second image data as vehicle image data is used for training of the one learning model M1. The other learning model M1 is trained to output the area of ​​the vehicle 2 scheduled to enter and the type of the vehicle 2 when the first image data is input. Training data including the first image data as vehicle image data is used for training of the other learning model M1. The information processing device 3 inputs the second image data to one learning model M1 to acquire the type of the vehicle 2 in the waste treatment facility 1, and inputs the first image data to the other learning model M1 to acquire the type of the vehicle 2 scheduled to enter.

[0083] The other learning model M1 may be trained to output only the type of vehicle 2 scheduled to enter among the multiple vehicles 2 even when multiple vehicles 2 are captured in the first image data. In this case, the types of vehicle 2 scheduled to enter are, for example, a garbage truck, a general vehicle, and no vehicle.

[0084] (Embodiment 2) Among the configurations according to the second embodiment, components similar to those in the first embodiment are given the same reference numerals, and detailed description thereof will be omitted. The display unit 4 in the second embodiment is, for example, a signal light that can light up and blink in blue and red. In the waste disposal plant 100 of the second embodiment, the information processing device 3 uses only the second image data out of the first image data and the second image data when determining whether or not to permit the entry of the vehicle 2.

[0085] In the second embodiment, the control unit 30, in determining whether or not to permit entry of the vehicle 2, identifies the type of vacant parking space in the same manner as in the first embodiment. The control unit 30 determines whether or not to permit entry of the vehicle 2 into the waste treatment facility 1 based on the identified type of vacant parking space.

[0086] When the identified type of vacant parking space is both a vacant parking space for a garbage truck and a vacant parking space for a general vehicle, the control unit 30 allows the garbage truck and the general vehicle to enter. That is, in the above case, the control unit 30 allows the vehicle 2 to enter regardless of whether the vehicle 2 is a garbage truck or a general vehicle. When allowing the garbage truck and the general vehicle to enter, the control unit 30 outputs first entry permission information indicating that the garbage truck and the general vehicle are allowed to enter to the display unit 4. The display unit 4 receives the first entry permission information and lights up in blue. The display unit 4 lighting up in blue indicates that the entry of both the garbage truck and the general vehicle 2 is allowed.

[0087] When the type of the identified vacant parking space is a vacant parking space for a garbage truck, the control unit 30 allows the garbage truck to enter. The control unit 30 outputs second entry permission information indicating that the garbage truck is allowed to enter to the display unit 4. The display unit 4 receives the second entry permission information and flashes blue. The display unit 4 flashing blue indicates that the garbage truck is allowed to enter.

[0088] When the type of the identified vacant parking space is a vacant parking space for general vehicles, the control unit 30 permits the general vehicle to enter. The control unit 30 outputs third entry permission information indicating that the general vehicle is permitted to enter to the display unit 4. The display unit 4 receives the third entry permission information and flashes red. The display unit 4 flashing red indicates that the general vehicle is permitted to enter.

[0089] If there is no available parking space, the control unit 30 outputs entry denial information to the display unit 4. The display unit 4 receives the entry denial information and turns red. The display unit 4 turning red indicates that the entry of the vehicle 2 is not permitted. The first entry permission information, the second entry permission information, the third entry permission information, and the entry denial information are included in the information on whether or not entry is permitted.

[0090] The colors that the display unit 4 lights up and flashes are not limited to blue and red. For example, the display unit 4 may be a signal light that can light up in four colors. The display unit 4 receives any one of four pieces of information: first entry permission information, second entry permission information, third entry permission information, and entry denial information, and lights up in a different color depending on the received information. The display unit 4 is not limited to a signal light. For example, the display unit 4 may be a display. The display displays words such as "Entry permitted", "Garbage trucks permitted", "General vehicles permitted", and "Entry prohibited" depending on the above received information.

[0091] 6 is a flowchart illustrating a determination process performed by the control unit 30 of embodiment 2. For example, the control unit 30 starts the following process based on an operation of an operation unit (not shown) provided in the information processing device 3 by a worker at the waste treatment facility 1.

[0092] The control unit 30 performs the processes of S21, S22, and S23. The processes of S21, S22, and S23 are similar to the processes of S11, S12, and S13, and therefore detailed explanations are omitted. The control unit 30 identifies the type of vacant parking space as described above (S24). The control unit 30 determines whether the identified type of vacant parking space is both a vacant parking space for a refuse truck and a vacant parking space for a general vehicle, i.e., whether there are both types of vacant parking spaces (S25).

[0093] If there are both types of vacant parking spaces (S25: YES), the control unit 30 outputs the first entry permission information to the display unit 4 (S26) and ends the determination process. If there are both types of vacant parking spaces, the type of the vacant parking space is both types of vacant parking spaces.

[0094] If there are no vacant parking spaces of both types (S25: NO), the control unit 30 judges whether the type of the identified vacant parking space is a vacant parking space for a garbage truck, i.e., whether there is a vacant parking space for a garbage truck (S251). If there is a vacant parking space for a garbage truck (S251: YES), the control unit 30 outputs the second entry permission information to the display unit 4 (S252) and ends the judgment process. If there is a vacant parking space for a garbage truck, the type of the vacant parking space is a vacant parking space for a garbage truck.

[0095] If there is no vacant parking space for a refuse compactor vehicle (S251: NO), the control unit 30 judges whether the type of the identified vacant parking space is a vacant parking space for a general vehicle, i.e., whether there is a vacant parking space for a general vehicle (S253). If there is a vacant parking space for a general vehicle (S253: YES), the control unit 30 outputs the third entry permission information to the display unit 4 (S254) and ends the judgment process. If there is a vacant parking space for a general vehicle, the type of the vacant parking space is a vacant parking space for a general vehicle.

[0096] If there is no vacant parking space for general vehicles (S253: NO), that is, if there is no vacant parking space, the control unit 30 outputs entry denial information to the display unit 4 (S255), and ends the determination process.

[0097] As described above, the control unit 30 outputs one of the first entry permission information, the second entry permission information, the third entry permission information, and the entry denial information according to the type of the vacant parking space. After outputting the information, the control unit 30 may return to the process of S21 instead of terminating the determination process.

[0098] For example, when there are no available parking spaces of both types, the control unit 30 may output entry denial information to the display unit 4. In other words, when there is no available parking space for general vehicles or no available parking space for refuse trucks, the control unit 30 may not permit the entry of the vehicle 2 into the waste treatment facility.

[0099] In this embodiment, the information processing device 3 identifies the type of vacant parking space, and determines whether or not to permit entry of the vehicle 2 based on the identified type of vacant parking space. Since parking spaces 6 are provided according to the type of vehicle 2, the type of parking space corresponds to the type of vehicle 2. The type of vehicle 2 is taken into consideration when determining whether or not to permit entry. As in the first embodiment, an excessive number of vehicles 2 are not allowed to enter the waste treatment facility 1, so that contact and collisions between vehicles 2 can be suppressed.

[0100] Since the information processing device 3 does not use the first image data in determining whether or not to permit entry of the vehicle 2, the first camera 5 does not need to be provided at the waste disposal site 100. Therefore, the number of pieces of equipment at the waste disposal site 100 can be reduced.

[0101] (Embodiment 3) 7 is a schematic diagram showing a waste disposal facility 100 according to embodiment 3. Among the components according to embodiment 3, components similar to those in embodiment 1 are given the same reference numerals, and detailed description thereof will be omitted. In the waste disposal facility 100 of embodiment 3, the information processing device 3 does not use the second image data when determining whether or not to permit entry of the vehicle 2. For example, the second camera 15 is not provided in the waste disposal facility 1.

[0102] In the third embodiment, each parking space 6 is provided with one detection unit 17 for detecting a vehicle 2 parked in the parking space 6. In the waste treatment facility 1 in FIG. 7, the number of detection units 17 is four. The detection unit 17 is, for example, a loop coil, and is buried in the parking space 6. The detection unit 17 is not limited to a loop coil, and may be, for example, a photoelectric tube. The detection unit 17 and the information processing device 3 are connected via the input / output unit 32 and can communicate with each other. Note that the connection line between the detection unit 17 and the information processing device 3 is omitted in the illustration. The information processing device 3 may have a wireless communication unit for wireless communication and communicate with the detection unit 17 via the wireless communication unit. For example, a plurality of detection units 17 may be provided in one parking space 6.

[0103] Each detection unit 17 outputs a detection result to the information processing device 3. For example, when the detection unit 17 detects a stopped vehicle 2, it outputs a signal indicating that the vehicle 2 is stopped to the information processing device 3. When the detection unit 17 does not detect a stopped vehicle 2, it outputs a signal indicating that the vehicle 2 is not stopped to the information processing device 3.

[0104] For example, each detection unit 17 is assigned an ID (Identification) number. For example, a detection unit DB (Database) in which the ID numbers of the detection units 17 and the parking spaces 6 in which the detection units 17 with the ID numbers are provided is stored in association with each other is stored in the storage unit 31. FIG. 8 is a conceptual diagram showing an example of the contents of the detection unit DB. The detection unit DB has a sequence of ID numbers of the detection units 17 and a sequence of parking spaces. In the example of FIG. 8, the ID number of the detection unit 17, 1, is associated with the first parking space 6a. At this time, the detection unit 17 with the ID number 1 is provided in the first parking space 6a.

[0105] The control unit 30 acquires the detection result output from the detection unit 17 via the input / output unit 32. The control unit 30 can identify the detection unit 17 that has output the detection result. For example, the detection unit 17 outputs a signal indicating its own ID number together with the detection result. Based on the acquired detection result, the control unit 30 refers to the detection unit DB to identify the parking space 6 in which the vehicle 2 is parked and the vacant parking space. Hereinafter, the parking space 6 in which the vehicle 2 is parked is also referred to as an in-use parking space 6.

[0106] For example, when the control unit 30 receives a signal indicating that the vehicle 2 is parked from the detection unit 17 having an ID number of 1 as a detection result, the control unit 30 identifies the first parking space 6a as an in-use parking space 6. When the control unit 30 receives a signal indicating that the vehicle 2 is not parked from the detection unit 17 having an ID number of 1 as a detection result, the control unit 30 identifies the first parking space 6a as an empty parking space. The detection unit 17 does not need to output a signal indicating that the vehicle 2 is not parked. For example, when the control unit 30 does not receive a signal indicating that the vehicle 2 is parked from the detection unit 17 having an ID number of 1, the control unit 30 identifies the first parking space 6a as an empty parking space.

[0107] In the manner described above, the control unit 30 specifies whether each of the four stopping spaces 6 is an in-use stopping space 6 or an empty stopping space 6. The control unit 30 specifies the type of the empty stopping space based on the specified in-use stopping space 6 and empty stopping space.

[0108] When the four parking spaces 6 are occupied parking spaces 6, the control unit 30 determines that there is no vacant parking space. When the vacant parking space is at least one of the first parking space 6a, the second parking space 6b, and the third parking space 6c, and the occupied parking space 6 is the fourth parking space 6d, the control unit 30 determines that the type of the vacant parking space is a vacant parking space for a refuse truck.

[0109] When the vacant parking spaces are at least one of the first parking space 6a, the second parking space 6b, and the third parking space 6c, and the fourth parking space 6d, the control unit 30 determines that the type of the vacant parking space is a vacant parking space for refuse vehicles and a vacant parking space for general vehicles. When the parking spaces in use are the first parking space 6a, the second parking space 6b, and the third parking space 6c, and the vacant parking space is the fourth parking space 6d, the control unit 30 determines that the type of the vacant parking space is a vacant parking space for general vehicles.

[0110] As in the first embodiment, the control unit 30 identifies the type of vehicle that is permitted to enter based on the acquired type of vacant parking space. As in the first embodiment, the control unit 30 acquires the type of vehicle 2 that is scheduled to enter. The control unit 30 determines whether or not to permit entry of the vehicle 2 into the waste treatment facility 1 based on the type of vehicle that is permitted to enter identified based on the type of vacant parking space and the acquired type of vehicle 2 that is scheduled to enter. In more detail, as in the first embodiment, the control unit 30 determines whether or not the type of vehicle 2 that is scheduled to enter is a type of vehicle that is permitted to enter. The control unit 30 outputs entry permission information or entry denial information to the display unit 4 depending on the determination result.

[0111] 9 is a flowchart illustrating a determination process performed by the control unit 30 of the embodiment 3. For example, the control unit 30 starts the following process based on an operation of an operation unit (not shown) provided in the information processing device 3 by a worker at the waste treatment facility 1.

[0112] The control unit 30 acquires detection results from each detection unit 17 (S31). As described above, the control unit 30 identifies the type of vacant parking space based on the acquired detection results, and identifies the vehicle type that is allowed to enter based on the identified type of vacant parking space (S32). The control unit 30 performs processes S33, S34, and S35. The processes of S33, S34, and S35 are similar to the processes of S15, S16, and S17, and therefore detailed explanations are omitted.

[0113] The control unit 30 determines whether the type of the vehicle 2 scheduled to enter is an allowed vehicle type (S36). If the type of the vehicle 2 scheduled to enter is an allowed vehicle type (S36: YES), the control unit 30 outputs entry permission information to the display unit 4 (S37) and ends the determination process. If the type of the vehicle 2 scheduled to enter is not an allowed vehicle type (S36: NO), the control unit 30 outputs entry denial information to the display unit 4 (S361) and ends the determination process.

[0114] For example, the control unit 30 may return to the process of S31 after outputting the entry permission information or the entry denial information. For example, when the learning model M1 to which the first image data is input outputs information indicating that the detection target has not been detected, the control unit 30 may end the determination process or may return to the process of S31.

[0115] The information processing device 3 acquires the detection result from the detection unit 17, and identifies an empty parking space based on the acquired detection result. In addition, the information processing device 3 acquires the first image data, and inputs the acquired first image data into the learning model M1 to acquire the type of the vehicle 2 scheduled to enter. In determining whether or not to permit entry, the information processing device 3 determines whether or not the type of the vehicle 2 scheduled to enter is an enterable vehicle type identified based on the type of the empty parking space. Therefore, it is possible to determine whether or not to permit entry of the vehicle 2 taking into consideration the type of the vehicle 2. The information processing device 3 can determine whether or not to permit entry without using the second image data. Since an excessive number of vehicles 2 do not enter the waste treatment facility 1, it is possible to suppress the occurrence of contact and collision between the vehicles 2.

[0116] (Embodiment 4) Fig. 10 is a schematic diagram showing a waste treatment plant 100 according to embodiment 4. Among the components according to embodiment 4, components similar to those in embodiment 1 are given the same reference numerals, and detailed description thereof will be omitted. The waste treatment facility 1 of embodiment 4 is provided with a second camera 15, similar to embodiment 1. Each parking space 6 of embodiment 4 is provided with a detection unit 17. Since the detection unit 17 is similar to the detection unit 17 of embodiment 3, detailed description thereof will be omitted.

[0117] In the information processing device 3 of the fourth embodiment, the control unit 30 acquires the detection result from the detection unit 17, similarly to the third embodiment, and identifies the parking space 6 in use based on the acquired detection result.

[0118] The control unit 30 acquires the second image data from the second camera 15. Since the range captured by the second camera 15 is fixed as described above, it is possible to specify in advance image coordinates indicating an area in the second image data in which the vehicle 2 parked in each parking space 6 is captured. The image coordinates indicating the above-mentioned area are specified for each parking space 6. For example, the image coordinates indicating the above-mentioned area are stored in the storage unit 31 in advance.

[0119] The control unit 30 inputs the second image data into the learning model M1 to obtain the type of vehicle 2 parked in the parking space 6 in use. In detail, when the control unit 30 inputs the second image data into the learning model M1, the control unit 30 causes the learning model M1 to perform the above-mentioned detection for the area related to the identified parking space 6 in use among the above areas in the second image data. For example, the control unit 30 inputs image data of the area related to the identified parking space 6 in use in the second image data into the learning model M1. At this time, the learning model M1 outputs the type of vehicle 2 parked in the identified parking space 6 in use.

[0120] The control unit 30 identifies the type of vacant parking space based on the acquired type of vehicle 2. Similar to the second embodiment, the control unit 30 determines whether or not to permit entry of the vehicle 2 into the waste treatment facility 1 based on the identified type of vacant parking space. In detail, the control unit 30 outputs one of first entry permission information, second entry permission information, third entry permission information, and entry denial information to the display unit 4 according to the type of vacant parking space. Similar to the second embodiment, the display unit 4 receives the output information and lights up or flashes blue or red.

[0121] 11 is a flowchart illustrating a determination process performed by the control unit 30 of the embodiment 4. For example, the control unit 30 starts the following process based on an operation of an operation unit (not shown) provided in the information processing device 3 by a worker at the waste treatment facility 1.

[0122] The control unit 30 acquires detection results from each detection unit 17 (S41). As described above, the control unit 30 identifies an occupied parking space 6 based on the acquired detection results (S42). The control unit 30 acquires second image data from the second camera 15 (S43).

[0123] As described above, the control unit 30 inputs the acquired second image data into the learning model M1 (S44), and has the learning model M1 perform the above-mentioned detection for the area related to the identified parking space 6 in use, thereby acquiring the type of vehicle 2 parked in the parking space 6 in use (S45).

[0124] As described above, the control unit 30 identifies the type of vacant parking space based on the acquired type of vehicle 2 (S46). The control unit 30 determines whether or not there are vacant parking spaces of both types (S47). If there are vacant parking spaces of both types (S47: YES), the control unit 30 outputs the first entry permission information to the display unit 4 (S48) and ends the determination process.

[0125] If there are no vacant parking spaces for both types of vehicles (S47: NO), the control unit 30 judges whether there is a vacant parking space for a refuse truck (S471). If there is a vacant parking space for a refuse truck (S471: YES), the control unit 30 outputs the second entry permission information to the display unit 4 (S472) and ends the judgment process.

[0126] If there is no vacant parking space for a refuse compactor vehicle (S471: NO), the control unit 30 judges whether there is a vacant parking space for a general vehicle (S473). If there is a vacant parking space for a general vehicle (S473: YES), the control unit 30 outputs third entry permission information to the display unit 4 (S474) and ends the judgment process. If there is no vacant parking space for a general vehicle (S473: NO), that is, if there is no vacant parking space, the control unit 30 outputs entry refusal information to the display unit 4 (S475) and ends the judgment process.

[0127] The control unit 30 may output any one of the first entry permission information, the second entry permission information, the third entry permission information, and the entry denial information, and then return to the process of S41 instead of terminating the determination process. Furthermore, the control unit 30 may output the entry denial information to the display unit 4 when there are no vacant parking spaces of both types.

[0128] The control unit 30 inputs the second image data to the learning model M1 and causes it to perform detection in the area related to the parking space 6 in use. The learning model M1 is easy to detect the vehicle 2 in the second image data. The accuracy of the type of the vehicle 2 in the waste treatment facility 1 can be improved.

[0129] (Embodiment 5) FIG. 12 is a block diagram showing a configuration example of an information processing system S according to embodiment 5. Among the configuration according to embodiment 5, components similar to those of embodiment 1 are given the same reference numerals, and detailed description thereof will be omitted. In the information processing device 3 of embodiment 5, a first learning model M1 (same as the learning model of embodiment 1) and a second learning model M2 are stored in a storage unit 31. The control unit 30 performs the above-mentioned judgment process using the first learning model M1. The second learning model M2 will be described later.

[0130] In the information processing device 3 of the fifth embodiment, a third camera 18 is connected to the input / output unit 32. The third camera 18 is, for example, a surveillance camera, and captures images inside the waste treatment facility 1. For example, the third camera 18 is attached to the ceiling of the waste treatment facility 1. The third camera 18 is disposed in an orientation that allows it to capture images of the vehicle 2, the parking space 6, and the hopper 13.

[0131] Hereinafter, image data captured by the third camera 18 inside the waste treatment facility 1 will also be referred to as in-facility image data. The in-facility image data may be a still image of one frame or a video of several frames. FIG. 13 is a schematic diagram showing an example of in-facility image data. The in-facility image data in FIG. 13 shows a vehicle 2, a parking space 6, a hopper 13, and a person 7. The person 7 is, for example, a worker performing work at the waste treatment facility 1 or a passenger of the vehicle 2.

[0132] The control unit 30 of the fifth embodiment communicates with the third camera 18 via the input / output unit 32. The control unit 30 acquires in-facility image data from the third camera 18. For example, the information processing device 3 may be configured to include a wireless communication unit (not shown) for performing wireless communication, and the control unit 30 and the third camera 18 may communicate with each other via the wireless communication unit.

[0133] The number of third cameras 18 may be one or two or more. In this embodiment, an example will be described in which third camera 18 and second camera 15 are different cameras, but third camera 18 and second camera 15 may be the same camera. In this case, since the in-facility image data captures an image of vehicle 2 inside waste treatment facility 1, the in-facility image data corresponds to the second image data.

[0134] In the second learning model M2, the relationship between the facility interior image data and the area and type of detection target in the facility interior image data is learned in advance. Detection targets of the second learning model M2 include vehicles 2, people 7, and hoppers 13. The hopper 13 corresponds to a specific device in the waste treatment facility 1. The specific device is not limited to the hopper 13, and may be a device or equipment in the waste treatment facility 1 other than the hopper 13. Multiple specific devices may be included in the detection targets.

[0135] The control unit 30 inputs the acquired in-facility image data to the second learning model M2. When the in-facility image data is input to the second learning model M2, the second learning model M2 outputs the area of ​​the detection target in the in-facility image data and the type of the detection target. For example, the types of detection target are garbage trucks, general vehicles, other vehicles, specific devices, and people 7. The area of ​​the detection target is, for example, a bounding box, and is indicated by an image coordinate system. When the detection target is not detected based on the in-facility image data, the second learning model M2 outputs information indicating that the detection target has not been detected.

[0136] The training data used in learning the second learning model M2 includes facility image data, and area data and type data related to the facility image data. The method of learning the second learning model M2 is similar to that of the learning model M1 of the first embodiment, so detailed description is omitted.

[0137] The control unit 30 inputs the acquired interior image data to the second learning model M2. The second learning model M2 outputs the area of ​​the detection target in the interior image data and the type of the detection target. The control unit 30 acquires the area of ​​the detection target and the type of the detection target in the output interior image data.

[0138] When the person 7 is detected, the control unit 30 derives the distance between the detected person 7 and the vehicle 2 or the specific device. For example, the control unit 30 derives the shortest distance between the area of ​​the detection target whose type is the person 7 and the area of ​​the detection target whose type is a garbage truck, a general vehicle, or another vehicle, based on the area of ​​each detection target, as the distance between the person 7 and the vehicle 2. The control unit 30 also derives the shortest distance between the area of ​​the detection target whose type is the person 7 and the area of ​​the detection target whose type is a specific device, based on the area of ​​each detection target, as the distance between the person 7 and the specific device. For example, the distance per pixel in the facility image data specified based on the resolution and dimensions of the facility image data is stored in the storage unit 31 in advance for deriving the distance. The control unit 30 derives the distance by multiplying the number of pixels between the areas of each detection target by the distance per pixel.

[0139] The control unit 30 determines whether the derived distance is equal to or less than a threshold. The threshold is stored in advance in, for example, the storage unit 31. For example, the threshold regarding the distance between the person 7 and the specific device may be different from the threshold regarding the distance between the person 7 and the vehicle 2. For example, the distance and the threshold may be indicated by the number of pixels. If the distance is equal to or less than the threshold, the control unit 30 outputs approach information to the speaker 16. The approach information is information indicating the approach of the person 7 and the vehicle 2 or the specific device.

[0140] The speaker 16 receives the output approach information and outputs a voice or a warning sound such as "Please move away from the vehicle or device" to notify the person 7 in the waste treatment facility 1 that the person 7 is approaching the vehicle 2 or a specific device. For example, the above voice or warning sound is included in the approach information. The approach information is stored in the memory unit 31. For example, the above voice or warning sound may be stored in the speaker in advance. The speaker 16 corresponds to the approach notification unit. Note that the approach notification unit is not limited to the speaker 16.

[0141] The control unit 30 may output approach information to the facility terminal instead of or in addition to the speaker 16. For example, the facility terminal receives the output approach information and outputs the above-mentioned voice or warning sound to the speaker of the facility terminal. The facility terminal may output the above-mentioned voice or warning sound to an earphone connected to the facility terminal. Furthermore, when the facility terminal receives the output approach information, it may display on the display unit of the facility terminal that the person 7 and the vehicle 2 or a specific device are approaching each other. It is possible to notify a worker in the waste treatment facility 1 that the person 7 and the vehicle 2 or a specific device are approaching each other. In this case, the facility terminal corresponds to the approach notification unit.

[0142] 14 is a flowchart illustrating a notification process performed by the control unit 30 in the fifth embodiment to notify the approach of a person 7 to a vehicle 2 or a specific device. For example, the notification process is performed in parallel with the determination process. For example, the control unit 30 starts the following process based on an operation of an operation unit (not shown) provided in the information processing device 3 by a worker at the waste treatment facility 1.

[0143] The control unit 30 acquires in-facility image data from the third camera 18 (S51) and inputs the acquired in-facility image data to the second learning model M2 (S52). The second learning model M2 outputs the area and type of the detection target in the in-facility image data. The control unit 30 acquires the area and type of the detection target in the output in-facility image data (S53).

[0144] As described above, the control unit 30 derives the distance between the person 7 and the vehicle 2 or a specific device based on the acquired area and type of the detection target (S54). For example, when the person 7 is not detected, the control unit 30 ends the notification process or performs the process of S51. As described above, the control unit 30 determines whether the derived distance is equal to or less than the threshold (S55).

[0145] If the distance is equal to or less than the threshold (S55: YES), the control unit 30 outputs approach information to the speaker 16 (S56) and ends the notification process. After outputting the approach information, the control unit 30 may return to the process of S51. If the distance is not equal to or less than the threshold (S55: NO), that is, if the distance is greater than the threshold, the control unit 30 ends the notification process. Instead of ending the notification process, the control unit 30 may return to the process of S51.

[0146] In this embodiment, when the distance between the person 7 and the vehicle 2 or the specific device is equal to or less than a threshold value, the person 7 is notified that the person 7 is approaching the vehicle 2 or the specific device. Therefore, an appropriate distance can be ensured between the person 7 and the vehicle 2 or the specific device. By ensuring an appropriate distance, for example, it is possible to prevent the person 7 from coming into contact with the vehicle 2. It is also possible to prevent the person 7 from getting too close to the hopper 13.

[0147] The control unit 30 may use the first learning model M1 in the notification process. For example, the detection targets of the first learning model M1 are the vehicle 2, the person 7, and a specific device. In this case, the control unit 30 acquires second image data from the second camera, inputs the second image data to the first learning model M1, and acquires the area and type of the detection target in the second image data. The control unit 30 derives the distance between the person 7 and the vehicle 2 or the specific device based on the area and type of the acquired detection target.

[0148] (Modification) 15 is a block diagram showing an example of the configuration of an information processing system S according to a modified example of the fifth embodiment. Among the configuration according to the modified example of the fifth embodiment, components similar to those of the fifth embodiment are given the same reference numerals, and detailed description thereof will be omitted. In the information processing device 3 according to the modified example of the fifth embodiment, the input / output unit 32 is connected to an interlock device 19 described below. The control unit 30 communicates with the interlock device 19 via the input / output unit 32. Note that the information processing device 3 may be provided with a wireless communication unit (not shown) for performing wireless communication, and the control unit 30 and the interlock device 19 may communicate with each other via the wireless communication unit.

[0149] In the modified example, detection targets of the second learning model M2 include the vehicle 2, the person 7, and the dumping box 14. The dumping box 14 corresponds to a specific device in the waste treatment facility 1. The dumping box 14 is imaged by the third camera 18.

[0150] In the modified example, the types of detection objects output from the second learning model M2 include the types of people 7. The types of people 7 are, for example, ordinary people and workers. Ordinary people are, for example, passengers such as drivers of ordinary vehicles. Therefore, the types of detection objects output from the second learning model M2 in the modified example are garbage trucks, ordinary vehicles, other vehicles, dumping boxes 14, ordinary people, and workers. For example, at the waste treatment facility 1, workers wear work clothes or work hats. In this case, ordinary people and workers can be distinguished based on whether or not they are wearing work clothes or the above-mentioned hats.

[0151] As in the fifth embodiment, the control unit 30 inputs the facility interior image data into the second learning model M2 to acquire the area of ​​the detection target in the facility interior image data and the type of the detection target. When a general public is detected, the control unit 30 derives the distance between the detected general public and the damping box 14 based on the acquired area and type of the detection target. The control unit 30 determines whether the derived distance is equal to or less than a threshold. If the distance is equal to or less than the threshold, the control unit 30 outputs approach information to the interlock device 19. Note that, if the above-mentioned distance is equal to or less than the threshold, the control unit 30 may output approach information to the speaker 16 and the interlock device 19.

[0152] The interlock device 19 is a device that emergency stops the drive of the damping box 14, and is, for example, built into the damping box 14. The interlock device 19 may be attached to the outside of the damping box 14. The interlock device 19 includes a control unit 190, an input / output unit 191, and an interlock mechanism 192 for stopping the drive of the damping box 14. The input / output unit 191 is an input / output interface that is connected to the information processing device 3.

[0153] The control unit 190 has one or more arithmetic processing devices such as CPUs or FPGAs (Field Programmable Gate Arrays). It also has memories such as ROMs or RAMs. The control unit 190 communicates with the information processing device 3 via the input / output unit 191. For example, the control unit 190 acquires approach information output from the information processing device 3 via the input / output unit 191. Furthermore, the control unit 190 controls the driving of an interlock mechanism 192.

[0154] When the control unit 190 acquires the approach information, it determines whether the damping box 14 is operating. When the damping box 14 is operating, the control unit 190 activates the interlock mechanism 192. That is, when the control unit 190 acquires the approach information and the damping box 14 is operating, it activates the interlock mechanism 192. The operation of the damping box 14 is stopped by the activation of the interlock mechanism 192. That is, when the distance between a general person and the operating damping box 14 is short, the operation of the damping box 14 is stopped. Therefore, the safety of the damping box 14 can be improved. The interlock device 19 corresponds to a stop unit.

[0155] For example, when the worker resumes driving the damping box 14, he or she operates the interlock device 19 to stop driving the interlock mechanism 192 (releases the interlock). Note that in this embodiment, even if the worker is close to the damping box 14, the information processing device 3 does not output approach information. In other words, even if the worker is close to the damping box 14, the interlock device 19 does not stop driving the damping box 14.

[0156] (Embodiment 6) 16 is a block diagram showing a configuration example of an information processing system S according to embodiment 6. In the configuration according to embodiment 6, components similar to those in embodiment 1 are given the same reference numerals, and detailed description thereof will be omitted.

[0157] The waste treatment plant 100 of the sixth embodiment has multiple, for example, two, waste treatment facilities 1. Each waste treatment facility 1 accepts a different type of waste. For example, the first waste treatment facility 1 accepts household waste. The second waste treatment facility 1 accepts non-burnable waste. For example, each waste treatment facility 1 is provided with an information processing device 3. The waste treatment plant 100 has a weighing building (not shown) for weighing the amount of waste loaded onto the vehicle 2.

[0158] Before going to waste treatment facility 1, vehicle 2 goes to a weighing house. At the weighing house, the amount of waste loaded onto vehicle 2 is weighed. A worker at the weighing house asks the driver of vehicle 2 what type of waste is loaded onto vehicle 2, and obtains the type of waste as a response. The worker at the weighing house tells the driver to go to a waste treatment facility 1 that corresponds to the obtained type of waste. For example, if the type of waste is household waste, the worker at the weighing house tells the driver to go to the first waste treatment facility 1.

[0159] In the information processing device 3 of the sixth embodiment, a waste type information DB 310, which will be described later, is stored in the memory unit 31. The information processing device 3 includes a communication unit 33. The communication unit 33 is a communication interface that realizes communication with an on-site computer 8 in the waste treatment plant 100 via a network N. The control unit 30 communicates with the on-site computer 8 via the communication unit 33. For example, the on-site computer 8 is provided in a weighing building.

[0160] The on-site computer 8 is a dedicated or general-purpose computer. The on-site computer 8 includes a control unit 80, a storage unit 81, an input / output unit 82, and a communication unit 83. The control unit 80, the storage unit 81, the input / output unit 82, and the communication unit 83 are connected to each other.

[0161] The control unit 80 has one or more arithmetic processing units such as a CPU or a GPU. It also has a memory such as a ROM or a RAM. The control unit 80 reads out programs stored in the storage unit 81 and executes various processes. The storage unit 81 includes a non-volatile memory such as a hard disk or an SSD. The communication unit 83 is a communication interface that realizes communication with the information processing device 3 via the network N. The control unit 80 communicates with the information processing device 3 via the communication unit 83.

[0162] The input / output unit 82 is an input / output interface connected to the fourth camera 84 and the input device 85. The input device 85 is, for example, a keyboard or a touch panel, and accepts input of the type of waste by a worker in the weighing building. The fourth camera 84 is provided in the weighing building and captures an image of the vehicle 2 in which the waste is being weighed. Hereinafter, image data captured of the vehicle 2 by the fourth camera 84 will also be referred to as weighing time image data. The license plate of the vehicle 2 is captured in the weighing time image data.

[0163] The control unit 80 acquires the weighing-time image data from the fourth camera 84 via the input / output unit 82. Based on the acquired weighing-time image data, the control unit 80 detects the number of the license plate of the vehicle 2 (hereinafter, the number of the vehicle 2) in the weighing-time image data using a known image recognition technology. Since the image recognition technology is a known technology, a detailed description will be omitted.

[0164] A worker uses the input device 85 to input the type of garbage loaded on the vehicle 2. The control unit 80 acquires the input type of garbage via the input / output unit 82. The control unit 80 stores garbage type information in the memory unit 81, which associates the acquired type of garbage with the license plate number of the vehicle 2 detected as described above. The control unit 80 outputs the garbage type information to the control unit 30 of the information processing device 3. It is noted that a worker at the weighing building may check the license plate number of the vehicle 2 and input the license plate number of the vehicle 2 using the input device 85.

[0165] The control unit 30 acquires the garbage type information output from the on-site computer 8, and stores the acquired garbage type information in the garbage type information DB 310. FIG. 17 is a conceptual diagram showing an example of the contents of the garbage type information DB 310. A plurality of pieces of garbage type information are stored in the garbage type information DB 310. More specifically, the garbage type information DB 310 has a vehicle 2 number column and a garbage type column. The garbage type information DB 310 stores a plurality of vehicle 2 number columns and a plurality of garbage types in association with each other.

[0166] The control unit 30 acquires the first image data. Based on the acquired first image data, the control unit 30 detects the license plate number of the vehicle 2 in the first image data using a known image recognition technique. The license plate number of the vehicle 2 in the first image data is the license plate number of the vehicle 2 scheduled to enter the waste treatment facility 1. The control unit 30 refers to the waste type information DB 310, and identifies the type of waste loaded on the vehicle 2 scheduled to enter, based on the license plate number and waste type information of the vehicle 2 scheduled to enter.

[0167] The control unit 30 outputs the identified type of waste to the speaker 16. For example, if the type of waste is household waste, the control unit 30 outputs voice data of a voice such as "A vehicle loaded with household waste is scheduled to enter" to the speaker 16. For example, the voice data is stored in the memory unit 31. The speaker 16 receives the output voice data and outputs the above voice to notify the worker at the waste treatment facility 1 of the type of waste loaded in the vehicle 2 scheduled to enter. Therefore, the worker at the waste treatment facility 1 does not need to confirm the type of waste loaded in the vehicle 2 scheduled to enter with the driver of the vehicle 2. The speaker 16 corresponds to a notification unit. Note that the notification unit is not limited to the speaker 16.

[0168] The control unit 30 may output the identified type of waste to the facility terminal instead of or in addition to the speaker 16. For example, if the type of waste is household waste, the control unit 30 outputs the above-mentioned voice data to the facility terminal. For example, the facility terminal receives the output voice data and outputs the above-mentioned voice to the speaker of the facility terminal or to a connected earphone. The type of waste loaded on the vehicle 2 scheduled to enter the waste treatment facility 1 can be notified to a worker. In this case, the facility terminal corresponds to the notification unit.

[0169] For example, when the control unit 30 is unable to identify the type of waste loaded, for example when the number of the detected vehicle 2 or the type of waste associated with that number is not stored in the waste type information DB, the control unit 30 may output information indicating that no waste type information exists to the speaker 16 or the facility terminal. For example, the information indicating that no waste type information exists includes voice data such as "The type of waste loaded in the vehicle scheduled to enter is not registered."

[0170] Notification of the type of waste is not limited to notification by voice. For example, the control unit 30 may output the type of waste to a display device such as a display (not shown) provided in the waste treatment facility 1. The display device displays the outputted type of waste and notifies a worker at the waste treatment facility 1 of the type of waste loaded on the vehicle 2 scheduled to enter.

[0171] For example, when the facility terminal receives the type of garbage output from the control unit 30, it may display the received type of garbage on the display unit of the facility terminal to notify workers at the waste treatment facility 1 of the type of garbage loaded on the vehicle 2 scheduled to enter.

[0172] FIG. 18 is a flowchart illustrating a notification process performed by the control unit 30 in embodiment 6 to notify the type of waste loaded on a vehicle 2 scheduled to enter. For example, the notification process is performed in parallel with the determination process. Note that the determination process may be performed after the notification process. For example, the control unit 30 starts the following process based on the operation of an operation unit (not shown) provided on the information processing device 3 by a worker at the waste treatment facility 1.

[0173] The control unit 30 acquires waste type information from the on-site computer 8 (S61) and stores the acquired waste type information in the waste type information DB 310. As described above, the control unit 30 acquires first image data (S62) and detects the license plate number of the vehicle 2 scheduled to enter based on the acquired first image data (S63). As described above, the control unit 30 refers to the waste type information DB 310 and identifies the type of waste loaded in the vehicle 2 scheduled to enter based on the detected license plate number and waste type information of the vehicle 2 (S64). The control unit 30 outputs the identified type of waste to the speaker 16 (S65) and ends the notification process.

[0174] As described above, speaker 16 notifies workers at waste treatment facility 1 of the type of waste loaded in vehicle 2 scheduled to enter. If the driver of vehicle 2 makes a mistake in the waste treatment facility 1 to which vehicle 2 is to enter, for example, when vehicle 2 loaded with non-burnable waste is scheduled to enter first waste treatment facility 1, workers at first waste treatment facility 1 will inform the driver that the driver has made a mistake in the waste treatment facility 1. It is possible to prevent vehicle 2 from entering a waste treatment facility 1 that does not correspond to the type of waste loaded.

[0175] For example, the information processing device 3 may store in the memory unit 31 the types of waste that can be accepted by the waste treatment facility 1 in which the device is installed. The control unit 30 determines whether the identified type of waste is an acceptable type of waste. If the identified type of waste is not an acceptable type of waste, the control unit 30 outputs a signal to the speaker 16 indicating that the vehicle 2 has entered the wrong waste treatment facility 1. The speaker 16 receives the signal and outputs a warning sound. The driver and workers at the waste treatment facility 1 can be notified that the vehicle 2 that is scheduled to enter has entered the wrong waste treatment facility 1.

[0176] In this embodiment, each waste treatment facility 1 is provided with an information processing device 3, but for example, one information processing device 3 may determine whether or not to permit entry to each waste treatment facility 1.

[0177] (Embodiment 7) FIG. 19 is a schematic diagram of a waste treatment plant 100 according to the seventh embodiment. Among the components according to the seventh embodiment, the same components as those in the sixth embodiment are given the same reference numerals, and detailed description thereof will be omitted. In the waste treatment plant 100 of the seventh embodiment, one waste treatment facility 1 receives multiple types of waste, for example, household waste and non-burnable waste. The number of waste treatment facilities 1 provided in the waste treatment plant 100 may be one or more. The vehicle 2 goes to the weighing building before going to the waste treatment facility 1, as in the sixth embodiment. The on-site computer 8 outputs waste type information to the information processing device 3.

[0178] The waste treatment facility 1 of the seventh embodiment is provided with a plurality of hoppers 13 (two in FIG. 19). One hopper 13 is disposed near the first parking space 6a and receives non-burnable waste. The other hopper 13 is disposed near the second parking space 6b and the third parking space 6c and receives household waste. A dumping box 14 for dumping household waste into the other hopper 13 is provided in the vicinity of the other hopper 13 and close to the fourth parking space 6d. Household waste loaded onto a general vehicle is dumped into the other hopper 13 by the dumping box 14.

[0179] The parking spaces 6 are provided according to the type of vehicle 2 and the type of waste loaded on the vehicle 2. A refuse truck loaded with non-burnable waste is parked in the first parking space 6a. A refuse truck loaded with household waste is parked in the second parking space 6b and the third parking space 6c. A general vehicle loaded with household waste is parked in the fourth parking space 6d. Therefore, the upper limit of the number of refuse trucks loaded with non-burnable waste that can enter the waste treatment facility 1 is one. The upper limit of the number of refuse trucks loaded with household waste that can enter the waste treatment facility 1 is two. The upper limit of the general vehicle loaded with household waste that can enter the waste treatment facility 1 is one. In this embodiment, a dumping box 14 for depositing non-burnable waste into one hopper 13 is not provided, so that non-burnable waste loaded on a general vehicle cannot be deposited into one hopper 13. Therefore, general vehicles loaded with non-burnable waste are not permitted to enter the waste treatment facility 1. In other words, the upper limit of the number of general vehicles loaded with non-burnable waste that can enter the waste treatment facility 1 is 0. For example, the upper limit of the number of these vehicles 2 that can enter is stored in the memory unit 31.

[0180] The control unit 30 acquires garbage type information from the on-site computer 8 and stores the acquired garbage type information in the garbage type information DB 310. The control unit 30 also acquires first image data from the first camera 5. As in the sixth embodiment, the control unit 30 detects the license plate number of the vehicle 2 scheduled to enter based on the first image data, and identifies the type of garbage loaded on the vehicle 2 scheduled to enter based on the license plate number and garbage type information of the detected vehicle 2 by referring to the garbage type information DB 310. As in the first embodiment, the control unit 30 inputs the acquired first image data into the learning model M1 to acquire the type of the vehicle 2 scheduled to enter. In other words, the control unit 30 identifies the type of the vehicle 2 scheduled to enter and the type of garbage loaded on the vehicle 2.

[0181] The control unit 30 acquires second image data from the second camera 15. The second image data shows the license plate of the vehicle 2 in the waste treatment facility 1. As in the first embodiment, the control unit 30 inputs the acquired second image data into the learning model M1. The learning model M1 outputs the area of ​​the vehicle 2 in the second image data and the type of the vehicle 2. The control unit 30 acquires the area of ​​the vehicle 2 and the type of the vehicle 2 in the output second image data.

[0182] When a vehicle 2 is detected, the control unit 30 uses a known image recognition technique based on the acquired area of ​​the vehicle 2 and the second image data to detect the license plate number of the detected vehicle 2. The control unit 30 refers to the waste type information DB 310 and identifies the type of waste loaded on the vehicle 2 based on the license plate number and waste type information of the detected vehicle 2. In other words, the control unit 30 identifies the type of vehicle 2 in the waste treatment facility 1 and the type of waste loaded on the vehicle 2.

[0183] The control unit 30 identifies the type of vacant parking space based on the type of vehicle 2 in the waste treatment facility 1 and the type of waste loaded on the vehicle 2. In this embodiment, the types of vacant parking spaces are a combination of three vacant parking spaces: a vacant parking space for a refuse truck loaded with household waste, a vacant parking space for a refuse truck loaded with non-burnable waste, and a vacant parking space for a general vehicle loaded with household waste. For example, if the type of the detected vehicle 2 is only a general vehicle and the type of waste loaded on the vehicle 2 is household waste, the control unit 30 identifies the types of vacant parking spaces as a vacant parking space for a refuse truck loaded with household waste and a vacant parking space for a refuse truck loaded with non-burnable waste.

[0184] In this embodiment, if a general vehicle is loaded with household waste, it enters the waste treatment facility 1 and stops at the fourth parking space 6d. Also, if a general vehicle is loaded with non-burnable waste, it does not enter the waste treatment facility 1 (entry of a general vehicle loaded with non-burnable waste is not permitted). Therefore, a general vehicle in the waste treatment facility 1 is considered to be a general vehicle loaded with household waste. Therefore, for example, if the type of the detected vehicle 2 is only a general vehicle, the control unit 30 may specify the type of the vacant parking space as a vacant parking space for a refuse truck loaded with household waste and a vacant parking space for a refuse truck loaded with non-burnable waste, without detecting the license plate number of the vehicle 2.

[0185] The control unit 30 determines whether or not to permit entry of the vehicle 2 based on the type of vehicle 2 that is to enter, the type of garbage loaded in the vehicle 2, and the type of vacant parking space. In more detail, the control unit 30 determines whether or not the type of vehicle 2 that is to enter and the type of garbage in the vehicle 2 correspond to the type of vacant parking space.

[0186] If the type of vacant parking space includes a vacant parking space for a garbage truck loaded with household waste, and a garbage truck loaded with household waste is scheduled to enter, the control unit 30 allows the entry. If the type of vacant parking space does not include a vacant parking space for a garbage truck loaded with household waste, and a garbage truck loaded with household waste is scheduled to enter, the control unit 30 does not allow the entry.

[0187] If the type of vacant parking space includes a vacant parking space for a refuse truck loaded with non-burnable waste and a refuse truck loaded with non-burnable waste is scheduled to enter, the control unit 30 permits the entry. If the type of vacant parking space does not include a vacant parking space for a refuse truck loaded with non-burnable waste and a refuse truck loaded with non-burnable waste is scheduled to enter, the control unit 30 does not permit the entry.

[0188] If the type of vacant parking space includes a vacant parking space for a general vehicle loaded with household waste, and a general vehicle loaded with household waste is scheduled to enter, the control unit 30 allows the entry. If the type of vacant parking space does not include a vacant parking space for a general vehicle loaded with household waste, and a general vehicle loaded with household waste is scheduled to enter, the control unit 30 does not allow the entry. If a general vehicle loaded with non-burnable waste is scheduled to enter, for example if the driver of the general vehicle has entered the wrong waste treatment facility 1, the control unit 30 does not allow the entry regardless of the type of vacant parking space.

[0189] The control unit 30 outputs entry permission information or entry denial information to the display unit 4 according to the result of the determination as to whether entry is permitted or not. The control unit 30 may output the type of waste loaded in the vehicle 2 scheduled to enter to the speaker 16 as in the sixth embodiment.

[0190] 20 is a flowchart illustrating a determination process performed by the control unit 30 of the seventh embodiment. For example, the control unit 30 starts the following process based on an operation of an operation unit (not shown) of the information processing device 3 by a worker at the waste treatment facility 1.

[0191] The control unit 30 performs the processes of S71 and S72. The processes of S71 and S72 are similar to the processes of S61 and S62, so detailed explanations will be omitted. As described above, the control unit 30 detects the license plate number of the vehicle 2 scheduled to enter based on the first image data, and identifies the type of garbage loaded on the vehicle 2 scheduled to enter based on the detected license plate number of the vehicle 2 and the garbage type information (S73). The control unit 30 performs the processes of S74 and S75. The processes of S74 and S75 are similar to the processes of S16 and S17, so detailed explanations will be omitted. The vehicle 2 scheduled to enter and the type of garbage loaded on the vehicle 2 are identified.

[0192] The control unit 30 performs the processes of S76, S77, and S78. The processes of S76, S77, and S78 are similar to the processes of S11, S12, and S13, so detailed explanations will be omitted. As described above, the control unit 30 detects the license plate number of the vehicle 2 in the waste treatment facility 1 based on the second image data, and identifies the type of waste of the vehicle 2 in the waste treatment facility 1 based on the detected license plate number and waste type information of the vehicle 2 (S79). The control unit 30 identifies the type of vacant parking space based on the type of vehicle 2 in the waste treatment facility 1 and the type of waste of the vehicle 2 (S80).

[0193] The control unit 30 determines whether the type of the vehicle 2 scheduled to enter and the type of garbage of the vehicle 2 correspond to the type of the vacant parking space (S81). If the type of the vehicle 2 scheduled to enter and the type of garbage of the vehicle 2 correspond to the type of the vacant parking space (S81: YES), it outputs entry permission information (S82) and ends the determination process. If the type of the vehicle 2 scheduled to enter and the type of garbage of the vehicle 2 do not correspond to the type of the vacant parking space (S81: NO), it outputs entry denial information (S811) and ends the determination process. After outputting the entry permission information or entry denial information, the control unit 30 may return to the process of S71.

[0194] Even when the parking spaces 6 are provided according to the type of vehicle 2 and the type of garbage, the information processing device 3 can determine whether or not to permit the entry of the vehicle 2, taking into consideration the type of vehicle 2 that is scheduled to enter and the type of garbage loaded in the vehicle 2. Note that there may be multiple parking spaces 6 for refuse trucks loaded with non-burnable garbage and multiple parking spaces 6 for general vehicles loaded with household garbage.

[0195] For example, the waste treatment facility 1 may be provided with a parking space 6 where general vehicles loaded with non-burnable waste can stop. That is, general vehicles loaded with non-burnable waste may be able to enter the waste treatment facility 1. In this case, for example, a dumping box 14 for dumping non-burnable waste into a hopper 13 is provided near the hopper 13 that receives the non-burnable waste.

[0196] For example, the waste treatment facility 1 may be provided with a container for storing household waste and a container for storing non-burnable waste. For example, each container may be arranged near the fourth parking space 6d. In this case, if there is an available parking space for a general vehicle, the general vehicle may be permitted to enter regardless of the type of waste loaded on the general vehicle. For example, the waste treatment facility 1 is configured so that a general vehicle loaded with household waste and a general vehicle loaded with non-burnable waste enter and stop at the fourth parking space 6d. At this time, when the type of the vehicle 2 detected based on the second image data is a general vehicle, the information processing device 3 does not need to detect the license plate number of the vehicle 2 and identify the type of waste loaded on the vehicle 2.

[0197] The embodiments disclosed herein are illustrative in all respects and should not be considered as limiting. The technical features described in each embodiment can be combined with each other, and the scope of the present invention is intended to include all modifications within the scope of the claims and the scope equivalent to the claims. [Explanation of symbols]

[0198] M1 learning model (first learning model) M2 Second Learning Model P Program S Information Processing System 100 Waste disposal plant 1. Waste treatment facilities 13 Hopper (specific device) 14 Damping Box 15 Second Camera 16 Speakers 17 Detection unit 2 Vehicles 3. Information processing equipment 30 Control section 4 Display section 5. First Camera 6 Parking space 7 people

Claims

1. Acquire vehicle image data including at least one of first image data in which a vehicle entering a waste treatment facility where a plurality of types of vehicles including a garbage truck that directly discharges garbage into a hopper and other vehicles that discharge garbage into the hopper through a garbage input device enters, and second image data in which the vehicle inside the waste treatment facility is imaged; inputting the acquired vehicle image data into a learning model that has been trained to output a vehicle type when vehicle image data is input, to acquire a vehicle type; a determination is made as to whether or not to permit the garbage truck or the other vehicle to enter the garbage treatment facility based on the availability of parking spaces within the facility, including a plurality of garbage truck parking spaces in which the garbage truck can be parked among the garbage truck and the other vehicle near the hopper, a shared parking space in which the garbage truck or the other vehicle can be parked among the garbage truck and the other vehicle near the garbage input device adjacent to the hopper, and an other vehicle parking space in which the other vehicle can be parked among the garbage truck and the other vehicle near the garbage input device adjacent to the hopper, and the acquired type of vehicle; In the determination, Whether or not to permit the entry of the garbage truck is determined based on the availability of the garbage truck parking space; Whether or not to permit the entry of the other vehicle is determined based on the availability of the parking space for the other vehicle, and whether or not to permit the entry of the garbage truck or the other vehicle is determined based on the availability of the shared parking space. A program that causes a computer to carry out processing.

2. A result notification unit notifies a result of a decision as to whether or not to permit entry, and outputs information as to whether or not to permit entry in accordance with the decision result. The program according to claim 1.

3. The program according to claim 2 , wherein the result notification unit includes at least one of a display unit visible from a position where the vehicle entering the waste treatment facility is waiting, and an audio output unit that outputs audio related to the determination result.

4. Acquiring the first image data from a first camera; The acquired first image data is input into the learning model to acquire the type of the vehicle scheduled to enter the waste treatment facility; acquiring the second image data from a second camera; The acquired second image data is input into the learning model to acquire the type of the vehicle in the waste treatment facility; A determination is made as to whether or not to permit entry based on the acquired vacant parking space in the waste treatment facility that is specified based on the type of the vehicle and the acquired type of the vehicle that is scheduled to enter. The program according to any one of claims 1 to 3.

5. acquiring the second image data; The acquired second image data is input into the learning model to acquire the type of the vehicle in the waste treatment facility; A determination is made as to whether or not to permit entry based on an available parking space identified based on the type of the vehicle within the waste treatment facility. The program according to any one of claims 1 to 3.

6. acquiring the first image data; The acquired first image data is input into the learning model to acquire the type of the vehicle scheduled to enter the waste treatment facility; acquiring a detection result of a detection unit that is provided in the in-facility parking space and detects the vehicle parked in the in-facility parking space; A determination is made as to whether or not to permit entry based on the vacant parking space identified based on the acquired detection result and the acquired type of the vehicle scheduled to enter. The program according to any one of claims 1 to 3.

7. Acquire vehicle image data including at least one of first image data in which a vehicle entering a waste treatment facility where a plurality of types of vehicles including a garbage truck that directly discharges garbage into a hopper and other vehicles that discharge garbage into the hopper through a garbage input device enters, and second image data in which the vehicle inside the waste treatment facility is imaged; inputting the acquired vehicle image data into a learning model that has been trained to output a vehicle type when vehicle image data is input, to acquire a vehicle type; a determination is made as to whether or not to permit the garbage truck or the other vehicle to enter the garbage treatment facility based on the availability of parking spaces within the facility, including a plurality of garbage truck parking spaces in which the garbage truck can be parked among the garbage truck and the other vehicle near the hopper, a shared parking space in which the garbage truck or the other vehicle can be parked among the garbage truck and the other vehicle near the garbage input device adjacent to the hopper, and an other vehicle parking space in which the other vehicle can be parked among the garbage truck and the other vehicle near the garbage input device adjacent to the hopper, and the acquired type of vehicle; In the determination, Whether or not to permit the entry of the garbage truck is determined based on the availability of the garbage truck parking space; Whether or not to permit the entry of the other vehicle is determined based on the availability of the parking space for the other vehicle, and whether or not to permit the entry of the garbage truck or the other vehicle is determined based on the availability of the shared parking space. An information processing method for causing a computer to execute processing.

8. an image data acquisition unit that acquires vehicle image data including at least one of first image data in which a vehicle entering a waste treatment facility where multiple types of vehicles, including a garbage truck that directly discharges garbage into a hopper and other vehicles that discharge garbage into the hopper through a garbage input device, are photographed, and second image data in which the vehicle inside the waste treatment facility is photographed; a type acquisition unit that acquires a type of a vehicle by inputting the acquired vehicle image data into a learning model that has been trained to output a type of a vehicle when the vehicle image data is input; a determination unit that determines whether or not to permit the garbage truck or the other vehicle to enter the garbage treatment facility based on the availability of parking spaces within the facility, including a plurality of garbage truck parking spaces in which the garbage truck can be parked among the garbage truck and the other vehicle near the hopper, a shared parking space in which the garbage truck or the other vehicle can be parked among the garbage truck and the other vehicle near the garbage input device adjacent to the hopper, and an other vehicle parking space in which the other vehicle can be parked among the garbage truck and the other vehicle near the garbage input device adjacent to the hopper, and the acquired type of vehicle; In the determination, the determination unit: Whether or not to permit the entry of the garbage truck is determined based on the availability of the garbage truck parking space; The information processing system determines whether or not to permit the entry of the other vehicle based on the availability of the other vehicle parking space, and also determines whether or not to permit the entry of the garbage truck or the other vehicle based on the availability of the shared parking space.

9. a first image data acquisition unit that acquires the first image data from a first camera; A first type acquisition unit that inputs the acquired first image data into the learning model to acquire the type of the vehicle scheduled to enter the waste treatment facility; a second image data acquisition unit that acquires the second image data from a second camera; A second type acquisition unit that inputs the acquired second image data into the learning model to acquire the type of the vehicle in the waste treatment facility, The determination unit determines whether or not to permit entry based on an available parking space specified based on the acquired type of the vehicle in the waste treatment facility and the acquired type of the vehicle scheduled to enter. The information processing system according to claim 8.

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