Vehicle contamination determination system, vehicle contamination determination device, vehicle contamination determination method, and vehicle contamination determination program

Abstract

To provide a vehicle contamination determination system capable of determining contamination of a vehicle.SOLUTION: A vehicle contamination determination system according to the present disclosure includes: photographing means for photographing an object vehicle; acquisition means for acquiring an image of the object vehicle from the photographing means; extraction means for extracting an image of a reference vehicle corresponding to the object vehicle from a prescribed image group on the basis of the image of the object vehicle; determination means for determining a contamination degree of the object vehicle on the basis of difference between color of the image of the object vehicle and color of the image of the reference vehicle; and outputting means for outputting a determination signal corresponding to the contamination degree.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a vehicle dirt determination system, a vehicle dirt determination device, a vehicle dirt determination method, and a vehicle dirt determination program. [Background technology]

[0002] Some drivers find it difficult to objectively evaluate the degree of dirtiness of their vehicle and are unable to determine the appropriate timing for washing the vehicle. As a technology to assist such drivers, for example, Patent Document 1 discloses a cleaning support system that estimates the appropriate timing for washing the vehicle based on the vehicle usage history. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-167988 Summary of the Invention [Problem to be solved by the invention]

[0004] The cleaning support system disclosed in Patent Document 1 is a technology that provides cleaning support to predetermined vehicles. On the other hand, if there was a technology that could determine the dirt level of unspecified vehicles, it would be convenient because it could be used, for example, in conjunction with a device at a gas station that has a car wash facility to provide cleaning support to vehicles that enter the station. Therefore, there was a problem in that it was possible to determine the dirt level of vehicles other than predetermined vehicles.

[0005] In view of the above-mentioned problems, an object of the present disclosure is to provide a vehicle dirt determination system, a vehicle dirt determination device, a vehicle dirt determination method, and a vehicle dirt determination program that can determine the dirt level of a vehicle. [Means for solving the problem]

[0006] The vehicle dirt determination system of the present disclosure comprises an imaging means for imaging a target vehicle, an acquisition means for acquiring an image of the target vehicle from the imaging means, an extraction means for extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined group of images based on the image of the target vehicle, a determination means for determining the degree of dirtiness of the target vehicle based on the difference between the color of the image of the target vehicle and the color of the image of the reference vehicle, and an output means for outputting a determination signal corresponding to the degree of dirtiness.

[0007] The vehicle dirt determination device of the present disclosure comprises an acquisition means for acquiring an image of a target vehicle, an extraction means for extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined group of images based on the image of the target vehicle, a determination means for determining the degree of dirtiness of the target vehicle based on the difference between the color of the image of the target vehicle and the color of the image of the reference vehicle, and an output means for outputting a determination signal corresponding to the degree of dirtiness.

[0008] The vehicle dirt determination method of the present disclosure involves capturing an image of a target vehicle, obtaining an image of the captured target vehicle, extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined group of images based on the image of the target vehicle, determining the degree of dirtiness of the target vehicle based on the difference between the color of the image of the target vehicle and the color of the image of the reference vehicle, and outputting a determination signal corresponding to the degree of dirtiness.

[0009] The vehicle dirt determination program of the present disclosure causes a computer to take an image of a target vehicle, acquire an image of the target vehicle, extract an image of a reference vehicle corresponding to the target vehicle from a predetermined group of images based on the image of the target vehicle, determine the degree of dirtiness of the target vehicle based on the difference between the color of the image of the target vehicle and the color of the image of the reference vehicle, and output a determination signal corresponding to the degree of dirtiness. [Effects of the Invention]

[0010] The present disclosure makes it possible to provide a vehicle dirt determination system, a vehicle dirt determination device, a vehicle dirt determination method, and a vehicle dirt determination program that can determine the dirt level of a vehicle. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram illustrating an example of a vehicle dirt determination system. [Figure 2] 1 is a flowchart showing an example of a flow of a vehicle contamination determination system. [Figure 3] FIG. 1 is a block diagram illustrating an example of a vehicle dirt determination system. [Figure 4] 1 is a schematic diagram showing an example of how the vehicle dirt determination system determines the dirtiness of a target vehicle. FIG. [Figure 5] 1 is a flowchart showing an example of a flow of a vehicle contamination determination system. [Figure 6] FIG. 10 is a diagram showing an example of a correspondence relationship stored in a second storage means. [Figure 7] 10A and 10B are diagrams illustrating an example of notification of a determination signal by a notification means. [Figure 8] 1 is a flowchart showing an example of a flow of a vehicle contamination determination system. [Figure 9] 1 is a block diagram showing an example of the configuration of a vehicle dirt determination device; [Figure 10] 1 is a block diagram showing an example of the configuration of a vehicle dirt determination device; DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, specific embodiments will be described in detail with reference to the drawings. In each drawing, the same or corresponding elements are designated by the same reference numerals, and for clarity of explanation, duplicate explanations will be omitted as necessary.

[0013] [First embodiment] 1 is a block diagram showing an example of a vehicle dirt determination system 1 according to the present disclosure. In the example of FIG. 1, the vehicle dirt determination system 1 includes an imaging unit 11, an acquisition unit 12, an extraction unit 13, a determination unit 14, and an output unit 15.

[0014] The vehicle dirt determination system 1 is a system for determining the degree of dirt on a target vehicle. For example, a camera can be used as the imaging means 11. For example, software or modules that operate when a processor executes a program stored in memory, or dedicated hardware or circuits can be used as the acquisition means 12, extraction means 13, determination means 14, and output means 15.

[0015] Fig. 2 is a flowchart showing an example of the flow of the vehicle dirt determination system 1 according to the present disclosure. In the example of Fig. 2, the flow of the vehicle dirt determination system 1 according to the present disclosure includes steps S11 to S15.

[0016] First, in step S11, the imaging means 11 captures an image of the target vehicle. Next, in step S12, the acquisition means 12 acquires an image of the target vehicle from the imaging means 11 and passes it to the extraction means 13.

[0017] Next, in step S13, the extraction means 13 extracts an image of a reference vehicle corresponding to the target vehicle from a predetermined group of images based on the image of the target vehicle. The reference vehicle is a vehicle that serves as a standard for determining the degree of dirtiness of the target vehicle. For example, a vehicle having the same body color as the target vehicle can be selected as the reference vehicle.

[0018] Next, in step S14, the determination means 14 determines the degree of dirtiness of the target vehicle based on the difference between the color of the image of the target vehicle and the color of the image of the reference vehicle. For example, the determination means 14 extracts the average value or distribution of RGB values ​​of pixels representing the target vehicle and the reference vehicle in the image and calculates the difference between them. Then, if the color difference is smaller than a predetermined threshold, the determination means 14 can determine that the degrees of dirtiness of the target vehicle and the reference vehicle are equivalent, and if the color difference is larger than the predetermined threshold, can determine that the degrees of dirtiness of the target vehicle and the reference vehicle are different. Finally, in step S15, the output means 15 outputs a determination signal associated with the degree of dirtiness. In this configuration, the vehicle dirt determination system 1 can determine the dirt level of the target vehicle.

[0019] [Second embodiment] Fig. 3 is a block diagram showing an example of a vehicle dirt determination system 2 according to the present disclosure. In the example of Fig. 3, the vehicle dirt determination system 2 includes an imaging unit 21, an acquisition unit 22, an extraction unit 23, a determination unit 24, and an output unit 25, as well as a first storage unit 26, a second storage unit 27, and a notification unit 28. Note that the imaging unit 21, the acquisition unit 22, the extraction unit 23, the determination unit 24, and the output unit 25 are the same as or directly correspond to the imaging unit 11, the acquisition unit 12, the extraction unit 13, the determination unit 14, and the output unit 15 of the vehicle dirt determination system 1, respectively. Therefore, the description of these units will be omitted where appropriate.

[0020] In the example shown below, the vehicle contamination determination system 2 is used in conjunction with equipment such as a POS (Point of Sales) system at a gas station. In this case, the vehicle contamination determination system 2 determines the contamination level of a target vehicle that has entered the gas station.

[0021] The first storage means 26 and the second storage means 27 are means for storing data and can be configured with storage such as a hard disk drive (HDD) or a solid state drive (SSD). The first storage means 26 stores an image group consisting of images of a plurality of vehicles. The image group preferably includes images of vehicles with clean appearances, and more preferably consists of only images of vehicles with clean appearances. The second storage means 27 stores the correspondence between the degree of contamination and the determination signal.

[0022] The notification means 28 outputs the determination signal acquired from the output means 25 in a form that can be perceived by humans, such as light, sound, or text. The notification means 28 can be, for example, a device that can output light, sound, text, or the like, such as a display, speaker, or printer.

[0023] FIG. 4 is a schematic diagram showing an example of how the vehicle contamination determination system 2 according to the present disclosure determines the contamination level of a target vehicle 90. In the example shown in FIG. 4, the target vehicle 90 is a vehicle being refueled from a fuel dispenser 91. The imaging means 21 captures images of an imaging area 92 provided in front of the fuel dispenser 91 from multiple directions. That is, the imaging means 21 captures images of the target vehicle 90 while it is being refueled. In the example of FIG. 4, the notification means 28 is a display provided in the fuel dispenser 91, and when the degree of contamination of the target vehicle 90 is determined, a determination signal corresponding to the degree of contamination is displayed. Examples of the determination signal include a signal notifying the degree of contamination of the target vehicle 90 or a signal notifying a car wash method suitable for the target vehicle 90. With this configuration, it is possible to notify the degree of contamination and suggest an appropriate car wash method to a vehicle during, before, or after refueling.

[0024] Fig. 5 is a flowchart showing an example of the flow of the vehicle dirt determination system 2 according to the present disclosure. In the example of Fig. 5, the flow of the vehicle dirt determination system 2 according to the present disclosure includes steps S20 to S25.

[0025] First, in step S20, the vehicle contamination determination system 2 determines whether or not a predetermined image capturing condition is satisfied. For example, the vehicle contamination determination system 2 may be linked with a sensor (not shown) to determine that the predetermined image capturing condition is satisfied when it determines that the target vehicle 90 is parked within the image capturing area 92. Alternatively, the vehicle contamination determination system 2 may be linked with a fuel pump 91 or the like to determine that the predetermined image capturing condition is satisfied when refueling begins. If the predetermined imaging conditions are not met (NO in step S20), the operation of step S20 is repeated. On the other hand, if the predetermined imaging conditions are met (YES in step S20), the process proceeds to step S21.

[0026] In step S21, the imaging means 21 captures images of the target vehicle 90 from multiple directions. For example, the imaging means 21 may capture images of the target vehicle 90 from the front, rear, and sides. Alternatively, the imaging means 21 may capture images of part or the entire target vehicle 90 so as to capture predetermined vehicle parts such as the windshield, side mirrors, wheels, etc. Next, in step S22, the acquisition means 22 acquires the images of the target vehicle 90 captured by the imaging means 21 and passes them to the extraction means 23.

[0027] Next, in step S23_1, the extraction means 23 determines whether or not there is a similar image in the first storage means 26 for each of the multiple images of the target vehicle 90 captured by the imaging means 21. For example, the extraction means 23 may use a known image search algorithm to determine whether or not there is an image of a vehicle in the first storage means 26 that is similar to the image of the target vehicle 90 in terms of angle of view, color, shape, etc. If there is no similar image for all the captured images of the target vehicle 90 (NO in step S23_1), the flow ends. If there is a similar image (YES in step S23_1), the flow proceeds to step S23_2, where the corresponding image is extracted as an image of a reference vehicle. Thereafter, the flow proceeds to step S24.

[0028] That is, in steps S23_1 and S23_2, the extraction means 23 extracts an image of a reference vehicle similar to the image of the target vehicle 90 from the group of images stored in the first storage means 26. In such a configuration, a vehicle of the same or similar model as the target vehicle 90 is used as the reference vehicle, thereby improving the accuracy of determining the degree of dirt.

[0029] Next, in step S24, the determination means 24 determines the degree of dirt for each predetermined vehicle part based on the color difference between the image of the target vehicle 90 and the image of the reference vehicle. For example, the determination means 24 extracts RGB values ​​of pixels corresponding to each part of the vehicle, such as the front, rear, left, and right body parts, as well as the windshield, side mirrors, and wheels, and calculates the difference between the images. With this configuration, dirt can be determined for each vehicle part.

[0030] If the image group stored in the first storage means 26 includes an image of a vehicle with a clean appearance, a vehicle with a clean appearance may be extracted as the reference vehicle. Alternatively, if the image group stored in the first storage means 26 is composed of only images of vehicles with a clean appearance, a vehicle with a clean appearance will always be extracted as the reference vehicle. When the reference vehicle has a clean appearance, if the difference in color between the image of the reference vehicle and the image of the target vehicle 90 is small, it can be estimated that the target vehicle 90 is clean, and if the difference in color is large, it can be estimated that the target vehicle 90 is dirty. Therefore, the determination means 24 can determine that the larger the difference, the greater the degree of dirtiness. In such a configuration, the degree of dirtiness can be evaluated in stages. For example, specifically, the degree of dirtiness can be evaluated in five stages from "1 (not dirty)" to "5 (dirty)."

[0031] The determination means 24 may also determine the degree of dirtiness of the coating state of the target vehicle 90 based on the color difference. For example, the determination means 24 may compare the HSV values ​​of pixels corresponding to the body, and determine that the lower the brightness and saturation in the image of the target vehicle 90, the worse the coating state of the target vehicle 90. In such a configuration, the degree of dirtiness of the body can be evaluated in more detail.

[0032] The determination means 24 may also calculate the color difference after multiplying the brightness of the image of the target vehicle 90 by a predetermined correction coefficient according to the imaging environment of the target vehicle 90. For example, in the case of rainy weather when the surroundings are dark, a correction coefficient greater than 1 may be multiplied, and in the case of nighttime when the vehicle is strongly lit, a correction coefficient smaller than 1 may be multiplied. In this way, the accuracy of the determination of the degree of dirt can be improved.

[0033] Finally, in step S25, the output means 25 determines a determination signal corresponding to the degree of dirt on each vehicle part and coating state based on the correspondence relationships stored in the second storage means 27, and outputs the determination signal through the notification means 28. Fig. 6 shows an example of the correspondence relationships stored in the second storage means 27. The example in Fig. 6 shows the correspondence relationship between the degree of dirt on the body of the target vehicle 90 and the determination signal indicating the corresponding car washing method.

[0034] FIG. 7 shows an example of notification of a determination signal by the notification means 28. In the example of FIG. 7, the notification means 28 has display areas A and B. Display area A can display the degree of dirtiness of each vehicle part and the coating state. Display area B can display the determination signal and information based on the determination signal. For example, specifically, the car wash method required for the target vehicle 90, as well as car wash fees and discount information, may be displayed.

[0035] The vehicle contamination determination system 2 may cooperate with a car wash (not shown) at a gas station or the like to provide services such as a car wash discount to the target vehicle 90 for which a car wash has been suggested. For example, a predetermined discount may be provided when a car wash is performed using a combination of car wash menus suggested for each vehicle part. Such a configuration can encourage people to wash their vehicles. In addition, the discount rate for the car wash fee may be changed depending on the degree of dirtiness, for example, the higher the degree of dirtiness, the higher the discount rate. By adopting such a configuration, it is possible to encourage car wash behavior for dirtier vehicles. Furthermore, the notification means 28 may further output a discount code that can be used at the car wash in the form of, for example, a PIN number, a barcode, etc. In such a configuration, it is possible to further encourage the target vehicle 90 to be washed.

[0036] In the above example, the notification means 28 is integrated with the fuel dispenser 91, but the form of the notification means 28 is not limited to this. For example, the notification means 28 may be provided in a fare adjustment machine, an automatic change dispenser, or any other external machine provided at a gas station. By providing the notification means 28 in an external machine, the notification of the degree of dirtiness of the target vehicle 90 and the suggestion to wash the car are more likely to be recognized by the driver.

[0037] [Third embodiment] Fig. 8 is a flowchart showing another example of a flow of the vehicle contamination determination system 2 according to the present disclosure. The example of the flow of Fig. 8 differs in that steps S33_1, S33_2, and S33_3 are included instead of steps S23_1 and S23_2 in the example of the flow shown in Fig. 5. Also, it is assumed that the first storage means 26 stores vehicle model information linked to each vehicle image.

[0038] In step S33_1, the extraction means 23 identifies the vehicle model of the target vehicle 90 from any of the images of the target vehicle 90 captured by the imaging means 21. For example, the extraction means 23 may identify the vehicle model of the target vehicle 90 using a known image recognition technique by extracting color or shape features from the image.

[0039] Next, in step S33_2, the extraction means 23 determines whether or not there is an image of a vehicle of the same model as the target vehicle 90 in the first storage means 26, from the group of images stored in the first storage means 26. For example, the extraction means 23 may refer to the model information linked to each image, and determine whether or not there is an image of a vehicle of the same model as the target vehicle 90. If there is no image of the vehicle of the same model as the target vehicle 90 (NO at step S33_2), the flow is ended. If there is a similar image (YES at step S33_2), the flow proceeds to step S33_3, where the corresponding image is extracted as an image of the reference vehicle. Thereafter, the flow proceeds to step S24.

[0040] That is, in steps S33_1 to S33_3, the extraction means 23 extracts, as an image of a reference vehicle, an image of a vehicle of the same model as the target vehicle 90 from the group of images stored in the first storage means 26. In such a configuration, since a vehicle of the same model as the target vehicle 90 is used as the reference vehicle, the accuracy of determining the degree of dirt can be improved.

[0041] In the above example, the imaging means, acquisition means, extraction means, determination means, output means, first storage means, second storage means, and notification means may be configured as different devices, or some or all of them may be configured as the same device. As an example, a block diagram of a vehicle contamination determination device 100 including an acquisition means 12, an extraction means 13, a determination means 14, and an output means 15 is shown in FIG. 9. For example, as shown in FIG. 10, the vehicle contamination determination device 100 may be configured with a typical computer hardware configuration including a processor 110 that executes a program, a memory 120 that stores the program, and an interface 130 that communicates with external devices.

[0042] Also in the above examples, the program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored on a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disk (DVD), Blu-ray® disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may also be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

[0043] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0044] Each drawing is merely an example for describing one or more embodiments. Each drawing may relate not only to one particular embodiment, but also to one or more other embodiments. As will be understood by those skilled in the art, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings to create, for example, an embodiment not explicitly shown or described. Not all features or steps shown in any one drawing are necessary to describe an exemplary embodiment, and some features or steps may be omitted. The order of steps described in any drawing may be changed as appropriate.

[0045] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes. (Appendix 1) imaging means for imaging a target vehicle; an acquisition means for acquiring an image of the target vehicle from the imaging means; an extraction means for extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined group of images based on the image of the target vehicle; a determining means for determining a degree of dirtiness of the target vehicle based on a difference between a color of the image of the target vehicle and a color of the image of the reference vehicle; and an output means for outputting a determination signal corresponding to the degree of contamination. Vehicle dirt detection system. (Appendix 2) The determining means determines the degree of dirtiness for each predetermined vehicle part. Attachment 1: A vehicle soiling determination system. (Appendix 3) The determination signal is a signal representing a car washing method according to the degree of dirt. Attachment 1: A vehicle soiling determination system. (Appendix 4) The reference vehicle is a vehicle with a clean appearance; The determination means determines that the degree of contamination is greater as the difference is greater. Attachment 1: A vehicle soiling determination system. (Appendix 5) the extraction means identifies a vehicle type of the target vehicle from the image of the target vehicle, and extracts an image of a vehicle of the same vehicle type as the target vehicle as the image of the reference vehicle; Attachment 1: A vehicle soiling determination system. (Appendix 6) further comprising a notification means for outputting the determination signal in a format that can be perceived by humans; Attachment 1: A vehicle soiling determination system. (Appendix 7) The target vehicle is a vehicle that has entered a gas station, The notification means is provided in an external device of the gas station. Attachment 6: A vehicle soiling determination system. (Appendix 8) The determination means further determines the degree of contamination based on the coating state of the target vehicle. Attachment 1: A vehicle soiling determination system. (Appendix 9) the extraction means extracts an image of the reference vehicle that is similar to the image of the target vehicle; Attachment 1: A vehicle soiling determination system. (Appendix 10) the imaging means images the target vehicle from a plurality of directions, the extraction means extracts an image of the reference vehicle for each of the plurality of images of the target vehicle; Attachment 1: A vehicle soiling determination system. (Appendix 11) an acquisition means for acquiring an image of a target vehicle; an extraction means for extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined group of images based on the image of the target vehicle; a determining means for determining a degree of dirtiness of the target vehicle based on a difference between a color of the image of the target vehicle and a color of the image of the reference vehicle; and an output means for outputting a determination signal corresponding to the degree of contamination. Vehicle dirt detection device. (Appendix 12) Taking an image of the target vehicle, Acquire an image of the target vehicle that has been captured; extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined image group based on the image of the target vehicle; determining a degree of dirtiness of the target vehicle based on a difference between a color of the image of the target vehicle and a color of the image of the reference vehicle; outputting a determination signal corresponding to the degree of contamination; Vehicle soiling determination method. (Appendix 13) Taking an image of the target vehicle, Acquire an image of the target vehicle that has been captured; extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined image group based on the image of the target vehicle; determining a degree of dirtiness of the target vehicle based on a difference between a color of the image of the target vehicle and a color of the image of the reference vehicle; and causing the computer to output a determination signal associated with the degree of contamination. Vehicle dirt detection program.

[0046] Some or all of the elements described in Supplementary Notes 2 to 8 that are dependent on Supplementary Note 1 may also be dependent on Supplementary Notes 9 to 11 in the same dependent relationship as Supplementary Notes 2 to 8. Some or all of the elements described in any Supplementary Note may be applied to various hardware, software, recording means for recording software, systems, and methods. [Explanation of symbols]

[0047] 1, 2 Vehicle contamination detection system 11, 21 Imaging means 12, 22 Acquisition method 13, 23 Extraction means 14, 24 Judgment means 15, 25 Output means 26 First storage means 27 Second storage means 28 Means of Notification 90 Target vehicles 91 Refueling Machine 92 Imaging area 100 Vehicle dirt detection device 110 processors 120 memory 130 Interface

Claims

1. imaging means for imaging a target vehicle; an acquisition means for acquiring an image of the target vehicle from the imaging means; an extraction means for extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined group of images based on the image of the target vehicle; a determining means for determining a degree of dirtiness of the target vehicle based on a difference between a color of the image of the target vehicle and a color of the image of the reference vehicle; and an output means for outputting a determination signal corresponding to the degree of contamination. Vehicle dirt detection system.

2. The determining means determines the degree of dirtiness for each predetermined vehicle part. The vehicle contamination determination system according to claim 1 .

3. The determination signal is a signal representing a car washing method according to the degree of dirt. The vehicle contamination determination system according to claim 1 .

4. The reference vehicle is a vehicle with a clean appearance; The determination means determines that the degree of contamination is greater as the difference is greater. The vehicle contamination determination system according to claim 1 .

5. the extraction means identifies a vehicle type of the target vehicle from the image of the target vehicle, and extracts an image of a vehicle of the same vehicle type as the target vehicle as the image of the reference vehicle; The vehicle contamination determination system according to claim 1 .

6. further comprising a notification means for outputting the determination signal in a format that can be perceived by humans; The vehicle contamination determination system according to claim 1 .

7. The target vehicle is a vehicle that has entered a gas station, The notification means is provided in an external device of the gas station. The vehicle contamination determination system according to claim 6 .

8. an acquisition means for acquiring an image of a target vehicle; an extraction means for extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined group of images based on the image of the target vehicle; a determining means for determining a degree of dirtiness of the target vehicle based on a difference between a color of the image of the target vehicle and a color of the image of the reference vehicle; and an output means for outputting a determination signal corresponding to the degree of contamination. Vehicle dirt detection device.

9. Taking an image of the target vehicle, Acquire an image of the target vehicle that has been captured; extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined image group based on the image of the target vehicle; determining a degree of dirtiness of the target vehicle based on a difference between a color of the image of the target vehicle and a color of the image of the reference vehicle; outputting a determination signal corresponding to the degree of contamination; Vehicle soiling determination method.

10. Taking an image of the target vehicle, Acquire an image of the target vehicle that has been captured; extracting an image of a reference vehicle corresponding to the target vehicle from a predetermined image group based on the image of the target vehicle; determining a degree of dirtiness of the target vehicle based on a difference between a color of the image of the target vehicle and a color of the image of the reference vehicle; and causing the computer to output a determination signal associated with the degree of contamination. Vehicle dirt detection program.

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