Vehicle type identification method, device, server, system and storage medium

Abstract

The invention provides a vehicle type identification method and device, a server, a system and a storage medium, and relates to the field of intelligent transportation. In the method, a server receives basic data of a to-be-identified vehicle sent by a data acquisition device and an image of the to-be-identified vehicle sent by an image processing device, the data acquisition device reads the basic data of the to-be-identified vehicle from a vehicle-mounted ETC device of the to-be-identified vehicle, and the image processing device captures the image of the to-be-identified vehicle; extracting appearance features of the to-be-recognized vehicle from the image of the to-be-recognized vehicle; and based on the basic data and the appearance characteristics, the vehicle type of the to-be-recognized vehicle is determined by using the pre-trained vehicle type recognition model, so that the problems of low recognition accuracy, slow response speed and the like caused by relying on manual observation or adopting a fixed camera to recognize the vehicle type are avoided, and the traffic management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation, and in particular to a vehicle type identification method, device, server, system and storage medium. Background Art

[0002] Vehicle type recognition is an important task in existing traffic management. However, traditional vehicle type recognition methods mainly rely on manual observation or use fixed cameras for image recognition, which has problems such as low recognition accuracy and slow response speed. Especially when the number of vehicles is large and the vehicle types are more diverse, the above problems are particularly prominent. Summary of the invention

[0003] The present invention provides a vehicle type identification method, device, server, system and storage medium, which can quickly and accurately identify the vehicle type.

[0004] The technical solution of the present invention can be implemented as follows:

[0005] In a first aspect, the present invention provides a vehicle type identification method, which is applied to a server in a vehicle type identification system, wherein the vehicle type identification system further includes a data acquisition device and an image processing device respectively connected to the server for communication, and the method includes:

[0006] Receiving basic data of the vehicle to be identified sent by the data acquisition device and an image of the vehicle to be identified sent by the image processing device, wherein the data acquisition device reads the basic data of the vehicle to be identified from the on-board ETC device of the vehicle to be identified, and the image processing device captures the image of the vehicle to be identified;

[0007] Extracting appearance features of the vehicle to be identified from the image of the vehicle to be identified;

[0008] Based on the basic data and the appearance features, the model of the vehicle to be identified is determined using a pre-trained model identification model.

[0009] Optionally, the method further comprises the step of training the vehicle type recognition model, which comprises:

[0010] Obtain basic data and vehicle images of multiple sample vehicles;

[0011] Determining the initial model of each of the sample vehicles according to the basic data of each of the sample vehicles;

[0012] Extracting appearance features of each of the sample vehicles from a vehicle image of each of the sample vehicles;

[0013] Using the initial model of each of the sample vehicles, the basic data and appearance features of each of the sample vehicles are annotated to obtain training data;

[0014] The training data is input into a pre-built deep learning model for training to obtain the vehicle type recognition model.

[0015] Optionally, the basic data includes a vehicle type code, and the step of determining the initial vehicle type of each sample vehicle according to the basic data of each sample vehicle includes:

[0016] According to the vehicle type code of each of the sample vehicles, the initial vehicle type of each of the sample vehicles is determined.

[0017] Optionally, before extracting the appearance features of the vehicle to be identified from the image of the vehicle to be identified, the method further includes:

[0018] The image of the vehicle to be identified is preprocessed.

[0019] Optionally, the method further comprises:

[0020] The basic data, appearance features and determined vehicle model of the vehicle to be identified are stored in a database, wherein the database stores the appearance features and basic data of vehicles corresponding to various vehicle models;

[0021] The vehicle type recognition model is optimized by utilizing the appearance features and basic data of vehicles corresponding to various vehicle types stored in the database.

[0022] In a second aspect, the present invention provides a vehicle type recognition method, which is applied to a vehicle type recognition system, wherein the vehicle type recognition system includes a server, a data acquisition device, and an image processing device, wherein the server is respectively connected to the data acquisition device and the image processing device for communication, and the method includes:

[0023] When the vehicle to be identified passes by, the data acquisition device communicates with the on-board ETC device of the vehicle to be identified, reads the basic data of the vehicle to be identified and sends it to the server;

[0024] When the vehicle to be identified passes by, the image processing device captures the image of the vehicle to be identified and sends it to the server;

[0025] The server receives basic data and an image of the vehicle to be identified, and extracts appearance features of the vehicle to be identified from the image of the vehicle to be identified;

[0026] The server determines the model of the vehicle to be identified based on the basic data and appearance features of the vehicle to be identified and using a pre-trained model identification model.

[0027] In a third aspect, the present invention provides a vehicle type identification device, which is applied to a server in a vehicle type identification system, wherein the vehicle type identification system further includes a data acquisition device and an image processing device respectively connected to the server for communication, and the device includes:

[0028] A receiving module, used for receiving the basic data of the vehicle to be identified sent by the data acquisition device and the image of the vehicle to be identified sent by the image processing device, wherein the data acquisition device reads the basic data of the vehicle to be identified from the on-board ETC device of the vehicle to be identified, and the image processing device captures the image of the vehicle to be identified;

[0029] An extraction module, used for extracting the appearance features of the vehicle to be identified from the image of the vehicle to be identified;

[0030] The determination module is used to determine the model of the vehicle to be identified based on the basic data and the appearance features using a pre-trained model identification model.

[0031] In a fourth aspect, the present invention provides a server comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the vehicle model identification method as described in the first aspect is implemented.

[0032] In a fifth aspect, the present invention provides a vehicle type identification system, which includes a data acquisition device and an image processing device as well as the server as described in the fourth aspect.

[0033] In a sixth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the vehicle model identification method as described in the first aspect above.

[0034] Compared with the prior art, the vehicle type identification method applied to the server in the vehicle type identification system provided by the present invention: receiving the basic data of the vehicle to be identified sent by the data acquisition device and the image of the vehicle to be identified sent by the image processing device, wherein the data acquisition device reads the basic data of the vehicle to be identified from the vehicle-mounted ETC device of the vehicle to be identified, and the image processing device captures the image of the vehicle to be identified; extracting the appearance features of the vehicle to be identified from the image of the vehicle to be identified; and determining the vehicle type of the vehicle to be identified based on the basic data and the appearance features using a pre-trained vehicle type identification model. Since the present invention determines the vehicle type of the vehicle to be identified by extracting the appearance features from the image of the vehicle to be identified, and inputting the basic data and the appearance features of the vehicle to be identified into a pre-trained vehicle type identification model, it avoids the problems of low recognition accuracy and slow response speed caused by relying on manual observation or using a fixed camera to identify the vehicle type, thereby improving the efficiency of traffic management.

[0035] Other features and advantages disclosed in the embodiments of the present application will be described in the subsequent description, or some features and advantages can be inferred or determined without doubt from the description, or can be learned by implementing the above-mentioned technologies disclosed in the embodiments of the present application.

[0036] In order to make the above-mentioned purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 A schematic block diagram of the structure of a vehicle type identification system provided by an embodiment of the present invention;

[0039] Figure 2 A schematic flow chart of a vehicle type identification method applied to a server provided in an embodiment of the present invention;

[0040] Figure 3 A schematic flow chart of a vehicle type identification method applied to a vehicle type identification system provided by an embodiment of the present invention;

[0041] Figure 4 A functional unit block diagram of a vehicle type identification device applied to a server provided by an embodiment of the present invention;

[0042] Figure 5 A schematic block diagram of the structure of a server provided in an embodiment of the present invention.

[0043] Icons: 100 - vehicle type recognition system; 110 - server; 120 - data acquisition equipment; 130 - image processing equipment; 111 - storage unit; 112 - processing unit; 200 - vehicle type recognition device; 201 - receiving module; 202 - extraction module; 203 - determination module; 204 - training module. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0045] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0047] It should be noted that, in the absence of conflict, the features in the embodiments of the present invention may be combined with each other.

[0048] In order to quickly and accurately identify the model of a car and improve traffic management efficiency, an embodiment of the present invention provides a model identification system, a model identification method of a server applied to the model identification system, and a model identification method applied to the model identification system, which are introduced in detail below.

[0049] Please refer to Figure 1 The vehicle type recognition system 100 provided in the embodiment of the present invention includes a server 110, a data acquisition device 120 and an image processing device 130. The server 110 is respectively connected to the data acquisition device 120 and the image processing device 130. The data acquisition device 120 also communicates with the vehicle-mounted ETC device.

[0050] The data collection device 120 is used to communicate with the vehicle-mounted ETC device (such as OBU (OnBoard Unit) device) when the vehicle passes through a toll station, etc., and read the basic data of the vehicle. The data collection device 120 can be an RSU (Road Side Unit) device.

[0051] The image processing device 130 is used to capture vehicle images when the vehicle passes through a toll booth or other places. The image processing device 130 may include a high-definition camera. When a vehicle enters its camera field of view, the image processing device 130 takes a photo and uploads the photo to the server 110 .

[0052] The server 110 is used to determine the vehicle model according to the basic vehicle data read by the data acquisition device 120 and the vehicle image captured by the image processing device 130 .

[0053] The server 110 is also used to send the vehicle model determination result to the corresponding user terminal.

[0054] Please refer to Figure 2 The vehicle type identification method applied to the server 110 provided in the embodiment of the present invention includes steps S101 to S103.

[0055] S101 , receiving basic data of a vehicle to be identified sent by a data acquisition device 120 and an image of a vehicle to be identified sent by an image processing device 130 .

[0056] The data acquisition device 120 reads basic data of the vehicle to be identified from the vehicle-mounted ETC device of the vehicle to be identified, and the image processing device 130 captures the image of the vehicle to be identified.

[0057] The vehicle to be identified refers to the vehicle that needs to be identified when passing through a highway intersection or toll station. The basic data includes the license plate number, vehicle type code, vehicle size, etc. of the vehicle to be identified. When the vehicle to be identified passes through the sensing area of ​​the data acquisition device 120, the data acquisition device 120 communicates with the on-board ETC device of the vehicle to be identified, and obtains the license plate number, vehicle type code, vehicle size, etc. of the vehicle to be identified from the on-board ETC device.

[0058] It can be understood that when a vehicle to be identified appears in a specific area, the data acquisition device 120 obtains basic data of the vehicle to be identified and sends it to the server 110 , and the image processing device 130 captures an image of the vehicle to be identified and sends it to the server 110 .

[0059] S102, extracting appearance features of the vehicle to be identified from the image of the vehicle to be identified.

[0060] Appearance features include vehicle body outline, light position, number of windows, and other vehicle model-related features. For example, the length of an ordinary car is usually between 4 and 6 meters, and the width is between 1.5 and 2 meters. The box size, wheelbase, and height of a truck can also be used as a basis for vehicle model identification.

[0061] The image recognition algorithm is used to digitize the image of the vehicle to be identified, and the body outline, light position, number of windows, etc. of the vehicle to be identified are determined.

[0062] In an embodiment of the present invention, in order to ensure the accuracy of the extracted appearance features, the image of the vehicle to be identified can be pre-processed by denoising, enhancing, segmenting, etc., and then the image recognition algorithm is used to digitize the pre-processed image of the vehicle to be identified to obtain the appearance features of the vehicle to be identified including the body contour, light position, number of windows, etc.

[0063] S103, based on the basic data and appearance features, using a pre-trained vehicle type recognition model, determine the vehicle type to be identified.

[0064] By inputting the basic data and appearance features of the vehicle to be identified into the pre-trained vehicle model identification model, the vehicle model identification model outputs the vehicle model to be identified.

[0065] The training process of the vehicle type recognition model may include steps S201 to S205.

[0066] S201, obtaining basic data and vehicle images of multiple sample vehicles.

[0067] The models of the multiple sample vehicles may be different. For example, the model of sample vehicle 1 is model A, the model of sample vehicle 2 is model B, the model of sample vehicle 3 is model C, and so on.

[0068] S201, determining the initial model of each sample vehicle based on the basic data of each sample vehicle.

[0069] The basic data includes a vehicle type code, and the initial vehicle model of each sample vehicle can be determined based on the vehicle type code of each sample vehicle.

[0070] S202: extracting appearance features of each sample vehicle from the vehicle image of each sample vehicle.

[0071] Among them, the image recognition algorithm can be used to digitize the vehicle image of each sample vehicle to determine the sample vehicle's body contour, headlight position, number of windows and other appearance features.

[0072] In an embodiment of the present invention, in order to ensure the accuracy of the extracted appearance features, the vehicle image of each sample vehicle can be pre-processed by denoising, enhancing, segmenting, etc., and then the pre-processed vehicle image of the sample vehicle can be digitized using an image recognition algorithm to obtain the appearance features of each sample vehicle including the body contour, light position, number of windows, etc.

[0073] S203, using the initial model of each sample vehicle, annotating the basic data and appearance features of each sample vehicle to obtain training data.

[0074] It can be understood that the training data includes the basic data and appearance features of each sample vehicle after being annotated.

[0075] S204: Input the training data into a pre-built deep learning model for training to obtain a vehicle model recognition model.

[0076] The deep learning model learns how to predict the model of a vehicle based on the basic data and appearance features of the input vehicle. When training the model, you can choose a suitable loss function (such as cross entropy loss) to evaluate the difference between the model's prediction results and the true label, and then use an optimization algorithm (such as gradient descent) to adjust the model parameters to minimize the loss function.

[0077] After determining the model of the vehicle to be identified, the model identification model can also be optimized based on the basic data, appearance features and the determined model of the vehicle to be identified, that is, the model identification method applied to the server also includes the steps of: storing the basic data, appearance features and the determined model of the vehicle to be identified in a database, wherein the database stores the appearance features and basic data of vehicles corresponding to various models; optimizing the model identification model using the appearance features and basic data of vehicles corresponding to various models stored in the database.

[0078] It can be understood that the appearance features and basic data of vehicles corresponding to various vehicle models stored in the database can be used as a validation set to evaluate the performance of the vehicle model recognition model. The vehicle model recognition model can be adjusted by obtaining evaluation indicators such as accuracy, recall rate, F1 score, etc., including modifying the network structure, adjusting hyperparameters, adding data enhancement strategies, etc.

[0079] Through iterative training and evaluation processes, the vehicle type recognition model is continuously optimized so that the vehicle recognition model can achieve higher accuracy and generalization ability.

[0080] The server 110 can realize the visual display of the vehicle type identification result through its display screen or by sending the vehicle type identification result to the user end.

[0081] Please refer to Figure 3 , a vehicle type identification method applied to a vehicle type identification system 100 provided by an embodiment of the present invention includes steps S301 to S304.

[0082] S301 , when a vehicle to be identified passes by, the data acquisition device 120 communicates with the onboard ETC device of the vehicle to be identified, reads the basic data of the vehicle to be identified and sends it to the server 110 .

[0083] The data acquisition device 120 can be installed on both sides of the lane. When the vehicle to be identified passes by, it communicates with the on-board ETC device of the vehicle to be identified, reads the basic data of the vehicle to be identified including the license plate number, vehicle type code, vehicle size, etc., and sends it to the server 110.

[0084] S302 , when the vehicle to be identified passes by, the image processing device 130 captures an image of the vehicle to be identified and sends the image to the server 110 .

[0085] The image processing device 130 may be installed at a position on both sides or above the lane where the vehicle image can be completely captured. When the vehicle to be identified passes by, the image of the vehicle to be identified is captured and sent to the server 110 .

[0086] S303, the server 110 receives basic data and an image of the vehicle to be identified, and extracts appearance features of the vehicle to be identified from the image of the vehicle to be identified.

[0087] After receiving the basic data and image of the vehicle to be identified, the server 110 can first perform pre-processing such as denoising, enhancement, and segmentation on the image of the vehicle to be identified, and then use the image recognition algorithm to digitize the pre-processed image of the vehicle to be identified to obtain the appearance features of the vehicle to be identified including the body contour, light position, number of windows, etc.

[0088] S304: The server 110 determines the model of the vehicle to be identified based on the basic data and appearance features of the vehicle to be identified and using a pre-trained model identification model.

[0089] The server 110 may input the basic data and appearance features of the vehicle to be identified into a pre-trained vehicle type identification model, and the vehicle type identification model outputs the vehicle type to be identified.

[0090] In order to execute the corresponding steps in the above method embodiment and each possible implementation mode, an implementation mode of the vehicle type identification device 200 applied to the server 110 is given below.

[0091] Please refer to Figure 4 The vehicle type recognition device 200 includes a receiving module 201, an extraction module 202, a determination module 203 and a training module 204.

[0092] The receiving module 201 is used to receive the basic data of the vehicle to be identified sent by the data acquisition device and the image of the vehicle to be identified sent by the image processing device, wherein the data acquisition device reads the basic data of the vehicle to be identified from the on-board ETC device of the vehicle to be identified, and the image processing device captures the image of the vehicle to be identified.

[0093] The extraction module 202 is used to extract the appearance features of the vehicle to be identified from the image of the vehicle to be identified.

[0094] The determination module 203 is used to determine the model of the vehicle to be identified based on the basic data and the appearance features using a pre-trained model identification model.

[0095] The training module 204 is used to obtain basic data and vehicle images of multiple sample vehicles; determine the initial vehicle model of each sample vehicle based on the basic data of each sample vehicle; extract the appearance features of each sample vehicle from the vehicle image of each sample vehicle; use the initial vehicle model of each sample vehicle to annotate the basic data and appearance features of each sample vehicle to obtain training data; input the training data into a pre-built deep learning model for training to obtain a vehicle model recognition model.

[0096] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the vehicle type identification device 200 described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0097] Furthermore, the embodiment of the present invention also provides a server 110, please refer to Figure 5 , the server 110 may include a storage unit 111 and a processing unit 112 .

[0098] Among them, the processing unit 112 can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of a program of a vehicle model identification method provided by the above method embodiment.

[0099] The storage unit 111 may be a ROM or other types of static storage devices that can store static information and instructions, a RAM or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The storage unit 111 may exist independently and be connected to the processing unit 112 via a communication bus. The storage unit 111 may also be integrated with the processing unit 112. Among them, the storage unit 111 is used to store machine executable instructions for executing the scheme of the present application. The processing unit 112 is used to execute the machine executable instructions stored in the storage unit 111 to implement the above-mentioned method embodiment.

[0100] An embodiment of the present invention further provides a computer-readable storage medium containing a computer program. When the computer program is executed, it can be used to perform relevant operations in a method for processing visitor data provided in the above method embodiment.

[0101] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A vehicle type identification method, characterized in that: A server used in a vehicle type recognition system, wherein the vehicle type recognition system further comprises a data acquisition device and an image processing device respectively connected to the server in communication, and the method comprises: Receiving basic data of the vehicle to be identified sent by the data acquisition device and an image of the vehicle to be identified sent by the image processing device, wherein the data acquisition device reads the basic data of the vehicle to be identified from the on-board ETC device of the vehicle to be identified, and the image processing device captures the image of the vehicle to be identified; Extracting appearance features of the vehicle to be identified from the image of the vehicle to be identified; Based on the basic data and the appearance features, the model of the vehicle to be identified is determined using a pre-trained model identification model.

2. The method according to claim 1, characterized in that The method further comprises the step of training the vehicle type recognition model, which comprises: Obtain basic data and vehicle images of multiple sample vehicles; Determining the initial model of each of the sample vehicles according to the basic data of each of the sample vehicles; Extracting appearance features of each of the sample vehicles from a vehicle image of each of the sample vehicles; Using the initial model of each of the sample vehicles, the basic data and appearance features of each of the sample vehicles are annotated to obtain training data; The training data is input into a pre-built deep learning model for training to obtain the vehicle type recognition model.

3. The method according to claim 2, characterized in that The basic data includes a vehicle type code, and the step of determining the initial vehicle type of each sample vehicle according to the basic data of each sample vehicle includes: According to the vehicle type code of each of the sample vehicles, the initial vehicle type of each of the sample vehicles is determined.

4. The method according to claim 1, characterized in that Before extracting the appearance features of the vehicle to be identified from the image of the vehicle to be identified, the method further includes: The image of the vehicle to be identified is preprocessed.

5. The method according to claim 1, characterized in that The method further comprises: The basic data, appearance features and determined vehicle model of the vehicle to be identified are stored in a database, wherein the database stores the appearance features and basic data of vehicles corresponding to various vehicle models; The vehicle type recognition model is optimized by utilizing the appearance features and basic data of vehicles corresponding to various vehicle types stored in the database.

6. A vehicle type identification method, characterized in that: Applied to a vehicle type recognition system, the vehicle type recognition system includes a server, a data acquisition device and an image processing device, the server is respectively connected to the data acquisition device and the image processing device for communication, the method includes: When the vehicle to be identified passes by, the data acquisition device communicates with the on-board ETC device of the vehicle to be identified, reads the basic data of the vehicle to be identified and sends it to the server; When the vehicle to be identified passes by, the image processing device captures the image of the vehicle to be identified and sends it to the server; The server receives basic data and an image of the vehicle to be identified, and extracts appearance features of the vehicle to be identified from the image of the vehicle to be identified; The server determines the model of the vehicle to be identified based on the basic data and appearance features of the vehicle to be identified and using a pre-trained model identification model.

7. A vehicle type identification device, characterized in that: A server used in a vehicle type recognition system, wherein the vehicle type recognition system further comprises a data acquisition device and an image processing device respectively connected to the server in communication, wherein the device comprises: A receiving module, used for receiving the basic data of the vehicle to be identified sent by the data acquisition device and the image of the vehicle to be identified sent by the image processing device, wherein the data acquisition device reads the basic data of the vehicle to be identified from the on-board ETC device of the vehicle to be identified, and the image processing device captures the image of the vehicle to be identified; An extraction module, used for extracting the appearance features of the vehicle to be identified from the image of the vehicle to be identified; The determination module is used to determine the model of the vehicle to be identified based on the basic data and the appearance features using a pre-trained model identification model.

8. A server, characterized in that: It includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the vehicle model identification method according to any one of claims 1 to 4 is implemented.

9. A vehicle type identification system, characterized in that: The vehicle type recognition system comprises a data acquisition device, an image processing device and a server as claimed in claim 8.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the vehicle model identification method as claimed in any one of claims 1 to 4.

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