CN113469259A

CN113469259A - Vehicle category identification method and system

Info

Publication number: CN113469259A
Application number: CN202110779600.2A
Authority: CN
Inventors: 杨帆; 郭敬娜; 王铭宇
Original assignee: Chengdu Star Innovation Technology Co ltd
Current assignee: Chengdu Star Innovation Technology Co ltd
Priority date: 2021-07-09
Filing date: 2021-07-09
Publication date: 2021-10-01

Abstract

The invention discloses a vehicle category identification method, which comprises the following steps: constructing a vehicle identification model, wherein the vehicle identification model is realized based on a CSPPe (China platform protocol for short) leenet-SE (secure element network) network; training and evaluating the constructed vehicle identification model; identifying the acquired picture based on the vehicle identification model which is evaluated; and acquiring an identification result, wherein the identification result comprises the probability that the vehicle in the picture belongs to the vehicle type of each category, so that the vehicle category identification can be realized, and the identification accuracy is higher.

Description

Vehicle category identification method and system

Technical Field

The invention relates to the technical field of image detection, in particular to a vehicle type identification method and a vehicle type identification system.

Background

With the development of society, private cars have become a main tool for people to go out, and the private cars seen everywhere on the street become a main cause of traffic jam, according to statistics of relevant departments, the number of cars in the whole country in 2020 reaches 2.81 million, and the number of car drivers is 4.18 million. In the presence of such a large number of vehicles, the problems of difficult parking and traffic jam often occur in cities, and a large amount of manpower and material resources are needed for charging the parked vehicles.

Nowadays, with the rapid development of science and technology, intelligent parking gradually comes into the vision of people. The intelligent parking is to replace manual charging with automatic charging, judge whether a vehicle is a charged vehicle through the entrance of the vehicle in a parking space, and automatically charge according to the parking time if the vehicle is the charged vehicle. In order to better serve an intelligent parking project, judging whether a toll vehicle is a non-toll vehicle is the first step of the project, and is very important, and only distinguishing whether the toll vehicle is the toll vehicle can be used for the next project.

Therefore, a vehicle category identification algorithm with higher accuracy and low false detection rate is needed.

Disclosure of Invention

One aspect of the embodiments of the present specification provides a vehicle category identification method including: constructing a vehicle identification model, wherein the vehicle identification model is realized based on a CSPPeleNet-SE network; training and evaluating the constructed vehicle identification model; identifying the acquired picture based on the vehicle identification model which is evaluated; and acquiring an identification result, wherein the identification result comprises the probability that the vehicle in the picture belongs to the vehicle type of each category.

In some embodiments, the vehicle identification model comprises a feature extraction model, a feature compression model, a feature excitation model, a feature fusion and classification model which are connected in sequence; the feature extraction model is used for extracting features of the input picture; the feature compression model is used for compressing the features extracted by the feature extraction model, the feature excitation model is used for processing the compressed features and obtaining the weight of each feature, and the feature fusion and classification model is used for fusing the weighted features, predicting the category of the fused feature map and outputting the recognition result.

In some embodiments, the feature excitation model includes two fully-connected layers, a first fully-connected layer is used for performing dimensionality reduction on input data to reduce the computation amount, the first function is a ReLU activation function and is used for performing function processing on data output by the first fully-connected layer, a second fully-connected layer is used for performing dimensionality enhancement on data processed by the first activation function, and the second function is a Sigmoid function and is used for calculating weights on data subjected to dimensionality enhancement.

In some embodiments, the features extracted by the feature extraction model are divided into two parts, one part enters the feature compression model and the feature excitation model in sequence for processing, and the other part directly enters the feature fusion and classification model.

An aspect of embodiments of the present specification provides a vehicle category identification device including: the vehicle identification module is used for constructing a vehicle identification model, and the vehicle identification model is realized based on a CSPPeleNet-SE network; the evaluation module is used for training and evaluating the constructed vehicle identification model; the identification module is used for identifying the acquired picture based on the evaluated vehicle identification model; and outputting a recognition result, wherein the recognition result comprises the probability that the vehicle in the picture belongs to the vehicle type of each category.

One aspect of embodiments of the present specification provides a vehicle class identification apparatus comprising at least one storage medium for storing computer instructions and at least one processor; the at least one processor is configured to execute the computer instructions to implement operations corresponding to the vehicle class identification method.

An aspect of embodiments of the present specification provides a computer-readable storage medium storing computer instructions that, when read by a computer, implement the vehicle class identification method.

Drawings

The present description will be further described by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic diagram of an application scenario of a vehicle class identification device according to some embodiments of the present application;

FIG. 2 is a schematic diagram of exemplary hardware and/or software components of an exemplary computing device on which a processing engine may be implemented, according to some embodiments of the present application;

FIG. 3 is a schematic diagram of exemplary hardware and/or software components of an exemplary mobile device on which one or more terminals may be implemented in accordance with some embodiments of the present application;

FIG. 4 is a schematic block diagram of an exemplary processing engine shown in accordance with some embodiments of the present application;

FIG. 5 is a flow diagram of a vehicle class identification method shown in accordance with some embodiments of the present description;

FIG. 6 is a CSPPeleNet-SE network architecture diagram of a vehicle identification model according to some embodiments shown herein;

FIG. 7 is a schematic diagram of a Squeeze-and-Excitation module shown in accordance with some embodiments of the present description;

FIG. 8 is a process flow diagram of a feature compression model according to some embodiments of the present description;

FIG. 9 is a process flow diagram of a feature incentive model according to some embodiments of the present description;

FIG. 10 is a StemBlock diagram of a vehicle identification model according to some embodiments of the present description;

FIG. 11 is a schematic view of a Two-Way sensor Layer of a vehicle identification model in accordance with certain embodiments of the present description;

FIG. 12 is a schematic diagram of a training flow of a vehicle recognition model, shown in accordance with some embodiments of the present description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used in this specification is a method for distinguishing different components, elements, parts or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

The vehicle may include a human powered vehicle (e.g., bicycle, tricycle), an electric vehicle (e.g., electric bicycle, electric tricycle), an automobile (e.g., taxi, bus, personal car, truck), and the like, or any combination thereof.

Embodiments of the present application may be applied to the identification of various vehicle classes. It should be understood that the application scenarios of the system and method of the present application are merely examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios without inventive effort based on these drawings. Although the present application has been described primarily in the context of a vehicle, and particularly an automobile, it should be noted that the principles of the present application are applicable to other articles for which the identity category may also be determined in accordance with the principles of the present application.

In the present application, the determination of the category identification of the vehicle is merely an example. It should be noted that the specific content of detecting the vehicle category is for illustrative purposes only and is not intended to limit the scope of the present application. In some embodiments, the present disclosure may be applied to other similar scenarios, such as, but not limited to, classification of products, and the like.

FIG. 1 is a schematic diagram of an exemplary monitoring system according to some embodiments of the present application. In some embodiments, the application scenario 100 may be configured as a scenario that needs to discriminate a vehicle type, such as a parking lot. Such as may be configured in a mall, hospital, school parking lot, etc. The vehicle class identification device may detect the type of vehicle entering the monitoring range. The application scenario 100 may include a server 110, a network 120, a user terminal 130, a storage device 140, and a photographing device 150. The server 110 may include a processing engine 112. In some embodiments, the server 110, the user terminal 130, the storage device 140, and the photographing device 150 may be connected to and/or communicate with each other via a wireless connection (e.g., the network 120), a wired connection, or a combination thereof.

The computing system 110 may be used to determine vehicle category content to be identified. In some embodiments, the method can be specifically used for tracking confirmation of the vehicle class, so that monitoring of the vehicle class is realized. The computing system 110 may identify vehicle category content based on the acquired data to determine vehicle information.

Computing system 110 refers to a system having computing capabilities, and in some embodiments, server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system). In some embodiments, the server 110 may be local or remote. For example, server 110 may access information and/or data stored in user terminal 130 and/or storage device 140 via network 120. As another example, server 110 may be directly connected to user terminal 130 and/or storage device 140 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof. In some embodiments, server 110 may be implemented on a computing device 200 having one or more of the components illustrated in FIG. 2 in the present application.

In some embodiments, the server 110 may include a processing engine 112. The processing engine 112 may process information related to vehicle category information. For example, the processing engine 112 may identify a vehicle category in the video data acquired by the photographing device 150 and determine vehicle information. In some embodiments, processing engine 112 may include one or more processing engines (e.g., a single core processing engine or a multi-core processor). By way of example only, the processing engine 112 may include one or more hardware processors, such as a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an application specific instruction set processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network 120 may facilitate the exchange of information and/or data. In some embodiments, one or more components in the application scenario 100 (e.g., the server 110, the user terminal 130, the storage device 140, and the photographing device 150) may send information and/or data to other components in the application scenario 100 over the network 120. For example, the processing engine 112 may send information of the identified vehicle category and information of the corresponding vehicle to the user terminal 130 via the network 120. In some embodiments, the network 120 may be a wired network or a wireless network, or the like, or any combination thereof. By way of example only, network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), the Public Switched Telephone Network (PSTN), a Bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, or the like, or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points, such as base stations and/or internet exchange points 120-1, 120-2, …, through which one or more components of the application scenario 100 may connect to the network 120 to exchange data and/or information.

In some embodiments, the user terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, or the like, or any combination thereof. In some embodiments, mobile device 140-1 may include a smart home device, a wearable device, a mobile device, a virtual reality device, an augmented reality device, and the like, or any combination thereof. In some embodiments, the smart home devices may include smart lighting devices, smart appliance control devices, smart monitoring devices, smart televisions, smart cameras, interphones, and the like, or any combination thereof. In some embodiments, the wearable device may include a bracelet, footwear, glasses, helmet, watch, clothing, backpack, smart accessory, and the like, or any combination thereof. In some embodiments, the mobile device may include a mobile phone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, a point of sale (POS) device, a laptop computer, a desktop computer, etc., or any combination thereof. In some embodiments, the virtual reality device and/or the enhanced virtual reality device may include a virtual reality helmet, virtual reality glasses, virtual reality eyecups, augmented reality helmets, augmented reality glasses, augmented reality eyecups, and the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a *** glass^TM、RiftCon^TM、Fragments^TM、GearVR^TMAnd the like. In some embodiments, the user terminal 130, which is also used by the driver of the vehicle corresponding to the identified vehicle category, may receive the notification sent from the processing engine 112. In some embodiments, a supervisory person (e.g., parking lot toll collector, etc.) may use the user terminal 130 to access the relevant records stored in the storage device 140.

In some embodiments, user terminal 130 may be a mobile terminal configured to include a camera. The user terminal 130 may send and/or receive information related to vehicle class identification to the processing engine 112 or a processor installed in the user terminal 130 via a user interface. For example, the user terminal 130 may transmit video data captured by a camera installed in the user terminal 130 to the processing engine 112 or a processor installed in the user terminal 120 via the user interface. The user interface may be in the form of an application implemented on the user terminal 130 for identifying the vehicle category. A user interface implemented on the user terminal 130 may facilitate communication between the user and the processing engine 112. For example, a user may input and/or need to identify a picture via a user interface. The processing engine 112 may receive an input picture via a user interface. As another example, the user may enter a request for vehicle class identification via a user interface implemented on the user terminal 130. In some embodiments, in response to a request for vehicle category identification, user terminal 130 may determine the category information of the vehicle directly via a processor of user terminal 130 based on video data captured by a camera installed in user terminal 130 as described elsewhere in this application. In some embodiments, in response to the request for vehicle category identification, the user terminal 130 may send the request for vehicle category identification to the processing engine 112 for determining vehicle category information based on video data captured by the photographing device 150 or a camera installed as described elsewhere in this application. In some embodiments, the user interface may facilitate presenting or displaying information and/or data (e.g., signals) related to vehicle class identification received from the processing engine 112. For example, the information and/or data may include results indicating vehicle category identification content, or vehicle information indicating that the identified vehicle category corresponds, or the like. In some embodiments, the information and/or data may be further configured to cause the user terminal 130 to display the results to the user.

Storage device 140 may store data and/or instructions. In some embodiments, the storage device 140 may store data obtained from the photographing device 150. Storage device 140 may store data and/or instructions that processing engine 112 may execute or use to perform the exemplary methods described herein. In some embodiments, storage device 140 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), the like, or any combination thereof. Exemplary mass storage devices may include magnetic disks, optical disks, solid state drives, and the like. Exemplary removable memories may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and the like. Exemplary volatile read and write memory can include Random Access Memory (RAM). Exemplary RAM may include Dynamic Random Access Memory (DRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Static Random Access Memory (SRAM), thyristor random access memory (T-RAM), zero capacitance random access memory (Z-RAM), and the like. Exemplary ROMs may include mask-type read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory, and the like. In some embodiments, the storage device 140 may execute on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.

In some embodiments, a storage device 140 may be connected to the network 120 to communicate with one or more components (e.g., server 110, user terminal 130) in the application scenario 100. One or more components in the application scenario 100 may access data or instructions stored in the storage device 140 via the network 120. In some embodiments, the storage device 140 may be directly connected to or in communication with one or more components in the application scenario 100 (e.g., server 110, user terminal 130). In some embodiments, the storage device 140 may be part of the server 110.

The photographing device 150 may acquire image or video data. In some embodiments, the acquired image or video data may be associated with a scene surrounding the vehicle. By way of example only, the photographing device 150 may be a video camera, a security camera, a web camera, a smartphone camera, a tablet camera, a laptop camera, and the like. The photographing apparatus 150 may be mounted on a vehicle or on an object (e.g., a traffic light, a utility pole, a pole). In some embodiments, the photographing device 150 can be powered by an energy unit (e.g., a generator, an electrical transmission line, a solar power supply unit). In addition, the photographing apparatus 150 can use a battery pack to expand the power. In some embodiments, the photographing device 150 may be configured with/coupled to a network module that enables the photographing device 150 to connect with the processing engine 112, the user terminal 130, and/or the storage device 140 via the network 120.

It should be noted that the above description is intended to be illustrative, and not to limit the scope of the application. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other characteristics of the exemplary embodiments described herein may be combined in various ways to obtain additional and/or alternative exemplary embodiments. For example, the photographing apparatus 150 may be configured with a storage module, a processing module, a communication module, and the like. However, such changes and modifications do not depart from the scope of the present application.

FIG. 2 is a schematic diagram of exemplary hardware and/or software components of an exemplary computing device on which a processing engine may be implemented in accordance with some embodiments of the present application. As shown in FIG. 2, computing device 200 may include a processor 210, memory 220, input/output (I/O)230, and communication ports 240.

The processor 210 (e.g., logic circuitry) may execute computer instructions (e.g., program code) and perform the functions of the processing engine 112 in accordance with the techniques described herein. In some embodiments, the processor 210 may be configured to process data and/or information related to one or more components of the application scenario 100. For example, the processor 210 may identify category information of the vehicle in the video data acquired by the photographing apparatus 150. For another example, the processor 210 may determine the content of the identified vehicle category based on a series of images. The processor 210 may also be configured to obtain information for vehicles corresponding to the identified vehicle category. The processor 210 may also transmit the identified category information and information of the corresponding vehicle to the server 110. In some embodiments, the processor 210 may send a notification to the associated user terminal 130.

In some embodiments, processor 210 may include interface circuitry 210-a and processing circuitry 210-b therein. The interface circuit may be configured to receive electrical signals from a bus (not shown in fig. 2), where the electrical signals encode structured data and/or instructions for processing by the processing circuit. The processing circuitry may perform logical computations and then encode the conclusions, results and/or instructions into electrical signals. The interface circuit may then send the electrical signals from the processing circuit via the bus.

The computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform particular functions described herein. For example, the processor 210 may process information related to the vehicle obtained from the user terminal 130, the storage device 140, and/or any other component of the application scenario 100. In some embodiments, processor 210 may include one or more hardware processors, such as microcontrollers, microprocessors, Reduced Instruction Set Computers (RISC), Application Specific Integrated Circuits (ASIC), application specific instruction set processors (ASIP), Central Processing Units (CPU), Graphics Processors (GPU), Physical Processors (PPU), microcontrollers, Digital Signal Processors (DSP), Field Programmable Gate Arrays (FPGA), Advanced RISC Machines (ARM), Programmable Logic Devices (PLD), any circuit or processor capable of executing one or more functions, or the like, or any combination thereof.

For illustration only, only one processor is depicted in computing device 200. However, it should be noted that the computing device 200 in the present application may also include multiple processors, and thus, operations and/or method steps performed by one processor as described herein may also be performed jointly or separately by multiple processors. For example, if in the present application, the processors of computing device 200 perform steps a and B simultaneously, it should be understood that steps a and B may also be performed jointly or separately by two or more different processors in computing device 200 (e.g., a first processor performing step a, a second processor performing step B, or a first processor and a second processor performing steps a and B together).

The memory 220 may store data/information obtained from the user terminal 130, the storage device 140, and/or any other component of the application scenario 100. In some embodiments, memory device 220 may include a mass memory device, a removable memory device, a volatile read-write memory, a read-only memory (ROM), the like, or any combination thereof. For example, mass storage may include magnetic disks, optical disks, solid state drives, and so forth. The removable storage device may include flash memory, floppy disks, optical disks, memory cards, zip disks, tapes, and the like. The volatile read and write memory may include Random Access Memory (RAM). RAM may include Dynamic RAM (DRAM), double-data-rate synchronous dynamic RAM (DDRSDRAM), Static RAM (SRAM), thyristor RAM (T-RAM), zero-capacitor RAM (Z-RAM), and the like. The ROM may include Masked ROM (MROM), Programmable ROM (PROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), digital versatile disk ROM, and the like. In some embodiments, memory 220 may store one or more programs and/or instructions to perform the example methods described herein. For example, the memory 220 may store a program for the processing engine 112 to determine vehicle values.

I/O230 may input and/or output signals, data, information, and the like. In some embodiments, I/O230 may enable a user to interact with processing engine 112. In some embodiments, I/O230 may include input devices and output devices. Examples of input devices may include a keyboard, mouse, touch screen, microphone, etc., or a combination thereof. Examples of output devices may include a display device, speakers, printer, projector, etc., or a combination thereof. Examples of a display device may include a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) based display, a flat panel display, a curved screen, a television device, a Cathode Ray Tube (CRT), a touch screen, etc., or any combination thereof.

The communication port 240 may be connected to a network (e.g., network 120) to facilitate data communication. The communication port 240 may establish a connection between the processing engine 112 and the user terminal 130, the photographing device 150, or the storage device 140. The connection may be a wired connection, a wireless connection, any other communication connection that may enable transmission and/or reception of data, and/or any combination of such connections. The wired connection may include, for example, an electrical cable, an optical cable, a telephone line, etc., or any combination thereof. The wireless connection may include, for example, a Bluetooth link, a Wi-FiTM link, a WiMax link, a WLAN link, a ZigBee link, a mobile network link (e.g., 3G, 4G, 5G), etc., or any combination thereof. In some embodiments, the communication port 240 may be and/or include a standardized communication port, such as RS232, RS485, and the like.

Fig. 3 is a schematic diagram of exemplary hardware and/or software components of an exemplary mobile device on which a user terminal may be implemented, according to some embodiments of the present application. In some embodiments, the mobile device 300 shown in FIG. 3 may be used by a user. The user may be a driver, a passenger, a parking place manager, etc. For example, the parking place manager may view parking information of the vehicle via the mobile device 300. In some embodiments, the driver may view information such as parking time of the vehicle via the mobile device 300.

As shown in FIG. 3, mobile device 300 may include a communication platform 310, a display 320, a Graphics Processing Unit (GPU)330, a Central Processing Unit (CPU)340, I/O350, memory 360, and storage 390. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown), may also be included in mobile device 300. In some embodiments, the operating system 370 (e.g., iOS) may be moved^TM、Android^TM、WindowsPhone^TM) And one or more applications 380 are loaded from storage 390 into memory 360 for execution by CPU 340. The application 380 may include a browser or any other suitable mobile application for receiving and rendering information related to image processing or other information from the processing engine 112. User interaction with the information flow may be enabled through the I/O350 and provided to the processing engine 112 and/or other components of the application scenario 100 through the network 120.

To implement the various modules, units, and their functions described herein, a computer hardware platform may be used as the hardware platform for one or more of the components described herein. A computer with user interface elements may be used to implement a Personal Computer (PC) or any other type of workstation or terminal device. The computer may also function as a server if appropriately programmed.

One of ordinary skill in the art will appreciate that when an element of the application scenario 100 executes, the element may execute via an electrical and/or electromagnetic signal. For example, when processing engine 112 processes a task, such as making a determination or identifying information, processing engine 112 may operate logic circuits in its processor to process the task. When the processing engine 112 transmits data (e.g., a current estimate of the target vehicle) to the user terminal 130, the processor of the processing engine 112 may generate an electrical signal encoding the data. The processor of the processing engine 112 may then send the electrical signal to an output port. If the user terminal 130 communicates with the processing engine 112 over a wired network, the output port may be physically connected to a cable that may further transmit the electrical signals to the input port of the server 110. If the user terminal 130 communicates with the processing engine 112 over a wireless network, the output port of the processing engine 112 may be one or more antennas that may convert electrical signals to electromagnetic signals. In an electronic device, such as user terminal 130 and/or server 110, when its processor processes instructions, issues instructions, and/or performs actions, the instructions and/or actions are performed by electrical signals. For example, when a processor retrieves or stores data from a storage medium (e.g., storage device 140), it may send electrical signals to a read/write device of the storage medium, which may read or write structured data in the storage medium. The configuration data may be transmitted to the processor in the form of electrical signals via a bus of the electronic device. Herein, an electrical signal may refer to an electrical signal, a series of electrical signals, and/or one or more discrete electrical signals.

FIG. 4 is a schematic block diagram of an exemplary processing engine shown in accordance with some embodiments of the present application.

As shown in FIG. 4, in some embodiments, the processing engine 112 may include a construction module 410, an evaluation module 420, and an identification module 430. The processing engine 140 may be implemented on various components (e.g., the processor 210 of the computing device 200 shown in fig. 2). For example, at least a portion of processing engine 140 may be implemented on a computing device as shown in FIG. 2 or a mobile device as shown in FIG. 3.

The build module 410 is used to build a vehicle identification model, which in some embodiments is based on a CSPPeleNet-SE network implementation.

The evaluation module 420 is used for evaluating the constructed vehicle identification model; the identification module 430 is used for identifying the acquired picture based on the vehicle identification model which is evaluated; and outputting the recognition result, wherein in some embodiments, the recognition result comprises the probability that the vehicle in the picture belongs to the vehicle type of each category.

In some embodiments, the processing engine 112 may further include a training module 440, and the training module 440 is configured to train the constructed vehicle recognition model according to the collected data to obtain a trained vehicle recognition model.

In some embodiments, the trained sample images may include images of vehicles of known vehicle classes. The vehicle images of the known vehicle category may be obtained in various manners, such as vehicle images collected by a historical driving recorder, vehicle images uploaded by a historical user, vehicle images collected by an electronic monitoring device, and the like. In some embodiments, data enhancement may be performed on existing vehicle images to increase the number of sample images. Methods of data enhancement include, but are not limited to, flipping, rotating, scaling, cropping, translating, adding noise, and the like. In some embodiments, the status data of the sample image may be tagged, which may be done manually or by a computer program. For example, the score of the vehicle may be counted by the user based on the history, and so on. For example only, the model may be trained with the sample image as input and the corresponding vehicle class as the correct criteria (Ground Truth). While the model parameters may be adjusted back according to the difference between the predicted output of the model (e.g., the predicted vehicle class) and the correct criteria. When a predetermined condition is satisfied, for example, the number of training sample images reaches a predetermined number, the predicted accuracy of the model is greater than a predetermined accuracy threshold, or the value of the loss function (LossFunction) is less than a predetermined value, the training process is stopped, and the trained model is designated as the vehicle recognition model. For more details on the vehicle identification model in this specification, refer to the following contents, which are not described herein.

In some embodiments, the processing engine 112 may obtain a vehicle identification model. In some embodiments, the vehicle recognition model may include a trained machine learning model. For example, the trained machine learning model may include a You only look once (YoLO) model, an enhanced Haar model, a FasterR-CNN model, a Mask R-CNN model, the like, or any combination thereof. In some embodiments, the processing engine 112 may obtain the recognition model directly from the storage device 140 via the network 120. In some embodiments, the processing engine 112 may obtain a machine learning model and train the machine learning model. For example, a set of sample images and a set of object recognition results (e.g., positive or negative labels, labels of object types) corresponding to the set of sample images may be used to train a machine learning model. The trained machine learning model may be used as a recognition model for recognizing vehicle classes in each of a series of frames.

In some embodiments, the vehicle identification model is implemented based on a csppelenet-SE network, and specific description of the csppelenet-SE network refers to details of fig. 6, which are not repeated herein.

The modules in the processing engine 112 may be connected to or in communication with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, and the like, or any combination thereof. The wireless connection may include a Local Area Network (LAN), a Wide Area Network (WAN), bluetooth, zigbee network, Near Field Communication (NFC), etc., or any combination thereof. Two or more modules may be combined into one module, and any one module may be split into two or more units. For example, the acquisition module 410 may be integrated into the identification module 420 as a single module that may identify the mobile terminal and an object associated with the mobile terminal.

It should be understood that the system and its modules shown in FIG. 4 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules in this specification may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It should be noted that the above description of the processing engine and its modules is for convenience only and should not limit the present disclosure to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, the building module and the identification module in fig. 4 may be different modules in one system, or one module may implement the functions of the two modules. For another example, the processing engine may share one memory module with each module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.

FIG. 5 is a flow diagram of a vehicle class identification method shown in accordance with some embodiments of the present description. In some embodiments, the process 500 shown in FIG. 5 may be implemented in the application scenario 100 shown in FIG. 1. For example, process 500 may be stored as instructions in a storage medium (e.g., storage device 140 or memory 220 of computing device 200) and invoked and/or executed by one or more modules in a processor (e.g., storage device 140), processing engine 112 of server 110, processor 220 of computing device 200, or processing engine 112 shown in fig. 4. The operations of the illustrated process 500 presented below are intended to be illustrative. In some embodiments, process 500 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of process 500 are illustrated in fig. 5 and described below is not intended to be limiting.

As shown in fig. 5, the process 500 may include the following steps:

and step 510, constructing a vehicle identification model, wherein the vehicle identification model is realized based on a CSPPeleNet-SE network.

In particular, this step may be performed by a building block.

In some embodiments, the basic idea of vehicle identification is: and the detected vehicle is taken as an input picture and sent into an optimized vehicle identification model, namely a classification task, and the input picture is subjected to feature extraction through a backbone network and then is classified through Softmax.

Fig. 6 is a schematic diagram illustrating a csppelenet-SE network structure of a vehicle identification model according to some embodiments of the present disclosure, and portions thereof will be described in detail later. In some embodiments, the vehicle identification model comprises a feature extraction model, a feature compression model, a feature excitation model, a feature fusion and classification model which are connected in sequence; the feature extraction model is used for extracting features of the input picture; the feature compression model is used for compressing the features extracted by the feature extraction model, the feature excitation model is used for processing the compressed features and obtaining the weight of each feature, and the feature fusion and classification model is used for fusing the weighted features, predicting the category of the fused feature map and outputting the recognition result. In some embodiments, the features extracted by the feature extraction model are divided into two parts, one part enters the feature compression model and the feature excitation model in sequence for processing, and the other part directly enters the feature fusion and classification model.

In some embodiments, the constructed vehicle identification model can be mainly referred to a lightweight Network PeleNet, and a Squeeze-and-excitation (SE) module and a Cross Stage Partial Network (CSPNet) are introduced on the basis of the lightweight Network PeleNet. The CSPNet is designed to reduce the calculation amount of the network and improve the accuracy and the reasoning speed. The reasoning computation is too high because there is a lot of repeated gradient information in the network optimization process. Therefore, the characteristic diagram of the base layer is divided into 2 parts, the gradient flow is separated, the gradient flow is transmitted in different network paths, and then the two parts are fused through cross-layer connection, so that the accuracy can be improved under the condition of reducing the calculation amount.

In some embodiments, SE is a channel attention mechanism, and the important features in each channel are strengthened by connections across channels, that is, by learning the correlation between channels, to obtain the importance degree of each feature channel, and according to the importance degree, the features useful for the task are promoted, and the features which do not contribute much to the task are suppressed.

FIG. 7 is a schematic diagram of the Squeeze-and-Excitation module according to some embodiments of the present description, and the Squeeze-and-Excitation module is mainly divided into the Squeeze (compression) process and the Excitation process, as shown in FIG. 7. Where Ftr is a convolution structure, X is the input of Ftr, and U is the output of Ftr.

In some embodiments, the feature extraction network is generally composed of one Block, and the SE module may be added at the end of any Block to refine information.

Fig. 8 is a schematic diagram illustrating a processing flow of a feature compression model according to some embodiments of the present disclosure, and as shown in fig. 8, in some embodiments, the processing of the feature compression model only includes a Global Average Pooling (Global Average power), the input feature map size is WxHxC, and after the Squeeze operation, the feature map is compressed to 1 × 1xC, so that the channel weight can be calculated by using the correlation between channels based on Global information.

FIG. 9 is a process flow diagram of a feature incentive model according to some embodiments of the present description. As shown in FIG. 9, in some embodiments, the process flow of the feature incentive model includes two fully connected layers and two activation functions. The first fully-connected layer is to reduce the number of channels to reduce the amount of calculation, the number of channels input to the specification is C, and the number of channels is compressed to C/r (r is a compression ratio) when passing through the first fully-connected layer. And secondly via a ReLU activation function. The second fully-connected layer changes the number of channels from C/r to C, i.e., restores the number of channels to keep the same as the number of channels input to the Excitation. And finally, outputting the weight of each feature map through a Sigmoid function.

It should be noted that, as shown in fig. 10, which is a schematic view of Stem Block of a vehicle identification model according to some embodiments of the present specification, peloenet involved in the present scheme is a lightweight network variant based on densoet, and is composed of Stem Block and Two-Way detect Layer. In some embodiments, Stem Block may perform one down-sampling (stride 2) and increasing the number of channels on the input image, and as shown in fig. 10, first perform a simple feature extraction operation on the input image by using a 3 × 3 convolution. And then dividing the operation into two branches to carry out different operations, wherein one branch is subjected to 3x3 convolution operation, and the other branch is subjected to maximum pooling operation.

In the scheme, the network comprises two network branches which are respectively used for capturing the information of the receptive fields with different scales, and is inspired by an Incepotion structure. After completing bottleeck through convolution of a layer of 1x1 in the first path, performing convolution of a layer of 3x3 in the first path; the second path is after bottleeck, and then convolved by Two layers of 3x3, and the specific Two-Way Dense Layer structure and flow are shown in fig. 11.

Specifically, the general structure of the peloenet classification network is shown in the following table:

CSPPeleNet-SE is a network used in the application, namely a CSPNet structure and an SE attention mechanism are introduced on the basis of PeleNet. The network architecture is shown in fig. 6. Before inputting the feature map into a DenseBlock, the feature map is divided into two parts, one part is subjected to feature extraction through the DenseBlock, then the feature map is sent to an SE module to learn the correlation among channels, and finally the feature map is sent to a transition layer. And the other part directly crosses the middle Dense Block and SE and is directly spliced with the output of the transition layer.

Finally, the vehicle identification model of the scheme can output the probability that the vehicle in the picture belongs to various vehicle types, and in practice, the vehicle type corresponding to the maximum probability value can be used as the final identification result.

And step 520, training and evaluating the constructed vehicle identification model.

In particular, this step may be performed by the evaluation module.

The constructed vehicle identification model needs to be trained, in some embodiments, images in the vehicle category data set serving as training samples can be collected and uploaded by special personnel, in some embodiments, the obtaining of the images in the vehicle category data set comprises extracting vehicle pictures by collecting vehicle videos and performing video segmentation, in some embodiments, the obtaining of the images in the vehicle category data set can directly shoot the images of the vehicle categories by monitoring equipment, camera equipment and the like, and in some embodiments, the collecting of the pictures comprises collecting the vehicle category pictures in night, uneven light, strong light reflection, rainy days and foggy days.

In some embodiments, one or more vehicle category photos may be stored in storage device 140. The processing engine 112 may obtain the vehicle category photograph from the storage device 140 via the network 120. For example, a user (e.g., traffic police) may enter a vehicle category photograph into storage device 140. The processing engine 112 may obtain a vehicle category photograph.

In some embodiments, the vehicle category photograph may be captured by a photographing device 150 as shown in fig. 1. In some embodiments, the vehicle category photographs may be captured by more than one photographing device 150. For example, the first photographing device 150-1 (not shown in the figure) may be configured to acquire a low resolution video (or a low resolution frame of an image) for analyzing the motion of the object. The second photographing device 150-2 (not shown in the figure) may be configured to acquire one or more high resolution images for identifying information of an object, such as a specific type of vehicle, and the like.

In some embodiments, the photographing device 150 may be removably mounted on the traffic sign or an object near the traffic sign. For example, the object may include a traffic light, a street light, a utility pole, a tree, a building, etc., or any combination thereof. In some embodiments, the photographing apparatus 150 may be installed on a vehicle parked at a parking place. As used herein, "mounted on a vehicle" means mounted on the exterior of the vehicle (e.g., on the roof, on the front window, on the rear view mirror) or mounted on the interior of the vehicle (e.g., above the panel, on the front window of the vehicle interior, or on the passenger seat).

In some embodiments, the preprocessing of the data includes performing noise reduction, data normalization, feature normalization, and the like. In some embodiments, noise regions are included in the photo scene. For example, the noise zone may include a lane, a static object, such as a tree, a building, a vehicle parked by a road, etc., or any combination thereof. In some embodiments, data preprocessing may be performed to remove noisy regions in the photograph before screening out the vehicle images.

In some embodiments, the vehicle category photographs employ a data set produced internally by a company, primarily classified as premium vehicles and non-premium vehicles. The toll vehicle comprises a car, a sports car and an police car. Non-toll vehicles include tricycles, express delivery vehicles, bicycles, motorcycles, and trucks. Each vehicle type has 1000 pictures, 8000 pictures in total. Wherein, the training set, the testing set and the verification set are divided according to the proportion of 7:2: 1.

In some embodiments, training data may be employed using weights previously trained on imagenet data sets, and the model is trained by migration learning. All layers will be trimmed and the last fully connected layer will be fully replaced with class 8. : since each vehicle picture used for training is only about 1000, data expansion is performed by random cropping, horizontal flipping, rotation, cropping, AddHueAndSaturation, AddMultiply, GaussianBlur, contrast normalization, sharpening, embossing, and the like.

Fig. 12 shows a schematic diagram of the training for the model. Training of the vehicle identification model may be performed by the training module 240, and the vehicle identification model may be obtained by training the historical vehicle category-related data. For example only, the model may be trained with historical basic information as an input and an appropriate similarity value corresponding to the historical basic information as a correct criterion (Ground Truth). And meanwhile, the model parameters can be reversely adjusted according to the difference between the prediction output of the model and the correct standard. When a predetermined condition is satisfied, for example, the number of training samples reaches a predetermined number, the predicted accuracy of the model is greater than a predetermined accuracy threshold, or the Loss Function (Loss Function) value is less than a predetermined value, the training process is stopped, and the trained model is designated as the vehicle identification model.

In some embodiments, the training process may be performed on the server 110, or may be performed on another device, and the trained model may be applied to the server 110. In some embodiments, the determination of the vehicle category content may also be made for the vehicle category photos from various different scenes.

After the training of the model is completed, the recognition result of the model needs to be evaluated. This step may be performed by the evaluation module.

Evaluation indexes are as follows: including Parameter, BFLOPs, Top-1, Top-5.

The Parameter is the Parameter number of the model.

BFLOPs are abbreviated as floating point operations per second, meaning the number of floating point operations per second, understood as the computation speed. Is an index for measuring the performance of the hardware.

ImageNet has roughly 1000 categories, and when the model predicts a picture, 1000 categories are ranked from high to low by probability. Top-1 is Top-1 Accuracy, and Top-5 is Top-5 Accuracy. The Top-1 Accuracy refers to the Accuracy rate that the first-ranked category matches the actual result, but refers to the Accuracy rate that the Top-five category contains the actual result. Accurancy is the accuracy, and the calculation formula is as follows:

and step 530, identifying the acquired picture based on the vehicle identification model which is evaluated.

In particular, this step may be performed by the identification module.

In some embodiments, the processing engine 112 may identify the vehicle category using a vehicle identification model. The recognition model may be obtained from the storage device 140. The processing engine 112 may execute the recognition model to recognize the vehicle class of the vehicle in the vehicle photograph. For a detailed description of the recognition model, reference is made to the relevant content of fig. 6, which is not repeated herein.

In some embodiments, the processing engine 112 may further obtain information of the vehicle based on obtaining the category information of the vehicle. Such as driver information, violation information of the vehicle, etc. Such as vehicle category information, may further be used to identify drivers who violate traffic regulations.

And 540, acquiring an identification result, wherein the identification result comprises the probability that the vehicle in the picture belongs to the vehicle type of each category.

In particular, this step may be performed by the identification module.

In some embodiments, the recognition result output by the model may be the probability that the vehicle in the current picture belongs to a certain category, and the category with the highest probability may be selected as the finally determined recognition result.

The vehicle category identification method of the embodiment of the present specification has beneficial effects including, but not limited to, the following: 1. based on an improved algorithm of the PeleNet, an Squeeze-and-excitation (SE) module and a Cross Stage Partial Network (CSPNet) are introduced on the basis, so that the parameter quantity is reduced, the edge device can be better served by the improved algorithm, and the accuracy is improved. 2. The vehicle type identification method can output the identification result of the vehicle type more quickly and accurately, and can be applied to various scenes.

Embodiments of the present description also provide a vehicle class identification device, comprising at least one storage medium for storing computer instructions and at least one processor; the at least one processor is configured to perform the aforementioned vehicle class identification method, the method comprising: constructing a vehicle identification model, wherein the vehicle identification model is realized based on a CSPPeleNet-SE network; training and evaluating the constructed vehicle identification model; identifying the acquired picture based on the vehicle identification model which is evaluated; and acquiring an identification result, wherein the identification result comprises the probability that the vehicle in the picture belongs to the vehicle type of each category.

The embodiment of the specification also provides a computer readable storage medium. The storage medium stores computer instructions, and after the computer instructions in the storage medium are read by the computer, the computer realizes the method for detecting the vehicle state, wherein the method comprises the following steps: constructing a vehicle identification model, wherein the vehicle identification model is realized based on a CSPPeleNet-SE network; training and evaluating the constructed vehicle identification model; identifying the acquired picture based on the vehicle identification model which is evaluated; and acquiring an identification result, wherein the identification result comprises the probability that the vehicle in the picture belongs to the vehicle type of each category.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or processing device. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing processing device or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A vehicle category identification method, characterized by comprising:

constructing a vehicle identification model, wherein the vehicle identification model is realized based on a CSPPeleNet-SE network;

training and evaluating the constructed vehicle identification model;

identifying the acquired picture based on the vehicle identification model which is evaluated;

and acquiring an identification result, wherein the identification result comprises the probability that the vehicle in the picture belongs to the vehicle type of each category.

2. The vehicle category identification method according to claim 1, wherein the vehicle identification model comprises a feature extraction model, a feature compression model, a feature excitation model, a feature fusion and classification model which are connected in sequence;

the feature extraction model is used for extracting features of the input picture; the feature compression model is used for compressing the features extracted by the feature extraction model, the feature excitation model is used for processing the compressed features and obtaining the weight of each feature, and the feature fusion and classification model is used for fusing the weighted features, predicting the category of the fused feature map and outputting the recognition result.

3. The vehicle type identification method according to claim 2, wherein the feature excitation model includes two fully connected layers, a first fully connected layer is used for performing dimension reduction processing on input data so as to reduce the calculation amount, the first function is a ReLU activation function and is used for performing function processing on data output by the first fully connected layer, a second fully connected layer is used for performing dimension enhancement processing on data processed by the first activation function, and the second function is a Sigmoid function and is used for calculating the weight of the data after dimension enhancement processing.

4. The vehicle category identification method according to claim 3, wherein the features extracted by the feature extraction model are divided into two parts, one part is sequentially processed in the feature compression model and the feature excitation model, and the other part is directly processed in the feature fusion and classification model.

5. A vehicle category identification system, comprising:

the vehicle identification module is used for constructing a vehicle identification model, and the vehicle identification model is realized based on a CSPPeleNet-SE network;

the evaluation module is used for training and evaluating the constructed vehicle identification model;

the identification module is used for identifying the acquired picture based on the evaluated vehicle identification model; and outputting a recognition result, wherein the recognition result comprises the probability that the vehicle in the picture belongs to the vehicle type of each category.

6. The vehicle category identification system according to claim 5, wherein the vehicle identification model comprises a feature extraction model, a feature compression model, a feature excitation model, a feature fusion and classification model which are connected in sequence;

7. The vehicle type identification system according to claim 6, wherein the feature excitation model includes two fully connected layers, a first fully connected layer is used for performing dimension reduction processing on input data so as to reduce the amount of calculation, the first function is a ReLU activation function used for performing function processing on data output by the first fully connected layer, a second fully connected layer is used for performing dimension enhancement processing on data processed by the first activation function, and the second function is a Sigmoid function used for calculating the weight of data after dimension enhancement processing.

8. The vehicle category identification system according to claim 4, wherein the features extracted by the feature extraction model are divided into two parts, one part is sequentially processed in the feature compression model and the feature excitation model, and the other part is directly processed in the feature fusion and classification model.

9. A vehicle class identification apparatus, the apparatus comprising a processor and a memory; the memory is configured to store instructions, and the instructions, when executed by the processor, cause the apparatus to implement operations corresponding to the vehicle category identification method according to any one of claims 1 to 4.

10. A computer-readable storage medium, wherein the storage medium stores computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes the vehicle category identification method according to any one of claims 1 to 4.