CN114550113A - Image recognition method, device, equipment and medium for intelligent traffic - Google Patents

Abstract

The present disclosure provides an image recognition method, device, electronic device, storage medium and program product for intelligent transportation, and relates to the technical field of image processing, in particular to the technical fields of deep learning and intelligent transportation. The specific implementation scheme is: determining the reference driving information related to the vehicle to be identified in the reference vehicle image; determining the target vehicle image subset from the vehicle image set based on the reference driving information; image to match the target vehicle image.

Description

Image recognition method, device, device and medium for intelligent transportation

technical field

The present disclosure relates to the technical field of image processing, and in particular, to the technical fields of deep learning and intelligent transportation. Specifically, it relates to an image recognition method, apparatus, electronic device, storage medium and program product for intelligent transportation.

Background technique

With the explosion in the amount of image data, it is difficult to rely on humans to analyze and process this image data. Computer vision technology offers great potential for liberating manpower. Computer vision is a science that studies how to use electronic devices to "see", that is, the science and technology of using cameras and computers instead of human eyes to identify, track and measure objects. Computer vision technology provides help for the development of applications in public security, information security, and financial security.

SUMMARY OF THE INVENTION

The present disclosure provides an image recognition method, apparatus, electronic device, storage medium and program product for intelligent transportation.

According to an aspect of the present disclosure, there is provided an image recognition method for intelligent transportation, comprising: determining reference driving information related to a vehicle to be recognized in a reference vehicle image; determining a subset of target vehicle images; and determining, from the subset of target vehicle images, a target vehicle image that matches the reference vehicle image.

According to another aspect of the present disclosure, there is provided an image recognition device for intelligent transportation, comprising: a first determination module for determining reference driving information related to a vehicle to be recognized in a reference vehicle image; a screening module for using for determining a target vehicle image subset from the vehicle image set based on the reference travel information; and a second determining module for determining a target vehicle image matching the reference vehicle image from the target vehicle image subset .

According to another aspect of the present disclosure, there is provided an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores information that can be used by the at least one processor Instructions to be executed, the instructions being executed by the at least one processor to enable the at least one processor to perform a method as disclosed.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are used to cause the computer to perform a method according to the present disclosure.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements a method according to the present disclosure.

It should be understood that what is described in this section is not intended to identify key or critical features of embodiments of the disclosure, nor is it intended to limit the scope of the disclosure. Other features of the present disclosure will become readily understood from the following description.

Description of drawings

The accompanying drawings are used for better understanding of the present solution, and do not constitute a limitation to the present disclosure. in:

FIG. 1 schematically shows an exemplary system architecture to which an image recognition method and apparatus for intelligent transportation can be applied according to an embodiment of the present disclosure;

FIG. 2 schematically shows a flowchart of an image recognition method for intelligent transportation according to an embodiment of the present disclosure;

FIG. 3 schematically shows a flowchart of an image recognition method for intelligent transportation according to another embodiment of the present disclosure;

FIG. 4 schematically shows a flow chart of generating a card identification record according to an embodiment of the present disclosure;

FIG. 5 schematically shows a block diagram of an image recognition apparatus for intelligent transportation according to an embodiment of the present disclosure; and

FIG. 6 schematically shows a block diagram of an electronic device suitable for implementing an image recognition method for intelligent transportation according to an embodiment of the present disclosure.

Detailed ways

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding and should be considered as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted from the following description for clarity and conciseness.

The present disclosure provides an image recognition method, apparatus, electronic device, storage medium and program product for intelligent transportation.

According to an embodiment of the present disclosure, an image recognition method for intelligent transportation is provided, which may include: determining reference driving information related to a vehicle to be recognized in a reference vehicle image; and determining a target vehicle image matching the reference vehicle image from the target vehicle image subset.

In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure and application of the user's personal information involved are all in compliance with the relevant laws and regulations, and necessary confidentiality measures have been taken, and do not violate the Public order and good customs.

In the technical solution of the present disclosure, the authorization or consent of the user is obtained before the user's personal information is obtained or collected.

FIG. 1 schematically shows an exemplary system architecture to which an image recognition method and apparatus for intelligent transportation can be applied according to an embodiment of the present disclosure.

It should be noted that FIG. 1 is only an example of a system architecture to which the embodiments of the present disclosure can be applied, so as to help those skilled in the art to understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used for other A device, system, environment or scene. For example, in another embodiment, the exemplary system architecture to which the image recognition method and apparatus for intelligent transportation can be applied may include a terminal device, but the terminal device may implement the methods provided by the embodiments of the present disclosure without interacting with the server. Image recognition method and device for intelligent transportation.

As shown in FIG. 1 , the system architecture 100 according to this embodiment may include an information collection apparatus 101 , a network 102 and a server 103 . The network 102 is used as a medium for providing a communication link between the information collection device 101 and the server 103 . Network 102 may include various connection types, such as wired and/or wireless communication links, and the like.

The information collection device 101 can be used to interact with the server 103 through the network 102 to receive or send messages and the like. The information collection apparatus 101 may be installed with a camera, a video camera, or other apparatus for collecting image or video information (just an example).

The information collection device 101 can be installed on the entrance and exit gantry of the parking lot, and is used to collect vehicle images of the vehicles 104 entering and exiting from the entrance and exit. But it is not limited to this. It can also be installed on the road section gantry of the expressway to collect vehicle images of the vehicles 104 driving on the expressway.

The server 103 may be a server that provides various services, such as a background management server that provides support for vehicle images collected by the information collection device 101 (just an example). The background management server can analyze and process the received vehicle images and other data.

It should be noted that, the image recognition method for intelligent transportation provided by the embodiment of the present disclosure may generally be executed by the server 103 . Correspondingly, the image recognition apparatus for intelligent transportation provided by the embodiment of the present disclosure may generally be provided in the server 103 . The image recognition method for intelligent transportation provided by the embodiments of the present disclosure may also be executed by a server or server cluster that is different from the server 103 and can communicate with the information collection device 101 and/or the server 103 . Correspondingly, the image recognition device for intelligent transportation provided by the embodiment of the present disclosure may also be provided in a server or server cluster that is different from the server 103 and can communicate with the information collection device 101 and/or the server 103 .

It should be understood that the numbers of terminal devices, networks and servers in FIG. 1 are merely illustrative. There can be any number of terminal devices, networks and servers according to implementation needs.

It should be noted that the sequence numbers of the respective operations in the following methods are only used as representations of the operations for the convenience of description, and should not be regarded as representing the execution order of the respective operations. The methods need not be performed in the exact order shown unless explicitly stated.

FIG. 2 schematically shows a flowchart of an image recognition method for intelligent transportation according to an embodiment of the present disclosure.

As shown in FIG. 2, the method includes operations S210-S230.

In operation S210, reference travel information related to the vehicle to be identified in the reference vehicle image is determined.

In operation S220, a target vehicle image subset is determined from the vehicle image set based on the reference travel information.

In operation S230, a target vehicle image matching the reference vehicle image is determined from the subset of target vehicle images.

According to an embodiment of the present disclosure, the reference vehicle image may include information of the vehicle to be identified. For example, the reference vehicle image is an image obtained by collecting the side information of the vehicle to be recognized, but it is not limited to this, and the reference vehicle image may also be an image obtained by collecting the front information of the vehicle to be recognized, as long as it can reflect The reference vehicle image of the information of the vehicle to be recognized is sufficient.

According to an embodiment of the present disclosure, the reference travel information related to the vehicle to be identified in the reference vehicle image may include travel position information, but is not limited thereto, and may also include travel time information. For example, the reference travel information may include: the vehicle to be identified traveled through the DD expressway intersection between 9:00 am on BB, month CC, year AA to 12:00 am on BB, month, CC, year AA.

According to an embodiment of the present disclosure, a subset of target vehicle images matching the reference driving information may be determined from the vehicle image set based on the reference driving information. The target vehicle image subset includes a plurality of vehicle images, and the travel information of the vehicle in each of the plurality of vehicle images is the same as the reference travel information. A vehicle whose travel information is the same as the reference travel information of the vehicle to be identified has a probability that it is the same vehicle as the vehicle to be identified.

According to the embodiments of the present disclosure, a part of vehicles in the vehicle image set can be deleted by using the reference driving information as the limiting condition of the preliminary screening operation to obtain the target vehicle image subset, thereby reducing the data processing amount for determining the target vehicle image and improving the processing efficiency.

According to the embodiment of the present disclosure, the target vehicle image that matches the reference vehicle image can be understood as the same vehicle as the vehicle to be identified and the target vehicle. The target vehicle image matching the reference vehicle image may be determined by matching the information of the vehicle to be identified in the reference vehicle image with the information of the target vehicle in the target vehicle image.

According to embodiments of the present disclosure, a feature extraction model may be used to extract reference image features of a reference vehicle image, and a feature extraction model may be used to target image features of each vehicle image in a subset of vehicle images. The similarity between the reference image features and the image features of each vehicle image is calculated. A vehicle image with a similarity greater than or equal to the similarity threshold is used as a target vehicle image matching the reference vehicle image. The feature extraction model may include one or more of trained convolutional neural networks, recurrent neural networks, and the like. Any image processing model capable of extracting image features of an image may be used.

According to the embodiment of the present disclosure, each vehicle image in the target vehicle image subset and the reference vehicle image can also be input into the image comparison model to obtain the similarity between the vehicle image and the reference vehicle image. A vehicle image with a similarity greater than or equal to the similarity threshold is used as a target vehicle image matching the reference vehicle image. The image contrast model may include one or more of trained convolutional neural networks, recurrent neural networks, and the like. As long as it is an image processing model capable of recognizing the similarity between two images.

According to other embodiments of the present disclosure, the target vehicle image that matches the reference vehicle image may be determined directly from the vehicle image set. For example, based on each vehicle image in the vehicle image set and the reference vehicle image, the similarity between each vehicle image in the vehicle image set and the reference vehicle image is calculated. The vehicle image with the similarity greater than or the similarity threshold is used as the target vehicle image matched with the reference vehicle image.

According to an embodiment of the present disclosure, compared to directly determining the target vehicle image matching the reference vehicle image from the vehicle image set, determining the target vehicle image matching the reference vehicle image from the target vehicle image subset, The preliminary screening operation for determining the target vehicle image subset in the vehicle image set simplifies the processing amount of determining the target vehicle image and improves the processing efficiency.

FIG. 3 schematically shows a flowchart of an image recognition method for intelligent transportation according to another embodiment of the present disclosure.

As shown in FIG. 3, the method includes operations S310-S360.

In operation S310, reference travel information related to the vehicle to be identified in the reference vehicle image is determined.

In operation S320, a target vehicle image subset is determined from the vehicle image set based on the reference travel information.

In operation S330, at least one initial target vehicle image matching the reference vehicle image is determined from the subset of target vehicle images.

In operation S340, it is determined whether the image number of the at least one initial target vehicle image is greater than a predetermined threshold. In a case where it is determined that the image number of the at least one initial target vehicle image is less than or equal to the predetermined threshold, operation S350 is performed. In a case where it is determined that the image number of the at least one initial target vehicle image is greater than the predetermined threshold, operation S360 is performed.

According to the embodiments of the present disclosure, the numerical range of the predetermined threshold is not limited. For example, it may be 1, but it is not limited to this, and an arbitrary integer larger than 1 may be used. As long as the target vehicle image can be identified based on a predetermined threshold value.

In operation S350, at least one initial target vehicle image is used as the target vehicle image.

In operation S360, a target vehicle image is determined from a plurality of initial target vehicle images based on the reference attribute information.

According to other embodiments of the present disclosure, the operations of determining the number of images of at least one initial target vehicle image and determining whether the number of images is greater than a predetermined threshold may not be performed. Operations S330 to S360 are replaced with the following operations.

For example, at least one initial target vehicle image that matches the reference vehicle image is determined from the subset of target vehicle images. A target vehicle image is determined from at least one initial target vehicle image based on the reference attribute information.

According to an embodiment of the present disclosure, in the case where a plurality of initial target vehicle images, eg, the number of images greater than a predetermined threshold, are determined from the target vehicle image subset, directly performing the method based on the reference attribute information from the plurality of initial target vehicle images. The operation of determining the target vehicle image, thereby simplifying the processing flow.

According to other embodiments of the present disclosure, when an initial target vehicle image of one or less than a predetermined threshold number of images is determined from the target vehicle image subset, operations S340 and S350 are performed, which can reduce the amount of calculation and improve the recognition efficiency.

According to an embodiment of the present disclosure, for operation S360, determining a target vehicle image from a plurality of initial target vehicle images based on the reference attribute information may include the following operations.

For example, for each initial target vehicle image of the plurality of initial target vehicle images, vehicle attribute information of the initial target vehicle image is identified. Based on the reference attribute information and the vehicle attribute information, the attribute similarity between the initial target vehicle image and the reference vehicle image is determined. Based on attribute similarity, a target vehicle image is determined from multiple initial target vehicle images.

According to an embodiment of the present disclosure, the vehicle attribute information includes at least one of the following: vehicle model information, vehicle color information, license plate information, and license plate type information. But it is not limited to this. The vehicle attribute information may also include: annual inspection mark information posted on the windshield of the vehicle, and information on ornaments in the vehicle, and the like.

According to the embodiments of the present disclosure, different identification methods can be used to identify different types of information in the vehicle attribute information. For example, for the license plate information, the initial target image including the license plate information can be used as input data into the character recognition model to obtain the license plate information. For vehicle color information, the initial target image can be used as input data into the image recognition model to obtain vehicle color information. Processing models with different functions can be used to process the initial target image to obtain the corresponding type of vehicle attribute information. The network structure of the processing model such as the character recognition model and the image recognition model is not limited, as long as it is a trained deep learning model and a model capable of obtaining vehicle attribute information based on an initial target image.

According to an embodiment of the present disclosure, before performing operation S360 and determining a target vehicle image from a plurality of initial target vehicle images based on the reference attribute information, the image recognition method for intelligent transportation may further include an operation of: identifying a reference for the reference vehicle image property information.

According to an embodiment of the present disclosure, the method of identifying the reference attribute information of the reference vehicle image may be the same as or similar to the method of identifying the vehicle attribute information of the initial target vehicle image.

According to an embodiment of the present disclosure, the reference attribute information is reference attribute information related to the vehicle to be identified in the reference vehicle image. The reference attribute information may include attribute information of the same or similar type as the vehicle attribute information. For example, one or more of vehicle model information, vehicle color information, license plate information, and license plate type information.

According to an embodiment of the present disclosure, a target vehicle image may be determined from a plurality of initial target vehicle images based on the reference attribute information and the vehicle attribute information. The identification method of the reference attribute information is the same as or similar to the identification method of the vehicle attribute information, and the type of the reference attribute information is the same or similar to the type of the vehicle attribute information. Match the target vehicle image to improve the image recognition accuracy.

According to an embodiment of the present disclosure, before determining the target vehicle image subset from the vehicle image set based on the reference travel information at operation S220, the image recognition method for intelligent transportation may further include an operation: for each of the vehicle image sets vehicle image, and determine the shooting location information and shooting time information of the vehicle image. Based on the photographing position information and the photographing time information, vehicle-related travel information in the vehicle image is determined. In order to perform an operation of determining a target vehicle image subset from the vehicle image set based on the reference travel information based on the reference travel information and the travel information.

According to the embodiment of the present disclosure, the shooting position information and shooting time information of the vehicle image may be acquired while collecting the image information of the vehicle traveling to the collecting area by the information collecting device. For example, an information collection device is installed on the road at an intersection, and the collection area of the information collection device may be the crossroad area directly opposite. Shooting position information and shooting time information of the vehicle image. Thereby, travel information related to the vehicle of the vehicle image is obtained.

According to the embodiments of the present disclosure, the information collection device may also be disposed on a road section gantry on a highway or at an ETC (Electronic Toll Collection) toll gate.

FIG. 4 schematically shows a schematic flow chart of generating a card identification record according to another embodiment of the present disclosure.

As shown in FIG. 4 , a vehicle image 410 and travel information 430 may be received from the information collection device. Based on the vehicle image 410 and the driving information 430, a license plate identification record 440 is generated. But it is not limited to this. The license plate record 440 may also include vehicle attribute information 420 for the vehicle image 410 . Vehicle attribute information 420 of the vehicle image 410 can be identified. And based on the vehicle attribute information 420 , the vehicle image 410 and the driving information 430 , a license plate identification record 440 is generated.

The card identification records 440 may be stored in the database 450 to generate a collection of card identification records. Image IDs such as PIC_ID can be configured for each vehicle image. An index table is generated based on the card identification records and the image identification. Each index entry in the index table may include travel information, image identification, and vehicle attribute information. So that in the process of image recognition, the driving information, image identification and vehicle attribute information can be mapped based on the index table, so as to facilitate the step-by-step screening to determine the target vehicle image.

According to other embodiments of the present disclosure, the non-license plate information in the reference attribute information can be used to determine a target vehicle image from a plurality of initial target vehicle images, thereby avoiding the need to compare the license plate information of the vehicle in the initial target vehicle image with the reference vehicle The phenomenon that the image of the target vehicle in which the license plate information of the vehicle to be recognized is inconsistent in the image is deleted. Whether the target vehicle in the target vehicle image has the phenomenon of changing the license plate can be determined based on the license plate information of the vehicle in the target vehicle image and the license plate information of the vehicle to be recognized in the reference vehicle image.

For example, the first license plate information of the vehicle to be recognized in the reference vehicle image is determined. The second license plate information of the target vehicle in the target vehicle image is determined. Determine the first license plate information and the second license plate information, and determine whether the target vehicle has the phenomenon of changing the license plate. When the first license plate information is the same as the second license plate information, the target vehicle does not have the phenomenon of changing the license plate; when the first license plate information and the second license plate information are different, the target vehicle has the phenomenon of changing the license plate.

Using the image recognition method for intelligent transportation provided by the embodiment of the present disclosure, the audit operation of determining whether the vehicle has a license plate replacement can be automatically completed, thereby liberating manpower and improving the processing efficiency of the audit operation.

According to an embodiment of the present disclosure, the image recognition method for intelligent transportation may further include the following operations.

For example, different multiple target vehicle images are determined based on different multiple reference travel information. Each of the plurality of target vehicle images is matched with the reference vehicle image, respectively. The travel information of each of the plurality of target vehicle images is different. The driving trajectory of the vehicle to be identified may be determined based on the respective driving information of the multiple target vehicle images.

By using the image recognition method for intelligent transportation provided by the embodiments of the present disclosure, the tracking of the driving track of the vehicle can be automatically completed, manpower is liberated, and the processing efficiency of the tracking of the vehicle can be improved.

FIG. 5 schematically shows a block diagram of an image recognition apparatus for intelligent transportation according to an embodiment of the present disclosure.

As shown in FIG. 5 , the image recognition apparatus 500 for intelligent transportation may include a first determination module 510 , a screening module 520 , and a second determination module 530 .

The first determination module 510 is configured to determine reference driving information related to the vehicle to be identified in the reference vehicle image.

The screening module 520 is configured to determine a target vehicle image subset from the vehicle image set based on the reference driving information.

The second determining module 530 is configured to determine a target vehicle image matching the reference vehicle image from the target vehicle image subset.

According to an embodiment of the present disclosure, the image recognition apparatus for intelligent transportation may further include a recognition module.

An identification module for identifying the reference attribute information of the reference vehicle image, wherein the reference attribute information is the reference attribute information related to the vehicle to be identified in the reference vehicle image, so as to perform the determination from the target vehicle image subset based on the reference attribute information Operations on the target vehicle image that match the reference vehicle image.

According to an embodiment of the present disclosure, the second determination module may include a first determination unit, and a first screening unit.

The first determining unit is configured to determine a plurality of initial target vehicle images from a subset of target vehicle images.

The first screening unit is configured to determine a target vehicle image from a plurality of initial target vehicle images based on the reference attribute information.

According to an embodiment of the present disclosure, the second determination module may include a second determination unit, a third determination unit, a fourth determination unit, and a second screening unit.

The second determining unit is configured to determine at least one initial target vehicle image that matches the reference vehicle image from the subset of target vehicle images.

The third determining unit is configured to determine the image quantity of at least one initial target vehicle image.

The fourth determining unit is configured to use the at least one initial target vehicle image as the target vehicle image when the image quantity of the at least one initial target vehicle image is determined to be less than or equal to a predetermined threshold.

The second screening unit is configured to determine a target vehicle image from a plurality of initial target vehicle images based on the reference attribute information when the number of images of the at least one initial target vehicle image is determined to be greater than a predetermined threshold.

According to an embodiment of the present disclosure, the image recognition apparatus for intelligent transportation may further include a third determination module, and a fourth determination module.

The third determining module is configured to determine, for each vehicle image in the vehicle image set, the photographing position information and photographing time information of the vehicle image.

The fourth determination module is configured to determine the vehicle-related driving information in the vehicle image based on the shooting position information and the shooting time information, so as to perform the reference driving information based on the reference driving information and the driving information, and determine the target vehicle from the vehicle image set. Operations on image subsets.

According to an embodiment of the present disclosure, the first screening unit or the second screening unit includes: an identification subunit, a calculation subunit, and a determination subunit.

The identifying subunit is used for identifying vehicle attribute information of the initial target vehicle image for each initial target vehicle image in the plurality of initial target vehicle images.

The calculation subunit is used for determining the attribute similarity between the initial target vehicle image and the reference vehicle image based on the reference attribute information and the vehicle attribute information.

The determining subunit is used for determining a target vehicle image from a plurality of initial target vehicle images based on the attribute similarity.

According to an embodiment of the present disclosure, the vehicle attribute information includes at least one of the following:

Vehicle model information, vehicle color information, license plate information, and license plate type information.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are processed by the at least one processor The processor executes to enable at least one processor to execute the method as an embodiment of the present disclosure.

According to an embodiment of the present disclosure, there is a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform a method according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a computer program product includes a computer program that, when executed by a processor, implements a method as an embodiment of the present disclosure.

6 shows a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in this chapter, their connections and relationships, and their functions are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 6 , the device 600 includes a computing unit 601 that can be executed according to a computer program stored in a read only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603 Various appropriate actions and handling. In the RAM 603, various programs and data necessary for the operation of the device 600 can also be stored. The computing unit 601 , the ROM 602 , and the RAM 603 are connected to each other through a bus 604 . An input/output (I/O) interface 605 is also connected to bus 604 .

Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, an optical disk, etc. ; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 609 allows the device 600 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

Computing unit 601 may be various general-purpose and/or special-purpose processing components with processing and computing capabilities. Some examples of computing units 601 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as an image recognition method for intelligent transportation. For example, in some embodiments, an image recognition method for intelligent transportation may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608 . In some embodiments, part or all of the computer program may be loaded and/or installed on device 600 via ROM 602 and/or communication unit 609 . When the computer program is loaded into RAM 603 and executed by computing unit 601, one or more steps of the image recognition method for intelligent transportation described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured by any other suitable means (eg, by means of firmware) to perform the image recognition method for intelligent transportation.

Various implementations of the systems and techniques described herein above may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips system (SOC), complex programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpretable on a programmable system including at least one programmable processor that The processor, which may be a special purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device an output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, performs the functions/functions specified in the flowcharts and/or block diagrams. Action is implemented. The program code may execute entirely on the machine, partly on the machine, partly on the machine and partly on a remote machine as a stand-alone software package or entirely on the remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with the instruction execution system, apparatus or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), fiber optics, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having a display device (eg, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user ); and a keyboard and pointing device (eg, a mouse or trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback); and can be in any form (including acoustic input, voice input, or tactile input) to receive input from the user.

The systems and techniques described herein may be implemented on a computing system that includes back-end components (eg, as a data server), or a computing system that includes middleware components (eg, an application server), or a computing system that includes front-end components (eg, a user's computer having a graphical user interface or web browser through which a user may interact with implementations of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

A computer system can include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a distributed system server, or a server combined with blockchain.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, the steps described in the present disclosure can be executed in parallel, sequentially, or in different orders. As long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, there is no limitation herein.

The above-mentioned specific embodiments do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure should be included within the protection scope of the present disclosure.

Claims (17)

1. An image recognition method for intelligent transportation, comprising: determining reference travel information related to the vehicle to be identified in the reference vehicle image; determining a target vehicle image subset from the vehicle image set based on the reference travel information; and A target vehicle image that matches the reference vehicle image is determined from the subset of target vehicle images. 2. The method of claim 1, further comprising: Identifying reference attribute information of the reference vehicle image, wherein the reference attribute information is reference attribute information related to a vehicle to be identified in the reference vehicle image, so that based on the reference attribute information, the An operation of determining a target vehicle image from a subset of target vehicle images that matches the reference vehicle image. 3. The method of claim 2, wherein the determining from the subset of target vehicle images a target vehicle image that matches the reference vehicle image comprises: determining a plurality of initial target vehicle images from the subset of target vehicle images; and The target vehicle image is determined from the plurality of initial target vehicle images based on the reference attribute information. 4. The method of claim 2, wherein the determining from the subset of target vehicle images a target vehicle image that matches the reference vehicle image comprises: determining from the subset of target vehicle images at least one initial target vehicle image that matches the reference vehicle image; determining an image number of the at least one initial target vehicle image; In the case where it is determined that the number of images of the at least one initial target vehicle image is less than or equal to a predetermined threshold, using the at least one initial target vehicle image as the target vehicle image; and In a case where it is determined that the number of images of the at least one initial target vehicle image is greater than the predetermined threshold, the target vehicle image is determined from the plurality of initial target vehicle images based on the reference attribute information. 5. The method of any one of claims 1 to 4, further comprising: For each vehicle image in the set of vehicle images, determining photographing location information and photographing time information of the vehicle image; and Based on the photographing position information and the photographing time information, vehicle-related travel information in the vehicle image is determined, so as to perform the based on the reference travel information based on the reference travel information and the travel information, from The operation of determining a subset of target vehicle images in a collection of vehicle images. 6. The method according to claim 3 or 4, wherein the determining the target vehicle image from the plurality of initial target vehicle images based on the reference attribute information comprises: For each initial target vehicle image in the plurality of initial target vehicle images, identifying vehicle attribute information of the initial target vehicle image; determining an attribute similarity between the initial target vehicle image and the reference vehicle image based on the reference attribute information and the vehicle attribute information; and The target vehicle image is determined from the plurality of initial target vehicle images based on the attribute similarity. 7. The method of claim 2, wherein the vehicle attribute information includes at least one of the following: Vehicle model information, vehicle color information, license plate information, and license plate type information. 8. An image recognition device for intelligent transportation, comprising: a first determining module, configured to determine reference driving information related to the vehicle to be identified in the reference vehicle image; a screening module for determining a target vehicle image subset from the vehicle image set based on the reference travel information; and A second determining module, configured to determine a target vehicle image matching the reference vehicle image from the target vehicle image subset. 9. The apparatus of claim 8, further comprising: An identification module, configured to identify reference attribute information of the reference vehicle image, wherein the reference attribute information is reference attribute information related to the vehicle to be identified in the reference vehicle image, so as to execute the execution based on the reference attribute information The operation of determining, from the subset of target vehicle images, a target vehicle image that matches the reference vehicle image. 10. The apparatus of claim 9, wherein the second determining module comprises: a first determining unit for determining a plurality of initial target vehicle images from the subset of target vehicle images; and A first screening unit, configured to determine the target vehicle image from the plurality of initial target vehicle images based on the reference attribute information. 11. The apparatus of claim 10, wherein the second determining module comprises: a second determining unit, configured to determine at least one initial target vehicle image that matches the reference vehicle image from the subset of target vehicle images; a third determining unit, configured to determine the image quantity of the at least one initial target vehicle image; a fourth determination unit, configured to use the at least one initial target vehicle image as the target vehicle image when the image quantity of the at least one initial target vehicle image is determined to be less than or equal to a predetermined threshold; and a second screening unit, configured to determine the target from the plurality of initial target vehicle images based on the reference attribute information when it is determined that the number of images of the at least one initial target vehicle image is greater than the predetermined threshold vehicle image. 12. The apparatus of any one of claims 8 to 11, further comprising: a third determining module, configured to determine, for each vehicle image in the vehicle image set, the photographing position information and photographing time information of the vehicle image; and A fourth determination module, configured to determine vehicle-related travel information in the vehicle image based on the photographing position information and the photographing time information, so as to execute the based on the reference travel information and the travel information. The reference travel information determines the operation of the target vehicle image subset from the vehicle image set. 13. The apparatus of claim 11, wherein the first screening unit or the second screening unit comprises: an identification subunit, configured to identify vehicle attribute information of the initial target vehicle image for each initial target vehicle image in the plurality of initial target vehicle images; a calculation subunit for determining an attribute similarity between the initial target vehicle image and the reference vehicle image based on the reference attribute information and the vehicle attribute information; and and a determination subunit for determining the target vehicle image from the plurality of initial target vehicle images based on the attribute similarity. 14. The apparatus of claim 9, wherein the vehicle attribute information includes at least one of the following: Vehicle model information, vehicle color information, license plate information, and license plate type information. 15. An electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the execution of any one of claims 1 to 7 Methods. 16. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1 to 7. 17. A computer program product comprising a computer program which, when executed by a processor, implements the method of any one of claims 1 to 7.

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