CN113569912A - Vehicle identification method and device, electronic equipment and storage medium - Google Patents
Vehicle identification method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The present disclosure provides a vehicle identification method, an apparatus, an electronic device and a storage medium, which relate to the field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, and the like, and can be specifically used in smart cities and intelligent traffic scenes. The specific implementation scheme is as follows: acquiring an image of a vehicle to be identified, and extracting first global feature information of the image; acquiring at least one candidate vehicle based on the first global feature information; extracting first component characteristic information of a vehicle to be identified from the image; and acquiring a target vehicle matched with the vehicle to be identified based on the first component characteristic information from at least one candidate vehicle. According to the vehicle identification method and device, the vehicle to be identified is accurately identified based on the global features and the component features, so that the vehicle with high similarity can be screened out from the appearance and/or the posture to serve as the target vehicle, and the accuracy of vehicle identification is improved.
Description
Technical Field
The utility model relates to an artificial intelligence field especially relates to technical field such as computer vision, deep learning, specifically can be used to under the scene of wisdom city and intelligent transportation.
Background
In the related art, the vehicle posture in the vehicle picture changes with the change of the shooting angle, and therefore, when vehicle recognition is performed through the appearance characteristics of the vehicle, it is often intended to recognize that two vehicles with similar postures are the same vehicle. Therefore, how to accurately identify the vehicle has become one of important research directions.
Disclosure of Invention
The disclosure provides a vehicle identification method, a vehicle identification device, an electronic device and a storage medium.
According to an aspect of the present disclosure, there is provided a vehicle identification method including:
acquiring an image of a vehicle to be identified, and extracting first global feature information of the image;
acquiring at least one candidate vehicle based on the first global feature information;
extracting first component characteristic information of a vehicle to be identified from the image;
and acquiring a target vehicle matched with the vehicle to be identified based on the first component characteristic information from at least one candidate vehicle.
According to the vehicle identification method and device, the vehicle to be identified is accurately identified based on the global features and the component features, so that the vehicle with high similarity can be screened out from the appearance and/or the posture to serve as the target vehicle, and the accuracy of vehicle identification is improved.
According to another aspect of the present disclosure, there is provided a vehicle identification device including:
the global feature extraction module is used for acquiring an image of a vehicle to be identified and extracting first global feature information of the image;
a candidate vehicle obtaining module for obtaining at least one candidate vehicle based on the first global feature information;
the component feature extraction module is used for extracting first component feature information of the vehicle to be identified from the image;
and the target vehicle acquisition module is used for acquiring a target vehicle matched with the vehicle to be identified from at least one candidate vehicle on the basis of the first component characteristic information.
According to another aspect of the present disclosure, there is provided an electronic device comprising at least one processor, and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the vehicle identification method of the embodiment of the first aspect of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the vehicle identification method of the first aspect of 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 the vehicle identification method of an embodiment of the first aspect of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a flow chart of a vehicle identification method according to one embodiment of the present disclosure;
FIG. 2 is a flow chart of a vehicle identification method according to one embodiment of the present disclosure;
FIG. 3 is a flow chart of a vehicle identification method according to one embodiment of the present disclosure;
FIG. 4 is a flow chart of a vehicle identification method according to one embodiment of the present disclosure;
FIG. 5 is a block diagram of a vehicle identification device according to one embodiment of the present disclosure;
fig. 6 is a block diagram of an electronic device for implementing a vehicle identification method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. 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 in the following description for clarity and conciseness.
In order to facilitate understanding of the present disclosure, the following description is first briefly made to the technical field to which the present disclosure relates.
Artificial intelligence is the subject of research that makes computers simulate some human mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), both at the hardware level and at the software level. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning technology, a deep learning technology, a big data processing technology, a knowledge map technology and the like.
Deep learning is the intrinsic law and expression level of the learning sample data, and the information obtained in the learning process is very helpful for the interpretation of data such as characters, images and sounds. The final aim of the method is to enable the machine to have the analysis and learning capability like a human, and to recognize data such as characters, images and sounds. Deep learning is a complex machine learning algorithm, and achieves the effect in speech and image recognition far exceeding the prior related art.
The intelligent transportation is a comprehensive transportation management technology which is established by effectively integrating and applying advanced information technology, data communication transmission technology, electronic sensing technology, control technology, computer technology and the like to the whole ground transportation management system, plays a role in a large range in all directions, and is real-time, accurate and efficient.
Computer vision is a interdisciplinary field of science, studying how computers gain a high level of understanding from digital images or videos. From an engineering point of view, it seeks for an automated task that the human visual system can accomplish. Computer vision tasks include methods of acquiring, processing, analyzing and understanding digital images, and methods of extracting high-dimensional data from the real world to produce numerical or symbolic information, for example, in the form of decisions.
The vehicle identification method, apparatus, electronic device, and storage medium of the present disclosure are described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a vehicle identification method according to an embodiment of the present disclosure, as shown in fig. 1, the method including the steps of:
s101, obtaining an image of a vehicle to be identified, and extracting first global feature information of the image.
And acquiring an image of the vehicle to be identified from a certain angle. In the embodiment of the present disclosure, the image of the vehicle to be recognized may be an image including a part of the vehicle to be recognized, or may be an image including the entire vehicle to be recognized. Alternatively, the image of the vehicle to be identified may be a still image taken, or may be a video image or a composite image in a sequence of video frames, or the like.
The method includes the steps of extracting global features from an image of a vehicle to be identified, and extracting first global feature information, optionally inputting the image of the vehicle to be identified into a neural network, and extracting the first global feature information of the image, wherein the first global feature information may be global features represented by vectors. For example, a feature extraction layer of a neural network is utilized to perform convolution operation and pooling operation on the image of the vehicle to be identified, so as to acquire first global feature information.
Alternatively, the neural network may be any suitable neural network that can extract global feature information, including but not limited to a global convolutional neural network, and the like.
And S102, acquiring at least one candidate vehicle based on the first global feature information.
In the present disclosure, images of different vehicles are collected in advance, and the images of different vehicles are stored in a database. When the first global feature information is acquired, at least one vehicle image similar to the image of the vehicle to be identified may be acquired based on the first global feature information, and then the vehicle corresponding to the vehicle image is taken as a candidate vehicle.
In some implementations, second global feature information of existing vehicle images in the database is extracted and matched with the first global feature information, and at least one candidate vehicle is obtained according to a matching result. The process of extracting the second global feature information may refer to the related description of extracting the first global feature information in step S101, and is not described herein again.
S103, extracting first component characteristic information of the vehicle to be identified from the image.
Due to the fact that the angles for collecting the images of the vehicles to be recognized are different, the vehicle parts contained in the images of the vehicles to be recognized are different. For example, in some implementations, the image of the vehicle to be recognized is captured from the front of the vehicle, and the captured image of the vehicle to be recognized may include a bumper, a logo, left and/or right rear view mirrors, left and/or right headlights, etc.; in some implementations, the vehicle to be recognized is image-captured from the rear of the vehicle, and the acquired image of the vehicle to be recognized may include a rear turn signal lamp, a trunk, and the like.
In order to improve the accuracy of vehicle identification, the embodiment of the disclosure further screens candidate vehicles according to vehicle components in the images, and acquires target vehicles from the candidate vehicles. In some implementations, a component of a vehicle to be identified in an image is identified, an image of a vehicle component region is obtained, and first component feature information of the vehicle component is extracted from the image of the vehicle component region, that is, the first component feature information of the vehicle to be identified is extracted. Alternatively, the first component feature information of the vehicle to be recognized may be extracted from the image using a neural network.
And S104, acquiring a target vehicle matched with the vehicle to be identified from at least one candidate vehicle based on the first component characteristic information.
After the first component feature information is acquired, at least one vehicle image similar to the image of the vehicle to be identified on the component can be acquired based on the first component feature information, and further screening of the candidate vehicles is achieved. As a possible implementation manner, the second component feature information may be extracted from a picture of the candidate vehicle, the similarity between the first component feature information of the vehicle to be identified and the second component feature information of the candidate vehicle may be determined, and the candidate vehicle matching the vehicle to be identified may be taken as the target vehicle according to the similarity of the vehicle components.
In the embodiment of the disclosure, an image of a vehicle to be identified is acquired, first global feature information of the image is extracted, at least one candidate vehicle is acquired based on the first global feature information, first component feature information of the vehicle to be identified is extracted from the image, and a target vehicle matched with the vehicle to be identified is acquired from the at least one candidate vehicle based on the first component feature information. According to the vehicle identification method and device, the vehicle to be identified is accurately identified based on the global features and the component features, so that the vehicle with high similarity can be screened out from the appearance and/or the posture to serve as the target vehicle, and the accuracy of vehicle identification is improved.
Fig. 2 is a flowchart of a vehicle identification method according to another embodiment of the present disclosure, and as shown in fig. 2, on the basis of the above embodiment, at least one candidate vehicle is obtained based on the first global feature information, including the following steps:
s201, obtaining the similarity between the first global feature information and the second global feature information of each vehicle in the database.
And extracting second global feature information of the image of the vehicle in the database, matching the second global feature information with the first global feature information, and obtaining the similarity between the first global feature information and the second global feature information as the similarity between the vehicle to be identified and each vehicle in the database. Optionally, the cosine distance between the first global feature information and the second global feature information may be obtained as the similarity between the vehicle to be identified and each vehicle in the database.
S202, sorting all vehicles in the database according to the similarity, and screening at least one candidate vehicle according to the sorting.
Sorting all vehicles in the database according to the similarity, optionally, taking the vehicle with the similarity larger than a preset threshold as a candidate vehicle, and also taking N vehicles with the maximum similarity as candidate vehicles; where N is a preset positive integer greater than 0, and the vehicle ranked in the top N after ranking may also be used as a candidate vehicle.
In the embodiment of the disclosure, the similarity between the first global feature information and the second global feature information of each vehicle in the database is obtained; and sorting all vehicles in the database according to the similarity, and screening at least one candidate vehicle according to the sorting. According to the method and the device, the candidate vehicles are primarily screened out from the database by effectively utilizing the first global feature information, the calculated amount of a subsequent screening process is reduced, and the efficiency of subsequently identifying the target vehicles is improved conveniently.
Fig. 3 is a flowchart of a vehicle identification method according to an embodiment of the present disclosure, as shown in fig. 3, the method including the steps of:
s301, carrying out component classification detection on the image to acquire a detection frame of the vehicle component.
In some implementations, the image of the vehicle to be recognized may be input to a classification detection network, the type, position, and area of the vehicle component may be output, and the circumscribed rectangle of the vehicle component area may be used as a detection frame of the vehicle component.
S302, extracting local feature information at the corresponding position of the detection frame.
Inputting the image slice region corresponding to the detection frame into the region-of-interest alignment layer, and extracting local feature information by the region-of-interest alignment layer, that is, dividing the image region contained in the detection frame into a plurality of units, calculating and fixing four coordinate positions in each unit, calculating the values of the four positions by using a bilinear interpolation method, and then performing maximum pooling operation to obtain the local feature information at the position corresponding to the detection frame.
And S303, generating first component characteristic information according to the local characteristic information.
And inputting the local characteristic information into the full-connection layer, and performing feature fusion between the parts on the local characteristic information corresponding to each vehicle part to acquire first part characteristic information.
In the embodiment of the disclosure, the image is subjected to component classification detection to obtain a detection frame of a vehicle component; and extracting local feature information at a position corresponding to the detection frame, and generating first part feature information according to the local feature information. The embodiment of the disclosure performs feature fusion on the local feature information to obtain the first component feature information, so that the target vehicle can be conveniently determined from the candidate vehicles in the follow-up process, and the accuracy of vehicle identification is improved.
Fig. 4 is a flowchart of a vehicle identification method according to an embodiment of the present disclosure, as shown in fig. 4, the method including the steps of:
s401, second component characteristic information of the candidate vehicle is obtained.
For the content of obtaining the second component feature information of the candidate vehicle in step S401, reference may be made to the description of obtaining the first component feature information in the foregoing embodiment, and details are not repeated here.
S402, for each candidate vehicle, acquiring similarity between first component characteristic information of any vehicle component and second component characteristic information of any vehicle component.
And for each candidate vehicle, matching the first component characteristic information of any vehicle component with the second component characteristic information of any vehicle component to acquire the similarity of the vehicle components.
And S403, identifying the target vehicle from at least one candidate vehicle according to the similarity of the vehicle parts.
In some implementations, a target candidate vehicle with the similarity of each vehicle component meeting a set similarity threshold is selected from at least one candidate vehicle, the number of the target candidate vehicles is obtained, the similarity threshold is increased in response to the number being greater than a set value, the target candidate vehicles are re-selected until the number is not greater than the set number, and the target vehicle is obtained. For example, in some implementations, the image of the vehicle to be recognized includes vehicle components such as a bumper, a right side rearview mirror, and a right side headlamp, and the candidate vehicle in which the similarity of each vehicle component satisfies a set similarity threshold is taken as a target candidate vehicle, if the number of the target candidate vehicles satisfies a set numerical value, the target candidate vehicle is determined to be the target vehicle, otherwise, the similarity threshold is increased, and the target candidate vehicle is reselected according to the updated similarity threshold until the number is not greater than the set number, so as to obtain the target vehicle. Alternatively, the set number may be 1.
In some implementations, for each candidate vehicle, the overall similarity between the candidate vehicle and the vehicle to be identified is obtained according to the similarity of each vehicle component, and the candidate vehicle with the greatest overall similarity is selected as the target vehicle. For example, in some implementations, the image of the vehicle to be recognized includes vehicle components such as a bumper, a right side rearview mirror, and a right side headlamp, the similarity of each vehicle component is averaged, the overall similarity between the candidate vehicle and the vehicle to be recognized is obtained, and the candidate vehicle with the largest overall similarity is selected as the target vehicle. Alternatively, the size or the importance degree of the vehicle component may be used as a weight, and a weighted average of the similarity of each vehicle component may be used as the overall similarity between the candidate vehicle and the vehicle to be identified.
In the embodiment of the disclosure, second component characteristic information of a candidate vehicle is acquired; for each candidate vehicle, acquiring similarity between first component characteristic information of any vehicle component and second component characteristic information of any vehicle component; and identifying the target vehicle from the at least one candidate vehicle according to the similarity of the vehicle parts. According to the method and the device for identifying the target vehicle, the target vehicle is determined from the candidate vehicles according to the first component characteristic information and the second component characteristic information, the influence of non-target vehicles similar to the vehicle to be identified in the candidate vehicles can be reduced, the vehicle to be identified is accurately identified, accordingly, vehicles with high similarity can be screened out from the appearance and/or the posture to serve as the target vehicle, and the accuracy of vehicle identification is improved.
Fig. 5 is a block diagram of a vehicle recognition device according to an embodiment of the present disclosure, and as shown in fig. 5, the vehicle recognition device 500 includes:
the global feature extraction module 510 is configured to obtain an image of a vehicle to be identified, and extract first global feature information of the image;
a candidate vehicle obtaining module 520, configured to obtain at least one candidate vehicle based on the first global feature information;
a component feature extraction module 530, configured to extract first component feature information of the vehicle to be identified from the image;
and a target vehicle obtaining module 540, configured to obtain, from the at least one candidate vehicle, a target vehicle matching the vehicle to be identified based on the first component feature information.
According to the vehicle identification method and device, the vehicle to be identified is accurately identified based on the global features and the component features, so that the vehicle with high similarity can be screened out from the appearance and/or the posture to serve as the target vehicle, and the accuracy of vehicle identification is improved.
It should be noted that the foregoing explanation of the embodiment of the vehicle identification method is also applicable to the vehicle identification device of the embodiment, and is not repeated here.
Further, in a possible implementation manner of the embodiment of the present disclosure, the candidate vehicle obtaining module 520 is further configured to: acquiring the similarity between the first global feature information and second global feature information of each vehicle in the database; and sorting all vehicles in the database according to the similarity, and screening at least one candidate vehicle according to the sorting.
Further, in a possible implementation manner of the embodiment of the present disclosure, the component feature extraction module 530 is further configured to: carrying out component classification detection on the image to acquire a detection frame of the vehicle component; extracting local characteristic information at a position corresponding to the detection frame; first component feature information is generated from the local feature information.
Further, in a possible implementation manner of the embodiment of the present disclosure, the component feature extraction module 530 is further configured to: and performing feature fusion among the parts on the local feature information corresponding to each vehicle part to acquire first part feature information.
Further, in a possible implementation manner of the embodiment of the present disclosure, the component feature extraction module 530 is further configured to: and inputting the image area corresponding to the detection frame into the region of interest alignment layer, and extracting local characteristic information by the region of interest alignment layer.
Further, in a possible implementation manner of the embodiment of the present disclosure, the target vehicle obtaining module 540 is further configured to: acquiring second component characteristic information of the candidate vehicle; for each candidate vehicle, acquiring similarity between first component characteristic information of any vehicle component and second component characteristic information of any vehicle component; and identifying the target vehicle from the at least one candidate vehicle according to the similarity of the vehicle parts.
Further, in a possible implementation manner of the embodiment of the present disclosure, the target vehicle obtaining module 540 is further configured to: selecting a target candidate vehicle with the similarity of each vehicle part meeting a set similarity threshold from at least one candidate vehicle; and acquiring the number of the target candidate vehicles, raising the similarity threshold in response to the fact that the number is larger than the set value, and reselecting the target candidate vehicles until the number is not larger than the set number, so as to obtain the target vehicles.
Further, in a possible implementation manner of the embodiment of the present disclosure, the target vehicle obtaining module 540 is further configured to: aiming at each candidate vehicle, acquiring the overall similarity between the candidate vehicle and the vehicle to be identified according to the similarity of each vehicle part; and selecting the candidate vehicle with the maximum overall similarity as the target vehicle.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes 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. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 executes the respective methods and processes described above, such as the vehicle identification method. For example, in some embodiments, the vehicle identification method may be implemented as a computer software program tangibly embodied in 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 onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the vehicle identification method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the vehicle identification method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one 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 codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may 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, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (19)
1. A vehicle identification method, comprising:
acquiring an image of a vehicle to be identified, and extracting first global feature information of the image;
acquiring at least one candidate vehicle based on the first global feature information;
extracting first component characteristic information of the vehicle to be identified from the image;
and acquiring a target vehicle matched with the vehicle to be identified based on the first component characteristic information from the at least one candidate vehicle.
2. The method of claim 1, wherein the obtaining at least one candidate vehicle based on the first global feature information comprises:
acquiring the similarity between the first global feature information and second global feature information of each vehicle in a database;
and sorting all vehicles in the database according to the similarity, and screening the at least one candidate vehicle according to the sorting.
3. The method according to claim 1 or 2, wherein the extracting first component feature information of the vehicle to be identified from the image comprises:
carrying out component classification detection on the image to acquire a detection frame of the vehicle component;
extracting local characteristic information at a position corresponding to the detection frame;
and generating the first part characteristic information according to the local characteristic information.
4. The method of claim 3, wherein the generating the first component feature information from the local feature information comprises:
and performing feature fusion among the parts on the local feature information corresponding to each vehicle part to acquire the first part feature information.
5. The method of claim 3, wherein the extracting the local feature information at the corresponding position of the detection frame comprises:
and inputting the image slice region corresponding to the detection frame into an interested region alignment layer, and extracting the local characteristic information by the interested region alignment layer.
6. The method of claim 3, wherein the obtaining, from the at least one candidate vehicle, a target vehicle matching the vehicle to be identified based on the first component feature information comprises:
acquiring second component characteristic information of the candidate vehicle;
for each candidate vehicle, acquiring similarity between the first component characteristic information of any vehicle component and the second component characteristic information of any vehicle component;
identifying the target vehicle from the at least one candidate vehicle based on the similarity of the vehicle components.
7. The method of claim 6, wherein said identifying the target vehicle from the at least one candidate vehicle based on the similarity of the vehicle components comprises:
selecting a target candidate vehicle with the similarity of each vehicle part meeting a set similarity threshold from the at least one candidate vehicle;
and acquiring the number of the target candidate vehicles, raising the similarity threshold in response to the fact that the number is larger than a set numerical value, and reselecting the target candidate vehicles until the number is not larger than the set number, so as to obtain the target vehicles.
8. The method of claim 6, wherein said identifying the target vehicle from the at least one candidate vehicle based on the similarity of the vehicle components comprises:
for each candidate vehicle, acquiring the overall similarity between the candidate vehicle and the vehicle to be identified according to the similarity of each vehicle component;
and selecting the candidate vehicle with the maximum overall similarity as the target vehicle.
9. A vehicle identification device comprising:
the global feature extraction module is used for acquiring an image of a vehicle to be identified and extracting first global feature information of the image;
a candidate vehicle obtaining module for obtaining at least one candidate vehicle based on the first global feature information;
the component feature extraction module is used for extracting first component feature information of the vehicle to be identified from the image;
and the target vehicle acquisition module is used for acquiring a target vehicle matched with the vehicle to be identified from the at least one candidate vehicle on the basis of the first component characteristic information.
10. The apparatus of claim 9, wherein the candidate vehicle acquisition module is further configured to:
acquiring the similarity between the first global feature information and second global feature information of each vehicle in a database;
and sorting all vehicles in the database according to the similarity, and screening the at least one candidate vehicle according to the sorting.
11. The apparatus of claim 9 or 10, wherein the component feature extraction module is further configured to:
carrying out component classification detection on the image to acquire a detection frame of the vehicle component;
extracting local characteristic information at a position corresponding to the detection frame;
and generating the first part characteristic information according to the local characteristic information.
12. The apparatus of claim 11, wherein the component feature extraction module is further configured to:
and performing feature fusion among the parts on the local feature information corresponding to each vehicle part to acquire the first part feature information.
13. The apparatus of claim 11, wherein the component feature extraction module is further configured to:
and inputting the image slice region corresponding to the detection frame into an interested region alignment layer, and extracting the local characteristic information by the interested region alignment layer.
14. The apparatus of claim 11, wherein the target vehicle acquisition module is further configured to:
acquiring second component characteristic information of the candidate vehicle;
for each candidate vehicle, acquiring similarity between the first component characteristic information of any vehicle component and the second component characteristic information of any vehicle component;
identifying the target vehicle from the at least one candidate vehicle based on the similarity of the vehicle components.
15. The apparatus of claim 14, wherein the target vehicle acquisition module is further configured to:
selecting a target candidate vehicle with the similarity of each vehicle part meeting a set similarity threshold from the at least one candidate vehicle;
and acquiring the number of the target candidate vehicles, raising the similarity threshold in response to the fact that the number is larger than a set numerical value, and reselecting the target candidate vehicles until the number is not larger than the set number, so as to obtain the target vehicles.
16. The apparatus of claim 14, wherein the target vehicle acquisition module is further configured to:
for each candidate vehicle, acquiring the overall similarity between the candidate vehicle and the vehicle to be identified according to the similarity of each vehicle component;
and selecting the candidate vehicle with the maximum overall similarity as the target vehicle.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.
19. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-8.
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