CN111145365A - Method, device, computer storage medium and terminal for realizing classification processing - Google Patents
Method, device, computer storage medium and terminal for realizing classification processing Download PDFInfo
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Abstract
A method, a device, a computer storage medium and a terminal for realizing classification processing comprise: determining the vehicle type of the vehicle to be analyzed based on a preset classification model; determining the vehicle model of the vehicle to be analyzed by adopting a regression model corresponding to the vehicle type of the vehicle to be analyzed; wherein the vehicle types include: passenger cars and vans; the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll. According to the embodiment of the invention, the regression model is adopted to determine the vehicle model after the vehicle type is determined, so that the accurate identification of the vehicle is realized, and the technical support is provided for an auxiliary Electronic Toll Collection (ETC) system to obtain accurate vehicle information.
Description
Technical Field
The present disclosure relates to, but not limited to, image processing technologies, and more particularly, to a method, an apparatus, a computer storage medium, and a terminal for implementing a classification process.
Background
The toll road refers to a road on which a toll is collected on a passing vehicle for repayment of loan or collection of capital. Different vehicles can have different charging standards, for example, passenger cars such as cars below seven seats, cars above seven seats, passenger buses, large buses and the like, low-load trucks, high-load trucks and the like can have different charging standards; the general formula is as follows: the larger the passenger car is, the higher the toll is collected; the larger the truck is, the higher the toll is collected; the toll collection standards for passenger cars and trucks are different. Electronic Toll Collection (ETC) system is used in highway or bridge, carries out the automated system that the Toll was collected to the transit vehicle, and the ETC discerns the vehicle through the on-vehicle Electronic tags who sets up on the vehicle, realizes the Toll settlement after networking through computer networking technology and bank, can make the vehicle need not to park can accomplish the payment of Toll through the ETC.
Although ETC can save the payment time of toll and improve the vehicle passing efficiency; however, the ETC charges communication fees based on the vehicle-mounted electronic tag mounted on the vehicle, and when the vehicle owner replaces the vehicle-mounted electronic tag manually, the ETC makes a misjudgment because of the vehicle-mounted electronic tag. In order to overcome the misjudgment problem caused by ETC, other means are needed to assist in determining the vehicle information. At present, the related art generally secondarily judges the vehicle type based on the technology of deep learning image classification, and includes a classification model based on image data, the classification model generally consists of a Convolutional Neural Network (CNN), and includes a feature extraction part and a classification part; the classification model requires a large amount of manually labeled training data for model training, and therefore, the implementation is difficult. The regression model based on the image data is similar to the classification model and can be divided into a feature extraction part and a regression part; the feature extraction part is the same as the classification model, but the regression part is simpler than the classification part and only outputs one regression value. The regression model is the same as the classification model, and the model training needs to be carried out through a large amount of artificially marked training data, so that the realization is still difficult; furthermore, the regression model cannot be used to obtain the vehicle type; although the sizes of the vehicles corresponding to different types of vehicles are different, the difference between the truck and the bus is different from that between the truck and the bus, and the bus is possibly misjudged as the truck and the bus by misjudging the bus as the bus by using the regression model for judging the types of the vehicles; therefore, the regression model cannot be used for identification of the vehicle type.
In summary, how to overcome the misjudgment generated by the ETC, and to assist the ETC to determine the vehicle information, is a technical problem to be solved.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
Embodiments of the present invention provide a method, an apparatus, a computer storage medium, and a terminal for implementing classification processing, which can provide technical support for assisting an ETC to obtain accurate vehicle information.
The embodiment of the invention provides a method for realizing classification processing, which comprises the following steps:
determining the vehicle type of the vehicle to be analyzed based on a preset classification model;
determining the vehicle model of the vehicle to be analyzed by adopting a regression model corresponding to the vehicle type of the vehicle to be analyzed;
wherein the vehicle type includes: passenger cars and vans; the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll.
In an exemplary embodiment, the classification model and the regression model include:
extracting the characteristics of the image of the vehicle to be analyzed based on the same characteristic extraction layer of the convolutional neural network CNN;
wherein, the relevant parameters of the feature extraction layer comprise: and (4) parameters configured by adopting a transfer learning method.
In an exemplary embodiment, the classification model includes:
and (4) determining a classification model according to a cross entropy loss function.
In an exemplary embodiment, the regression model is obtained based on the following loss function training:
the lambda is a super parameter with a preset value of (0, 1), y is an actual vehicle model, and y _ p is a vehicle model predicted by the regression model.
In one exemplary embodiment, after determining the vehicle model of the vehicle to be analyzed, the method further comprises:
and feeding back the determined vehicle type and vehicle model of the vehicle to be analyzed to an ETC system so that the ETC system processes the vehicle to be analyzed according to a preset strategy.
On the other hand, an embodiment of the present invention further provides an apparatus for implementing classification processing, including: a type determining unit and a model determining unit; wherein,
determining a type unit for: determining the vehicle type of the vehicle to be analyzed based on a preset classification model;
the model determining unit is used for: determining the vehicle model of the vehicle to be analyzed by adopting a regression model corresponding to the vehicle type of the vehicle to be analyzed;
wherein the vehicle type includes: passenger cars and vans; the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll.
In an exemplary embodiment, the regression model is obtained based on the following loss function training:
the lambda is a super parameter with a preset value of (0, 1), y is an actual vehicle model, and y _ p is a vehicle model predicted by the regression model.
In an exemplary embodiment, the apparatus further comprises a feedback unit for:
and feeding back the determined vehicle type and vehicle model of the vehicle to be analyzed to an ETC system so that the ETC system processes the vehicle to be analyzed according to a preset strategy.
In still another aspect, an embodiment of the present invention further provides a computer storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for implementing classification processing is implemented.
In another aspect, an embodiment of the present invention further provides a terminal, including: a memory and a processor, the memory having a computer program stored therein; wherein,
the processor is configured to execute the computer program in the memory;
the computer program, when executed by the processor, implements a method of implementing a classification process as described above.
Compared with the related art, the technical scheme of the application comprises the following steps: determining the vehicle type of the vehicle to be analyzed based on a preset classification model; determining the vehicle model of the vehicle to be analyzed by adopting a regression model corresponding to the vehicle type of the vehicle to be analyzed; wherein the vehicle types include: passenger cars and vans; the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll. According to the embodiment of the invention, the vehicle type is determined by adopting the regression model after the vehicle type is determined, so that the accurate identification of the vehicle is realized, and the technical support is provided for assisting ETC to obtain accurate vehicle information.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a flow chart of a method for implementing a classification process according to an embodiment of the present invention;
FIG. 2 is a block diagram of an apparatus for implementing classification processing according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a system composition of an application example of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
The steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
Fig. 1 is a flowchart of a method for implementing classification processing according to an embodiment of the present invention, as shown in fig. 1, including:
wherein the vehicle type includes: passenger cars and vans;
wherein the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll.
In an exemplary embodiment, the classification model and the regression model include:
extracting the features of the image of the vehicle to be analyzed based on the same feature extraction layer of the Convolutional Neural Network (CNN);
wherein, the relevant parameters of the feature extraction layer comprise: and (4) parameters configured by adopting a transfer learning method.
In an exemplary embodiment, the classification model includes:
and (4) determining a classification model according to a cross entropy loss function.
In an exemplary embodiment, the regression model is obtained based on the following loss function training:
the lambda is a super parameter with a preset value of (0, 1), y is an actual vehicle model, and y _ p is a vehicle model predicted by the regression model.
It should be noted that the smaller lambda of the embodiment of the present invention, the more the regression model tends to predict that the vehicle model is not greater than a for the vehicle with type a.
In an exemplary embodiment, after determining the vehicle model of the vehicle to be analyzed, the method of the embodiment of the present invention further includes:
and feeding back the determined vehicle type and vehicle model of the vehicle to be analyzed to an ETC system so that the ETC system processes the vehicle to be analyzed according to a preset strategy.
Compared with the related art, the technical scheme of the application comprises the following steps: determining the vehicle type of the vehicle to be analyzed based on a preset classification model; determining the vehicle model of the vehicle to be analyzed by adopting a regression model corresponding to the vehicle type of the vehicle to be analyzed; wherein the vehicle types include: passenger cars and vans; the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll. According to the embodiment of the invention, the vehicle type is determined by adopting the regression model after the vehicle type is determined, so that the accurate identification of the vehicle is realized, and the technical support is provided for assisting ETC to obtain accurate vehicle information.
Fig. 2 is a block diagram of a device for implementing classification processing according to an embodiment of the present invention, as shown in fig. 2, including: a type determining unit and a model determining unit; wherein,
determining a type unit for: determining the vehicle type of the vehicle to be analyzed based on a preset classification model;
the model determining unit is used for: determining the vehicle model of the vehicle to be analyzed by adopting a regression model corresponding to the vehicle type of the vehicle to be analyzed;
wherein the vehicle type includes: passenger cars and vans; the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll.
In an exemplary embodiment, the classification model and the regression model of the embodiment of the present invention are based on the same feature extraction layer of a Convolutional Neural Network (CNN) to perform feature extraction of an image of a vehicle to be analyzed;
wherein, the relevant parameters of the feature extraction layer comprise: and (4) parameters configured by adopting a transfer learning method.
In an exemplary embodiment, the classification model includes:
and (4) determining a classification model according to a cross entropy loss function.
In an exemplary embodiment, the regression model is obtained based on the following loss function training:
the lambda is a super parameter with a preset value of (0, 1), y is an actual vehicle model, and y _ p is a vehicle model predicted by the regression model.
It should be noted that the smaller lambda of the embodiment of the present invention, the more the regression model tends to predict that the vehicle model is not greater than a for the vehicle with type a.
In an exemplary embodiment, the apparatus further comprises a feedback unit for:
and feeding back the determined vehicle type and vehicle model of the vehicle to be analyzed to an ETC system so that the ETC system processes the vehicle to be analyzed according to a preset strategy.
Compared with the related art, the technical scheme of the application comprises the following steps: determining the vehicle type of the vehicle to be analyzed based on a preset classification model; determining the vehicle model of the vehicle to be analyzed by adopting a regression model corresponding to the vehicle type of the vehicle to be analyzed; wherein the vehicle types include: passenger cars and vans; the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll. According to the embodiment of the invention, the vehicle type is determined by adopting the regression model after the vehicle type is determined, so that the accurate identification of the vehicle is realized, and the technical support is provided for assisting ETC to obtain accurate vehicle information.
The embodiment of the invention also provides a computer storage medium, wherein a computer program is stored in the computer storage medium, and when being executed by a processor, the computer program realizes the method for realizing the classification processing.
An embodiment of the present invention further provides a terminal, including: a memory and a processor, the memory having a computer program stored therein; wherein,
the processor is configured to execute the computer program in the memory;
the computer program, when executed by the processor, implements a method of implementing a classification process as described above.
The following embodiments of the present invention are briefly described in detail by using application examples, which are only used for illustrating the present invention and are not used for limiting the protection scope of the present invention.
Application example
Fig. 3 is a schematic diagram of a system configuration of an application example of the present invention, and as shown in fig. 3, the apparatus according to the embodiment of the present invention is connected to an ETC system, where the ETC system includes: the classification processing device comprises a detection unit and a processing unit, and the classification processing device comprises: the device comprises an image acquisition unit, a type determining unit and a model determining unit; wherein,
the detection unit is used for: and detecting a vehicle-mounted electronic tag arranged on the vehicle to be analyzed.
The processing unit is used for: and obtaining vehicle information based on the detected vehicle-mounted electronic tag, and assuming that the identified vehicle information is as follows: ETC _ pred _ type.
The image acquisition unit is used for: an image of a vehicle to be analyzed to pass through a toll booth (a detection area of an ETC detection unit) is acquired.
Determining a type unit for: determining the vehicle type of the vehicle to be analyzed based on a preset classification model;
the model determining unit is used for: determining the vehicle model of the vehicle to be analyzed by adopting a regression model corresponding to the vehicle type of the vehicle to be analyzed;
wherein the vehicle type includes: passenger cars and vans; the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll.
In an exemplary embodiment, the classification model and the regression model of the embodiment of the present invention are based on the same feature extraction layer of a Convolutional Neural Network (CNN) to perform feature extraction of an image of a vehicle to be analyzed;
wherein, the relevant parameters of the feature extraction layer comprise: and (4) parameters configured by adopting a transfer learning method.
Note that the feature extraction layer of the present application example includes a feature extraction layer including an existing residual neural network (RESNET), a Visual Geometry Group (VGG)16, and the like.
In an exemplary embodiment, the classification model includes:
and (4) determining a classification model according to a cross entropy loss function.
In an exemplary embodiment, the regression model is obtained based on the following loss function training:
the lambda is a super parameter with a preset value of (0, 1), y is an actual vehicle model, and y _ p is a vehicle model predicted by the regression model.
It should be noted that the smaller lambda of the embodiment of the present invention, the more the regression model tends to predict that the vehicle model is not greater than a for the vehicle with type a.
The regression model of the truck and the passenger car are the same in the application example of the invention. However, the regression models for the passenger car and the freight car use different parameters, in short two independent regression models. The present application example determines the charge type division itself of the vehicle based on whether the vehicle is a cargo or a carrier and the size and load of the vehicle, which is itself a classification value, but is actually a regression value, for different passenger cars and trucks. In the application example, the classification model and the regression model contain many model parameters, and the parameters need to be initialized randomly before training by using training set data; however, when the training data is not large, the generalization capability of the model obtained by random initialization training is not good. The present application example uses a migration learning method to configure parameters of the feature extraction layer. Taking RESNET as an example, training a RESNET model based on massive sample data, and then directly copying parameters of a feature extraction layer of the trained RESNET model to the feature extraction layer of the application example.
In the application example, the classification model and the regression model share the same feature extraction layer; in model training, the classification model and the regression model may be trained simultaneously. For an image of a vehicle to be analyzed, the result output by the present application example apparatus contains both vehicle type and vehicle model information.
In an exemplary embodiment, the present application example apparatus further includes a feedback unit, configured to:
and feeding back the determined vehicle type and the determined vehicle model of the vehicle to be analyzed to the ETC system so that the ETC system processes the vehicle to be analyzed according to a preset strategy.
The preset policy may include: when the output result of the ETC system and the result of the present application example are different in the vehicle type, a warning is made.
When the output result of the ETC system is the same as the result of the application example in vehicle type, if the vehicle types are different, the warning can be carried out, and the warning is not carried out when the vehicle type represented by the vehicle information of the output result of the ETC system is larger than the vehicle type (the traffic cost is high) of the device of the application example; when the vehicle model represented by the vehicle information of the ETC system output result is smaller than that of the vehicle model of the application example device, an alarm is given.
When an alarm occurs, after the output result of the ETC system and the output result of the classification processing device of the application example are fed back in a preset mode, relevant workers verify the charging standard manually.
According to the application example, the vehicle model is determined by adopting the regression model after the vehicle type is determined, the accurate identification of the vehicle is realized, and the technical support is provided for assisting ETC to obtain accurate vehicle information.
"one of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art. "
Claims (10)
1. A method of implementing a classification process, comprising:
determining the vehicle type of the vehicle to be analyzed based on a preset classification model;
determining the vehicle model of the vehicle to be analyzed by adopting a regression model corresponding to the vehicle type of the vehicle to be analyzed;
wherein the vehicle type includes: passenger cars and vans; the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll.
2. The method of claim 1, wherein the classification model and the regression model comprise:
extracting the characteristics of the image of the vehicle to be analyzed based on the same characteristic extraction layer of the convolutional neural network CNN;
wherein, the relevant parameters of the feature extraction layer comprise: and (4) parameters configured by adopting a transfer learning method.
3. The method of claim 1, wherein the classification model comprises:
and (4) determining a classification model according to a cross entropy loss function.
4. The method according to any one of claims 1 to 3, wherein the regression model is obtained based on the following loss function training:
the lambda is a super parameter with a preset value of (0, 1), y is an actual vehicle model, and y _ p is a vehicle model predicted by the regression model.
5. A method according to any one of claims 1 to 3, wherein after determining the vehicle model of the vehicle to be analysed, the method further comprises:
and feeding back the determined vehicle type and vehicle model of the vehicle to be analyzed to an ETC system so that the ETC system processes the vehicle to be analyzed according to a preset strategy.
6. An apparatus for implementing a classification process, comprising: a type determining unit and a model determining unit; wherein,
determining a type unit for: determining the vehicle type of the vehicle to be analyzed based on a preset classification model;
the model determining unit is used for: determining the vehicle model of the vehicle to be analyzed by adopting a regression model corresponding to the vehicle type of the vehicle to be analyzed;
wherein the vehicle type includes: passenger cars and vans; the vehicle model includes: and the preset classification number is used for distinguishing the vehicle toll.
7. The apparatus of claim 6, wherein the regression model is obtained based on the following loss function training:
the lambda is a super parameter with a preset value of (0, 1), y is an actual vehicle model, and y _ p is a vehicle model predicted by the regression model.
8. The apparatus according to claim 6 or 7, characterized in that the apparatus further comprises a feedback unit for:
and feeding back the determined vehicle type and vehicle model of the vehicle to be analyzed to an ETC system so that the ETC system processes the vehicle to be analyzed according to a preset strategy.
9. A computer storage medium having stored thereon a computer program which, when executed by a processor, implements a method of implementing a classification process according to any one of claims 1 to 5.
10. A terminal, comprising: a memory and a processor, the memory having a computer program stored therein; wherein,
the processor is configured to execute the computer program in the memory;
the computer program, when executed by the processor, implements a method of implementing a classification process as claimed in any one of claims 1 to 5.
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