CN112307236A - Data labeling method and device - Google Patents
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Abstract
The application discloses a data labeling method and a device thereof, wherein the method comprises the following steps: acquiring a vehicle-mounted image of a vehicle as sample data and acquiring state data of the vehicle; determining the state information of the vehicle according to the state data; and marking the sample data by using the state information as marking information to generate training data. By the method and the device, the vehicle-mounted image can be marked by using the determined state information while the vehicle-mounted image is acquired, and automatic data marking in the field of vehicles is realized.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a data annotation method and apparatus.
Background
Machine learning can generate various models from data, thus requiring a user to provide training data. In order to train the model by using the training data, data tagging needs to be performed on sample data in advance, where the data tagging refers to a process of describing or labeling data such as text, pictures, voice, and the like, for example, tagging a picture sample of a driving car as a danger.
The existing data labeling process is as follows: manually segmenting a data (e.g., vehicle-mounted image) annotation task into a plurality of subtasks and distributing the subtasks to a plurality of annotators; each annotator selects a corresponding single edition annotation tool for annotation according to the data type of the data of the subtasks; and after the labeling work of all the subtasks is finished, integrating and storing the data labeled by each label maker.
It can be seen that this requires a significant amount of effort by the annotator to manually annotate the data. Therefore, there is a need in the art for a solution that can automatically label data.
Disclosure of Invention
The embodiment of the application provides a data labeling method and a device thereof, which are used for at least solving the technical problems.
The embodiment of the present application further provides a data annotation method, where the method includes: acquiring sample data and marking information corresponding to the sample data; and marking the sample data by using the marking information to generate training data.
The embodiment of the application also provides a data annotation method, which comprises the steps of acquiring a vehicle-mounted image of a vehicle as sample data, and acquiring state data of the vehicle by using a vehicle-mounted auxiliary device; determining the state information of the vehicle according to the state data; and marking the sample data by using the state information as marking information to generate training data.
The embodiment of the present application further provides a data annotation device, and the method includes: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to: acquiring a vehicle-mounted image of a vehicle as sample data, and acquiring state data of the vehicle by using a vehicle-mounted auxiliary device; determining the state information of the vehicle according to the state data; and marking the sample data by using the state information as marking information to generate training data.
The embodiment of the application also provides a data labeling method, which comprises the steps of acquiring voice data as sample data and acquiring text data corresponding to the voice data; and marking the sample data by using the text data as marking information to generate training data.
The embodiment of the present application further provides a data annotation device, and the method includes: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to: acquiring voice data as sample data and acquiring text data corresponding to the voice data; and marking the sample data by using the text data as marking information to generate training data.
According to the technical scheme, the vehicle-mounted image can be marked by using the determined state information while the vehicle-mounted image is acquired, and automatic data marking in the field of vehicles is achieved. And automatic marking of sample data can be realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a diagram of a scenario of data annotation according to an exemplary embodiment of the present application;
FIG. 2 is a flow chart of a data annotation process according to an exemplary embodiment of the present application;
FIG. 3 is a scene diagram of data annotation for in-vehicle images according to an exemplary embodiment of the present application;
FIG. 4 is a scene diagram of data annotation for speech according to an exemplary embodiment of the present application;
FIG. 5 is a block diagram of a data annotation device according to an exemplary embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings. For a better understanding of the present application, a scenario of data annotation of an exemplary embodiment of the present application will be described below in conjunction with fig. 1.
As described in fig. 1, data related to the vehicle may be transmitted to the computer device 100 during the driving of the vehicle. The computer device 100 may label the data according to the data labeling method of the exemplary embodiment of the present application after receiving the data, and store the labeled data.
In addition, after receiving the voice data and the corresponding text data, the computer device 100 may perform data tagging on the voice data by using the text data, and store the tagged data as training data.
It should be noted that the computer device 100 refers to an intelligent electronic device that can execute a predetermined process such as numerical calculation and/or logical calculation by executing a predetermined program learning instruction, and includes, but is not limited to, a server, a personal computer, a notebook, a tablet computer, and an intelligent terminal.
In addition, the computer device 100 can be operated alone to implement the present application, and can also be connected to a network and operated by interoperation with other computer devices in the network to implement the present application. The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a local area network, a metropolitan area network, a VPN network, and the like.
A flowchart of a data annotation method according to an exemplary embodiment of the present application will be described in detail below with reference to fig. 2.
In step S210, the vehicle-mounted image of the vehicle is acquired as sample data, and at the same time, the state data of the vehicle is acquired.
Specifically, in the prior art, after acquiring a large amount of sample data, a annotator needs to manually determine the annotation information of each sample data one by one, but in the present application, the method can directly acquire the sample data and the annotation information corresponding to the sample data, where the annotation information may include one or more types of annotation information, that is, the same sample data may be annotated as different annotation information according to a predetermined rule, where the predetermined rule may be set according to specific requirements.
Further, in the present application, the annotation information corresponding to the sample data may be determined while the sample data is acquired. That is, the sample data and annotation information are generated simultaneously and in real time. This will be described in detail below in conjunction with fig. 3 and 4.
In the case where the method is applied to the field of vehicles, as illustrated in fig. 3, the sample data may include an in-vehicle image for a vehicle, which may include an image related to the vehicle acquired by an in-vehicle image acquisition device. As shown in fig. 3, a vehicle-mounted image acquired by a vehicle during driving is acquired by a vehicle data recorder. In addition, other on-vehicle image capturing devices may be used, such as an image capturing device externally mounted on the outside of the vehicle or a reverse vision device that can capture an image of the rear of the vehicle.
Then, while acquiring the on-board image, the on-board auxiliary device may be used to acquire the state data of the vehicle, wherein the on-board auxiliary device may be a device for acquiring the running state of the vehicle, for example, the on-board auxiliary device may be an on-board sensor that can acquire various state information of the vehicle in running, such as the vehicle speed, the temperature of various media, the running state of the engine, and the like, and convert the information into an electric signal to be transmitted to a computer device, and further, the on-board auxiliary device may be an on-board diagnostic system (OBD) that can acquire data related to the engine in real time. It should be noted that the in-vehicle assist system is not limited to the above example, and all devices that can acquire the state information of the vehicle in operation can be applied thereto.
As shown in fig. 3, the data provided by the in-vehicle image acquisition device and the vehicle auxiliary device may be simultaneously provided to the computer apparatus 100 in fig. 1, and the computer apparatus 100 may perform step S210 using the data.
Subsequently, step S220 may be performed, wherein the state information of the vehicle is determined according to the state data. As shown in fig. 3, the normal driving or the dangerous driving is marked in fig. 3. In an embodiment, the state information of the vehicle running may be determined according to a preset rule, and the state information is not limited to whether the vehicle normally runs as provided above, but also includes direction information of the vehicle running, for example, the vehicle is running straight, reversing, turning left, turning right, and the like. Additionally, speed information for the vehicle may also be included, for example, the vehicle is traveling over 80 miles, the vehicle is traveling under 30 miles, and so forth.
These status information may be directly acquired from the in-vehicle auxiliary device, or the status information may be determined based on status data provided by the in-vehicle auxiliary device, and for example, the status of the vehicle during running, such as normal running and dangerous running, may be determined based on the running speed of the vehicle and the operating speed of the engine. Finally, the status information can be used as the labeling information of the vehicle.
In implementation, the same vehicle-mounted image can correspond to different labeling information, that is, various state information of the vehicle can be acquired for the same vehicle-mounted image, and all the state information can be used as the labeling information. For example, the labeled information corresponding to the same vehicle-mounted image includes: left turn, dangerous driving, and transmitter anomaly.
In addition, the method can also be applied to the field of voice. Specifically, the speech from the speaker can be acquired by the speech acquisition device, and for example, the audio from the speaker can be acquired by the audio acquisition device while the speaker is speaking at the conference, and at the same time, the translated content can be acquired by, for example, a stenographer through manual stenography. The audio acquiring device is a device capable of acquiring voice, and may include, but is not limited to, a recording pen, a conference recorder, and the like.
Then, the data can be transmitted to the computer device 100 in fig. 1 by wireless or wire, and the computer device 100 can perform step S210 by using the data, in which in implementation, the voice data is acquired as sample data and the text data corresponding to the voice data is acquired as annotation information.
Finally, step S230 may be executed, and the state information is used as labeling information to label the sample data, so as to generate training data. Specifically, based on a data labeling algorithm, the labeling information is used for labeling the sample data, so that training data for subsequently training the model is generated. It should be noted that the data annotation algorithm may be a data annotation algorithm that is already known in the related art. For example, the data annotation of the sample data can be realized by correspondingly storing the sample data and the annotation information.
The embodiment of the application also provides a data labeling method, which comprises the steps of acquiring voice data as sample data and acquiring text data corresponding to the voice data; and marking the sample data by using the text data as marking information to generate training data.
In summary, according to the data labeling method of the exemplary embodiment of the present application, the labeling information can be determined while the sample data is acquired, and the labeling information is used to label the sample data, so that the automatic marking of the sample data is realized, and a large amount of manpower and material resources are saved. Furthermore, marking information can be preset, so that different marking requirements can be met. Furthermore, the same sample data can acquire different labeling information, so that the sample data can be more fully utilized. Furthermore, the method can be applied to the field of vehicles, and automatic labeling of the vehicle-mounted images is achieved. Furthermore, the method can be applied to the field of voice, and realizes automatic labeling of voice, particularly conference voice.
In order to more clearly understand the inventive concept of the exemplary embodiment of the present application, a block diagram of a data annotation device of the exemplary embodiment of the present application will be described below with reference to fig. 5. Those of ordinary skill in the art will understand that: the apparatus in fig. 5 shows only components related to the present exemplary embodiment, and common components other than those shown in fig. 5 are also included in the apparatus.
FIG. 5 shows a block diagram of a data annotation device of an exemplary embodiment of the present application. It should be noted that the data annotation device may be the computer device 100 in fig. 1, or may be a device for executing the data annotation method shown in fig. 2.
Referring to fig. 5, the apparatus includes, at a hardware level, a processor, an internal bus, and a computer-readable storage medium, wherein the computer-readable storage medium includes a volatile memory and a non-volatile memory. The processor reads the corresponding computer program from the non-volatile memory and then runs it. Of course, besides the software implementation, the present application does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.
Specifically, the processor performs the following operations: acquiring a vehicle-mounted image of a vehicle as sample data and acquiring state data of the vehicle; determining the state information of the vehicle according to the state data; and marking the sample data by using the state information as marking information to generate training data.
Optionally, the processor acquiring the state data of the vehicle while acquiring the on-vehicle image of the vehicle as the sample data in the implementing step includes: the vehicle-mounted image of the vehicle is acquired as sample data by the vehicle-mounted image acquisition device, and the vehicle state data is acquired by the vehicle-mounted auxiliary device.
Optionally, the vehicle-mounted auxiliary device is a device for acquiring a vehicle running state.
According to an example embodiment of the present application, the processor may further perform the following operations: acquiring voice data as sample data and acquiring text data corresponding to the voice data; and marking the sample data by using the text data as marking information to generate training data.
In summary, the data labeling device according to the exemplary embodiment of the present application can determine the labeling information while acquiring the sample data, and label the sample data by using the labeling information, thereby realizing automatic marking of the sample data, and saving a large amount of manpower and material resources. Furthermore, marking information can be preset, so that different marking requirements can be met. Furthermore, the same sample data can acquire different labeling information, so that the sample data can be more fully utilized. Furthermore, the method can be applied to the field of vehicles, and automatic labeling of the vehicle-mounted images is achieved. Furthermore, the method can be applied to the field of voice, and realizes automatic labeling of voice, particularly conference voice.
It should be noted that the execution subjects of the steps of the method provided in embodiment 1 may be the same device, or different devices may be used as the execution subjects of the method. For example, the execution subject of steps 21 and 22 may be device 1, and the execution subject of step 23 may be device 2; for another example, the execution subject of step 21 may be device 1, and the execution subjects of steps 22 and 23 may be device 2; and so on.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (8)
1. A method for annotating data, comprising:
acquiring a vehicle-mounted image of a vehicle as sample data and acquiring state data of the vehicle;
determining the state information of the vehicle according to the state data;
and marking the sample data by using the state information as marking information to generate training data.
2. The method of claim 1, wherein acquiring the vehicle-mounted image of the vehicle as sample data while acquiring the status data of the vehicle comprises:
the vehicle-mounted image of the vehicle is acquired as sample data by the vehicle-mounted image acquisition device, and the vehicle state data is acquired by the vehicle-mounted auxiliary device.
3. The method according to claim 2, characterized in that the on-board auxiliary device is a device for acquiring a vehicle operating state.
4. A method for annotating data, comprising:
acquiring voice data as sample data and acquiring text data corresponding to the voice data;
and marking the sample data by using the text data as marking information to generate training data.
5. A data annotation device, comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
acquiring a vehicle-mounted image of a vehicle as sample data and acquiring state data of the vehicle;
determining the state information of the vehicle according to the state data;
and marking the sample data by using the state information as marking information to generate training data.
6. The apparatus of claim 5, wherein the processor acquiring the status data of the vehicle while the implementing step acquires the on-board image of the vehicle as the sample data comprises:
the vehicle-mounted image of the vehicle is acquired as sample data by the vehicle-mounted image acquisition device, and the vehicle state data is acquired by the vehicle-mounted auxiliary device.
7. The device according to claim 6, characterized in that the on-board auxiliary device is a device for acquiring a vehicle running state.
8. A data annotation device, comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
acquiring voice data as sample data and acquiring text data corresponding to the voice data;
and marking the sample data by using the text data as marking information to generate training data.
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