CN110569692A - multi-vehicle identification method, device and equipment - Google Patents
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Abstract
The embodiment of the specification provides a multi-vehicle identification method, a multi-vehicle identification device and multi-vehicle identification equipment. The group of shot pictures comprises at least one shot picture. And inputting at least one shot picture into a multi-classification classifier to predict the confidence vector and the most probable color class of each shot picture. The confidence vector here consists of the confidence that the pictures taken belong to the respective predefined color class. And merging the confidence vectors and the maximum possibility color categories of all the shot pictures. And inputting the merged confidence coefficient vector and the maximum possibility color category into a multi-vehicle classification model to identify whether the group of shot pictures covers vehicles with multiple colors.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a method, an apparatus, and a device for multi-vehicle identification.
background
In a vehicle damage scenario, an insurance company typically performs damage for a particular vehicle. However, when taking damaged pictures of vehicles, it often happens that the taken pictures of vehicles with different colors are placed in the same case, that is, the taken pictures in the same case may cover vehicles with multiple colors. There is a need to be able to automatically identify whether the photographed picture in the same case covers vehicles of multiple colors, i.e., whether the current case is multiple vehicles. And then, according to the recognition result, vehicle damage assessment is carried out.
Disclosure of Invention
one or more embodiments of the present specification describe a method, an apparatus, and a device for multi-vehicle identification, which can implement case-level multi-vehicle identification.
In a first aspect, a method for identifying multiple vehicles is provided, including:
Acquiring a group of taken pictures of vehicles corresponding to a case; the group of shot pictures comprises at least one shot picture;
Inputting the at least one shot picture into a multi-classification classifier to predict a confidence vector and a maximum likelihood color class of each shot picture; the confidence coefficient vector is formed by the confidence coefficients of the shot pictures respectively belonging to the predefined color categories;
Merging the confidence vectors and the maximum possibility color categories of the shot pictures;
And inputting the merged confidence coefficient vector and the maximum possibility color category into a multi-vehicle classification model to identify whether the group of shot pictures covers vehicles with multiple colors.
In a second aspect, there is provided a multi-vehicle recognition apparatus including:
An acquisition unit for acquiring a photographed picture of a group of vehicles corresponding to one case; the group of shot pictures comprises at least one shot picture;
a prediction unit configured to input the at least one captured picture acquired by the acquisition unit into a multi-classification classifier to predict a confidence vector and a maximum likelihood color class of each captured picture; the confidence coefficient vector is formed by the confidence coefficients of the shot pictures respectively belonging to the predefined color categories;
A merging unit configured to merge the confidence vectors and the most probable color categories of the respective captured pictures predicted by the prediction unit;
And the identification unit is used for inputting the confidence coefficient vector and the maximum possibility color category which are combined by the combination unit into a multi-vehicle classification model so as to identify whether the group of shot pictures covers vehicles with various colors.
in a third aspect, a multi-vehicle recognition apparatus is provided, including:
A receiver for obtaining a photographic picture of a group of vehicles corresponding to a case; the group of shot pictures comprises at least one shot picture;
at least one processor configured to input the at least one captured picture into a multi-classification classifier to predict a confidence vector and a most likely color class for each captured picture; the confidence coefficient vector is formed by the confidence coefficients of the shot pictures respectively belonging to the predefined color categories; merging the confidence vectors and the maximum possibility color categories of the shot pictures; and inputting the merged confidence coefficient vector and the maximum possibility color category into a multi-vehicle classification model to identify whether the group of shot pictures covers vehicles with multiple colors.
The multi-vehicle identification method, the multi-vehicle identification device and the multi-vehicle identification equipment provided by one or more embodiments of the specification acquire a group of taken pictures of vehicles corresponding to a case. The group of shot pictures comprises at least one shot picture. And inputting at least one shot picture into a multi-classification classifier to predict the confidence vector and the most probable color class of each shot picture. The confidence vector here consists of the confidence that the pictures taken belong to the respective predefined color class. And merging the confidence vectors and the maximum possibility color categories of all the shot pictures. And inputting the merged confidence coefficient vector and the maximum possibility color category into a multi-vehicle classification model to identify whether the group of shot pictures covers vehicles with multiple colors. Therefore, the multi-vehicle identification method provided by the specification can realize case-level multi-vehicle identification. In addition, usually, one group or one case comprises a plurality of shot pictures, and the method realizes the identification of a plurality of pictures and a plurality of vehicles.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic view of an application scenario of a multi-vehicle identification method provided in the present specification;
FIG. 2 is a flow chart of a multi-vehicle identification method provided in one embodiment of the present disclosure;
FIG. 3 is a schematic view of a multi-vehicle identification apparatus provided in one embodiment of the present disclosure;
Fig. 4 is a schematic diagram of a multi-vehicle identification device according to an embodiment of the present disclosure.
Detailed Description
The scheme provided by the specification is described below with reference to the accompanying drawings.
the multi-vehicle identification method provided by one or more embodiments of the present description can be applied to the scenario shown in fig. 1. In fig. 1, the multi-vehicle recognition module 20 is used to recognize whether the photographed pictures in the same case cover vehicles of multiple colors. Specifically, the multi-vehicle recognition module 20 may obtain a photographed picture of a group of vehicles corresponding to one case. The group of shot pictures comprises at least one shot picture. And inputting at least one shot picture into a multi-classification classifier to predict the confidence vector and the most probable color class of each shot picture. The confidence vector here consists of the confidence that the pictures taken belong to the respective predefined color class. And merging the confidence vectors and the maximum possibility color categories of all the shot pictures. And inputting the merged confidence coefficient vector and the maximum possibility color category into a multi-vehicle classification model to identify whether the group of shot pictures covers vehicles with multiple colors.
the multi-vehicle recognition module 20 may input the recognition result (e.g., the result of whether to cover the multi-color vehicles) to the vehicle damage assessment module 40 after acquiring the recognition result. In one implementation, when the recognition result is a vehicle that covers only one color, the vehicle damage module 40 damages the vehicle of the one color. When the recognition result is a vehicle covering a plurality of colors, the target color or each color of the vehicle is damaged by the vehicle damage module 40. The damage assessment process may be: the method and the system automatically identify the lost parts of the vehicle and the loss degree thereof reflected in the shot picture of the vehicle with a certain color and automatically give a maintenance scheme.
When the vehicle damage assessment module 40 only assesses a vehicle of a target color, it may perform processing such as splitting a captured image of a vehicle of another color.
Fig. 2 is a flowchart of a multi-vehicle identification method according to an embodiment of the present disclosure. The execution subject of the method may have a device with processing capabilities: a server or system or module, such as multi-vehicle identification module 20 of fig. 1, for example. As shown in fig. 2, the method may specifically include:
in step 202, a set of photographs of a vehicle corresponding to a case is obtained.
The set of captured pictures may include at least one captured picture.
Step 204, inputting at least one shot picture into the multi-classification classifier to predict the confidence vector and the maximum possibility color category of each shot picture.
the confidence vector here consists of the confidence that the pictures taken belong to the respective predefined color class. The multi-classification classifier herein may also be referred to as a multi-classification model. The multi-classification classifier can be obtained by training a light neural network model according to a plurality of sample pictures with color class labels. The lightweight neural network model herein may include, but is not limited to, Mobi LeNet, SqueezeNet, inclusion, Xcept, ShuffleNet, and the like. Here, obtaining a multi-class classifier by training a lightweight neural network model can improve prediction efficiency. In addition, the multi-classification classifier trained by the specification can predict the color classes of a plurality of shot pictures, so that the comprehensiveness of prediction can be improved.
the sample pictures may be taken of vehicles previously collected by data collectors (including C-end users and insurance company's loss-assessment personnel). After the plurality of sample pictures are collected, the sample pictures can be divided into M groups according to case information, wherein M is a positive integer. One or more sample pictures may be included in each group. It is to be understood that one group described above corresponds to one case.
It should be noted that the color type label of the sample picture may be preset manually, and may include, but is not limited to, black, blue, red, silver, white, other colors, and the like.
the definition of the predefined color class in step 204 may refer to the color class label of the sample picture. Furthermore, the most probable color class in step 204 may be determined according to the confidence that the taken picture belongs to the respective predefined color class. For example, the predefined color class corresponding to the maximum confidence may be determined as the most likely color class for taking the picture; the predefined color class for which the confidence level exceeds the threshold may also be determined as the most likely color class for taking the picture.
It should be noted that, when the second method is adopted, a shot picture can also be assigned to a color class with the highest possibility. For example, the plurality of predefined color categories may be ordered according to a priority from high to low. And then, sequentially judging whether the confidence corresponding to each predefined color class is greater than a threshold value, if the confidence of a certain predefined color class is greater than the threshold value, selecting the predefined color class as the most probable color class, and not judging the subsequent predefined color classes any more.
And step 206, merging the confidence vectors and the maximum possibility color categories of all the shot pictures.
Step 208, the merged confidence vectors and the most probable color categories are input into a multi-vehicle classification model to identify whether the group of captured images covers vehicles with multiple colors.
The multi-vehicle classification model may also be referred to as a binary classification model. The multi-vehicle classification model can be obtained by training a decision tree, a support vector machine or a random forest by taking the confidence coefficient vector and the maximum possibility color category of the sample pictures in the groups as input data. Wherein the confidence vector is composed of the confidence of the sample picture belonging to each predefined color class. In the present specification, when training a multi-vehicle classification model, the confidence vectors and the most probable color classes of sample pictures are input into the classification model (including a decision tree, a support vector machine, a random forest, or the like) according to groups, so that the features (including the confidence vectors, the most probable color classes, and the like) of a plurality of sample pictures in the same group can be learned. The method and the device realize the fusion of the characteristics of a plurality of sample pictures of the same case, thereby avoiding the problem of inaccurate identification result caused by identifying whether a certain case is a plurality of vehicles only depending on a single shot picture.
It should be noted that each group of sample pictures may have a sample label for multiple cars. The sample label may be pre-calibrated by a human. In addition, the confidence vector and the most probable color class of the sample picture can be obtained by inputting the sample picture into a multi-class classifier trained in advance.
In steps 206 and 208, the confidence vectors and the most probable color categories of the respective pictures are combined, and the combined result is input into the multi-vehicle classification model, so that the features of all the pictures of the current case can be fused. Therefore, the accuracy of multi-vehicle identification can be improved.
In summary, the multi-vehicle identification method provided in one or more embodiments of the present disclosure may implement case-level multi-vehicle identification. In addition, since a plurality of shot pictures are usually included in one group, the multi-vehicle classification model can fuse the multi-image features, so that the problem that when a single shot picture is only relied on to identify whether a certain case is a plurality of cases, if the shot picture is influenced by illumination and the like and the color changes, the identification result is inaccurate can be avoided. Finally, the method can identify whether a plurality of shot pictures in the same group cover vehicles with various colors, so that the identification of a plurality of pictures and a plurality of vehicles is realized.
corresponding to the above multi-vehicle identification method, an embodiment of the present specification further provides a multi-vehicle identification apparatus, as shown in fig. 3, the apparatus may include:
an acquisition unit 302 for acquiring a photographed picture of a group of vehicles corresponding to one case. The group of shot pictures comprises at least one shot picture.
a prediction unit 304, configured to input the at least one captured picture acquired by the acquisition unit 302 into the multi-classification classifier to predict the confidence vector and the most probable color class of each captured picture. The confidence vector is formed by the confidence that the taken picture belongs to each predefined color class respectively.
A merging unit 306, configured to merge the confidence vectors and the most probable color classes of the respective captured pictures predicted by the prediction unit 304.
And the identifying unit 308 is configured to input the confidence vectors and the most probable color classes merged by the merging unit 304 into the multi-vehicle classification model to identify whether the group of captured pictures covers vehicles with multiple colors.
The multi-vehicle classification model may be a two-classification model.
In one example, the multi-vehicle classification model may be obtained by training a decision tree, a support vector machine, or a random forest using the confidence vectors and the most probable color classes of the sample pictures in the plurality of groups as input data. The confidence vector here consists of the confidence with which the sample picture belongs to the respective predefined color class.
the functions of each functional module of the device in the above embodiments of the present description may be implemented through each step of the above method embodiments, and therefore, a specific working process of the device provided in one embodiment of the present description is not repeated herein.
In the multi-vehicle recognition apparatus provided in one embodiment of the present specification, the obtaining unit 302 obtains a captured picture of a group of vehicles corresponding to one case. The group of shot pictures comprises at least one shot picture. The prediction unit 304 inputs at least one captured picture into the multi-classification classifier to predict the confidence vector and the most likely color class of each captured picture. The confidence vector is formed by the confidence that the taken picture belongs to each predefined color class respectively. The merging unit 306 merges the confidence vectors of the respective taken pictures and the most likely color class. The recognition unit 308 inputs the merged confidence vectors and the most likely color class into the multi-vehicle classification model to recognize whether the group of captured pictures covers vehicles of multiple colors. Therefore, the accuracy of multi-case identification can be improved.
The multi-vehicle identification apparatus provided in one embodiment of the present disclosure may be a sub-module or sub-unit of the multi-vehicle identification module 20 shown in fig. 1.
Corresponding to the above multi-vehicle identification method, an embodiment of the present specification further provides a multi-vehicle identification device, as shown in fig. 4, the device may include:
A receiver 402 for obtaining a photographic image of a group of vehicles corresponding to a case. The group of shot pictures comprises at least one shot picture.
At least one processor 404 for inputting the at least one captured picture into a multi-classification classifier to predict a confidence vector and a most likely color class for each captured picture. The confidence vector is formed by the confidence that the taken picture belongs to each predefined color class respectively. And merging the confidence vectors and the maximum possibility color categories of all the shot pictures. And inputting the merged confidence coefficient vector and the maximum possibility color category into a multi-vehicle classification model so as to identify whether a group of shot pictures covers vehicles with multiple colors.
The multi-vehicle identification device provided by one embodiment of the specification can improve the accuracy of multi-vehicle case identification.
Fig. 4 shows an example in which the multi-vehicle identification device provided in the embodiment of the present specification is a server. In practical applications, the device may also be a terminal, which is not limited in this specification.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The steps of a method or algorithm described in connection with the disclosure herein may be embodied in hardware or may be embodied in software instructions executed by a processor. The software instructions may consist of corresponding software modules that may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. Additionally, the ASIC may reside in a server. Of course, the processor and the storage medium may reside as discrete components in a server.
Those skilled in the art will recognize that, in one or more of the examples described above, the functions described in this invention may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
the foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The above-mentioned embodiments, objects, technical solutions and advantages of the present specification are further described in detail, it should be understood that the above-mentioned embodiments are only specific embodiments of the present specification, and are not intended to limit the scope of the present specification, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present specification should be included in the scope of the present specification.
Claims (9)
1. a multi-vehicle identification method, comprising:
Acquiring a group of taken pictures of vehicles corresponding to a case; the group of shot pictures comprises at least one shot picture;
inputting the at least one shot picture into a multi-classification classifier to predict a confidence vector and a maximum likelihood color class of each shot picture; the confidence coefficient vector is formed by the confidence coefficients of the shot pictures respectively belonging to the predefined color categories;
merging the confidence vectors and the maximum possibility color categories of the shot pictures;
and inputting the merged confidence coefficient vector and the maximum possibility color category into a multi-vehicle classification model to identify whether the group of shot pictures covers vehicles with multiple colors.
2. the method of claim 1, wherein the multi-classification classifier is obtained by training a lightweight neural network model according to a plurality of sample pictures with color class labels; the sample picture covers the target vehicle.
3. The method of claim 1, the multi-vehicle classification model being a bi-classification model.
4. The method according to any one of claims 1-3, wherein the multi-vehicle classification model is obtained by training a decision tree, a support vector machine or a random forest with the confidence vectors and the most probable color classes of the sample pictures in the plurality of groups as input data; the confidence vectors are comprised of the confidence with which the sample picture belongs to each predefined color class.
5. A multiple vehicle identification device comprising:
An acquisition unit for acquiring a photographed picture of a group of vehicles corresponding to one case; the group of shot pictures comprises at least one shot picture;
A prediction unit configured to input the at least one captured picture acquired by the acquisition unit into a multi-classification classifier to predict a confidence vector and a maximum likelihood color class of each captured picture; the confidence coefficient vector is formed by the confidence coefficients of the shot pictures respectively belonging to the predefined color categories;
A merging unit configured to merge the confidence vectors and the most probable color categories of the respective captured pictures predicted by the prediction unit;
And the identification unit is used for inputting the confidence coefficient vector and the maximum possibility color category which are combined by the combination unit into a multi-vehicle classification model so as to identify whether the group of shot pictures covers vehicles with various colors.
6. the apparatus of claim 5, wherein the multi-classification classifier is obtained by training a lightweight neural network model according to a plurality of sample pictures with color class labels; the sample picture covers the target vehicle.
7. the apparatus of claim 5, the multi-vehicle classification model being a bi-classification model.
8. The apparatus according to any one of claims 5-7, wherein the multi-vehicle classification model is obtained by training a decision tree, a support vector machine or a random forest with the confidence vectors and the most probable color classes of the sample pictures in the plurality of groups as input data; the confidence vectors are comprised of the confidence with which the sample picture belongs to each predefined color class.
9. A multiple vehicle identification apparatus comprising:
a receiver for obtaining a photographic picture of a group of vehicles corresponding to a case; the group of shot pictures comprises at least one shot picture;
At least one processor configured to input the at least one captured picture into a multi-classification classifier to predict a confidence vector and a most likely color class for each captured picture; the confidence coefficient vector is formed by the confidence coefficients of the shot pictures respectively belonging to the predefined color categories; merging the confidence vectors and the maximum possibility color categories of the shot pictures; and inputting the merged confidence coefficient vector and the maximum possibility color category into a multi-vehicle classification model to identify whether the group of shot pictures covers vehicles with multiple colors.
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