CN112966730A - Vehicle damage identification method, device, equipment and storage medium - Google Patents

Vehicle damage identification method, device, equipment and storage medium Download PDF

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CN112966730A
CN112966730A CN202110226715.9A CN202110226715A CN112966730A CN 112966730 A CN112966730 A CN 112966730A CN 202110226715 A CN202110226715 A CN 202110226715A CN 112966730 A CN112966730 A CN 112966730A
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张发恩
郭慧娟
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Innovation Wisdom Shanghai Technology Co ltd
AInnovation Shanghai Technology Co Ltd
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Abstract

The application provides a vehicle damage identification method, a device, equipment and a storage medium, wherein the vehicle damage identification method comprises the following steps: acquiring a damage picture of a vehicle to be monitored; extracting image characteristics of the damage picture of the vehicle to be monitored according to a neural network; determining a plurality of damage candidate regions in the damage picture according to the image characteristics; processing the damage picture with the damage candidate regions according to an example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, wherein the damage detection result comprises information of at least one damage, and the information of the damage comprises the category and the position information of the damage. The application can improve the accuracy of vehicle damage identification.

Description

Vehicle damage identification method, device, equipment and storage medium
Technical Field
The application relates to the field of computer vision, in particular to a vehicle damage identification method, device, equipment and storage medium.
Background
Owing to the rapid development of deep learning, the commercial value of computer vision is gradually reflected in a plurality of fields such as security, internet, industrial manufacturing and the like. And the artificial intelligence algorithm is transferred, reformed and innovated, and is also suitable for auxiliary analysis of vehicle damage assessment. AI may help improve the efficiency and accuracy of determining vehicle damage. The current vehicle damage assessment process is as follows: and identifying and judging the vehicle damage condition according to the picture of the vehicle damage shot by the user on site. The user experience can be improved and the cost of the insurance company can be reduced.
The biggest difficulty of the existing intelligent damage assessment is that the identification of vehicle damage has the requirement of higher precision, the damage position is required to be accurately positioned, the damage category is required to be judged, and the precision of the existing damage assessment scheme on the vehicle damage is not high.
Disclosure of Invention
The embodiment of the application aims to provide a vehicle damage identification method, a vehicle damage identification device, vehicle damage identification equipment and a storage medium, which are used for improving the accuracy of vehicle damage identification.
To this end, a first aspect of the present application discloses a vehicle damage identification method, the method comprising:
acquiring a damage picture of a vehicle to be monitored;
extracting image characteristics of the damage picture of the vehicle to be monitored according to a neural network;
determining a plurality of damage candidate regions in the damage picture according to the image characteristics;
processing the damage picture with the damage candidate regions according to an example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, wherein the damage detection result comprises information of at least one damage, and the information of the damage comprises the category and the position information of the damage.
In the first aspect of the application, by obtaining a damage picture of a vehicle to be monitored, image features of the damage picture of the vehicle to be monitored can be extracted according to a neural network, a plurality of damage candidate regions in the damage picture can be determined according to the image features, and the damage picture with the plurality of damage candidate regions can be processed according to an example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, the damage detection result comprises at least one piece of damage information, and the damage information comprises the category and the position information of damage. Compared with the prior art, the embodiment of the application can process the damage picture by utilizing the example segmentation model, so that the damage can be positioned to each pixel point of the image, and the identification and positioning accuracy of the vehicle damage can be further improved.
In the first aspect of the present application, as an optional implementation manner, the example segmentation model includes a classification branch network, a bounding box regression branch network, and a mask prediction branch network;
and determining a plurality of damage candidate regions in the damage picture according to the image features, wherein the determining comprises the following steps:
classifying the plurality of damage candidate regions in the damage picture according to the classification branch network to obtain a first prediction result;
performing frame regression processing on the plurality of damage candidate regions in the damage picture according to the frame regression branch network to obtain a second prediction result;
performing mask prediction on the plurality of damage candidate regions in the damage picture according to the mask prediction branch network to obtain a third prediction result;
and outputting the damage detection result of the vehicle to be monitored according to the first prediction result, the second prediction result and the third prediction result.
In the optional embodiment, a plurality of damage candidate regions in a damage picture are classified through a classification branch network, so that a first prediction result can be obtained; performing frame regression processing on a plurality of damage candidate regions in the damage picture according to a frame regression branch network, and further obtaining a second prediction result; on the other hand, mask prediction is carried out on a plurality of damage candidate areas in the damage picture according to a mask prediction branch network, a third prediction result can be obtained, and therefore the damage detection result of the vehicle to be monitored is output according to the first prediction result, the second prediction result and the third prediction result.
In the first aspect of the present application, as an optional implementation manner, the mask predicted branch network includes a plurality of depth-separable convolutional networks and a deconvolution network;
and performing mask prediction on the plurality of candidate damage regions in the damaged picture according to the mask prediction branch network to obtain a third prediction result, wherein the mask prediction comprises:
taking the plurality of damage candidate regions in the damage picture as an input of the deconvolution network, so that the deconvolution network outputs shallow features of the plurality of damage candidate regions in the damage picture;
processing the plurality of damage candidate regions in the damage picture according to the plurality of depth-separable convolutional networks to output deep features of the plurality of damage candidate regions in the damage picture;
and obtaining the third prediction result according to the shallow feature and the deep feature.
In an optional embodiment, the plurality of damage candidate regions in the damage picture are used as input of a deconvolution network, and then the plurality of damage candidate regions in the damage picture can be sampled through the deconvolution network to extract shallow features, on the other hand, deep features can be extracted through the plurality of depth separable convolution networks, so that the shallow features and the deep features can be fused and identified and positioned based on fused output, wherein the transmission of spatial position information in the image can be enhanced due to the fact that the shallow features do not need to be subjected to multilayer continuous convolution, and then the identification and positioning accuracy of vehicle damage is improved, and particularly the identification and positioning accuracy is better for small damage of a vehicle.
In the first aspect of the present application, as an optional implementation manner, the classification branching network includes a sigmod function;
and classifying the plurality of damage candidate regions in the damage picture according to the classification branch network to obtain a first prediction result, including:
and classifying the plurality of damage candidate regions in the damage picture according to the sigmod function to obtain the first prediction result.
In the optional embodiment, the sigmod function is used in the classification branch network, so that the problem of inter-class competition caused by the softmax classification function can be solved, each damage class is independently predicted respectively, prediction of each damage class is decoupled, and accuracy of damage identification and positioning is further improved.
In the first aspect of the present application, as an optional implementation manner, the classification branching network further includes a loss function, where the calculation formula of the loss function is:
Lcls＝L+L0-1；
and the number of the first and second groups,
Figure BDA0002956693730000041
where L represents the cross entropy loss function. L is0-1Represents the 0-1 loss function, σ (α)i) For the sigmod function, N represents the number of classes.
In the first aspect of the present application, as an optional implementation manner, the damage category is one of scratch, corner deformation, non-corner deformation, dead fold, crack, fracture, displacement, partial deletion, complete deletion, lamp damage, glass damage, and severe damage.
In the optional embodiment, the damage category is scratch, corner deformation, non-corner deformation, dead fold, crack, fracture, displacement, partial deletion, complete deletion, lamp damage, glass damage and severe damage, that is, compared with the prior art, the optional embodiment can identify and position more types of damage types so as to further improve the precision of damage identification and positioning.
A second aspect of the present application discloses a vehicle damage identifying device, the device including:
the acquisition module is used for acquiring a damage picture of the vehicle to be monitored;
the extraction module is used for extracting the image characteristics of the damage picture of the vehicle to be monitored according to the neural network;
the determining module is used for determining a plurality of damage candidate regions in the damage picture according to the image characteristics;
the identification module is configured to process the damage picture with the plurality of damage candidate regions according to an example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, the damage detection result includes information of at least one damage, and the information of the damage includes category and position information of the damage.
The device of the second aspect of the application can further determine a plurality of damage candidate regions in the damage picture by executing the vehicle damage identification method, further can extract image features of the damage picture of the vehicle to be monitored according to the neural network, further can determine the plurality of damage candidate regions in the damage picture according to the image features, further can process the damage picture with the plurality of damage candidate regions according to the example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, the damage detection result comprises at least one piece of damage information, and the damage information comprises the type and the position information of the damage. Compared with the prior art, the embodiment of the application can process the damage picture by utilizing the example segmentation model, so that the damage can be positioned to each pixel point of the image, and the identification and positioning accuracy of the vehicle damage can be further improved.
In the second aspect of the present application, as an optional implementation manner, the example segmentation model includes a classification branch network, a bounding box regression branch network, and a mask prediction branch network;
and, the determining module comprises:
the classification submodule is used for classifying the damage candidate areas in the damage picture according to the classification branch network to obtain a first prediction result;
the frame regression processing submodule is used for carrying out frame regression processing on the plurality of damage candidate regions in the damage picture according to the frame regression branch network to obtain a second prediction result;
the prediction sub-module is used for performing mask prediction on the plurality of damage candidate regions in the damage picture according to the mask prediction branch network to obtain a third prediction result;
and the output module is used for outputting the damage detection result of the vehicle to be monitored according to the first prediction result, the second prediction result and the third prediction result.
Classifying a plurality of damage candidate regions in the damage picture through a classification branch network, and further obtaining a first prediction result; performing frame regression processing on a plurality of damage candidate regions in the damage picture according to a frame regression branch network, and further obtaining a second prediction result; on the other hand, mask prediction is carried out on a plurality of damage candidate areas in the damage picture according to a mask prediction branch network, a third prediction result can be obtained, and therefore the damage detection result of the vehicle to be monitored is output according to the first prediction result, the second prediction result and the third prediction result.
A third aspect of the present application discloses a vehicle damage identifying apparatus, the apparatus including:
a processor; and
a memory configured to store machine readable instructions which, when executed by the processor, cause the processor to perform the vehicle impairment identification method of the first aspect of the present application.
The device of the third aspect of the present application, by executing the vehicle damage identification method, can further obtain a damage picture of the vehicle to be monitored, can further extract image features of the damage picture of the vehicle to be monitored according to the neural network, and can further determine a plurality of damage candidate regions in the damage picture according to the image features, and further can process the damage picture with the plurality of damage candidate regions according to the example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, the damage detection result includes information of at least one damage, and the damage information includes a category and position information of the damage. Compared with the prior art, the embodiment of the application can process the damage picture by utilizing the example segmentation model, so that the damage can be positioned to each pixel point of the image, and the identification and positioning accuracy of the vehicle damage can be further improved.
A fourth aspect of the present application discloses a storage medium storing a computer program executed by a processor to perform the vehicle damage identification method of the first aspect of the present application.
The storage medium of the fourth aspect of the present application executes the vehicle damage identification method, and then can obtain a damage picture of a vehicle to be monitored, and then can extract image features of the damage picture of the vehicle to be monitored according to the neural network, and then can determine a plurality of damage candidate regions in the damage picture according to the image features, and then can process the damage picture with the plurality of damage candidate regions according to the example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, the damage detection result includes information of at least one damage, and the damage information includes a category and position information of the damage. Compared with the prior art, the embodiment of the application can process the damage picture by utilizing the example segmentation model, so that the damage can be positioned to each pixel point of the image, and the identification and positioning accuracy of the vehicle damage can be further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a schematic flow chart of a vehicle damage identification method disclosed in an embodiment of the present application;
FIG. 2 is a diagram illustrating a mask predicted branch network in the prior art;
FIG. 3 is a schematic structural diagram of a mask predicted branch network disclosed in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a vehicle damage identification device disclosed in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a vehicle damage identification device disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a vehicle damage identification method according to an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application includes the steps of:
101. acquiring a damage picture of a vehicle to be monitored;
102. extracting image characteristics of a damage picture of a vehicle to be monitored according to a neural network;
103. determining a plurality of damage candidate regions in a damage picture according to the image characteristics;
104. processing a damage picture with a plurality of damage candidate regions according to an example segmentation model so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, wherein the damage detection result comprises at least one piece of damage information, and the damage information comprises the category and position information of the damage.
In the embodiment of the application, the damage picture of the vehicle to be monitored is obtained, the image characteristics of the damage picture of the vehicle to be monitored can be extracted according to the neural network, a plurality of damage candidate areas in the damage picture can be determined according to the image characteristics, the damage picture with the plurality of damage candidate areas can be processed according to the example segmentation model, the example segmentation model outputs the damage detection result of the vehicle to be monitored, the damage detection result comprises at least one piece of damage information, and the damage information comprises the category and the position information of the damage. Compared with the prior art, the embodiment of the application can process the damage picture by utilizing the example segmentation model, so that the damage can be positioned to each pixel point of the image, and the identification and positioning accuracy of the vehicle damage can be further improved.
In the embodiment of the present application, the image feature may be one or a combination of features including color, texture, shape, and the like, as an example. On the other hand, the candidate region is a region where it is currently impossible to determine that the region is a damaged region.
In the embodiment of the application, 9 candidate frames with different size proportions are taken for each pixel point on an image with image characteristics, wherein a region defined by the candidate frames is a damage candidate region.
In the embodiment of the present application, the example segmentation model is MASKRCNN.
In the embodiment of the present application, as an optional implementation manner, the example segmentation model includes a classification branch network, a frame regression branch network, and a mask prediction branch network;
and, the step: determining a plurality of damage candidate regions in a damage picture according to the image characteristics, comprising:
classifying a plurality of damage candidate regions in the damage picture according to a classification branch network to obtain a first prediction result;
performing frame regression processing on a plurality of damage candidate regions in the damage picture according to a frame regression branch network to obtain a second prediction result;
performing mask prediction on a plurality of damage candidate regions in the damage picture according to a mask prediction branch network to obtain a third prediction result;
and outputting a damage detection result of the vehicle to be monitored according to the first prediction result, the second prediction result and the third prediction result.
In the optional embodiment, a plurality of damage candidate regions in a damage picture are classified through a classification branch network, so that a first prediction result can be obtained; performing frame regression processing on a plurality of damage candidate regions in the damage picture according to a frame regression branch network, and further obtaining a second prediction result; on the other hand, mask prediction is carried out on a plurality of damage candidate areas in the damage picture according to a mask prediction branch network, a third prediction result can be obtained, and therefore the damage detection result of the vehicle to be monitored is output according to the first prediction result, the second prediction result and the third prediction result.
In the embodiment of the present application, a plurality of candidate damage regions in a damage picture are classified according to a classification branch network, and a specific manner of obtaining a first prediction result is as follows:
the classification branch network calculates a score of each category to which the candidate region belongs based on the feature of each candidate region (i.e., the candidate box), wherein the category with the highest score is taken as the category of the candidate region. For example, the classification branching network calculates a score of the candidate region belonging to the damage category according to the color, texture, and shape of each candidate region (i.e., candidate frame), and the damage category having the highest score is used as the damage category of the candidate region.
It should be noted that the damage category of the candidate region refers to one of scratch, corner deformation, non-corner deformation, dead fold, crack, fracture, displacement, partial deletion, complete deletion, lamp damage, glass damage, and severe damage.
In the embodiment of the present application, the frame regression branch network performs frame regression processing on a plurality of damage candidate regions in the damage picture, and a specific manner of obtaining the second prediction result is as follows:
and determining which candidate frames are closer to the frame of the real damage occurrence position through a frame regression branch network, continuously adjusting the positions of the candidate frames to be closer to the position of the real damage frame, and finally taking the area defined by the optimal candidate frame as a second prediction result. Further, the bounding box regression branch network determines which candidate boxes are closer to the boxes at the true damage occurrence location by Non-Maximum Suppression (NMS) to screen for redundant boxes and determine which candidate boxes are closer to the boxes at the true damage occurrence location. The number of the plurality of candidate damage regions may be 2 or 3.
In the embodiment of the application, as an optional implementation manner, the mask prediction branch network comprises a plurality of depth separable convolution networks and a deconvolution network;
and performing mask prediction on a plurality of damage candidate regions in the damage picture according to a mask prediction branch network to obtain a third prediction result, wherein the third prediction result comprises the following steps:
taking a plurality of damage candidate areas in the damage picture as the input of a deconvolution network, so that the deconvolution network outputs the shallow features of the plurality of damage candidate areas in the damage picture;
processing a plurality of damage candidate regions in the damage picture according to a plurality of depth-divisible convolutional networks to output deep features of the plurality of damage candidate regions in the damage picture;
and obtaining a third prediction result according to the shallow feature and the deep feature.
In an optional embodiment, a plurality of damage candidate regions in the damage picture are used as input of a deconvolution network, and then the plurality of damage candidate regions in the damage picture can be sampled through the deconvolution network to extract shallow features, on the other hand, deep features can be extracted through a plurality of depth separable convolution networks, so that the shallow features and the deep features can be fused and identified and positioned based on fused output, wherein the transmission of spatial position information in the image can be enhanced due to the fact that the shallow features do not need to be subjected to multilayer continuous convolution, and then the identification and positioning accuracy of vehicle damage is improved, and particularly the identification and positioning accuracy is better for small damage of a vehicle.
In this alternative embodiment, as an example, as shown in fig. 2 and fig. 3, the mask predicted branch network in the prior art includes only several depth-separable convolutional networks, whereas the mask predicted branch network in the embodiment of the present application includes several depth-separable convolutional networks and one deconvolution network, specifically, includes 4 depth-separable convolutional networks.
In the embodiment of the present application, as an optional implementation manner, the classification branch network includes a sigmod function;
and, the step: classifying a plurality of damage candidate regions in the damage picture according to a classification branch network to obtain a first prediction result, wherein the method comprises the following steps:
and classifying a plurality of damage candidate regions in the damage picture according to a sigmod function to obtain a first prediction result.
In the optional embodiment, the sigmod function is used in the classification branch network, so that the problem of inter-class competition caused by the softmax classification function can be solved, each damage class is independently predicted respectively, prediction of each damage class is decoupled, and accuracy of damage identification and positioning is further improved.
In this embodiment of the present application, as an optional implementation manner, the classification branch network further includes a loss function, and a calculation formula of the loss function is:
Lcls＝L+L0-1；
and the number of the first and second groups,
Figure BDA0002956693730000121
where L represents the cross entropy loss function. L is0-1Represents the 0-1 loss function, σ (α)i) For the sigmod function, N represents the number of classes.
In this alternative embodiment, by combining L0-1Added to L as a regularization termclsIn the cross entropy loss function, the final loss function can guide the distinction between the categories to be more obvious, and the confidence coefficient of the classification is improved.
In the first aspect of the present application, as an alternative embodiment, the damage category is one of scratch, corner deformation, non-corner deformation, dead fold, crack, fracture, displacement, partial deletion, complete deletion, lamp damage, glass damage, and severe damage.
In the optional embodiment, the damage category is scratch, corner deformation, non-corner deformation, dead fold, crack, fracture, displacement, partial deletion, complete deletion, lamp damage, glass damage and severe damage, that is, compared with the prior art, the optional embodiment can identify and position more types of damage types so as to further improve the precision of damage identification and positioning.
Example two
Referring to fig. 4, fig. 4 is a schematic structural diagram of a vehicle damage identification device disclosed in the embodiment of the present application. As shown in fig. 4, the apparatus of the embodiment of the present application includes:
the acquiring module 201 is used for acquiring a damage picture of a vehicle to be monitored;
the extraction module 202 is used for extracting image characteristics of a damage picture of a vehicle to be monitored according to the neural network;
the determining module 203 is used for determining a plurality of damage candidate regions in the damage picture according to the image characteristics;
the identifying module 204 is configured to process a damage picture with a plurality of damage candidate regions according to the example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, where the damage detection result includes information of at least one damage, and the damage information includes a category and location information of the damage.
The device of the embodiment of the application can further determine a plurality of damage candidate areas in the damage picture according to the image characteristics by executing the vehicle damage identification method, and further can extract the image characteristics of the damage picture of the vehicle to be monitored according to the neural network, and further can process the damage picture with the plurality of damage candidate areas according to the example segmentation model, so that the example segmentation model outputs the damage detection result of the vehicle to be monitored, the damage detection result comprises at least one piece of damage information, and the damage information comprises the type and the position information of the damage. Compared with the prior art, the embodiment of the application can process the damage picture by utilizing the example segmentation model, so that the damage can be positioned to each pixel point of the image, and the identification and positioning accuracy of the vehicle damage can be further improved.
In the embodiment of the present application, as an optional implementation manner, the example segmentation model includes a classification branch network, a frame regression branch network, and a mask prediction branch network;
and, the determining module includes:
the classification submodule is used for classifying a plurality of damage candidate regions in the damage picture according to the classification branch network to obtain a first prediction result;
the frame regression processing submodule is used for carrying out frame regression processing on a plurality of damage candidate regions in the damage picture according to the frame regression branch network to obtain a second prediction result;
the prediction sub-module is used for performing mask prediction on a plurality of damage candidate regions in the damage picture according to a mask prediction branch network to obtain a third prediction result;
and the output module is used for outputting the damage detection result of the vehicle to be monitored according to the first prediction result, the second prediction result and the third prediction result.
Classifying a plurality of damage candidate regions in the damage picture through a classification branch network, and further obtaining a first prediction result; performing frame regression processing on a plurality of damage candidate regions in the damage picture according to a frame regression branch network, and further obtaining a second prediction result; on the other hand, mask prediction is carried out on a plurality of damage candidate areas in the damage picture according to a mask prediction branch network, a third prediction result can be obtained, and therefore the damage detection result of the vehicle to be monitored is output according to the first prediction result, the second prediction result and the third prediction result.
Please refer to the detailed description of the first embodiment of the present application for other descriptions of the embodiments of the present application, which are not repeated herein.
EXAMPLE III
Referring to fig. 5, fig. 5 is a schematic structural diagram of a vehicle damage identification device according to an embodiment of the present application. As shown in fig. 5, the apparatus of the embodiment of the present application includes:
a processor 301; and
the memory 302 is configured to store machine readable instructions, and when the instructions are executed by the processor 301, the processor 301 executes the vehicle damage identification method according to the first embodiment of the present application.
The device of the embodiment of the application can further determine a plurality of damage candidate regions in the damage picture according to the image characteristics by executing the vehicle damage identification method, and further can process the damage picture with the plurality of damage candidate regions according to the example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, the damage detection result comprises at least one damage information, and the damage information comprises the category and the position information of the damage. Compared with the prior art, the embodiment of the application can process the damage picture by utilizing the example segmentation model, so that the damage can be positioned to each pixel point of the image, and the identification and positioning accuracy of the vehicle damage can be further improved.
Example four
The embodiment of the application discloses a storage medium, wherein a computer program is stored in the storage medium, and the computer program is executed by a processor to execute the vehicle damage identification method in the embodiment of the application.
The storage medium of the embodiment of the application can further determine a plurality of damage candidate regions in the damage picture by executing the vehicle damage identification method, further can extract image features of the damage picture of the vehicle to be monitored according to the neural network, further can determine the plurality of damage candidate regions in the damage picture according to the image features, further can process the damage picture with the plurality of damage candidate regions according to the example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, the damage detection result comprises at least one piece of damage information, and the damage information comprises the type and the position information of the damage. Compared with the prior art, the embodiment of the application can process the damage picture by utilizing the example segmentation model, so that the damage can be positioned to each pixel point of the image, and the identification and positioning accuracy of the vehicle damage can be further improved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a division of one logic function, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
It should be noted that the functions, if implemented in the form of software functional modules and sold or used as independent products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. A vehicle damage identification method, characterized in that the method comprises:
acquiring a damage picture of a vehicle to be monitored;
extracting image characteristics of the damage picture of the vehicle to be monitored according to a neural network;
determining a plurality of damage candidate regions in the damage picture according to the image characteristics;
processing the damage picture with the damage candidate regions according to an example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, wherein the damage detection result comprises information of at least one damage, and the information of the damage comprises the category and the position information of the damage.
2. The method of claim 1, wherein the instance segmentation model comprises a classification branch network, a bounding box regression branch network, a mask prediction branch network;
and determining a plurality of damage candidate regions in the damage picture according to the image features, wherein the determining comprises the following steps:
classifying the plurality of damage candidate regions in the damage picture according to the classification branch network to obtain a first prediction result;
performing frame regression processing on the plurality of damage candidate regions in the damage picture according to the frame regression branch network to obtain a second prediction result;
performing mask prediction on the plurality of damage candidate regions in the damage picture according to the mask prediction branch network to obtain a third prediction result;
and outputting the damage detection result of the vehicle to be monitored according to the first prediction result, the second prediction result and the third prediction result.
3. The method of claim 2, wherein said mask predicted branch network comprises a number of deep separable convolutional networks and a deconvolution network;
and performing mask prediction on the plurality of candidate damage regions in the damaged picture according to the mask prediction branch network to obtain a third prediction result, wherein the mask prediction comprises:
taking the plurality of damage candidate regions in the damage picture as an input of the deconvolution network, so that the deconvolution network outputs shallow features of the plurality of damage candidate regions in the damage picture;
processing the plurality of damage candidate regions in the damage picture according to the plurality of depth-separable convolutional networks to output deep features of the plurality of damage candidate regions in the damage picture;
and obtaining the third prediction result according to the shallow feature and the deep feature.
4. The method of claim 2, wherein the classification branching network comprises a sigmod function;
and classifying the plurality of damage candidate regions in the damage picture according to the classification branch network to obtain a first prediction result, including:
and classifying the plurality of damage candidate regions in the damage picture according to the sigmod function to obtain the first prediction result.
5. The method of claim 4, wherein the classification branching network further comprises a loss function calculated as:
Lcls＝L+L0-1；
and the number of the first and second groups,
Figure FDA0002956693720000021
wherein L represents a cross entropy loss function, L0-1Represents the 0-1 loss function, σ (α)i) For the sigmod function, N represents the number of classes.
6. The method of claim 1, wherein the damage is one of scratch, corner distortion, non-corner distortion, dead fold, crack, fracture, displacement, partial loss, complete loss, lamp failure, glass failure, and severe damage.
7. A vehicle damage identification device, characterized in that the device comprises:
the acquisition module is used for acquiring a damage picture of the vehicle to be monitored;
the extraction module is used for extracting the image characteristics of the damage picture of the vehicle to be monitored according to the neural network;
the determining module is used for determining a plurality of damage candidate regions in the damage picture according to the image characteristics;
the identification module is used for processing the damage picture with the damage candidate areas according to an example segmentation model, so that the example segmentation model outputs a damage detection result of the vehicle to be monitored, the damage detection result comprises at least one piece of damage information, and the damage information comprises the category and the position information of the damage.
8. The apparatus of claim 7, in which the instance segmentation model comprises a classification branch network, a bounding box regression branch network, a mask prediction branch network;
and, the determining module comprises:
the classification submodule is used for classifying the damage candidate areas in the damage picture according to the classification branch network to obtain a first prediction result;
the frame regression processing submodule is used for carrying out frame regression processing on the plurality of damage candidate regions in the damage picture according to the frame regression branch network to obtain a second prediction result;
the prediction sub-module is used for performing mask prediction on the plurality of damage candidate regions in the damage picture according to the mask prediction branch network to obtain a third prediction result;
and the output module is used for outputting the damage detection result of the vehicle to be monitored according to the first prediction result, the second prediction result and the third prediction result.
9. A vehicle damage identification device, characterized in that the device comprises:
a processor; and
a memory configured to store machine readable instructions that, when executed by the processor, cause the processor to perform the vehicle injury identification method of any of claims 1-6.
10. A storage medium characterized in that the storage medium stores a computer program which is executed by a processor to perform the vehicle damage identification method according to any one of claims 1 to 6.
CN202110226715.9A 2021-03-01 2021-03-01 Vehicle damage identification method, device, equipment and storage medium Pending CN112966730A (en)

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Application publication date: 20210615