CN113902045B - Vehicle insurance field rapid damage assessment method based on image recognition - Google Patents

Vehicle insurance field rapid damage assessment method based on image recognition Download PDF

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CN113902045B
CN113902045B CN202111499929.XA CN202111499929A CN113902045B CN 113902045 B CN113902045 B CN 113902045B CN 202111499929 A CN202111499929 A CN 202111499929A CN 113902045 B CN113902045 B CN 113902045B
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CN113902045A (en
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陈振
唐彬
刘洪丹
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BEIJING CHEXIAO TECHNOLOGY CO.,LTD.
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Abstract

The invention provides a vehicle insurance field rapid loss assessment method based on image recognition, after an accident vehicle corresponding to the loss assessment is found by information provided by initial image data, image data with high repeatability is removed through an SSIM algorithm, the recognition efficiency is improved, as the collected image data is usually multi-dimensional, the image data recognition is slow, the pressure of a recognition system is increased, the image data after the duplication removal is subjected to nonlinear dimension reduction through a PCA dimension reduction algorithm, the dimension of the image data is reduced under the condition of ensuring the least loss of the initial data, and the recognition capability is improved; and 3D modeling is carried out on the data image subjected to dimensionality reduction through 3Dcloud, the 3D model is subjected to rapid loss assessment, and the loss assessment accuracy obtained through the three-dimensional structure is greater than that of the plane data image.

Description

Vehicle insurance field rapid damage assessment method based on image recognition
Technical Field
The invention relates to the technical field of image recognition, in particular to a vehicle insurance field rapid loss assessment method based on image recognition.
Background
The current vehicle insurance loss checking business of the insurance company is manually processed by a loss checking specialist, wherein the most important link is to check case pictures uploaded to an information system of the insurance company and check whether repair items in a loss assessment list accord with vehicle damage positions and degrees presented in the pictures one by one. The number of pictures in a car insurance case is dozens of pictures and hundreds of pictures, and the items in the loss assessment list are numerous, so that the inspection work is time-consuming and labor-consuming even for experienced loss checking personnel, and in order to ensure the time limit requirement of business processing, the inspection of some pictures is often not required to be given up without full inspection, so that the risk of virtual increase and loss expansion in loss assessment cannot be well controlled, and the loss is brought to insurance companies for settlement.
In addition, various contents of accident scene pictures and vehicle damage pictures are contained in the vehicle insurance case pictures, and although various insurance companies have normative requirements on uploading sequences of pictures shot in the link of surveying and determining damage, picture data are messy due to various reasons in actual production implementation, the picture data for rapid damage determination cannot be effectively provided, the difficulty of later-stage examination is improved, and the working efficiency is reduced.
Disclosure of Invention
The invention provides a vehicle insurance field rapid damage assessment method based on image recognition.
The invention is realized by the following technical scheme:
a vehicle insurance site rapid damage assessment method based on image recognition comprises the following steps.
S1, acquiring image data of a car accident site, wherein the image data comprises an image set outside the regional vehicle and an image set of the environment of the site where the regional vehicle is located;
s2, comparing the vehicle identity information provided by the regional vehicle external image set with the environment data information provided by the regional vehicle field environment image to match accident vehicles needing damage assessment, wherein the regional vehicle field environment image set at least comprises one vehicle;
s3, after the accident vehicle needing damage assessment is confirmed, images with the information characteristics of the accident vehicle are screened from the regional vehicle external image set to form a preliminary damage assessment vehicle image set;
s4, marking each accident vehicle information characteristic image in the preliminary damage assessment vehicle image set as n1, n2 and n3 … nn, calculating an SSIM value by the aid of an SSIM algorithm through n1 and n2 and n3 … nn respectively, taking all images with the SSIM value calculated in the first round being 0-1 out to form a first repeated set, carrying out de-duplication on the first repeated set, and continuing the steps until the preliminary damage assessment vehicle image set is completely de-duplicated to obtain a standard damage assessment vehicle image set;
s5, marking each de-duplicated accident vehicle information characteristic image in the standard damage assessment vehicle image set as p1, p2 and p3 … pn, and carrying out nonlinear dimension reduction on the p1-pn by using a PCA dimension reduction method, wherein the p1-pn is changed into a dimension reduced image from a multi-dimensional image;
s6, carrying out model construction on the dimension reduction image of p1-pn through a 3Dcloud model construction system to generate a 3D model with the overall characteristics of the accident vehicle;
and S7, importing the 3D model of the accident vehicle into an intelligent damage assessment database, and comparing the data provided by the intelligent damage assessment database with the 3D model of the accident vehicle to assess damage quickly.
Further, the device for acquiring image data in step S1 includes an electronic camera, an unmanned aerial vehicle, and a mobile phone intelligent terminal.
Further, in step S2, the accident vehicle needing damage assessment can be found through the vehicle license plate, the vehicle shape and the color provided by the regional vehicle exterior image set and the regional vehicle environment image set.
Further, the image of the accident vehicle information feature in the step S3 includes a front view, a side view and a rear view of the vehicle having headlights, rear view mirrors, bumpers, center nets, fender panels, covers, a pillar, doors, fog lights and tail lights.
Further, the step S4 adopts a formula
Figure 222607DEST_PATH_IMAGE001
，c1=（k1L）2 ，c2=（k2L）2SSIM values were calculated, where n1, n2 are two images, μn1Is the average value of n1, μn2Is the average value of n2 and,
Figure 202064DEST_PATH_IMAGE002
is the variance of n1
Figure 778539DEST_PATH_IMAGE003
Is the variance of n2 and,
Figure 970486DEST_PATH_IMAGE004
is the covariance of n1 and n2,
Figure 316017DEST_PATH_IMAGE005
and
Figure 935217DEST_PATH_IMAGE006
is a constant for maintaining stability, L is the dynamic range of pixel values, k1=0.01， k2=0.03。
Further, the step S5 includes the following sub-steps:
s501, forming a matrix X with n rows and m columns by each image in all standard damage assessment vehicle image sets according to columns, wherein m represents the number of feature vectors of the images, and n represents the dimensionality of the images;
s502, subtracting the average value of each row of the matrix array X;
s503, obtaining a covariance matrix C =
Figure 733409DEST_PATH_IMAGE007
XXT；
S504, solving eigenvalues of the covariance matrix and corresponding eigenvectors;
s505, arranging the eigenvectors into a matrix from top to bottom according to the corresponding eigenvalue size, and taking the first k rows to form a matrix P;
s506, Y = PX, wherein Y is an image reduced to K-dimension.
Further, the rapid damage assessment in the step S7 includes five damage types identification of scraping, sinking, cracking, wrinkling and perforating for the accident vehicle.
The invention has the beneficial effects that:
(1) after the accident vehicle corresponding to the damage to be determined is found out through the information provided by the initial image data, the image data with high repeatability is removed through an SSIM algorithm, and the identification efficiency is improved;
(2) because the acquired image data is usually multidimensional, the image data is slowly identified, and the pressure of an identification system is increased, so that the image data after the duplication removal is subjected to nonlinear dimension reduction through a PCA dimension reduction algorithm, the dimension of the image data is reduced under the condition of ensuring the least loss of original data, and the identification capability is improved;
(3) and 3D modeling is carried out on the data image subjected to dimensionality reduction through 3Dcloud, the 3D model is subjected to rapid loss assessment, and the loss assessment accuracy obtained through the three-dimensional structure is greater than that of the plane data image.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a first block diagram of a rapid loss assessment method for a vehicle insurance scene based on image recognition, provided by the invention;
FIG. 2 is a schematic structural diagram of a terminal device of a rapid loss assessment method for a vehicle insurance scene based on image recognition according to the present invention;
fig. 3 is a schematic structural diagram of a computer-readable storage medium of a vehicle insurance field rapid damage assessment method based on image recognition according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
Referring to fig. 1, a vehicle insurance scene rapid damage assessment method based on image recognition includes the following steps.
S1, acquiring image data of a car accident site, wherein the image data comprises an image set outside the regional vehicle and an image set of the environment of the site where the regional vehicle is located;
s2, comparing the vehicle identity information provided by the regional vehicle external image set with the environment data information provided by the regional vehicle field environment image to match accident vehicles needing damage assessment, wherein the regional vehicle field environment image set at least comprises one vehicle;
s3, after the accident vehicle needing damage assessment is confirmed, images with the information characteristics of the accident vehicle are screened from the regional vehicle external image set to form a preliminary damage assessment vehicle image set;
s4, marking each accident vehicle information characteristic image in the preliminary damage assessment vehicle image set as n1, n2 and n3 … nn, calculating an SSIM value by the aid of an SSIM algorithm through n1 and n2 and n3 … nn respectively, taking all images with the SSIM value calculated in the first round being 0-1 out to form a first repeated set, carrying out de-duplication on the first repeated set, and continuing the steps until the preliminary damage assessment vehicle image set is completely de-duplicated to obtain a standard damage assessment vehicle image set;
s5, marking each de-duplicated accident vehicle information characteristic image in the standard damage assessment vehicle image set as p1, p2 and p3 … pn, and carrying out nonlinear dimension reduction on the p1-pn by using a PCA dimension reduction method, wherein the p1-pn is changed into a dimension reduced image from a multi-dimensional image;
s6, carrying out model construction on the dimension reduction image of p1-pn through a 3Dcloud model construction system to generate a 3D model with the overall characteristics of the accident vehicle;
and S7, importing the 3D model of the accident vehicle into an intelligent damage assessment database, and comparing the data provided by the intelligent damage assessment database with the 3D model of the accident vehicle to assess damage quickly.
Example 2
On the basis of embodiment 1, the present embodiment provides a specific implementation of a vehicle insurance site rapid damage assessment method based on image recognition.
Further, the manner is as follows:
the device for collecting image data in the step S1 comprises an electronic camera, an unmanned aerial vehicle and a mobile phone intelligent terminal.
Further, in step S2, the accident vehicle needing damage assessment can be found through the vehicle license plate, the vehicle shape and the color provided by the regional vehicle exterior image set and the regional vehicle environment image set.
Further, the image of the accident vehicle information feature in the step S3 includes a front view, a side view and a rear view of the vehicle having headlights, rear view mirrors, bumpers, center nets, fender panels, covers, a pillar, doors, fog lights and tail lights.
Further, the step S4 adopts a formula
Figure 729046DEST_PATH_IMAGE001
，c1=（k1L）2 ，c2=（k2L）2SSIM values were calculated, where n1, n2 are two images, μn1Is the average value of n1, μn2Is the average value of n2 and,
Figure 929084DEST_PATH_IMAGE002
is the variance of n1
Figure 453606DEST_PATH_IMAGE003
Is the variance of n2 and,
Figure 4673DEST_PATH_IMAGE004
is the covariance of n1 and n2,
Figure 272843DEST_PATH_IMAGE005
and
Figure 592966DEST_PATH_IMAGE006
is a constant for maintaining stability, L is the dynamic range of pixel values, k1=0.01， k2=0.03。
Further, the step S5 includes the following sub-steps:
s501, forming a matrix X with n rows and m columns by each image in all standard damage assessment vehicle image sets according to columns, wherein m represents the number of feature vectors of the images, and n represents the dimensionality of the images;
s502, subtracting the average value of each row of the matrix array X;
S503、solving a covariance matrix C =
Figure 553969DEST_PATH_IMAGE007
XXT；
S504, solving eigenvalues of the covariance matrix and corresponding eigenvectors;
s505, arranging the eigenvectors into a matrix from top to bottom according to the corresponding eigenvalue size, and taking the first k rows to form a matrix P;
s506, Y = PX, wherein Y is an image reduced to K-dimension.
Further, the rapid damage assessment in the step S7 includes five damage types identification of scraping, sinking, cracking, wrinkling and perforating for the accident vehicle.
Example 3
Referring to fig. 2, the present embodiment provides a terminal device for a rapid damage assessment method for a vehicle insurance field based on image recognition, and the terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.
The memory 210 may include readable media in the form of volatile memory, such as Random Access Memory (RAM)211 and/or cache memory 212, and may further include Read Only Memory (ROM) 213.
The memory 210 further stores a computer program, and the computer program can be executed by the processor 220, so that the processor 220 executes any one of the aforementioned image recognition-based rapid damage assessment methods for vehicle insurance sites in this embodiment of the present application, and a specific implementation manner of the method is consistent with the implementation manner and the achieved technical effect described in the aforementioned embodiment, and some contents are not described again. Memory 210 may also include a program/utility 214 having a set (at least one) of program modules 215, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Accordingly, processor 220 may execute the computer programs described above, as well as may execute programs/utilities 214.
Bus 230 may be a local bus representing one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or any other type of bus structure.
Terminal device 200 may also communicate with one or more external devices 240, such as a keyboard, pointing device, Bluetooth device, etc., as well as with one or more devices capable of interacting with terminal device 200, and/or with any device (e.g., router, modem, etc.) that enables terminal device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the terminal device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) through the network adapter 260. The network adapter 260 may communicate with other modules of the terminal device 200 via the bus 230. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.
Example 4
Referring to fig. 3, the embodiment provides a computer-readable storage medium for a rapid loss assessment method for a vehicle insurance site based on image recognition, where the computer-readable storage medium has instructions stored thereon, and the instructions are executed by a processor to implement any one of the above-mentioned rapid loss assessment methods for a vehicle insurance site based on image recognition. The specific implementation manner is consistent with the implementation manner and the achieved technical effect described in the above embodiments, and some contents are not described again.
Fig. 3 shows a program product 300 provided by the present embodiment for implementing the method, which may employ a portable compact disc read only memory (CD-ROM) and include program codes, and may be run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not so limited, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. Program product 300 may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (5)

1. A vehicle insurance field rapid damage assessment method based on image recognition is characterized by comprising the following steps:
s1, acquiring image data of a car accident site, wherein the image data comprises an image set outside the regional vehicle and an image set of the environment of the site where the regional vehicle is located;
s2, the field environment image set where the regional vehicle is located at least comprises one vehicle, the accident vehicle needing damage assessment is matched by comparing the vehicle identity information provided by the region vehicle external image set with the environment data information provided by the field environment image where the regional vehicle is located, and the accident vehicle needing damage assessment is found according to the vehicle license plate, the vehicle shape and the color respectively provided by the region vehicle external image set and the field environment image set where the regional vehicle is located in the step S2;
s3, after the accident vehicle needing damage assessment is confirmed, images with the information characteristics of the accident vehicle are screened from the regional vehicle external image set to form a preliminary damage assessment vehicle image set;
s4, marking each accident vehicle information characteristic image in the preliminary damage assessment vehicle image set as n1, n2 and n3 … nn, calculating an SSIM value by the aid of an SSIM algorithm through n1 and n2 and n3 … nn respectively, taking all images with the SSIM value calculated in the first round being 0-1 out to form a first repeated set, carrying out de-duplication on the first repeated set, and continuing the steps until the preliminary damage assessment vehicle image set is completely de-duplicated to obtain a standard damage assessment vehicle image set;
s5, marking each de-duplicated accident vehicle information characteristic image in the standard damage assessment vehicle image set as p1, p2 and p3 … pn, and carrying out nonlinear dimension reduction on the p1-pn by using a PCA dimension reduction method, wherein the p1-pn is changed into a dimension reduced image from a multidimensional image;
s6, carrying out model construction on the dimension reduction image of p1-pn through a 3Dcloud model construction system to generate a 3D model with the overall characteristics of the accident vehicle;
and S7, importing the 3D model of the accident vehicle into an intelligent damage assessment database, and comparing the data provided by the intelligent damage assessment database with the 3D model of the accident vehicle to assess damage quickly, wherein the quick damage assessment in the step S7 comprises five damage type identifications of scraping, sinking, cracking, wrinkling and perforating on the accident vehicle.
2. The vehicle insurance scene rapid damage assessment method based on image recognition according to claim 1, wherein the device for collecting image data in step S1 comprises an electronic camera, a unmanned aerial vehicle and a mobile phone intelligent terminal.
3. The method of claim 1, wherein the image of the accident vehicle information feature in step S3 includes a front view, a side view and a back view of the vehicle with headlights, rearview mirrors, bumpers, center screen, fender, cover, A-pillar, doors, fog lights and tail lights.
4. The vehicle insurance scene rapid damage assessment method based on image recognition as claimed in claim 1, wherein said step S4 adopts formula
Figure 433911DEST_PATH_IMAGE001
，c1=（k1L）2 ，c2=（k2L）2SSIM values were calculated, where n1, n2 are two images, μn1Is the average value of n1, μn2Is the average value of n2 and,
Figure 451545DEST_PATH_IMAGE002
is the variance of n1
Figure 352636DEST_PATH_IMAGE003
Is the variance of n2 and,
Figure 213145DEST_PATH_IMAGE004
is the covariance of n1 and n2,
Figure 615307DEST_PATH_IMAGE005
and
Figure 861350DEST_PATH_IMAGE006
is a constant for maintaining stability, L is the dynamic range of pixel values, k1=0.01， k2=0.03。
5. The vehicle insurance scene rapid damage assessment method based on image recognition as claimed in claim 1, wherein said step S5 includes the following sub-steps:
s501, forming a matrix X with n rows and m columns by each image in all standard damage assessment vehicle image sets according to columns, wherein m represents the number of feature vectors of the images, and n represents the dimensionality of the images;
s502, subtracting the average value of each row of the matrix array X;
s503, obtaining a covariance matrix C =
Figure 182610DEST_PATH_IMAGE007
XXT；
S504, solving eigenvalues of the covariance matrix and corresponding eigenvectors;
s505, arranging the eigenvectors into a matrix from top to bottom according to the corresponding eigenvalue size, and taking the first k rows to form a matrix P;
s506, Y = PX, wherein Y is an image reduced to K-dimension.
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