Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A method for rapid loss determination of auto insurance on the spot based on image recognition

A technology of image recognition and auto insurance, applied in the field of image recognition, can solve the problems of reducing work efficiency, time-consuming and labor-intensive, and difficult post-audit, etc., and achieve the effect of improving recognition ability and recognition efficiency

Active Publication Date: 2022-04-01
北京车晓科技有限公司
View PDF12 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The number of pictures in a car insurance case ranges from dozens to hundreds, and there are many items in the loss assessment list. Therefore, this inspection work is very time-consuming and laborious even for experienced loss checkers. In order to ensure business Due to the time limit requirements for processing, it is often impossible to conduct a comprehensive inspection and have to give up the verification of some pictures. Therefore, it is impossible to well control the risk of inflated and expanded losses in the loss determination, which will bring losses to the insurance company.
[0003] In addition, the pictures of auto insurance cases include photos of the accident scene and photos of car damage. Various reasons lead to messy picture data, which cannot effectively provide picture data for quick damage assessment, which increases the difficulty of later review and reduces work efficiency

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method for rapid loss determination of auto insurance on the spot based on image recognition
  • A method for rapid loss determination of auto insurance on the spot based on image recognition
  • A method for rapid loss determination of auto insurance on the spot based on image recognition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036] Such as figure 1 , A rapid loss method based on image recognition, including the following steps.

[0037] S1, capture image data on the scene of the car accident, the image data including the area vehicle external image set and the field environment image set at the area vehicle;

[0038] S2, the field environment image of the area vehicle includes at least one car, according to the vehicle identity information provided by the area vehicle external image set and the environmental data information provided by the area vehicle, matching the accident required to be bound vehicle;

[0039] S3, after confirming the required accident vehicle, the image set of the occurrence of the accident vehicle information characteristics is screened in the outer image of the area vehicle;

[0040] S4, the output of each accident vehicle information feature image score in the initial loss of the vehicle image is N1, N2, N3 ... NN, and the N1 is calculated from N2, N3 ... Nn, respectively, and...

Embodiment 2

[0045] Based on the first embodiment, the present embodiment proposes a specific embodiment of a fast loss method based on image recognition.

[0046] Further, the manner is as follows:

[0047] The apparatus for acquiring image data in step S1 includes an electronic camera, a drone, and a mobile phone intelligent terminal.

[0048] Further, a vehicle license, vehicle profile, and color, which can be provided, the vehicle profile, and color, respectively, the vehicle license, vehicle profile, and color, respectively, the vehicle license, vehicle profile, and color, respectively, the vehicle license, vehicle profile, and color, respectively.

[0049] Further, the image of the incident vehicle information characteristic in the step S3 includes front view of a vehicle, a rear view mirror, a bumper, a medium, a leaf plate, a cover, a column, a door, a fog lamp, a taillight, a side Figure and rear view.

[0050] Further, the step S4 uses a formula , C 1 (K) 1 L) 2 , C 2 (K) 2 L) 2 The S...

Embodiment 3

[0060] Such as figure 2 This embodiment proposes a terminal device of a fast loss method based on image recognition, and the terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connected to different platform systems.

[0061] Memory 210 can include readable media in the form of a volatile memory, such as a random access memory (RAM) 211, and / or cache memory 212, further comprising a read only memory (ROM) 213.

[0062] Wherein, the memory 210 also stores a computer program, and the computer program can be executed by the processor 220 such that the processor 220 performs the above-described one of the above-described one of the type-based autograph-based auto-loss method, specific implementation With the embodiments described in the above embodiment, the technical effects achieved, and some content will not be described again. Memory 210 may also include program / utilities 214 having a set (at least one) program module 215, including...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention proposes a rapid on-site damage assessment method for auto insurance based on image recognition. After the information provided by the initial image data finds the corresponding accident vehicle that needs to be assessed, the SSIM algorithm is used to remove the image data with a high degree of repetition to improve the recognition efficiency. Since the collected image data is usually multi-dimensional, it will cause slow image data recognition and increase the pressure on the recognition system. Therefore, the deduplicated image data is non-linearly reduced through the PCA dimensionality reduction algorithm to ensure the least loss of original data. Reduce the dimension of the image data and improve the recognition ability; use 3Dcloud to perform 3D modeling on the data image after dimension reduction, and quickly determine the damage of the 3D model. The accuracy of the damage determination obtained by the three-dimensional structure is greater than that of the plane data image. loss of precision.

Description

Technical field [0001] The present invention relates to the field of image recognition, and more particularly to a rapid loss method of a vehicle insurance on image recognition. Background technique [0002] The current auto insurance business is handled by the nuclear damage commissioner, and the most important part of which is to check the case of the case of the insurance company information system. Whether the position and extent of vehicle damage are consistent. The number of pictures in an auto insurance case is less than that tens, hundreds of items, there are many projects in the order list, so this inspection work is also very expensive for the experience of experienced nuclear loss, in order to ensure business Time-limited requirements, often not fully inspect and have to give up the verification of some pictures, so it is not good to control the deficiency, retransmond risk, and bring losses to the insurance company claims. [0003] In addition, the accident in the cas...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/64G06V10/75G06V10/77G06K9/62
CPCG06F18/22G06F18/2135
Inventor 陈振唐彬刘洪丹
Owner 北京车晓科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com