AI Vehicle Damage Estimation Using Heat Map Analysis
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Solution Overview
Problem
Current methods for estimating vehicle repair data are laborious and time-consuming, often leading to inaccurate results, as they require extensive searching of reference materials and user-provided textual data, and cannot accurately identify damaged components without textual information.
Innovation Solution
The use of artificial intelligence models trained on vehicle damage images to identify damaged components and determine repair data, including a heat map analysis for severity assessment, allowing for automated and real-time repair-or-replace designations without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual inspection and searching methods are used to identify damaged components and generate repair estimates, then the inspector can access reference materials and databases to identify parts, but the process becomes laborious and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of inspecting vehicles, searching databases, and generating repair estimates with an automated optical system. The image capture device takes photographs of damaged vehicles, and an processing system automatically analyzes these images to identify damaged components, determine repairability, and generate repair data without requiring manual inspection or database searching.
Solution Approach 2:
The system enables self-service by allowing the image processing system to autonomously identify damaged components, assess repairability, and generate repair estimates without human intervention. The system processes images, extracts damage information, queries databases automatically, and produces repair data independently, eliminating the need for inspectors to manually perform these tasks.
2Extent of automation
If existing automated technologies use images combined with detailed textual summaries to identify damage, then the system can process damage information, but the dependency on textual data leads to incorrect identification when user-provided text is inaccurate
Solution Approach 1:
The patent extracts and removes the dependency on user-provided textual data from the damage identification process. Instead of requiring users to input damage descriptions, the system captures only images of the damaged vehicle and automatically extracts all necessary damage information directly from the visual data through image processing and pattern recognition algorithms.
Solution Approach 2:
The patent introduces an image processing system as an intermediary between the damaged vehicle and the repair estimation process. Rather than relying on direct user input or manual inspection, the system uses image analysis algorithms as a mediator to objectively identify and characterize damage, eliminating the errors introduced by inaccurate user descriptions.
3Reliability
If inspectors manually search for and access correct reference materials for each type of damage, then accurate repair information can be obtained, but the process requires significant effort and expertise
Solution Approach 1:
The patent creates a universal system that handles multiple types of damage and repair scenarios through a single integrated image processing platform. The system can identify various damaged components, assess different types of damage, determine repairability across multiple vehicle types, and generate comprehensive repair data without requiring inspectors to manually search different reference materials for each specific case.
Solution Approach 2:
The system performs preliminary actions by pre-processing and analyzing damage images to automatically identify damaged components and their characteristics before the repair estimation process begins. This preliminary image analysis prepares structured damage information that can be directly used to query databases and generate repair data, eliminating the need for inspectors to manually search reference materials during the estimation process.
Data Source
AI summary
A method, non-transitory computer readable medium, and an apparatus for automated estimation of repair data includes applying a first generated artificial intelligence model on a received vehicle damage image associated with an electronic claim to identify damaged component(s) on a vehicle without using any metadata. A heat map analysis is performed on the received actual vehicle damage image to identify a damage severity value associated with at least one of the identified damaged component(s). A second generated artificial intelligence model is applied on the received actual vehicle damage image and the damage severity value associated with the identified damaged component(s) to determine repair data and a repair-or-replace designation. The determined repair data and the determined repair-or-replace designation for at least one of the identified one or more damaged components is provided in response to the received actual vehicle damage image associated with the electronic claim.