Deep Learning Vehicle Imaging for Remote Damage Estimation
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Solution Overview
Problem
Insured clients face difficulties in obtaining quick and accurate estimates of vehicle damage level, repair time, and repair cost due to transportation challenges and the lack of utilization of existing insurance claim data containing vehicle images and associated information.
Innovation Solution
A deep learning image processing method that utilizes a predictive similarity model trained on historical vehicle images and claim data to compare and estimate damage level, repair time, and repair cost by analyzing digital images of damaged vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a client takes the damaged vehicle to a repair shop for damage estimation, then the damage level, repair time, and repair cost can be assessed, but the process becomes time-consuming and difficult due to transportation challenges
Solution Approach 1:
The system creates a digital copy (image) of the damaged vehicle and processes this copy through image recognition and comparison algorithms to estimate damage, eliminating the need to physically transport the vehicle to a repair shop while maintaining estimation accuracy
Solution Approach 2:
The patent replaces the mechanical process of physically transporting vehicles to repair shops with an automated digital image processing system that uses computer vision and machine learning to perform damage assessment remotely
2Productivity
If insurance companies utilize existing databases of damaged vehicle images and claim data, then rapid damage estimation can be provided, but the system requires complex deep learning model training and processing
Solution Approach 1:
The system performs preliminary actions by pre-training deep learning models on historical claim data and damaged vehicle images before actual damage estimation is needed, so that when a new damage case arises, the pre-trained model can quickly process and provide estimates without requiring complex real-time training
Solution Approach 2:
The deep learning model automatically learns and extracts relevant features from images and claim data through self-supervised learning, eliminating the need for manual feature engineering and reducing system complexity while improving processing speed
Data Source
AI summary
In a computer-implemented method and associated tangible non-transitory computer-readable medium, an image of a damaged vehicle may be analyzed to generate a repair estimate. A dataset populated with digital images of damaged vehicles and associated claim data may be used to train a deep learning neural network to learn damaged vehicle image characteristics that are predictive of claim data characteristics, and a predictive similarity model may be generated. Using the predictive similarity model, one or more similarity scores may be generated for a digital image of a newly damaged vehicle, indicating its similarity to one or more digital images of damaged vehicles with known damage level, repair time, and/or repair cost. A repair estimate may be generated for the newly damaged vehicle based on the claim data associated with images that are most similar to the image of the newly damaged vehicle.