Vehicle Damage Image Processing System for Accurate Parts Identification
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Solution Overview
Problem
Current methods for assessing vehicle damage in insurance claims often underestimate additional damages found during thorough inspections by repair facilities, leading to unforeseen costs and supplemental claims.
Innovation Solution
An image processing system that analyzes images of damaged vehicles, extracts relevant attributes, and uses historical claim data to predict the necessary replacement parts and estimate costs, thereby providing more accurate initial assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a representative of the insurance company performs an initial assessment of vehicle damage, then the assessment process is quick and efficient, but additional damages are often underestimated or missed
Solution Approach 1:
The system segments the damage assessment process into multiple independent analysis components: initial visual assessment by adjusters, detailed image attribute extraction by computer vision algorithms, and comprehensive parts identification. This segmentation allows the initial quick assessment to be supplemented by automated detailed analysis without requiring the entire process to be slow.
Solution Approach 2:
The patent introduces an intermediary automated image processing system that bridges the gap between the quick initial assessment by insurance representatives and the thorough inspection by repair facilities. This intermediary system processes images to extract attributes and predict parts, providing a more accurate preliminary assessment that reduces the need for supplemental claims.
2Measurement precision
If a repair facility performs a thorough inspection, then additional damages are identified, but this requires time and access that insurance adjusters cannot provide
Solution Approach 1:
The system performs preliminary damage assessment and parts identification using image processing before the vehicle reaches the repair facility. By extracting attributes from images taken at the accident scene or initial inspection, the system predicts required parts in advance, reducing the time needed at the repair facility while maintaining high detection completeness.
Solution Approach 2:
The patent creates a digital copy of the vehicle damage through image processing and attribute extraction. This digital representation allows comprehensive analysis without requiring physical disassembly or extensive hands-on inspection, enabling thorough damage detection while minimizing time loss and preserving vehicle accessibility.
3Device complexity
If insurance adjusters base estimates on visible damages only, then the assessment process is simple and fast, but additional hidden damages are not identified
Solution Approach 1:
The system transitions from two-dimensional visual inspection by adjusters to multi-dimensional image attribute analysis using computer vision. The image processing extracts numerous attributes (color, texture, geometric features, damage patterns) that reveal hidden damage information not apparent in simple visual assessment, while maintaining process simplicity through automated analysis.
Data Source
AI summary
An image processing system automatically processes a plurality of images of a damaged vehicle and determines replacements parts that are needed to repair the vehicle therefrom. The system includes a network interface via which the images are received, an image extraction component that generates a set of image attributes from the received images, a parts identifier component that generates indications of the needed replacement parts based on the image attributes and an information identification model, and an output interface via which the generated indications are provided. Additionally, the system includes a data storage entity storing data from multiple historical vehicle insurance claims, and a model generation component that generates the information identification model based on the historical claim data. The information identification model includes independent variable(s) corresponding to a set of image attributes that are more strongly correlated to replacement parts than are other attributes of the historical claim data.