Deep Neural Network for Vehicle Damage Appraisal
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Solution Overview
Problem
Existing automated property damage appraisal technologies face challenges with accuracy due to subjective user input, historical data bias, and the need for costly and specialized instrumentation, leading to inefficiencies and inconsistencies.
Innovation Solution
The use of a deep neural network with multiple hidden layers to analyze property images, identify damaged vehicle parts, and generate OEM-specific repair estimates by leveraging a parts dictionary and VIN information, enabling cost-effective and accurate damage assessments without requiring specialized training or instrumentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural network with stored knowledge data is used to identify damaged parts, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system performs preliminary encoding of knowledge from stored property damage images into the deep neural network during a training phase. This pre-processing of information allows the network to automatically identify damaged parts and assess damage severity without requiring manual annotation during actual appraisal, resolving the contradiction by preparing the system in advance to handle complex pattern recognition tasks.
Solution Approach 2:
The system creates a digital representation (copy) of damage patterns from stored property damage images and encodes them into the neural network's knowledge structure. This copying mechanism allows the system to learn from historical damage data and replicate expert appraisal knowledge, improving measurement precision while keeping the operational system relatively simple.
2Productivity
If automated damage appraisal system is implemented, then productivity is improved, but loss of information increases due to subjective user input and historical data bias
Solution Approach 1:
The system incorporates feedback mechanisms by using stored property damage images as training data to continuously improve its assessment capabilities. The neural network learns from historical examples and adjusts its internal parameters based on the patterns it observes, creating a feedback loop that reduces information loss and improves accuracy over time while maintaining high productivity.
Solution Approach 2:
The system transforms subjective user inputs and historical data into standardized numerical parameters that the neural network can process objectively. By converting qualitative damage assessments into quantifiable features and parameters, the system eliminates subjective bias while preserving essential damage information, thereby improving both productivity and information retention.
Data Source
AI summary
A method, non-transitory computer readable medium, and apparatus that improves automated damage appraisal includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify the part of the vehicle that has sustained damage. Damage data on an extent of the damage in the identified part of the vehicle is determined using the deep neural network which has stored knowledge data encoded from one or more stored property damage images. The identified generic part of the vehicle is used to obtain a corresponding Part ID Code by using a parts dictionary. Part Qualifier information obtained using VIN information is then used in conjunction with the Part ID to obtain the OEM-specific part and then generate one or more repair lines for the repair estimate.