Damage Identification Optimization Using LSTM and Attention
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Solution Overview
Problem
Current intelligent damage determination solutions for vehicle damage identification lack accuracy, leading to suboptimal results and increased costs for insurance companies and poor user experience due to lengthy claim cycles.
Innovation Solution
A method and apparatus utilizing a recurrent neural network and a long short-term memory (LSTM) network to optimize vehicle damage identification results by incorporating previous damage identification results and user interactions, allowing for iterative refinement of damage identification based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection and damage determination are performed by professional staff, then damage identification accuracy is maintained, but labor costs increase and claim cycle duration extends
Solution Approach 1:
The system performs preliminary damage identification automatically using computer vision technology before manual inspection. The preliminary results provide a head start, reducing the need for extensive manual verification and accelerating the overall claim process while maintaining accuracy through subsequent human review of automated results
Solution Approach 2:
The patent introduces an automated computer vision system as an intermediary between image capture and final damage determination. This intermediary performs initial analysis to generate damage identification results, which then serve as input for manual verification, effectively bridging the gap between rapid automated processing and accurate human judgment
2Productivity
If automated computer vision technology is used for damage identification, then labor costs and claim cycle duration are reduced, but damage identification accuracy decreases
Solution Approach 1:
The patent merges automated computer vision technology with manual inspection processes into a hybrid system. The automated system handles initial damage identification to maintain high productivity, while manual inspection is integrated to verify and correct automated results, ensuring accuracy is not compromised by automation alone
Solution Approach 2:
The system implements feedback mechanisms where manual inspection results are used to validate and refine automated damage identification. The feedback loop allows the automated system to learn from human corrections, improving its accuracy over time while maintaining the productivity benefits of automation
3Measurement precision
If iterative refinement with user feedback is implemented, then damage identification accuracy improves, but system complexity increases
Solution Approach 1:
The system employs dynamic refinement where the complexity of processing adapts based on user feedback. Initially, the system provides basic automated results, and only when users provide corrections or feedback does the system engage in more complex iterative refinement processes, maintaining simplicity for straightforward cases while enabling enhanced accuracy when needed
Data Source
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AI summary
Embodiments of the specification provide a damage identification result optimization method. According to an aspect, the method includes a method of optimizing a damage identification result of a single image according to data of interaction with a user. The method specifically includes: firstly, on the basis of a CNN algorithm, identifying a preliminary damage identification result of a single image, displaying the same to a user, receiving a modification made by the user to the preliminary damage identification result, and then with reference to this modification, outputting a damage identification result again by means of effects of an LSTM and an attention mechanism, and displaying the same to the user again until the user is satisfied. According to another aspect, the method further includes a method of optimizing a damage identification result of a current image on the basis of a damage identification result of another image. The method specifically includes: on the basis of the CNN algorithm, identifying a preliminary damage identification result of a current image, and optimizing the preliminary damage identification result of the current image by means of the LSTM and the attention mechanism with reference to a damage identification result of another image.