Neural Network Vehicle Part Recognition via Conditional Random Field Optimization
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Solution Overview
Problem
Current vehicle damage assessment methods involving manual surveys and comparisons of images are time-consuming and costly, and while automated approaches using AI and machine learning reduce labor costs, they still face challenges in accurately identifying and recognizing vehicle parts and their damage.
Innovation Solution
A neural network system that generates convolution feature maps to identify proposed regions of vehicle parts, uses a conditional random field to optimize class and bounding box predictions, and jointly trains parameters to improve the accuracy of vehicle part recognition, enabling simultaneous detection of multiple parts and generating reports on damage assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual survey and image comparison methods are used, then accuracy of damage assessment can be maintained through professional judgment, but processing time increases and labor costs increase
Solution Approach 1:
The patent replaces the manual mechanical process of professional surveyors examining vehicles with an automated computer vision system using convolutional neural networks. The system automatically processes images to detect damaged parts and generate assessments, eliminating the time-consuming manual survey process while maintaining assessment accuracy through advanced image recognition algorithms.
Solution Approach 2:
The patent creates a digital copy of the vehicle damage assessment process through trained neural network models. These models are trained on large datasets of vehicle images with annotated damage, enabling the system to replicate and automate the decision-making process of professional assessors without requiring their physical presence, thus reducing processing time while preserving assessment quality.
2Measurement precision
If manual survey with professionals is conducted, then accurate identification of vehicle parts and damage can be achieved, but labor costs and training costs increase significantly
Solution Approach 1:
The patent implements a self-service automated assessment system where the neural network models independently perform vehicle part identification and damage assessment without requiring human professionals. The system serves itself by automatically processing images, detecting parts, identifying damage, and generating reports, thereby eliminating the need for hiring, training, and deploying human surveyors while maintaining high identification accuracy.
Solution Approach 2:
The patent transforms the assessment process from a human-dependent manual procedure to an automated computational process by changing the fundamental parameter of who performs the assessment. The system uses trained neural networks with parameters optimized through large-scale dataset training to achieve accurate part identification and damage assessment, replacing human expertise with algorithmic intelligence that requires no ongoing training.
3Productivity
If automated image-based AI approaches are used, then processing time and labor costs are reduced, but accuracy of vehicle part recognition and damage identification deteriorates
Solution Approach 1:
The patent applies preliminary action by extensively training the neural network models on large datasets of vehicle images with detailed annotations of parts and damage before deployment. This pre-training phase prepares the models to accurately recognize various vehicle parts and damage types, ensuring high recognition accuracy is established beforehand so that the automated system can maintain both speed and precision during actual assessments.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously improves its part recognition and damage identification accuracy by learning from assessment results and comparing predictions with ground truth data. The neural networks are refined through feedback from training data and can be retrained to improve performance, ensuring that automated accuracy matches or exceeds manual assessment quality while maintaining high processing efficiency.
Data Source
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AI summary
One embodiment facilitates recognizing parts of a vehicle. A convolution module is configured to generate a convolution feature map of a vehicle image. A region proposal module is configured to determine, based on the convolution feature map, one or more proposed regions, wherein a respective proposed region corresponds to a target of a respective vehicle part. A classification module is configured to determine a class and a bounding box of a vehicle part corresponding to a proposed region based on a feature of the proposed region. A conditional random field module is configured to optimize classes and bounding boxes of the vehicle parts based on correlated features of the corresponding proposed regions. A reporting module is configured to generate a result which indicates a list including an insurance claim item and corresponding damages based on the optimized classes and bounding boxes of the vehicle parts.