ML Vehicle Defect Detection via Segmented Models
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Solution Overview
Problem
Conventional methods for identifying vehicle defects are time-consuming and costly, making it impractical for large-scale vehicle inspections, such as those conducted by car dealers or auction marketplaces.
Innovation Solution
A method and system using trained machine learning (ML) models to assist inspectors by providing information on potential vehicle defects through a mobile device. This involves obtaining vehicle information, generating features, processing these features with trained ML models to identify defects, and notifying the inspector of potential issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods using professional mechanics are used to identify vehicle defects, then measurement precision is improved, but productivity deteriorates and loss of time increases
Solution Approach 1:
The inspection process is segmented into multiple independent ML models, each specialized in detecting specific defect types (engine defects, transmission defects, exhaust defects, etc.). This allows parallel processing of different defect categories, improving both throughput and specialized detection accuracy without requiring a single mechanic to evaluate all aspects.
Solution Approach 2:
The patent replaces the mechanical inspection system (professional mechanics physically examining vehicles) with an automated ML-based system that processes vehicle data, images, and sensor information. This substitution dramatically increases productivity while maintaining or improving detection precision through consistent, data-driven analysis.
2Measurement precision
If conventional methods using professional mechanics are used to identify vehicle defects, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary analysis by processing available vehicle data, historical information, and preliminary sensor readings through ML models before the actual inspection is complete. This allows defect identification to begin in advance, reducing the overall time required while maintaining accurate detection through the precision of trained models.
Solution Approach 2:
By replacing time-consuming manual mechanical inspection with automated ML processing that can analyze multiple data streams simultaneously, the system dramatically reduces inspection time while maintaining or improving detection precision through consistent algorithmic evaluation.
3Productivity
If automated ML models are used to identify vehicle defects, then productivity is improved and loss of time is reduced, but device complexity increases
Solution Approach 1:
The complex ML system is segmented into multiple specialized models, each handling specific defect types. This modular approach manages complexity by dividing the overall system into manageable, independent components that can be developed, trained, and maintained separately while working together to provide comprehensive inspection capabilities.
4Measurement precision
If multiple trained ML models are used to detect different defect types, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The detection system is segmented into specialized ML models, each optimized for specific defect types (engine, transmission, exhaust, etc.). This segmentation improves measurement precision by dedicating computational resources to specific detection tasks while managing complexity through modular, independent model development and deployment.
Data Source
AI summary
The inventors have developed technology to facilitate the inspection of vehicles, such as cars, for the presence of defects. The technology may be used to facilitate detection of any defects of one or more vehicle defects before, during, and/or after inspection of the vehicle. The technology includes software and trained machine learning models for performing analyses to determine likely vehicle defects prior to completion of a vehicle inspection, to determine whether any vehicle defects are present based on data acquired during the vehicle inspection, and/or to determine, after completion of the vehicle inspection, whether there are any discrepancies between any defects identified by an inspector during the vehicle inspection and defects automatically detected by analyzing data collected during the inspection of the vehicle.