Vehicle Image Grading With ML for Faster Condition Assessment
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Solution Overview
Problem
Manually determining the condition grade of a vehicle is a time-consuming and costly process, requiring extensive multi-point inspections that are often inaccurate and inefficient.
Innovation Solution
An automated vehicle condition grading system using a machine learning engine processes images of a vehicle to generate optical condition grades, which are aggregated and adjusted with additional vehicle information to produce a final condition grade, reducing the need for manual evaluations and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual multi-point inspection is used to determine vehicle condition grade, then measurement precision may be improved, but productivity deteriorates due to time-consuming process
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image processing system using machine learning models. The system captures images of vehicle surfaces and uses trained machine learning models to automatically detect damage and determine condition grades, eliminating the need for manual multi-point inspection while maintaining measurement precision and significantly improving productivity.
2Measurement precision
If manual multi-point inspection is used to determine vehicle condition grade, then measurement precision may be improved, but loss of time increases due to extensive evaluation required
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive datasets of vehicle images with known damage conditions. This preliminary training enables the system to perform rapid, accurate condition assessments without requiring time-consuming manual inspection during actual vehicle grading operations.
3Productivity
If automated image processing with machine learning is used, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal image processing platform that handles multiple vehicle types, damage conditions, and grading criteria through a single machine learning system. The system is designed to be adaptable and reusable across different applications, reducing overall system complexity while maintaining high productivity through standardized processing workflows.
Data Source
AI summary
Aspects of the present disclosure relate to automated vehicle condition grading. In examples, a set of images for a vehicle are processed using a machine learning engine to generate an optical vehicle condition grade for each image of the set. The resulting set of optical vehicle condition grades may be aggregated to generate an aggregate optical vehicle condition grade for the vehicle. In some examples, additional information associated with the vehicle is processed, which may be used to generate an adjustment grade. Accordingly, the adjustment grade and the aggregate optical vehicle condition grade are used to generate a final vehicle condition grade for the vehicle. The final vehicle condition grade is associated with the vehicle in a vehicle condition grade data store for subsequent use by the vehicle grading service and/or by an associated client device, among other examples.