ML-Based Vehicle Panel Paint Blending Identification

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Solution Overview

Problem

Current methods for determining whether to repaint surrounding vehicle panels after damage are subjective and error-prone, leading to inaccurate and incomplete cost estimates, as they fail to accurately identify areas requiring paint blending and account for the complexity of color-matching and damage extent.

Innovation Solution

A system utilizing trained machine learning models to analyze images of damaged vehicles, identify regions requiring repair and paint blending, and generate accurate cost estimates by determining the severity of damage and querying a repainting cost database, thereby providing a comprehensive repair and repainting cost estimate.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual appraisal methods are used to determine paint blending requirements, then the process is simple and quick, but the accuracy and completeness of cost estimates deteriorate due to subjectivity and errors

Engineering Contradiction:

Improveaccuracy of paint blending identificationVSAvoidcomplexity of appraisal system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces the manual mechanical appraisal process with an automated image processing system using machine learning models. The system captures images of damaged vehicle panels and uses trained models to automatically identify regions requiring repair and paint blending, eliminating human subjectivity and error while maintaining operational efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates digital copies of the damaged vehicle panels through image capture and processing. These digital representations are then analyzed by machine learning models to identify repair regions and paint blending requirements, allowing accurate assessment without physical inspection errors.

Inventive Principle:

Principle #26Copying

2Reliability

If comprehensive analysis of all vehicle panels is performed to identify paint blending requirements, then the completeness of cost estimates improves, but the time and computational resources required increase

Engineering Contradiction:

Improvecompleteness of cost estimateVSAvoidappraisal time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The machine learning models are pre-trained on extensive datasets of damaged vehicle panels before deployment. This preliminary training enables the models to quickly and accurately assess new damage cases without requiring time-consuming manual analysis, achieving both completeness and efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The automated image analysis system replaces time-consuming manual inspection with rapid computational processing. The system can evaluate multiple vehicle panels simultaneously using parallel processing, significantly reducing appraisal time while maintaining comprehensive analysis of paint blending requirements.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If automated image processing is used to identify repair regions, then the objectivity and consistency of assessments improve, but the complexity of the system and training requirements increase

Engineering Contradiction:

Improveconsistency of damage assessmentVSAvoidease of system implementation

Core Design Contradiction:

ReliabilityVSEase of manufacture

Solution Approach 1:

The assessment process is segmented into distinct functional modules: image capture, preprocessing, machine learning inference, and result generation. Each module can be independently developed, tested, and deployed, reducing implementation complexity while maintaining overall system consistency and objectivity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The machine learning models are designed to be universal, capable of assessing various types of vehicle panel damage across different vehicle models. This multi-functionality reduces the need for multiple specialized systems, simplifying implementation while ensuring consistent and objective assessments across diverse cases.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20240087330A1System and method for automatically identifying vehicle panels requiring paint blending

Publication Date: 2024.03.14 MITCHELL INTERNATIONAL INC

AI summary

A computer-implemented method comprises receiving an image of a vehicle having damage to a first exterior body panel; providing the image to one or more trained machine learning models that are configured to identify a first region of the first exterior body panel to be repaired and a second region to be paint-blended, when the second region contains a second exterior body panel of the vehicle other than the first exterior body panel, generating an exterior body panel repainting list that includes the identification of the first exterior body panel of the vehicle and the identification of the second exterior body panel of the vehicle; querying a repainting cost database using the exterior body panel repainting list; and receiving a repainting cost estimate from the repainting cost database responsive to the querying.