Vehicle Damage Training Image Annotation for CNN Detection

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Solution Overview

Problem

Existing image processing systems struggle to quickly and accurately detect and quantify changes, such as damage, to objects in images, particularly due to variations in perspective, lighting, and camera distortions, requiring manual user analysis and complex AI models.

Innovation Solution

An image processing system utilizing classification engines and convolutional neural networks (CNNs) analyzes multiple views and zoom levels of an object to tag and enhance images, then applies segmentation and characterization to determine precise damage locations, sizes, and types using trained models.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Difficulty of detecting and measuring

If traditional image processing systems compare images from different cameras and perspectives, then they can detect changes to objects, but the detection accuracy deteriorates due to variations in lighting, perspective, and camera distortions

Engineering Contradiction:

Improvechange detection accuracyVSAvoiddamage quantification precision

Core Design Contradiction:

Difficulty of detecting and measuringVSMeasurement precision

Solution Approach 1:

The patent creates a digital twin or virtual model of the object that can be rendered from any perspective. This virtual model serves as a reference copy that can be compared against actual images taken from different angles and lighting conditions, eliminating the need for perfect image matching while maintaining high detection accuracy

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system adjusts and normalizes image parameters such as lighting conditions, perspective angles, and distortion levels before comparison. By standardizing these parameters across different images, the system can accurately detect changes without being affected by variations in capture conditions

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If manual user analysis is used to estimate damage, then detailed assessment can be performed, but the processing time and labor costs increase significantly

Engineering Contradiction:

Improvedamage assessment accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables automatic self-assessment of damage by using AI algorithms to analyze images, generate heat maps, and quantify damage without human intervention. The automated system performs the assessment function that previously required manual analysis, dramatically reducing processing time while maintaining consistent accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical process of manual visual inspection with an automated computer vision system. The AI-based image analysis system processes images and quantifies damage automatically, substituting human labor with computational algorithms that can analyze multiple images simultaneously and rapidly

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If complex AI models are deployed for damage detection, then detection accuracy improves, but computational complexity and resource requirements increase

Engineering Contradiction:

Improvedamage detection accuracyVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent divides the complex damage detection task into multiple simpler sub-tasks: initial image processing, heat map generation, damage region identification, and quantification. Each sub-task uses appropriately complex algorithms, avoiding the need for a single overly complex model while achieving high overall accuracy

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies AI processing selectively to only the regions of interest identified in preliminary analysis, rather than processing entire images uniformly. This partial action approach reduces computational complexity by focusing resources on areas where damage is likely present

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12437564B2Tool for labelling training images for a vehicle characteristic detection model

Publication Date: 2025.10.07 CCC INTELLIGENT SOLUTIONS INC

AI summary

A tool used in an image processing system assists a user to train one or more statistical image models or classification engines (e.g., the CNN models) that are used to detect damaged areas on a vehicle, to detect damage types and/or to detect segments of the depiction of the vehicle. The tool enables a user to select and annotate various different training images to be used to train the models, wherein each of the training images depicts damage of one or more damage types to various different vehicles or automobiles (including automobiles of different years/makes/models). The tool displays each of a set of selected training images and enables a user to indicate, on the displayed selected training image, using an electronic pen, a touch screen or any other type of selector device, one or more sets of pixels within the displayed image that are associated with or that depict damage to the vehicle, one or more sets of pixels within the displayed image that are associated with or that depict a particular type of vehicle damage and/or one or more sets of pixels within the displayed image that are associated with or included in a particular segment of the depiction of the vehicle within the image. The marked or tagged images are then used to train an image model to detect vehicle damage, types of vehicle damage and/or vehicle segments in new vehicle images.