Automated 3D Model Damage Analysis via Convolutional Neural Networks
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Solution Overview
Problem
The existing process of damage assessment in objects, such as buildings or vehicles, is laborious and time-intensive, requiring human experts to inspect and estimate repair costs, which is costly and inefficient for all parties involved.
Innovation Solution
An automated system using convolutional neural networks for image analysis, which acquires images or videos, constructs three-dimensional models, identifies and evaluates damage areas, and guides users through the damage management process, potentially substituting or supporting human experts with remote live inspections and sensor data integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually inspect and estimate damage, then assessment accuracy can be maintained, but the process becomes laborious and time-intensive
Solution Approach 1:
The patent replaces the mechanical system of manual human inspection with an automated image analysis system using convolutional neural networks. The system processes images of damaged objects automatically, identifying and evaluating damage areas without human intervention, thereby eliminating the time-consuming nature of manual inspection while maintaining assessment accuracy through advanced AI algorithms.
Solution Approach 2:
The patent creates a digital copy (three-dimensional model) of the damaged object from acquired images. This digital replica allows for automated analysis and evaluation of damage areas, enabling the system to assess damage without physically inspecting the original object, thus significantly reducing inspection time while preserving measurement precision.
2Measurement precision
If human experts are deployed for damage inspection, then detailed evaluation can be performed, but the process becomes costly for all parties involved
Solution Approach 1:
The patent substitutes expensive human expert deployment with an automated AI-based image analysis system. The convolutional neural network performs detailed damage evaluation without requiring human experts to travel to inspection locations, thereby eliminating travel costs, labor costs, and other expenses associated with manual inspection while maintaining high evaluation detail.
Solution Approach 2:
The system enables self-service damage assessment where the damaged object itself (through its images) provides all necessary information for evaluation. The automated system extracts and analyzes damage features directly from images without requiring external human expertise, making the process cost-effective while preserving detailed evaluation capabilities.
3Productivity
If automated image analysis is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent employs a universal convolutional neural network architecture that can handle various types of damaged objects (buildings, vehicles, electronic devices, furniture) with a single system. This multi-functional approach increases processing speed across different object types while managing system complexity by using a standardized AI model rather than requiring separate specialized systems for each object category.
Data Source
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AI summary
The present disclosure relates to a method for identifying and / or evaluating a damage in an object, comprising the steps:acquiring at least two images of the object, electronically constructing a three-dimensional model of the object, and analyzing the three-dimensional model of the object by means of a convolutional neural network to identify and / or evaluate ast least one area of damage to the object. Another aspect of the invention relates to a system for performing such a method.