Video-Based Structural Damage Detection With AI Part Classification
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Solution Overview
Problem
Existing methods for assessing damage to physical structures, such as vehicles, are laborious, time-consuming, and impractical, often requiring manual inspection and image analysis with specific standards, which can increase complexity and delay the repair assessment process.
Innovation Solution
Utilizing AI/ML systems to analyze videos and optionally associated audio to automatically detect and classify damaged regions of structures, providing a vision-based damage estimate by training modules to identify and classify parts and damage types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection and image analysis are used to assess damage, then detailed evaluation can be performed, but the process becomes laborious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical inspection processes with an automated computer vision system using machine learning models. The system processes video frames automatically to detect and classify damaged regions, eliminating the need for human inspectors to manually examine and document damage while maintaining assessment accuracy.
Solution Approach 2:
The system creates a digital representation of the physical structure by processing video frames to generate a detailed damage map. This digital copy includes classified damaged regions and their properties, allowing for accurate assessment without requiring physical inspection of the actual structure.
2Reliability
If strict image capture standards are enforced to ensure accurate damage determination, then assessment reliability improves, but operational complexity increases
Solution Approach 1:
The system dynamically adapts to varying capture conditions by using machine learning models that are trained to recognize damaged regions regardless of lighting, angle, or distance. The ML models automatically adjust to different video qualities and perspectives, eliminating the need for strict capture standards while maintaining reliable damage determination.
Solution Approach 2:
The system changes the approach from controlling capture parameters (lighting, angle, zoom) to analyzing parameter variations directly. The ML models process videos with varying parameters and extract damage information robustly, allowing flexible operation without requiring standardized capture conditions.
3Measurement precision
If base images prior to damage are required for comparison, then accurate damage assessment is achieved, but the process complexity and delays increase
Solution Approach 1:
The system performs preliminary analysis by detecting damaged regions directly from the current video frames without requiring comparison to base images. The ML models are trained to identify damage characteristics and classify damaged regions autonomously, eliminating the need for prior images while maintaining assessment accuracy.
Solution Approach 2:
The system extracts damage information directly from the current video data by using ML models to identify and classify damaged regions. This extraction approach isolates the damage assessment function from the image comparison process, enabling accurate assessment using only the current video frames.
Data Source
AI summary
A method and system are provided for assessing damage to a structure. According to one embodiment, the method includes detecting one or more external parts of the structure from a video of the structure using a first machine learning (ML) module trained to identify in one or more frames of a video of a structure an external part of the structure. The method also includes using a second ML module, trained to detect and classify damaged regions of a structure from one or more frames of the video: (i) identifying one or more damaged regions of the structure, and (ii) classifying the one or more damaged regions based on damage types. The method further includes associating the one or more damaged regions and corresponding damage types with the one or more external parts, providing a respective vision-based damage estimate for each of the one or more external parts.