Structure Damage Cause Estimation Using Similar Image Matching
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Solution Overview
Problem
Existing systems struggle to accurately estimate the cause of structure damage due to the variability in inspector skills and the lack of a comprehensive database for damage cause identification, leading to inconsistent and potentially inaccurate assessments.
Innovation Solution
A structure damage cause estimation system and server that utilize a database of past damage causes, incorporating image analysis and similarity extraction to identify and present damage causes based on captured images and actual size information, enhancing the accuracy of damage cause estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection methods are used to estimate damage causes, then inspection can be performed, but the estimation accuracy varies due to inspector skill differences
Solution Approach 1:
The system creates a database of reference damage images with known causes, and uses image recognition technology to automatically identify and match current damage images against the reference database. This copying approach replaces human inspector judgment with automated pattern recognition, ensuring consistent and accurate damage cause estimation across different inspectors and time points.
Solution Approach 2:
The patent replaces the mechanical human inspection process with an automated image processing system. The image acquisition unit captures damage images, the recognition unit processes these images to identify damage causes, and the presentation unit displays results. This substitution eliminates human variability and provides reliable, repeatable estimation results.
2Measurement precision
If comprehensive damage cause identification is performed, then estimation accuracy improves, but the complexity of the inspection process increases
Solution Approach 1:
The system segments the damage identification process into distinct functional modules: an image acquisition unit for capturing damage images, a recognition unit for processing images and identifying causes, and a presentation unit for displaying results. This segmentation allows each module to be optimized independently and simplifies the overall system architecture while maintaining high accuracy.
Solution Approach 2:
The system performs preliminary action by pre-storing reference damage images and their corresponding causes in a database before actual inspection. During inspection, the system only needs to compare current images against this pre-prepared database, significantly reducing the complexity of real-time analysis while maintaining comprehensive identification capability.
Data Source
AI summary
A structure damage cause estimation system, a structure damage cause estimation method, and a structure damage cause estimation server that enable a damage cause to be estimated with a high probability are provided. A structure damage cause estimation system (100) includes a database (110) that has data of a captured image and a damage cause of a structure, an image acquisition unit (115) that acquires a captured image of a target structure to be inspected, a damage detection unit (125) that detects damage from the captured image, a similar damage extraction unit (130) that extracts similar damage similar to the damage by using the database (110), and a damage cause presentation unit (135) that presents damage causes of the similar damage. Also provided are a damage cause estimation method that uses the structure damage cause estimation system (100), and a damage cause estimation server.