Selective Image Compression for Infrastructure Damage Detection
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Solution Overview
Problem
In the maintenance and management of infrastructure, such as construction, there is a challenge in efficiently inspecting and managing large datasets of high-resolution image data captured for anomaly detection and prediction, which leads to increased storage demands and potential deterioration of data quality due to compression methods that may not accurately predict damage progression.
Innovation Solution
An image processing program that analyzes captured images of infrastructure to specify damaged areas, predicts the spread of damage based on design data, and employs a compression method that keeps critical regions uncompressed to maintain data quality while reducing storage needs by compressing non-critical regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compression methods are applied to reduce storage demands, then storage requirements are reduced, but data quality deteriorates
Solution Approach 1:
The patent applies different compression strategies to different regions of the image based on their importance. Critical regions containing damage or anomalies are preserved with high quality or uncompressed, while non-critical regions are compressed more aggressively. This local differentiation resolves the contradiction by maintaining data quality where needed while reducing overall storage requirements.
2Quantity of substance
If compression methods are applied to reduce storage demands, then storage requirements are reduced, but anomaly detection accuracy deteriorates
Solution Approach 1:
The system performs preliminary analysis to identify regions containing damage or anomalies before applying compression. By预先 identifying critical areas, the system can preserve these regions from compression or apply minimal compression, ensuring anomaly detection accuracy is maintained while still achieving storage reduction in non-critical areas.
3Measurement precision
If high-resolution image data is captured for anomaly detection, then detection accuracy is improved, but storage demands increase
Solution Approach 1:
The patent captures high-resolution image data overall but applies selective compression to different regions. Critical regions containing damage or anomalies are preserved at high resolution to maintain detection accuracy, while non-critical regions are compressed to reduce storage demands. This local quality differentiation resolves the contradiction between detection accuracy and storage requirements.
Data Source
AI summary
A non-transitory computer-readable recording medium recording an image processing program that causes a computer to execute processing of: specifying a damaged portion by analyzing a captured image of a construction; and predicting, in the captured image, a range to which damage spreads based on the specified damaged portion and design data associated with the construction.