AI Image Defect Commenting for Concrete Inspection Reports
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Solution Overview
Problem
Existing image processing systems for inspecting concrete structures struggle with accurately distinguishing between accurate and inaccurate defect detections, requiring manual effort to prepare inspection reports, as they often mix both types of results, and lack automated generation of appropriate comments and determination ranks.
Innovation Solution
An image processing apparatus using a trained AI model generates comments and individual determination ranks based on defect information and instruction information, displaying text that includes the comment and defect level, considering local government criteria and structural details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic defect detection is used, then inspection efficiency is improved, but the ability to distinguish accurate from inaccurate detection results deteriorates
Solution Approach 1:
The patent segments detection results into multiple categories (accurate detections, false positives, false negatives) and processes each segment differently. The system divides the inspection workflow into separate modules: automatic detection, manual verification, and report generation, allowing each segment to be optimized independently while maintaining overall efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where inspection workers can mark detection results as accurate or inaccurate, and this feedback is used to refine the automatic detection system. The system continuously learns from worker corrections to improve its detection accuracy over time, resolving the contradiction between automation and precision.
2Device complexity
If all detected defects are outputted in a superimposed manner, then system complexity is reduced, but the ease of preparing inspection reports deteriorates
Solution Approach 1:
The patent segments defect information into structured data fields (location, type, severity, detection confidence) and presents them in an organized manner. Instead of a simple superimposed display, the system creates categorized lists and prioritized views that make report preparation easier while maintaining manageable complexity.
Solution Approach 2:
The patent performs preliminary sorting and filtering of defect results before presentation to the user. The system pre-ranks detections by accuracy and relevance, and pre-organizes them by location and type, so that when workers prepare reports, they receive already-processed information rather than raw data, significantly easing the report preparation process.
3Measurement precision
If manual verification of each defect is required, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent applies partial manual verification rather than requiring complete manual checking of every defect. The system identifies high-confidence detections that can be automatically accepted and low-confidence detections that require manual review, applying manual action only where necessary. This partial verification approach maintains sufficient accuracy while dramatically improving productivity.
Solution Approach 2:
The patent implements self-service through automated detection and preliminary processing that reduces the manual verification burden. The automatic detection system performs initial analysis and triage, allowing the system to serve itself in filtering and organizing data, while workers only intervene for ambiguous or critical cases, thereby maintaining precision without sacrificing productivity.
Data Source
AI summary
An image processing apparatus that obtains defect information related to a defect detected from a captured image and instruction information for instructing content to include in a comment on the defect generates the comment and a level of the defect as an output result of a trained model that is based on the defect information and the instruction information, and displays text including the comment and the level of the defect.