Image Defect Detection Correction Using Target and Reference Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image-based inspection methods for infrastructure defects, such as cracking and delamination, face challenges with false positives and negatives due to factors like formwork traces and shadows, leading to high correction workloads.
Innovation Solution
An image processing apparatus that detects regions using multiple criteria, allows user input for correction, and integrates a target and reference image to correct false positives and negatives by setting unit regions and performing correction processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic detection technology is used to detect defect regions from images, then inspection efficiency is improved, but false positive and false negative rates increase due to factors like formwork traces and shadows
Solution Approach 1:
The patent introduces a reference image as an intermediary to mediate between the automatically detected target image and the final detection result. The reference image, created by manual tracing, serves as a mediator to correct false positives and false negatives in the automatic detection, thereby improving detection accuracy while maintaining high inspection efficiency
Solution Approach 2:
The system implements feedback by using the manually traced reference image to correct the automatically detected target image. The correction results are fed back to improve the detection algorithm, creating a continuous improvement loop that enhances both accuracy and efficiency over time
2Reliability
If manual tracing is performed to ensure accurate defect detection, then detection accuracy is improved, but inspection time increases significantly
Solution Approach 1:
The patent applies partial action by having users perform manual tracing only on specific regions that require correction, rather than tracing the entire image manually. The system automatically detects most defect regions, and users only need to correct false positives and false negatives, significantly reducing the time investment while maintaining high accuracy
Solution Approach 2:
The inspection process is segmented into automatic detection of defect regions followed by selective manual correction of specific problematic areas. This segmentation allows the system to leverage the speed of automatic detection while incorporating manual accuracy only where needed, optimizing the balance between time and accuracy
3Reliability
If multiple detection criteria are applied to improve detection accuracy, then false positive and false negative rates are reduced, but system complexity increases
Solution Approach 1:
The reference image serves multiple functions: it acts as a correction source for false positives, fills in false negatives, and provides a basis for verifying automatic detection results. This multi-functionality allows the system to improve detection accuracy without proportionally increasing system complexity, as the same reference image structure supports multiple correction operations
Data Source
AI summary
An image processing apparatus includes detecting means for detecting a first detected region and a second detected region from an input image, on the basis of a first detection criterion and a second detection criterion, respectively; image setting means for setting, as a target image subjected to correction, an image including the first detected region, and setting, as a reference image that is referred to in the correction, an image including the second detected region; accepting means for accepting, from a user, designation of a region in the target image and a correction instruction for the designated region; correction region setting means for identifying, in the reference image, a region corresponding to the designated region, and for setting a to-be-corrected region on the basis of the identified region and the second detected region; and correcting means for correcting the first detected region in the target image on the basis of the to-be-corrected region set in the reference image.