Automated Defect Detection Interface with Human Feedback Loop
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Solution Overview
Problem
Current automated inspection techniques for images captured by borescopes, such as those used in aircraft engine blade inspection, are prone to errors due to human inattention and struggle to detect defects outside pre-defined classes, limiting their effectiveness in identifying all types of blade damage.
Innovation Solution
An automated defect detection system that uses an image capture device to transmit data to a monitoring and analysis site for automated analysis, employing Robust Principal Component Analysis (PCA) and a classifier to identify defects, with human inspector feedback refining the system's accuracy and storing results in a database for future reference and training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated inspection techniques categorize defects into pre-defined classes, then common defects can be detected, but defects outside those classes are not detected
Solution Approach 1:
The system incorporates feedback loops where automated analysis results are reviewed by human inspectors, and their corrections are fed back to refine the automated analysis algorithms. This continuous feedback mechanism enables the system to adapt to new defect types and improve detection accuracy over time without being limited by pre-defined classes.
2Reliability
If human inspectors review images for defect detection, then defect interpretation can be performed, but errors result from human inattention
Solution Approach 1:
The inspection process is segmented into two stages: automated analysis for initial defect identification and filtering, followed by human inspector review only for potential defects. This segmentation reduces the time burden on human inspectors while maintaining high detection accuracy through the combination of automated efficiency and human expertise.
Solution Approach 2:
The automated analysis system acts as an intermediary between the raw images and human inspectors, pre-processing and filtering images to identify potential defects. This intermediary role eliminates the need for human inspectors to review every image individually, reducing time loss while maintaining reliability.
3Productivity
If automated analysis is performed without human feedback, then processing speed increases, but accuracy improves through continuous training
Solution Approach 1:
Human inspector feedback on automated analysis results is systematically collected and used to retrain and refine the automated analysis algorithms. This feedback loop enables continuous improvement of detection precision while maintaining high productivity, as the system learns from human expertise without requiring manual review of every image.
Data Source
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AI summary
A system and method for improving human-machine interface while performing automated defect detection is disclosed. The system and method may include an image capture device for capturing and transmitting data of an object, performing automated analysis of the data and reviewing results of the automated analysis by a human inspector and providing feedback. The system and method may further include refining the automated analysis of the data based upon the feedback of the human inspector.