AI Surface Defect Analysis for Textiles
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Solution Overview
Problem
Existing methods for analyzing surface defects in two-dimensional products, such as textiles and leather components, are not fully automated and interactive, limiting their effectiveness in detecting defects exhaustively and accurately.
Innovation Solution
A method utilizing artificial intelligence-based image analysis, involving multiple deep learning models for defect localization, background elimination, and irrelevant defect filtering, integrated with interactive software for operator feedback and data storage, enabling exhaustive and automated defect analysis on personal and mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing methods for analyzing surface defects are used, then some defect detection capability is provided, but the process cannot be fully automated and interactive
Solution Approach 1:
The defect analysis process is segmented into multiple specialized deep learning models: a first model for defect localization, a second model for background elimination, and a third model for filtering irrelevant defects. This segmentation allows each model to specialize in a specific aspect of analysis while maintaining overall automation, resolving the contradiction between automation extent and operational interactivity.
Solution Approach 2:
The system incorporates feedback mechanisms where operators can interact with the analysis results, provide corrections, and feed this information back into the system. This feedback loop maintains high automation while preserving necessary human oversight and interactivity, allowing the system to learn and improve from operator input.
2Measurement precision
If multiple deep learning models are used for comprehensive defect analysis, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
Instead of using one complex monolithic model, the system segments the analysis into three specialized models, each handling a specific aspect (localization, background elimination, filtering). This segmentation improves accuracy for each specific task while making the overall system more manageable and less complex than a single comprehensive model would be.
Solution Approach 2:
The deep learning models are designed to be universal and can be applied across different product types and defect scenarios. This multi-functionality allows the same framework to handle various analysis tasks, reducing the need for multiple specialized systems and thereby controlling complexity while maintaining high detection accuracy.
3Reliability
If interactive software with operator feedback is implemented, then analysis completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary automated analysis using deep learning models to identify and flag potential defects before operator review. This preliminary action filters out obvious cases and prepares structured information for operators, reducing the time they need to spend on each case while ensuring completeness through their final verification.
Solution Approach 2:
The system provides self-service capabilities where operators can review, confirm, or correct automated analysis results at their own pace. The automated models handle routine identification tasks, allowing operators to focus only on ambiguous or critical cases, thereby maintaining reliability without significantly increasing overall processing time.
Data Source
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AI summary
Method for the analysis of surface defects of substantially two-dimensional products, the method being suitable for implementation on personal computers or other similar devices, the method comprising the following phases: - arranging a product in substantially two-dimensional development on a work surface, near an optical means, - acquiring images of the product in substantially two-dimensional development by means of the optical means, - analyzing the images by means of software, based on artificial intelligence, implemented on a computer or other analysis device, including mobile device, - visualizing the result of the defect analysis on a screen, - storing the collected data on physical devices for production documentation, and - archiving the collected data on virtual devices.