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FOC winding defect detection based on improved texture features and low-rank representation model.
Applied Optics
|October 18, 2022
Summary
This study introduces a novel winding defect detection method for fiber-optic coils (FOCs) using low-rank representation (LRR) technology. The LRR approach enhances FOC quality control by improving detection accuracy and reducing false alarms.
Area of Science:
- Materials Science
- Optical Engineering
- Quality Control
Background:
- Fiber-optic coils (FOCs) are critical components, necessitating robust quality control.
- Existing defect detection methods for FOCs suffer from poor performance and low reliability.
Purpose of the Study:
- To develop an effective and reliable method for detecting winding defects in FOCs.
- To address the limitations of current FOC defect detection techniques.
Main Methods:
- A feature matrix was designed to represent FOC images.
- Low-rank representation (LRR) technology was applied for defect detection.
- Laplacian regularization was incorporated to enhance defect region separation.
Main Results:
- The proposed LRR-based method demonstrated superior performance in detecting winding defects.
- The algorithm achieved the highest detection accuracy among tested methods.
- A significantly lower false alarm rate was observed compared to existing techniques.
Conclusions:
- The LRR-based approach offers an effective solution for FOC winding defect detection.
- The method improves the quality control of FOC production.
- The study validates the effectiveness and reliability of the proposed technique.
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