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Real-time batch inspection system for surface defects on circular optical filters.

Jishi Zheng, Wenying Yu, Zhigang Ding

    Applied Optics
    |January 6, 2023
    PubMed
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    This study introduces a deep learning and image processing method for real-time optical filter surface quality inspection. The new technique significantly improves defect detection accuracy and efficiency, reducing manual workload by 90%.

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    Area of Science:

    • Optical Engineering
    • Materials Science
    • Computer Vision

    Background:

    • Optical filters are crucial components in optical instruments, requiring stringent surface quality for optimal performance.
    • Traditional machine learning methods for optical filter inspection struggle with minor or numerous defects due to reliance on manual feature extraction.
    • Existing methods exhibit limitations in detection efficiency and accuracy for complex defect scenarios.

    Purpose of the Study:

    • To develop a real-time, efficient, and accurate method for inspecting optical filter surface quality.
    • To address the limitations of traditional techniques in detecting diverse and subtle optical filter defects.
    • To enhance the automated inspection process for optical filters, improving product quality and reducing labor.

    Main Methods:

    • A deep learning model was trained on a custom dataset for detecting and identifying seven typical optical filter surface defects.
    • Image processing techniques were employed to accurately locate defects, assess their position relative to the effective aperture, and analyze critical defect features.
    • The proposed method integrates deep learning for defect recognition with image processing for detailed analysis.

    Main Results:

    • The developed method demonstrated significant improvements in inspection productivity and product quality.
    • A 90% reduction in manual workload was achieved through the automated inspection process.
    • Experimental validation against field measurement data confirmed the method's high recognition accuracy and improved efficiency.

    Conclusions:

    • The proposed deep learning and image processing approach offers a robust solution for real-time optical filter surface quality inspection.
    • This method effectively overcomes the challenges posed by minor and numerous defects, enhancing detection capabilities.
    • The successful implementation leads to increased efficiency, superior product quality, and reduced operational costs in optical filter manufacturing.