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Algorithm for real-time defect detection of micro pipe inner surface.

Xinyu Zhao, Bin Wu

    Applied Optics
    |October 8, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces new algorithms for identifying defects in micro pipes. The lightweight convolution neural network (LCNN) achieves high accuracy in defect detection and segmentation for quality control.

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    Area of Science:

    • Materials Science
    • Computer Vision
    • Quality Control

    Background:

    • Quantitative analysis of defects in micro pipes is challenging.
    • Accurate defect identification is crucial for micro pipe quality control.

    Purpose of the Study:

    • To develop and present algorithms for defect detection and feature recognition in micro pipes.
    • To improve the accuracy and efficiency of quality control processes for micro pipes.

    Main Methods:

    • A lightweight convolution neural network (LCNN) was developed for defect discrimination.
    • A shallow segmentation network was used with LCNN for pixel-wise crack detection.
    • A feature recognition algorithm was implemented for quantitative measurement.

    Main Results:

    • The proposed algorithms achieved 98.5% accuracy in defect detection.
    • Segmentation accuracy reached a mean intersection over union of 0.834.
    • The system demonstrated a low latency of 0.2 seconds for real-time processing.

    Conclusions:

    • The developed algorithms are effective for online feature recognition and defect detection on the inner surface of holes.
    • The LCNN-based approach offers a robust solution for micro pipe quality assurance.
    • This method enhances the precision and speed of identifying unknown shaped defects.