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Petr Šmíd, Vítězslav Havránek, Georgi Ivanov

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    This study introduces a digital image processing method for detecting defects on rotationally symmetric objects by comparing surface brightness. It reliably identifies larger defects on uniform surfaces, optimizing defect characterization.

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    Area of Science:

    • Industrial Engineering
    • Computer Vision
    • Quality Control

    Background:

    • Automated visual inspection is crucial for manufacturing quality control.
    • Detecting subtle defects on symmetric objects presents unique challenges.
    • Existing methods may struggle with vaguely defined or surface-based defects.

    Purpose of the Study:

    • To develop and validate a digital image processing technique for identifying visual defects on rotationally symmetric objects.
    • To establish an optimal brightness difference threshold for defect detection.
    • To analyze the limitations and influencing factors of the proposed defect detection method.

    Main Methods:

    • Utilizing digital image processing to analyze surface brightness variations.
    • Exploiting rotational symmetry to compare corresponding surface areas.
    • Locating defects based on deviations from the average brightness of symmetric regions.

    Main Results:

    • The method successfully detects vaguely defined defects on symmetric objects like automotive wheels and turbines.
    • Optimal brightness difference for defect characterization was determined through testing.
    • Reliability is contingent on surface properties (opaque, non-fragmented) and defect isolation.

    Conclusions:

    • The developed image processing method offers an effective approach for detecting larger defects on rotationally symmetric items.
    • Factors such as object shape uncertainty, camera resolution, and illumination uniformity significantly impact minimum detectable defect size.
    • Further analysis is needed to refine defect size limitations based on geometric and imaging parameters.