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Related Experiment Video

Updated: Mar 26, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Steel-surface defect detection using a switching-lighting scheme.

Yong-Ju Jeon, Doo-Chul Choi, Sang Jun Lee

    Applied Optics
    |February 3, 2016
    PubMed
    Summary

    This study introduces a new method for detecting defects on steel surfaces using dual-light switching lighting (DLSL) and a sub-optimal filter. This approach effectively identifies defects despite varying surface brightness and textures.

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    Area of Science:

    • Materials Science
    • Computer Vision
    • Industrial Automation

    Background:

    • Steel surfaces exhibit non-uniform brightness and diverse defect shapes, complicating automated detection.
    • Surface scales and variations in steel grade/temperature create challenging image textures for conventional filters.

    Purpose of the Study:

    • To propose a novel filtering scheme combined with a lighting method for improved steel surface defect detection.
    • To address the challenges posed by non-uniform brightness and variable textures in steel surface imaging.

    Main Methods:

    • Dual-light switching lighting (DLSL) to standardize surface brightness and enhance defect contrast.
    • A sub-optimal filtering scheme using an optimized general-finite impulse-response filter for robust defect identification.

    Main Results:

    • DLSL represents defects as consistent black and white patterns, simplifying detection.
    • The proposed sub-optimal filter effectively detects defects across varied steel surface textures.
    • Experimental results on actual production line data confirm the algorithm's effectiveness.

    Conclusions:

    • The combined DLSL and sub-optimal filtering approach significantly enhances steel surface defect detection accuracy.
    • This method offers a robust solution for automated quality control in steel production.