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    This study introduces an automated genetic image segmentation algorithm for precise crack detection in thermographic inspection. The method enhances accuracy in defect sizing without manual intervention, improving non-destructive testing.

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    Area of Science:

    • Materials Science and Engineering
    • Non-Destructive Testing and Evaluation (NDT&E)
    • Image Processing and Computer Vision

    Background:

    • Thermographic inspection is vital for rapid, contactless, non-destructive testing and evaluation.
    • Accurate defect identification and sizing rely heavily on image segmentation techniques.
    • Current methods often require manual intervention, leading to potential inaccuracies and inefficiencies.

    Purpose of the Study:

    • To propose an effective genetic first-order statistical image segmentation algorithm for quantitative crack detection.
    • To automate the extraction of spatial-temporal patterns, eliminating human bias in thermal video analysis.
    • To enhance the accuracy of crack sizing through an internally integrated genetic threshold control.

    Main Methods:

    • Development of a genetic first-order statistical image segmentation algorithm.
    • Utilizing unsupervised feature extraction for spatial-temporal pattern identification.
    • Implementation of an internal genetic algorithm for automatic segmentation threshold optimization.
    • Demonstration using Eddy Current Pulsed Thermography (ECPT) for surface crack detection.

    Main Results:

    • The proposed algorithm successfully automates crack detection and sizing in thermographic data.
    • Elimination of manual frame selection significantly reduces processing time and human error.
    • Experimental validation using ECPT confirms the method's efficacy in quantitative crack assessment.
    • The F-score metric was employed for objective performance evaluation against other segmentation algorithms.

    Conclusions:

    • The developed genetic image segmentation algorithm offers a robust and accurate solution for quantitative crack detection in thermography.
    • This automated approach improves efficiency and reliability in non-destructive testing applications.
    • The method holds significant potential for advancing defect analysis in various industrial sectors.