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Representation Learning Based on Co-Evolutionary Combined With Probability Distribution Optimization for Precise

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    This study introduces a new method for industrial defect detection, addressing sample scarcity and improving accuracy for blurred defects. The feature co-evolution interaction architecture (CIA) enhances detection in challenging industrial settings.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Industrial Automation

    Background:

    • Representation learning methods are crucial for industrial visual defect detection but face challenges with limited data, blurred defect edges, and inaccurate positioning.
    • Existing methods struggle with sample scarcity, fuzzy defect separation, and lack of precise localization, hindering industrial application.

    Purpose of the Study:

    • To address the challenges of sample scarcity, blurred defect detection, and inaccurate positioning in industrial visual defect detection.
    • To propose a novel feature co-evolution interaction architecture (CIA) and an enforced IoU loss (IIoU loss) for enhanced defect detection.
    • To create a comprehensive glass container defect dataset to mitigate data limitations.

    Main Methods:

    • Developed a glass container image acquisition system combining RGB and polarization information to create a large-scale defect dataset (>60,000 samples).
    • Designed the feature co-evolution interaction architecture (CIA) to optimize feature distributions by co-evolving edge and context features.
    • Introduced an enforced IoU loss (IIoU loss) that accounts for scale changes in predicted boxes for improved localization.

    Main Results:

    • The proposed CIA achieved high mean average precision (mAP) across diverse industrial datasets: NEU-Det (88.74%), glass containers (95.38%), and wood (68.42%).
    • The method demonstrates superior performance compared to state-of-the-art techniques while maintaining computational efficiency (22.5 GFLOPs).
    • The new dataset and CIA effectively alleviate sample scarcity and improve detection of blurred defects and noisy environments.

    Conclusions:

    • The feature co-evolution interaction architecture (CIA) significantly enhances visual defect detection accuracy and localization in industrial settings.
    • The developed glass container defect dataset and IIoU loss contribute to overcoming key limitations in current defect detection methodologies.
    • The proposed approach offers a robust and efficient solution for real-world industrial defect detection applications.