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Related Experiment Video

Updated: Jun 11, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Automated detection of damage precursors on specular surfaces using an enhanced deep-learning framework.

Xing Peng, Haozhe Li

    Applied Optics
    |June 10, 2026
    PubMed
    Summary

    This study introduces an enhanced deep learning model integrating polarized imaging for detecting damage precursors on reflective metal surfaces in additive manufacturing. The new SOutlook-YOLOv11 framework significantly improves defect detection accuracy and reliability.

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    Area of Science:

    • Materials Science
    • Computer Vision
    • Optical Physics

    Background:

    • Industrial quality assurance for metal additive manufacturing (AM) faces challenges in detecting damage precursors on reflective surfaces.
    • Conventional optical inspection methods are hindered by surface reflectivity, impacting small-defect recognition and environmental robustness.

    Purpose of the Study:

    • To propose an enhanced SOutlook-YOLOv11 framework for improved detection of damage precursors in metal AM components.
    • To address limitations in small-defect recognition and environmental robustness for quality inspection.

    Main Methods:

    • Integration of polarized imaging with an optimized deep-learning architecture (SOutlook-YOLOv11).
    • Introduction of the C3K2-Outlook module for enhanced feature representation.

    Related Experiment Videos

    Last Updated: Jun 11, 2026

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
    03:31

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

    Published on: December 15, 2023

  • Insertion of a spatial adaptive feature modulation (SAFM) module for improved multi-scale feature fusion.
  • Replacement of CIoU loss with Inner-CIoU loss for enhanced localization accuracy.

    • Main Results:

    • The proposed model achieved a 4.2% increase in precision, 0.8% in recall, and 1.9% in mAP@50 compared to the baseline YOLOv11.
    • A recall rate of 0.937 was reached, demonstrating high reliability in identifying damage precursors.
    • Substantial performance improvements over the baseline YOLOv11 were observed.

    Conclusions:

    • The enhanced SOutlook-YOLOv11 framework offers a more reliable and intelligent solution for quality inspection in high-precision additive manufacturing.
    • This work presents a novel strategy combining optical physics and deep learning for defect detection.
    • The proposed method effectively overcomes challenges posed by reflective surfaces in metal AM quality assurance.