Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The association between parents' marital status, problematic internet use, and negative emotions among Chinese adolescents: a cross-lagged panel network analysis.

BMC public health·2026
Same author

Clinical Utility of Cytological Methylation Assay in Cervical Cancer Screening Across Various Cervical Transformation Zones.

International journal of cancer·2026
Same author

Internet Addiction and Suicidal Ideation in Chinese Adolescents: Sex Differences and the Interactive Roles of Trauma and Depression.

Cyberpsychology, behavior and social networking·2026
Same author

Network based associations between peer rejection and adolescent depression: evidence from general population and clinical samples.

BMC psychology·2026
Same author

Deep learning-assisted versus manual reading in routine cervical cytopathology: a multicentre randomised crossover trial.

NPJ digital medicine·2026
Same author

Childhood trauma profiles and addictive features of adolescent non-suicidal self-injury: The roles of rumination and age of onset.

Journal of health psychology·2026

Related Experiment Video

Updated: Oct 28, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

11.3K

Automatic optical inspection platform for real-time surface defects detection on plane optical components based on

Jules Karangwa, Linghua Kong, Dingrong Yi

    Applied Optics
    |July 15, 2021
    PubMed
    Summary

    Automated optical inspection systems using machine learning can significantly improve defect detection accuracy and speed for optical components. This study developed a deep learning platform for precise surface defect identification, surpassing traditional methods.

    More Related Videos

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.1K
    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
    10:28

    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

    Published on: July 5, 2016

    10.5K

    Related Experiment Videos

    Last Updated: Oct 28, 2025

    Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
    11:34

    Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

    Published on: May 15, 2017

    11.3K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.1K
    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
    10:28

    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

    Published on: July 5, 2016

    10.5K

    Area of Science:

    • Optics and Materials Science
    • Computer Science and Artificial Intelligence

    Background:

    • Optical component manufacturing requires high-precision inspection to ensure quality.
    • Human-based inspection is subjective, slow, and inadequate for modern digital workflows and ISO standards.
    • Automated visual inspection systems are crucial for meeting high-quality product demands.

    Purpose of the Study:

    • To develop an advanced optical inspection platform for automated surface defect detection on optical plane components.
    • To leverage deep learning and machine vision for accurate and efficient defect classification.
    • To address the limitations of manual inspection in optical manufacturing.

    Main Methods:

    • Developed an optical inspection platform integrating optomechanical modules and parallel deep learning algorithms.
    • Utilized a high-resolution line-scanning CMOS camera for dark-field image acquisition.
    • Employed convolutional neural networks and semantic segmentation for defect detection and classification.

    Main Results:

    • The developed system achieved a detection speed of 0.07 seconds per image.
    • An overall detection pixel accuracy of 0.923 was reached for optical bandpass filters.
    • The platform demonstrated superior performance compared to traditional inspection methods.

    Conclusions:

    • The proposed optical inspection platform effectively detects and classifies surface defects on optical components.
    • Deep learning-based machine vision offers a robust and efficient solution for optical part quality control.
    • The system meets the demands for high-accuracy, high-speed inspection in optical manufacturing.