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

Curvature-Controlled Field Effect Enables Thermal Localization for Low-Temperature C─F Bond Activation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

LINC00511 Promotes Breast Cancer Cell Proliferation and Invasion by Mediating MYC-Mediated Regulation of VASP.

Clinical breast cancer·2026
Same author

Non-Destructive Testing and Evaluation of Hybrid and Advanced Structures: A Comprehensive Review of Methods, Applications, and Emerging Trends.

Sensors (Basel, Switzerland)·2025
Same author

Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics.

BMC cancer·2025
Same author

Deep-Optimal Leucorrhea Detection Through Fluorescent Benchmark Data Analysis.

Journal of imaging informatics in medicine·2025
Same author

Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review.

Sensors (Basel, Switzerland)·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 30, 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

11.2K

Automatic Detection and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data.

Qiang Fang1, Clemente Ibarra-Castanedo1, Iván Garrido2

  • 1Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Université Laval, 1065, av. de la Médecine, Québec, QC G1V 0A6, Canada.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning models were applied to infrared thermography (IRT) for automated defect detection in materials. These advanced algorithms show promise in assisting human inspectors for enhanced quality management.

Keywords:
automatic defect identification and segmentationconvolutional neural networkdeep-learning non-destructive evaluation (NDE)infrared image processinginfrared thermographypulsed thermography

More Related Videos

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.9K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Related Experiment Videos

Last Updated: Jul 30, 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

11.2K
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.9K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Area of Science:

  • Materials Science
  • Computer Vision
  • Non-Destructive Testing

Background:

  • Infrared thermography (IRT) is crucial for material quality management, identifying defects like delamination and damage.
  • Deep learning (DL) algorithms are increasingly used in image processing but are underutilized in the IRT field.
  • Automated defect detection and identification in thermal images are needed for efficient quality management (QM).

Purpose of the Study:

  • To investigate and integrate spatial deep learning image processing methods for automated defect detection and identification in thermal images.
  • To evaluate the accuracy of various DL models for assisting human inspectors in quality management.
  • To compare the performance of DL methods against traditional infrared image segmentation techniques.

Main Methods:

  • Exploration of deep Convolutional Neural Networks (CNNs) for image detection.
  • Implementation of instance segmentation models: Mask Region-based Convolutional Neural Networks (Mask-RCNN) and Center-Mask.
  • Utilization of semantic segmentation models: U-net and Resnet-U-net.
  • Application of objective localization models: You Only Look Once (YOLO-v3) and Faster Region-based Convolutional Neural Networks (Faster-RCNN).
  • Comparison with a traditional method combining Absolute Thermal Contrast (ATC) and global thresholding.

Main Results:

  • Evaluation of the efficacy and performance of proposed deep learning algorithms on academic samples with artificial defects.
  • Assessment of different DL architectures, including instance segmentation, semantic segmentation, and object localization methods.
  • Comparative analysis of DL-based defect detection against traditional image processing techniques.

Conclusions:

  • Deep learning models demonstrate potential for automated interpretation of thermal images in material quality management.
  • The study provides insights into the performance of various DL algorithms for defect detection in IRT.
  • Further development and training are required to achieve high accuracy for practical assistance to human inspectors.