A Convolution Residual Network for Heating-Invariant Defect Segmentation in Composite Materials Inspected by Lock-in Thermography

Morelli, Davide; Marani, Roberto; D’Accardi, Ester; Palumbo, Davide; Galietti, Umberto; D’Orazio, Tiziana

doi:10.1109/tim.2021.3116300

Cited by 14 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This diagnosis concerns mainly the rail industry, where sliding railway wheels and hot bearings are detected [58], and the alloy industry, where defects are detected in heat-resistant alloy coating structures using the same technologies [59]. Besides, inclusion defects in composite materials are detected through thermal imagery and segmentation techniques [60]. Moreover, the fiber placement defects' detection, location, and classification using the fusion of infrared and visible images are also studied [61].…”

Section: Focus On the Defects' Detection Issue Using Infrared Thermal...mentioning

confidence: 99%

A Review of the Use of Thermal Imaging and Computer Vision for Pattern Recognition

Clero,

Nadour,

Ed-Diny

et al. 2023

Cryptography and Blockchain

View full text Add to dashboard Cite

The use of thermal imaging and computer vision has seen a sharp increase in recent years. This paper presents the current state of the art concerning these fields. Thus, a review of research articles studying the combination of these technologies for pattern recognition is established. After presenting an overview of the topic, a bibliometric analysis is selected based on 1160 articles extracted from the Scopus database, which allows assessing the temporal evolution of the publications and the study of their most relevant sources, authors, keywords, trend topics, and publishing countries. Besides, a particular focus is given to the issue of defect detection using thermal imaging and computer vision. The related articles are analyzed and classified into their primary domains. The results confirm that combining thermal imaging with computer vision techniques has become commonly used in several fields. However, the defects detection case studies using these technologies are focused on a few application fields. Thus, more contributions are required regarding this specific issue in more innovative applications. This review will help to guide future research contributions within all fields where creative uses of infrared thermal imaging and computer vision can be developed to recognize different patterns and issues.

show abstract

Section: Focus On the Defects' Detection Issue Using Infrared Thermal...mentioning

confidence: 99%

A Review of the Use of Thermal Imaging and Computer Vision for Pattern Recognition

Clero,

Nadour,

Ed-Diny

et al. 2023

Cryptography and Blockchain

View full text Add to dashboard Cite

show abstract

Depth classification of defects in composite materials by long-pulsed thermography and blind linear unmixing

Marani

Campos-Delgado²

2023

Composites Part B: Engineering

View full text Add to dashboard Cite

Automated CFRP impact damage detection with statistical thermographic data and machine learning

Moskovchenko,

Švantner

2025

International Journal of Thermal Sciences

View full text Add to dashboard Cite

A Convolution Residual Network for Heating-Invariant Defect Segmentation in Composite Materials Inspected by Lock-in Thermography

Cited by 14 publications

References 37 publications

A Review of the Use of Thermal Imaging and Computer Vision for Pattern Recognition

A Review of the Use of Thermal Imaging and Computer Vision for Pattern Recognition

Depth classification of defects in composite materials by long-pulsed thermography and blind linear unmixing

Automated CFRP impact damage detection with statistical thermographic data and machine learning

Contact Info

Product

Resources

About