Big Data and Artificial Intelligence Applications in Infrared Thermography
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".
Deadline for manuscript submissions: 20 December 2024 | Viewed by 263
Special Issue Editors
Interests: non-destructive testing and evaluation; structural health monitoring; infrared thermography; machine learning; composite materials
2. Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
Interests: NDT; diagnostics; non-invasive imaging; composites; structures; thermal imaging; monitoring
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Special Issue Information
Dear Colleagues,
Many experimental non-destructive testing methods have been proposed in recent decades to assess the internal structure of components and structures. Infrared thermography currently holds a prestigious place of prominence. It is based on the principle that heat flow in a material is altered by the presence of some types of anomalies, and it usually produces a lot of data. Thousands of images could be acquired during one single experiment. These data must be processed to extract knowledge about the inner structures of the components. In this regard, new applications for artificial intelligence approaches and Big Data analyses focused on material characterization and defect and damage assessment offer great potential.
Machine learning methods are subsets of artificial intelligence that have dramatically evolved over the last few years. They can be used to find solutions to complex problems, for improved signal-to-noise ratio, for interpolation and extrapolation, as well as for prediction and regression. In the past few years, many methods have been developed in the area of image and data science, which can also be applied in other areas, such as non-destructive testing. This Special Issue invites the submission of both review and original research articles related but not limit to the application of artificial intelligence and big data algorithms to enhance data acquired by infrared thermography.
Dr. Henrique Fernandes
Prof. Dr. Nicolas P. Avdelidis
Guest Editors
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Keywords
- big data
- artificial intelligence
- infrared thermography
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