Big Data and Artificial Intelligence Approaches for Infrared Thermography Inspection

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 2666

Special Issue Editors

Faculty of Computing, Federal University of Uberlandia, Uberlandia, Brazil
Interests: non-destructive testing and evaluation; infrared thermography; digital image processing; machine learning; composite materials; medical images
Department of Industrial and Information Engineering and Economics, University of L’Aquila, L'Aquila, Italy
Interests: building heritage; building pathology; infrared thermography; hygrothermal behaviour of buildings; energy efficiency; thermal comfort; numerical modelling; heat transfer; optical metrology; composite materials; NDT
Special Issues, Collections and Topics in MDPI journals
Sensor Systems for Intelligent Data Acquisition and Advanced Data Processing, Saarland University of Applied Sciences, Saarbrücken, Germany
Interests: non-destructive evaluation; intelligent sensing systems for industrial inspection; image processing; deep and machine learning
School of Physics and Electronics, Central South University, Changsha, China
Interests: signal processing for infrared thermography; vision system for industrial inspection; terahertz spectroscopy and imaging; artificial intelligence for automated inspection and pedestrian detection at nighttime

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 to the application of artificial intelligence and big data algorithms to enhance data acquired by infrared thermography inspections. In addition, articles on numerical analysis as well as remaining lifetime estimation are welcome.

Prof. Dr. Henrique Fernandes
Prof. Dr. Stefano Sfarra
Prof. Dr. Ahmad Osman
Prof. Dr. Yuxia Duan
Guest Editors

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Keywords

  • artificial intelligence
  • big data machine learning
  • deep learning
  • infrared thermography
  • composite materials
  • metals
  • concrete and buldings
  • cultural heritage
  • structural heal monitoring
  • lifetime estimation
  • numerical models

Published Papers (1 paper)

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Research

17 pages, 6152 KiB  
Article
Research on Key Technologies of Super-Resolution Reconstruction of Medium and Long Wave Maritime Infrared Image
by Zhipeng Ren, Jianping Zhao, Chao Wang, Xiaocong Ma, Yan Lou and Peng Wang
Appl. Sci. 2022, 12(21), 10871; https://0-doi-org.brum.beds.ac.uk/10.3390/app122110871 - 26 Oct 2022
Cited by 2 | Viewed by 1197
Abstract
Complex illumination, solar flares and heavy smog on the sea surface have caused difficulties to accurately obtain high-quality imaging and multi-dimensional information of marine monitoring targets, such as oil spill, red tide and underwater vehicle wake. The principle of existing imaging mechanism is [...] Read more.
Complex illumination, solar flares and heavy smog on the sea surface have caused difficulties to accurately obtain high-quality imaging and multi-dimensional information of marine monitoring targets, such as oil spill, red tide and underwater vehicle wake. The principle of existing imaging mechanism is complex, and thus it is not practical to capture high-resolution infrared images efficiently. To combat this challenge by utilizing new infrared optical materials and single point diamond-turning technology, we designed and processed a simple, light and strong condensing ability medium and long wavelength infrared imaging optical system with large relative aperture, which can obtain high-quality infrared images. On top of this, with the training from a combination of infrared and visible light images, we also proposed a super-resolution network model, which is composed of a feature extraction layer, an information extraction block and a reconstruction block. The initial features of the input images are recognized in feature extraction layer. Next, to supply missing feature information and recover more details on infrared image extracted from a dense connection block, a feature mapping attention mechanism is introduced. Its main function is to transfer the important feature information of the visible light images in the information extraction block. Finally, the global feature information is integrated in the reconstruction block to reconstruct the high-resolution infrared image. We experimented our algorithm on both of the public Kaist datasets and self-collected datasets, and then compared it with several relevant algorithms. The results showed that our algorithm can significantly improve the reconstruction performance and reveal more detail information, and enhance the visual effect. Therefore, it brings excellent potential in dealing with the problem of low resolution of optical infrared imaging in complex marine environment. Full article
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