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Thermography Sensing-Based Non-destructive Testing Methods and Applications
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Thermography Sensing-Based Non-destructive Testing Methods and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 1343

Special Issue Editors

Prof. Dr. Bin Gao

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Interests: audio and image processing; social signal processing; multi-physics mathematical modeling; non-destructive evaluation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junyan Liu

E-Mail Website
Guest Editor
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: non-destructive testing and evulation; machine learning; silicon solar cell; 3D printing; in-process monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yunze He

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: intelligent sensing; NDT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thermography non-destructive testing is an overarching field of research focusing on the physics–mathematical foundations and practical applications of thermography NDT and its multi-excitation, interpretation, system, signal processing and artificial intelligent algorithms that learn, reason and act. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent thermography-based NDT methods and applications. The core of thermography lies in its use of and proposed physic meaning in various thermography NDT, physically guided AI methodologies combined with convex and non-convex optimization of machine learning/deep learning neural networks. The novel developments in these areas specialised to the processing of a variety of modalities include infrastructure, chemical, bio area, multi-physics signals, images, multispectral, and video, among others.

The focus of the Special Issue will be on a broad range of sensors, physic interpretation, signal and artificial intelligent processing involving the introduction and development of new advanced theoretical and practical algorithms.

Potential topics include but are not limited to the following:

  • Induction, optical, laser, ultrasound, and flash thermography NDT;
  • Multimodality excitation, such as lock in, pulsed, step heating, etc.;
  • Physical guided thermography processing and machine learning;
  • Different thermography NDT applications;
  • Computer vision and 3D reconstruction by multimodal sensor data fusion;
  • Fusion of thermography NDT with other NDT methods;
  • Non-destructive testing and evaluation and structure health monitoring for material characterization, structural integrity, defect detection and identification, stress and lifecycle assessment.

Prof. Dr. Bin Gao
Prof. Dr. Junyan Liu
Prof. Dr. Yunze He
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • photothermal radiometry
  • infrared thermography
  • multi-information fusion detection
  • physic guided machine learning
  • thermal feature extraction
  • photothermal heteredyne imaging
  • heat conduction
  • thermography sensing

Published Papers (2 papers)

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Research

21 pages, 12843 KiB  
Article
Integration of Finite Element Analysis and Laboratory Analysis on 3D Models for Methodology Calibration
by Sara Gonizzi Barsanti, Rosa De Finis and Riccardo Nobile
Sensors 2024, 24(13), 4048; https://doi.org/10.3390/s24134048 - 21 Jun 2024
Viewed by 399
Abstract
To better address mechanical behavior, it is necessary to make use of modern tools through which it is possible to run predictions, simulate scenarios, and optimize decisions. sources integration. This will increase the capability of detecting material modifications that forerun damage and/or to [...] Read more.
To better address mechanical behavior, it is necessary to make use of modern tools through which it is possible to run predictions, simulate scenarios, and optimize decisions. sources integration. This will increase the capability of detecting material modifications that forerun damage and/or to forecast the stage in the future when very likely fatigue is initiating and propagating cracks. Early warning outcomes obtained by the synergetic implementation of NDE-based protocols for studying mechanical and fatigue and fracture behavior will enhance the preparedness toward economically sustainable future damage control scenarios. Specifically, these early warning outcomes will be developed in the form of retopologized models to be used coupled with FEA. This paper presents the first stage of calibration and the combination of a system of different sensors (photogrammetry, laser scanning and strain gages) for the creation of volumetric models suitable for the prediction of failure of FEA software. The test objects were two components of car suspension to which strain gauges were attached to measure its deformation under cyclic loading. The calibration of the methodology was carried out using models obtained from photogrammetry and experimental strain gauge measurements. Full article
(This article belongs to the Special Issue Thermography Sensing-Based Non-destructive Testing Methods and Applications)
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14 pages, 5025 KiB  
Article
Personnel Detection in Dark Aquatic Environments Based on Infrared Thermal Imaging Technology and an Improved YOLOv5s Model
by Liang Cheng, Yunze He, Yankai Mao, Zhenkang Liu, Xiangzhao Dang, Yilong Dong and Liangliang Wu
Sensors 2024, 24(11), 3321; https://doi.org/10.3390/s24113321 - 23 May 2024
Viewed by 549
Abstract
This study presents a novel method for the nighttime detection of waterborne individuals using an enhanced YOLOv5s algorithm tailored for infrared thermal imaging. To address the unique challenges of nighttime water rescue operations, we have constructed a specialized dataset comprising 5736 thermal images [...] Read more.
This study presents a novel method for the nighttime detection of waterborne individuals using an enhanced YOLOv5s algorithm tailored for infrared thermal imaging. To address the unique challenges of nighttime water rescue operations, we have constructed a specialized dataset comprising 5736 thermal images collected from diverse aquatic environments. This dataset was further expanded through synthetic image generation using CycleGAN and a newly developed color gamut transformation technique, which significantly improves the data variance and model training effectiveness. Furthermore, we integrated the Convolutional Block Attention Module (CBAM) at the end of the last encoder’s feedforward network. This integration maximizes the utilization of channel and spatial information to capture more intricate details in the feature maps. To decrease the computational demands of the network while maintaining model accuracy, Ghost convolution was employed, thereby boosting the inference speed as much as possible. Additionally, we applied hyperparameter evolution to refine the training parameters. The improved algorithm achieved an average detection accuracy of 85.49% on our proprietary dataset, significantly outperforming its predecessor, with a prediction speed of 23.51 FPS. The experimental outcomes demonstrate the proposed solution’s high recognition capabilities and robustness, fulfilling the demands of intelligent lifesaving missions. Full article
(This article belongs to the Special Issue Thermography Sensing-Based Non-destructive Testing Methods and Applications)
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