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Latest Advances and Applications of Infrared Thermography Non-destructive Testing (NDT): 2nd Edition

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

Deadline for manuscript submissions: closed (10 August 2024) | Viewed by 4516

Special Issue Editor

Prof. Dr. Wontae Kim

E-Mail Website
Guest Editor
Division of Mechanical & Automotive Engineering, Kongju National University, Cheonan-si 31080, Republic of Korea
Interests: infrared thermography; thermal system design; numerical simulation; condition monitoring; non-destructive testing; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Infrared thermography has undergone a remarkable transformation over the last century, owing to the enormous progress in microsystem technologies of infrared detector design, electronics, and computer science. Currently, thermal imaging plays an essential role in research and development; safety and law enforcement; medicine; wildlife; energy efficiency; and a variety of different fields in the industry, such as condition monitoring, predictive maintenance, and non-destructive testing and evaluation. This Special Issue explores theoretical, numerical, and experimental advances in infrared thermography and its application in a variety of fields. We invite researchers to contribute original research, case studies, industrial and bio-medical applications, and review articles with a focus on the current state of the art.

Prof. Dr. Wontae Kim
Guest Editor

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Keywords

  • infrared thermography
  • thermal imaging
  • image data processing
  • infrared thermal modeling
  • NDT

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Related Special Issue

Published Papers (4 papers)

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Research

15 pages, 4533 KiB  
Article
Neural Network-Based Analysis and Its Application to Spectroscopy for Mango
by Zicheng Zhang, Tianshuo Wang and Hanhan Fan
Appl. Sci. 2024, 14(6), 2402; https://doi.org/10.3390/app14062402 - 13 Mar 2024
Viewed by 1015
Abstract
Sugar derived from crops is a crucial organic energy source studied in the Earth sciences, serving as a renewable and clean energy alternative. Biofuels produced from crop sugars are more environmentally friendly than traditional fossil fuel sources and contribute to solar energy storage [...] Read more.
Sugar derived from crops is a crucial organic energy source studied in the Earth sciences, serving as a renewable and clean energy alternative. Biofuels produced from crop sugars are more environmentally friendly than traditional fossil fuel sources and contribute to solar energy storage and conversion within the Earth’s cycle. Using mangoes as a case study, this research employs near-infrared spectral analysis technology to develop an algorithm for a mango brix detection device. The study investigates the relationship between brix and absorbance, as well as changes in brix levels, and their application for on-site mango brix detection. Near-infrared spectral data in the range of 1300 nm to 2300 nm were collected during the mango ripening season in summer and preprocessed using various techniques. A neural network-based least squares modeling approach was utilized to develop a mango sugar content detection model, resulting in a correlation coefficient of 0.9055 and a root-mean-square error of 0.2192. To enhance model accuracy and avoid local optimization issues, this study incorporated the simulated annealing algorithm for model optimization, leading to a correlation coefficient of 0.9854 and a root-mean-square error of 0.0431. The findings demonstrate that the non-destructive testing model of mangoes based on near-infrared spectroscopy effectively detects brix changes and storage potential post-harvest, offering valuable insights for mango quality assessment, optimal picking and selling times, and market selection. Full article
(This article belongs to the Special Issue Latest Advances and Applications of Infrared Thermography Non-destructive Testing (NDT): 2nd Edition)
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13 pages, 3432 KiB  
Article
Multiscale Feature-Based Infrared Ship Detection
by Dongming Lu, Haolong Tang, Longyin Teng, Jiangyun Tan, Mengke Wang, Zechen Tian and Liping Wang
Appl. Sci. 2024, 14(1), 246; https://doi.org/10.3390/app14010246 - 27 Dec 2023
Viewed by 912
Abstract
In this paper, based on the idea of “step-by-step accuracy”, a novel multiscale feature-based infrared ship-detection method (MSFISD) is proposed. The proposed method can achieve efficient and effective infrared ship detection in complex scenarios, which may provide assistance in applications such as night [...] Read more.
In this paper, based on the idea of “step-by-step accuracy”, a novel multiscale feature-based infrared ship-detection method (MSFISD) is proposed. The proposed method can achieve efficient and effective infrared ship detection in complex scenarios, which may provide assistance in applications such as night surveillance. First, candidate regions (CRs) are extracted from the whole image by extracting the sea–sky line and region of interest (ROI). The real sea–sky line is extracted based on the gradient features enhanced by large-scale gradient operators. The coarse segmentation results are obtained by the optimization method and are then refined by incorporating the edge features of the ship to reduce false alarms and obtain the CRs. Second, by analyzing the shape features of ships, the feature quantity is established, and the ships in CRs are finally accurately segmented. Experimental results demonstrate that compared with the other five methods, the proposed method has higher detection accuracy with a lower false-alarm rate and performs better in complex sea scenarios. Full article
(This article belongs to the Special Issue Latest Advances and Applications of Infrared Thermography Non-destructive Testing (NDT): 2nd Edition)
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8 pages, 2893 KiB  
Communication
Scene-Based Nonuniformity Correction Method Using Principal Component Analysis for Infrared Focal Plane Arrays
by Dongming Lu, Longyin Teng, Jianle Ren, Jiangyun Tan, Mengke Wang, Liping Wang and Guohua Gu
Appl. Sci. 2023, 13(24), 13331; https://doi.org/10.3390/app132413331 - 18 Dec 2023
Viewed by 1032
Abstract
In this paper, principal component analysis is introduced to form a scene-based nonuniformity correction method for infrared focal plane arrays. The estimation of the gain and offset of the infrared detector and the correction of nonuniformity based on the neural network method with [...] Read more.
In this paper, principal component analysis is introduced to form a scene-based nonuniformity correction method for infrared focal plane arrays. The estimation of the gain and offset of the infrared detector and the correction of nonuniformity based on the neural network method with a novel estimation of desired target value are achieved concurrently. The current frame and several adjacent registered frames are decomposed onto a set of principal components, and then the first principal component is extracted to construct the desired target value. It is practical, forms fewer ghosting artifacts, and considerably promotes correction precision. Numerical experiments demonstrate that the proposed method presents excellent performance when dealing with clean infrared data with synthetic pattern noise as well as the real infrared video sequence. Full article
(This article belongs to the Special Issue Latest Advances and Applications of Infrared Thermography Non-destructive Testing (NDT): 2nd Edition)
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14 pages, 7705 KiB  
Article
Quantitative Investigation of Containment Liner Plate Thinning with Combined Thermal Wave Signal and Image Processing in Thermography Testing
by Yoonjae Chung, Seungju Lee, Chunyoung Kim and Wontae Kim
Appl. Sci. 2023, 13(24), 13180; https://doi.org/10.3390/app132413180 - 12 Dec 2023
Viewed by 880
Abstract
This study presents a process for the quantitative investigation of thinning defects occurring in the containment liner plate (CLP) of a nuclear power plant according to various depths with a combined thermal wave signal and image processing in a lock-in thermography (LIT) technique. [...] Read more.
This study presents a process for the quantitative investigation of thinning defects occurring in the containment liner plate (CLP) of a nuclear power plant according to various depths with a combined thermal wave signal and image processing in a lock-in thermography (LIT) technique. For that, a plate sample with a size of 300 × 300 mm was produced considering the 6 mm thickness applied to an actual CLP. The sample was designed with nine thinning defects on the back side with defect sizes of 40 × 40 mm and varying thinning rates from 10% to 90%. LIT experiments were conducted under various modulation frequency conditions, and phase angle data was calculated and evaluated through four-point method processing. The calculated phase angle was correlated with the defect depth. Then, the phase image was binarized by the Otsu algorithm to evaluate defect detection ability and shape. Furthermore, the accuracy of defect depth assessment was evaluated through third-order polynomial curve fitting. The detectability was analyzed by comparing the number of pixels of the thinning defect in the binarized image and the theoretical calculation. Finally, it was concluded that LIT can be applied for fast thinning defect detection and accurate thinning depth evaluation. Full article
(This article belongs to the Special Issue Latest Advances and Applications of Infrared Thermography Non-destructive Testing (NDT): 2nd Edition)
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