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Thermal Imaging Sensors and Their Applications

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 32782

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Prof. Dr. Constantin Vertan

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Guest Editor

Image Processing and Analysis Laboratory, University Politehnica of Bucharest
Interests: fundamental image processing and analysis; medical image analysis; computer vision; fuzzy logic
Special Issues, Collections and Topics in MDPI journals

Dr. Valeriu Vrabie

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Guest Editor

Université de Reims Champagne-Ardenne
Interests: Signal processing

Special Issue Information

Dear colleagues,

Thermal sensors are used to measure the amount of heat or cold energy generated by an object or system, and allow us to assess or detect any temperature difference or evolution. Predicted to top $7 billion by 2023, thermal sensor markets, including markets for resistance temperature detectors, thermocouples, and infrared (terahertz) sensors, are growing in several applications such as the automotive and chemical industries, non-destructive testing, agriculture, and medicine, the most recent example being the use of thermal imaging cameras for COVID screening.

This Special Issue aims to cover a wide range of topics, from detection and measurement mechanisms and techniques to theoretical and experimental results of signal- and image-processing methods, data mining, and machine learning algorithms for different applications, such as medical diagnostics and treatment, non-destructive industrial inspection and testing, and environmental monitoring, with a particular interest in the area of applications. Applications that involve the use of machine learning techniques are particularly welcome. Still, methodological approaches, review papers, and prospective articles also fall within the scope of this Special Issue.

Prof. Dr. Constantin Vertan
Dr. Valeriu Vrabie
Guest Editors

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Keywords

thermal imaging
imaging applications
multispectral imaging
multimodal imaging.

Published Papers (12 papers)

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15 pages, 2385 KiB

Open AccessArticle

Automatic Detection of Human Maxillofacial Tumors by Using Thermal Imaging: A Preliminary Study

by Diana Mačianskytė and Rimas Adaškevičius

Sensors 2022, 22(5), 1985; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051985 - 03 Mar 2022

Cited by 3 | Viewed by 1496

Abstract

Traditional computed tomography (CT) delivers a relatively high dose of radiation to the patient and cannot be used as a method for screening of pathologies. Instead, infrared thermography (IRT) might help in the detection of pathologies, but interpreting thermal imaging (TI) is difficult even for the expert. The main objective of this work is to present a new, automated IRT method capable to discern the absence or presence of tumor in the orofacial/maxillofacial region of patients. We evaluated the use of a special feature vector extracted from face and mouth cavity thermograms in classifying TIs against the absence/presence of tumor (n = 23 patients per group). Eight statistical features extracted from TI were used in a k-nearest neighbor (kNN) classifier. Classification accuracy of kNN was evaluated by CT, and by creating a vector with the true class labels for TIs. The presented algorithm, constructed from a training data set, gives good results of classification accuracy of kNN: sensitivity of 77.9%, specificity of 94.9%, and accuracy of 94.1%. The new algorithm exhibited almost the same accuracy in detecting the absence/presence of tumor as CT, and is a proof-of-principle that IRT could be useful as an additional reliable screening tool for detecting orofacial/maxillofacial tumors. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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16 pages, 4082 KiB

Open AccessArticle

Imitating Emergencies: Generating Thermal Surveillance Fall Data Using Low-Cost Human-like Dolls

by Ivan Nikolov, Jinsong Liu and Thomas Moeslund

Sensors 2022, 22(3), 825; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030825 - 22 Jan 2022

Cited by 1 | Viewed by 2080

Abstract

Outdoor fall detection, in the context of accidents, such as falling from heights or in water, is a research area that has not received as much attention as other automated surveillance areas. Gathering sufficient data for developing deep-learning models for such applications has also proven to be not a straight-forward task. Normally, footage of volunteer people falling is used for providing data, but that can be a complicated and dangerous process. In this paper, we propose an application for thermal images of a low-cost rubber doll falling in a harbor, for simulating real emergencies. We achieve thermal signatures similar to a human on different parts of the doll’s body. The change of these thermal signatures over time is measured, and its stability is verified. We demonstrate that, even with the size and weight differences of the doll, the produced videos of falls have a similar motion and appearance to what is expected from real people. We show that the captured thermal doll data can be used for the real-world application of pedestrian detection by running the captured data through a state-of-the-art object detector trained on real people. An average confidence score of 0.730 is achieved, compared to a confidence score of 0.761 when using footage of real people falling. The captured fall sequences using the doll can be used as a substitute to sequences of people. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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20 pages, 2463 KiB

Open AccessArticle

Clothing Insulation Rate and Metabolic Rate Estimation for Individual Thermal Comfort Assessment in Real Life

by Jinsong Liu, Isak Worre Foged and Thomas B. Moeslund

Sensors 2022, 22(2), 619; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020619 - 14 Jan 2022

Cited by 12 | Viewed by 3016

Abstract

Satisfactory indoor thermal environments can improve working efficiencies of office staff. To build such satisfactory indoor microclimates, individual thermal comfort assessment is important, for which personal clothing insulation rate (

I_{c l}

) and metabolic rate (M) need to be [...] Read more.

I_{c l}

) and metabolic rate (M) need to be estimated dynamically. Therefore, this paper proposes a vision-based method. Specifically, a human tracking-by-detection framework is implemented to acquire each person’s clothing status (short-sleeved, long-sleeved), key posture (sitting, standing), and bounding box information simultaneously. The clothing status together with a key body points detector locate the person’s skin region and clothes region, allowing the measurement of skin temperature (

T_{s}

) and clothes temperature (

T_{c}

), and realizing the calculation of

I_{c l}

from

T_{s}

and

T_{c}

. The key posture and the bounding box change across time can category the person’s activity intensity into a corresponding level, from which the M value is estimated. Moreover, we have collected a multi-person thermal dataset to evaluate the method. The tracking-by-detection framework achieves a mAP₅₀ (Mean Average Precision) rate of 89.1% and a MOTA (Multiple Object Tracking Accuracy) rate of 99.5%. The

I_{c l}

estimation module gets an accuracy of 96.2% in locating skin and clothes. The M estimation module obtains a classification rate of 95.6% in categorizing activity level. All of these prove the usefulness of the proposed method in a multi-person scenario of real-life applications. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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22 pages, 10174 KiB

Open AccessArticle

Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications

by Pablo Venegas, Eugenio Ivorra, Mario Ortega and Idurre Sáez de Ocáriz

Sensors 2022, 22(2), 613; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020613 - 13 Jan 2022

Cited by 6 | Viewed by 2543

Abstract

The maintenance of industrial equipment extends its useful life, improves its efficiency, reduces the number of failures, and increases the safety of its use. This study proposes a methodology to develop a predictive maintenance tool based on infrared thermographic measures capable of anticipating failures in industrial equipment. The thermal response of selected equipment in normal operation and in controlled induced anomalous operation was analyzed. The characterization of these situations enabled the development of a machine learning system capable of predicting malfunctions. Different options within the available conventional machine learning techniques were analyzed, assessed, and finally selected for electronic equipment maintenance activities. This study provides advances towards the robust application of machine learning combined with infrared thermography and augmented reality for maintenance applications of industrial equipment. The predictive maintenance system finally selected enables automatic quick hand-held thermal inspections using 3D object detection and a pose estimation algorithm, making predictions with an accuracy of 94% at an inference time of 0.006 s. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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20 pages, 3312 KiB

Open AccessEditor’s ChoiceArticle

Robust Data Association Using Fusion of Data-Driven and Engineered Features for Real-Time Pedestrian Tracking in Thermal Images

by Mircea Paul Muresan, Sergiu Nedevschi and Radu Danescu

Sensors 2021, 21(23), 8005; https://0-doi-org.brum.beds.ac.uk/10.3390/s21238005 - 30 Nov 2021

Cited by 21 | Viewed by 2478

Abstract

Object tracking is an essential problem in computer vision that has been extensively researched for decades. Tracking objects in thermal images is particularly difficult because of the lack of color information, low image resolution, or high similarity between objects of the same class. One of the main challenges in multi-object tracking, also referred to as the data association problem, is finding the correct correspondences between measurements and tracks and adapting the object appearance changes over time. We addressed this challenge of data association for thermal images by proposing three contributions. The first contribution consisted of the creation of a data-driven appearance score using five Siamese Networks, which operate on the image detection and on parts of it. Secondly, we engineered an original edge-based descriptor that improves the data association process. Lastly, we proposed a dataset consisting of pedestrian instances that were recorded in different scenarios and are used for training the Siamese Networks. The data-driven part of the data association score offers robustness, while feature engineering offers adaptability to unknown scenarios and their combination leads to a more powerful tracking solution. Our approach had a running time of 25 ms and achieved an average precision of 86.2% on publicly available benchmarks, containing real-world scenarios, as shown in the evaluation section. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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11 pages, 3026 KiB

Open AccessCommunication

The Use of Thermal Imaging in the Evaluation of Temperature Effects of Radiotherapy in Patients after Mastectomy—First Study

by Agnieszka Baic, Dominika Plaza, Barbara Lange, Marta Reudelsdorf-Ullmann, Łukasz Michalecki, Agata Stanek, Krzysztof Ślosarek and Armand Cholewka

Sensors 2021, 21(21), 7068; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217068 - 25 Oct 2021

Cited by 4 | Viewed by 2548

Abstract

The aim of the study was to evaluate the temperature parameter of the breast area in patients undergoing radiotherapy at various intervals. The relationship between temperature changes on the patient’s skin and the time after the end of radiotherapy was studied. Measurements with a thermal imaging camera were performed in a group of twelve volunteers. Six of them were healthy women who did not have thermal asymmetry between the breasts, whereas six were diagnosed with breast cancer and underwent mastectomy due to the advanced stage of the disease. The patients were qualified for radiation therapy. Thermographic examinations were performed before treatment, two months later and then six months after the end of the treatment. Temperature differences between the healthy breasts and the treated areas were assessed. Additionally, the correlation between a patient’s skin temperature changes and the time after the end of radiotherapy was analyzed. The highest skin temperature increase (1.47 °C) was observed 6 months after the end of RT compared to the measurement before treatment. It seems that thermovision may bring a new tool for quantitative analyses of the temperature effects of radiotherapy. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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18 pages, 2928 KiB

Open AccessArticle

Reconstruction of Microscopic Thermal Fields from Oversampled Infrared Images in Laser-Based Powder Bed Fusion

by Leigh Stanger, Thomas Rockett, Alistair Lyle, Matthew Davies, Magnus Anderson, Iain Todd, Hector Basoalto and Jon R. Willmott

Sensors 2021, 21(14), 4859; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144859 - 16 Jul 2021

Cited by 2 | Viewed by 1989

Abstract

This article elucidates the need to consider the inherent spatial transfer function (blur), of any thermographic instrument used to measure thermal fields. Infrared thermographic data were acquired from a modified, commercial, laser-based powder bed fusion printer. A validated methodology was used to correct for spatial transfer function errors in the measured thermal fields. The methodology was found to make a difference of 40% to the measured signal levels and a 174 °C difference to the calculated effective temperature. The spatial gradients in the processed thermal fields were found to increase significantly. These corrections make a significant difference to the accuracy of validation data for process and microstructure modeling. We demonstrate the need for consideration of image blur when quantifying the thermal fields in laser-based powder bed fusion in this work. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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24 pages, 4330 KiB

Open AccessArticle

Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

by Roger Resmini, Lincoln Silva, Adriel S. Araujo, Petrucio Medeiros, Débora Muchaluat-Saade and Aura Conci

Sensors 2021, 21(14), 4802; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144802 - 14 Jul 2021

Cited by 26 | Viewed by 3439

Abstract

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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13 pages, 2131 KiB

Open AccessArticle

Palm Temperature Differences after Static and Dynamic Load on High Bar

by Karmen Šibanc, Ivan Čuk, Maja Pajek and Igor Pušnik

Sensors 2021, 21(13), 4497; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134497 - 30 Jun 2021

Cited by 2 | Viewed by 1643

Abstract

Thermal imaging is used in various fields of industry and research to measure temperature and its possible differences. Since there is a lack of research and literature on palm temperatures and prevention of blisters on hands, our question was how palm temperature differs in human hands after different loads (Hang and Swing in Hang) for 30 s on a high bar. Thirty-eight students from the Faculty of Sport at the University of Ljubljana were measured with a high-quality thermal imaging camera. Palm temperatures were measured before the load was applied, immediately after and every 30 s for a period of 5 min after the load. Each hand was divided into nine different regions of interest (ROIs). Mean (XA), standard deviation (SD), maximum and minimum, and number of pixels were calculated. We found that there was no difference between the left and right hand. The temperature right after the load was applied decreased significantly for both loads and then increased above the level before the load was applied. After the static load, the temperature reached a constant higher level after 3 min. After the dynamic load, the temperatures continued to increase throughout the measurement period. Further investigation is needed to determine the time period in which the hand temperature reaches the temperature before the load is applied. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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24 pages, 2647 KiB

Open AccessArticle

A Computational Study on the Role of Parameters for Identification of Thyroid Nodules by Infrared Images (and Comparison with Real Data)

by José R. González, Charbel Damião, Maira Moran, Cristina A. Pantaleão, Rubens A. Cruz, Giovanna A. Balarini and Aura Conci

Sensors 2021, 21(13), 4459; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134459 - 29 Jun 2021

Cited by 4 | Viewed by 2106

Abstract

According to experts and medical literature, healthy thyroids and thyroids containing benign nodules tend to be less inflamed and less active than those with malignant nodules. It seems to be a consensus that malignant nodules have more blood veins and more blood circulation. This may be related to the maintenance of the nodule’s heat at a higher level compared with neighboring tissues. If the internal heat modifies the skin radiation, then it could be detected by infrared sensors. The goal of this work is the investigation of the factors that allow this detection, and the possible relation with any pattern referent to nodule malignancy. We aim to consider a wide range of factors, so a great number of numerical simulations of the heat transfer in the region under analysis, based on the Finite Element method, are performed to study the influence of each nodule and patient characteristics on the infrared sensor acquisition. To do so, the protocol for infrared thyroid examination used in our university’s hospital is simulated in the numerical study. This protocol presents two phases. In the first one, the body under observation is in steady state. In the second one, it is submitted to thermal stress (transient state). Both are simulated in order to verify if it is possible (by infrared sensors) to identify different behavior referent to malignant nodules. Moreover, when the simulation indicates possible important aspects, patients with and without similar characteristics are examined to confirm such influences. The results show that the tissues between skin and thyroid, as well as the nodule size, have an influence on superficial temperatures. Other thermal parameters of thyroid nodules show little influence on surface infrared emissions, for instance, those related to the vascularization of the nodule. All details of the physical parameters used in the simulations, characteristics of the real nodules and thermal examinations are publicly available, allowing these simulations to be compared with other types of heat transfer solutions and infrared examination protocols. Among the main contributions of this work, we highlight the simulation of the possible range of parameters, and definition of the simulation approach for mapping the used infrared protocol, promoting the investigation of a possible relation between the heat transfer process and the data obtained by infrared acquisitions. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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12 pages, 1805 KiB

Open AccessArticle

Estimation of Respiratory Rate from Thermography Using Respiratory Likelihood Index

by Yudai Takahashi, Yi Gu, Takaaki Nakada, Ryuzo Abe and Toshiya Nakaguchi

Sensors 2021, 21(13), 4406; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134406 - 27 Jun 2021

Cited by 11 | Viewed by 3214

Abstract

Respiration is a key vital sign used to monitor human health status. Monitoring respiratory rate (RR) under non-contact is particularly important for providing appropriate pre-hospital care in emergencies. We propose an RR estimation system using thermal imaging cameras, which are increasingly being used in the medical field, such as recently during the COVID-19 pandemic. By measuring temperature changes during exhalation and inhalation, we aim to track the respiration of the subject in a supine or seated position in real-time without any physical contact. The proposed method automatically selects the respiration-related regions from the detected facial regions and estimates the respiration rate. Most existing methods rely on signals from nostrils and require close-up or high-resolution images, while our method only requires the facial region to be captured. Facial region is detected using YOLO v3, an object detection model based on deep learning. The detected facial region is divided into subregions. By calculating the respiratory likelihood of each segmented region using the newly proposed index, called the Respiratory Quality Index, the respiratory region is automatically selected and the RR is estimated. An evaluation of the proposed RR estimation method was conducted on seven subjects in their early twenties, with four 15 s measurements being taken. The results showed a mean absolute error of 0.66 bpm. The proposed method can be useful as an RR estimation method. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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23 pages, 2718 KiB

Open AccessBrief Report

Measurement and Analysis of the Parameters of Modern Long-Range Thermal Imaging Cameras

by Jaroslaw Barela, Krzysztof Firmanty and Mariusz Kastek

Sensors 2021, 21(17), 5700; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175700 - 24 Aug 2021

Cited by 2 | Viewed by 3638

Abstract

Today’s long-range infrared cameras (LRIRC) are used in many systems for the protection of critical infrastructure or national borders. The basic technical parameters of such systems are noise equivalent temperature difference (NETD); minimum resolvable temperature difference (MRTD); and the range of detection, recognition and identification of selected objects (DRI). This paper presents a methodology of the theoretical determination of these parameters on the basis of technical data of LRIRCs. The first part of the paper presents the methods used for the determination of the detection, recognition and identification ranges based on the well-known Johnson criteria. The theoretical backgrounds for both approaches are given, and the laboratory test stand is described together with a brief description of the methodology adopted for the measurements of the selected necessary characteristics of a tested observation system. The measurements were performed in the Accredited Testing Laboratory of the Institute of Optoelectronics of the Military University of Technology (AL IOE MUT), whose activity is based on the ISO/IEC 17025 standard. The measurement results are presented, and the calculated ranges for a selected set of IR cameras are given, obtained on the basis of the Johnson criteria. In the final part of the article, the obtained measurement results are presented together with an analysis of the measurement uncertainty for 10 LRIRCs. The obtained measurement results were compared to the technical parameters presented by the manufacturers. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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