Pub Date : 2021-07-28DOI: 10.1080/17686733.2021.1953226
B. Oswald-Tranta
ABSTRACT Inductive thermography can be excellently used to detect surface cracks in metals. A short induction heating pulse (0.1-1s) induces eddy currents in the sample and an infrared camera records the surface temperature distribution. As cracks disturb the eddy current distribution and the heat diffusion, they become visible in the infrared images. In this paper it is investigated, how different parameters influence the surface pattern around short cracks (0.5-12mm length). The main emphasis is on finite element simulations, but some experimental results are presented, too. The influence of crack geometry, as crack depth, length, inclination angle and crack shape below the surface are investigated for ferro-magnetic and austenitic steel. Around the crack tips high temperature ‘hot spots’ can be observed, which intensity increases with the crack depth. But this intensity is strongly affected by the crack shape, whether it is rectangular, trapezoid or half-penny shape. For longer cracks (6-8mm length) simulation results show, that in the middle of the crack the phase distribution can be used to estimate the crack depth. Furthermore, the effect of experimental parameters, as excitation frequency, heating pulse duration and the angle between crack line and induction coil are investigated in order to optimize an experimental setup.
{"title":"Detection and characterisation of short fatigue cracks by inductive thermography","authors":"B. Oswald-Tranta","doi":"10.1080/17686733.2021.1953226","DOIUrl":"https://doi.org/10.1080/17686733.2021.1953226","url":null,"abstract":"ABSTRACT Inductive thermography can be excellently used to detect surface cracks in metals. A short induction heating pulse (0.1-1s) induces eddy currents in the sample and an infrared camera records the surface temperature distribution. As cracks disturb the eddy current distribution and the heat diffusion, they become visible in the infrared images. In this paper it is investigated, how different parameters influence the surface pattern around short cracks (0.5-12mm length). The main emphasis is on finite element simulations, but some experimental results are presented, too. The influence of crack geometry, as crack depth, length, inclination angle and crack shape below the surface are investigated for ferro-magnetic and austenitic steel. Around the crack tips high temperature ‘hot spots’ can be observed, which intensity increases with the crack depth. But this intensity is strongly affected by the crack shape, whether it is rectangular, trapezoid or half-penny shape. For longer cracks (6-8mm length) simulation results show, that in the middle of the crack the phase distribution can be used to estimate the crack depth. Furthermore, the effect of experimental parameters, as excitation frequency, heating pulse duration and the angle between crack line and induction coil are investigated in order to optimize an experimental setup.","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"19 1","pages":"239 - 260"},"PeriodicalIF":2.5,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17686733.2021.1953226","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43955893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.1080/17686733.2021.1947595
Ahmet Özdil, B. Yılmaz
ABSTRACT Automatisation and standardisation of the diagnosis process in medical infrared thermal imaging (MITI) is crucial because the number of medical experts in this area is highly limited.The current studies generally need manual intervention. One of the manual operations requires physician’s determination of the body part and orientation. In this study automatic pose and body part detection on medical thermal images is investigated. The database (957 thermal images - 59 patients) was divided into four classes upper-lower body parts with back-front views. First, histogram equalization (HE) method was applied on the pixels only within the body determined using Otsu’sthresholding approach. Secondly, DarkNet-19 architecture was used for feature extraction, and principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) approaches for feature selection. Finally, the performances of various machine learning based classification methods were examined. Upper vs. lower body parts and back vs. front of upper body were classified with 100% accuracy, and back vs. front classification of lower body part success rate was 93.38%. This approach will improve the automatisation process of thermal images to group them for comparing one image with the others and to perform queries on the labeled images in a more user-friendly manner.
{"title":"Automatic body part and pose detection in medical infrared thermal images","authors":"Ahmet Özdil, B. Yılmaz","doi":"10.1080/17686733.2021.1947595","DOIUrl":"https://doi.org/10.1080/17686733.2021.1947595","url":null,"abstract":"ABSTRACT Automatisation and standardisation of the diagnosis process in medical infrared thermal imaging (MITI) is crucial because the number of medical experts in this area is highly limited.The current studies generally need manual intervention. One of the manual operations requires physician’s determination of the body part and orientation. In this study automatic pose and body part detection on medical thermal images is investigated. The database (957 thermal images - 59 patients) was divided into four classes upper-lower body parts with back-front views. First, histogram equalization (HE) method was applied on the pixels only within the body determined using Otsu’sthresholding approach. Secondly, DarkNet-19 architecture was used for feature extraction, and principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) approaches for feature selection. Finally, the performances of various machine learning based classification methods were examined. Upper vs. lower body parts and back vs. front of upper body were classified with 100% accuracy, and back vs. front classification of lower body part success rate was 93.38%. This approach will improve the automatisation process of thermal images to group them for comparing one image with the others and to perform queries on the labeled images in a more user-friendly manner.","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"19 1","pages":"223 - 238"},"PeriodicalIF":2.5,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17686733.2021.1947595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48988299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-10DOI: 10.1080/17686733.2021.1933698
António Ramos Silva, M. Vaz, Sofia Leite, J. Gabriel
ABSTRACT Among the Non-Destructive Testing (NDT) techniques available today, Active Infrared Thermal Testing (AIRTT) is certainly one of the most flexible and promising. The goal of this work was to compare the results obtained with a common Lock-in Thermal Test (LTT) and the same test using a true sinusoidal stimulation obtained through a closed-loop controller. The results showed a poor dynamic response of the common system and a lack of proportionality between the reference signal and the generated optical stimulation. To improve its response, it was implemented a PID controller using a light sensor to close the feedback loop. The amplitude images obtained with this controller showed a significant improvement in the results. Defects invisible with the common LTT were now identifiable. The phase images obtained using the controller with feedback revealed higher sensitivity with lower noise. Despite only one system was tested, the results show that the optical stimulation used in LTT is not very accurate and can/should be improved and, that a sensitivity 2.5 times higher than the common LTT was achieved with a real sinusoidal stimulation. Abbreviation: NDT: Non-Destructive Tests; AIRTT: Active Infrared Thermal Testing; LTT: Lock-in Thermal Test; cLTT: common LTT; PID: Proportional, Integral and Derivative; CFRP: Carbon Fibre Reinforced Polymers; TTT: Transient Thermal Tests; LDR: light-dependent resistor; PMMA: Poly(methyl methacrylate)
{"title":"Lock-in thermal test with corrected optical stimulation","authors":"António Ramos Silva, M. Vaz, Sofia Leite, J. Gabriel","doi":"10.1080/17686733.2021.1933698","DOIUrl":"https://doi.org/10.1080/17686733.2021.1933698","url":null,"abstract":"ABSTRACT Among the Non-Destructive Testing (NDT) techniques available today, Active Infrared Thermal Testing (AIRTT) is certainly one of the most flexible and promising. The goal of this work was to compare the results obtained with a common Lock-in Thermal Test (LTT) and the same test using a true sinusoidal stimulation obtained through a closed-loop controller. The results showed a poor dynamic response of the common system and a lack of proportionality between the reference signal and the generated optical stimulation. To improve its response, it was implemented a PID controller using a light sensor to close the feedback loop. The amplitude images obtained with this controller showed a significant improvement in the results. Defects invisible with the common LTT were now identifiable. The phase images obtained using the controller with feedback revealed higher sensitivity with lower noise. Despite only one system was tested, the results show that the optical stimulation used in LTT is not very accurate and can/should be improved and, that a sensitivity 2.5 times higher than the common LTT was achieved with a real sinusoidal stimulation. Abbreviation: NDT: Non-Destructive Tests; AIRTT: Active Infrared Thermal Testing; LTT: Lock-in Thermal Test; cLTT: common LTT; PID: Proportional, Integral and Derivative; CFRP: Carbon Fibre Reinforced Polymers; TTT: Transient Thermal Tests; LDR: light-dependent resistor; PMMA: Poly(methyl methacrylate)","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"19 1","pages":"261 - 282"},"PeriodicalIF":2.5,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17686733.2021.1933698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49284040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-06DOI: 10.1080/17686733.2021.1918514
Juan Carlos Torres-Galván, E. Guevara, E. Kolosovas-Machuca, A. Oceguera-Villanueva, J. Flores, F. J. González
ABSTRACT Infrared thermography is a technique that can detect anomalies in temperature patterns which can indicate some breast pathologies including breast cancer. One limitation of the method is the absence of standardised thermography interpretation procedures. Deep learning models have been used for pattern recognition and classification of objects and have been adopted as an adjunct methodology in medical imaging diagnosis. In this paper, the use of a deep convolutional neural network (CNN) with transfer learning is proposed to automatically classify thermograms into two classes (normal and abnormal). A population of 311 female subjects was considered analysing two approaches to test the CNN’s performance: one with a balanced class distribution and the second study in a typical screening cohort, with a low prevalence of abnormal thermograms. Results showed that the transfer-learned ResNet-101 model had a sensitivity of 92.3% and a specificity of 53.8%, while with an unbalanced distribution the values were 84.6% and 65.3%, respectively. These results suggest that the model presented in this work can classify abnormal thermograms with high sensitivity which validates the use of infrared thermography as an adjunct method for breast cancer screening.
{"title":"Deep convolutional neural networks for classifying breast cancer using infrared thermography","authors":"Juan Carlos Torres-Galván, E. Guevara, E. Kolosovas-Machuca, A. Oceguera-Villanueva, J. Flores, F. J. González","doi":"10.1080/17686733.2021.1918514","DOIUrl":"https://doi.org/10.1080/17686733.2021.1918514","url":null,"abstract":"ABSTRACT Infrared thermography is a technique that can detect anomalies in temperature patterns which can indicate some breast pathologies including breast cancer. One limitation of the method is the absence of standardised thermography interpretation procedures. Deep learning models have been used for pattern recognition and classification of objects and have been adopted as an adjunct methodology in medical imaging diagnosis. In this paper, the use of a deep convolutional neural network (CNN) with transfer learning is proposed to automatically classify thermograms into two classes (normal and abnormal). A population of 311 female subjects was considered analysing two approaches to test the CNN’s performance: one with a balanced class distribution and the second study in a typical screening cohort, with a low prevalence of abnormal thermograms. Results showed that the transfer-learned ResNet-101 model had a sensitivity of 92.3% and a specificity of 53.8%, while with an unbalanced distribution the values were 84.6% and 65.3%, respectively. These results suggest that the model presented in this work can classify abnormal thermograms with high sensitivity which validates the use of infrared thermography as an adjunct method for breast cancer screening.","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"19 1","pages":"283 - 294"},"PeriodicalIF":2.5,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17686733.2021.1918514","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49654699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-10DOI: 10.1080/17686733.2021.1882075
A. M. Gabbi, G. Kolling, V. Fischer, L. R. Pereira, T. R. Tomich, F. S. Machado, M. M. Campos, M. V. G. B. Silva, C. S. Cunha, M. K. R. Santos, C. Pimentel
ABSTRACT The trial aimed to investigate the use of infrared thermography (IRT) to estimate enteric methane production in dairy heifers. The study lasted 5 days with 36 Gyr, Gyr x Holstein and Holstein heifers. The sulphur hexafluoride (SF6) tracer technique was used to estimate methane emission. Superficial body temperature was obtained with an infrared camera (FLIR® T300) on each side of the animals at 20-minute intervals during eight hours after the morning feeding. Data were analysed using Pearson’s correlation analysis and multivariate regression analysis, as well as two multivariate tests to investigate the relationship with methane emission. The analysis considered all the data together (total) and three subsets: initial (0–150 minutes), middle (150–300) and final period (300–442 minutes after feeding). Based on R2 and canonical correlations, the best predictive capacity of methane emission by IRT occurred in the initial and final periods after feeding. Infrared thermography may be a good estimator of methane emission using the SF6-tracer technique when considering the flanks’ temperature.
摘要本试验旨在研究利用红外热像仪（IRT）来估计奶牛肠道甲烷产量。该研究对36 Gyr、Gyr x Holstein和Holstein小母牛进行了为期5天的研究。六氟化硫（SF6）示踪技术用于估算甲烷排放量。在早上喂食后的8小时内，用红外相机（FLIR®T300）每隔20分钟在动物两侧测量体表体温。使用Pearson相关分析和多元回归分析以及两个多元检验对数据进行分析，以调查与甲烷排放的关系。该分析综合考虑了所有数据（总计）和三个子集：初期（0–150分钟）、中期（150–300分钟）和末期（喂食后300–442分钟）。基于R2和典型相关性，IRT对甲烷排放的最佳预测能力出现在喂食后的初始和最后阶段。在考虑侧面温度时，使用SF6示踪技术，红外热成像可以很好地估算甲烷排放。
{"title":"Use of infrared thermography to estimate enteric methane production in dairy heifers","authors":"A. M. Gabbi, G. Kolling, V. Fischer, L. R. Pereira, T. R. Tomich, F. S. Machado, M. M. Campos, M. V. G. B. Silva, C. S. Cunha, M. K. R. Santos, C. Pimentel","doi":"10.1080/17686733.2021.1882075","DOIUrl":"https://doi.org/10.1080/17686733.2021.1882075","url":null,"abstract":"ABSTRACT The trial aimed to investigate the use of infrared thermography (IRT) to estimate enteric methane production in dairy heifers. The study lasted 5 days with 36 Gyr, Gyr x Holstein and Holstein heifers. The sulphur hexafluoride (SF6) tracer technique was used to estimate methane emission. Superficial body temperature was obtained with an infrared camera (FLIR® T300) on each side of the animals at 20-minute intervals during eight hours after the morning feeding. Data were analysed using Pearson’s correlation analysis and multivariate regression analysis, as well as two multivariate tests to investigate the relationship with methane emission. The analysis considered all the data together (total) and three subsets: initial (0–150 minutes), middle (150–300) and final period (300–442 minutes after feeding). Based on R2 and canonical correlations, the best predictive capacity of methane emission by IRT occurred in the initial and final periods after feeding. Infrared thermography may be a good estimator of methane emission using the SF6-tracer technique when considering the flanks’ temperature.","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"19 1","pages":"187 - 195"},"PeriodicalIF":2.5,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17686733.2021.1882075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48585930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-26DOI: 10.1080/17686733.2020.1846113
E. Koroteeva, A. A. Bashkatov
ABSTRACT As a result of physiological thermoregulation, human eccrine sweat glands produce sessile droplets on the skin. This paper studies the thermal signatures associated with droplet dynamics on the face and the fingers of an individual using a mid-wave infrared camera. The thermal image analysis focuses on the morphology and evolution of individual droplets induced by various emotional stimuli. We follow the growth and evaporation rate of droplets with different lifetimes ranging from seconds to tens of seconds. The results are of interest in the fields of medicine and psychophysiology (in the studies of human emotional sweating) or physics (for the development of models of evaporative surface cooling).
{"title":"Thermal signatures of liquid droplets on a skin induced by emotional sweating","authors":"E. Koroteeva, A. A. Bashkatov","doi":"10.1080/17686733.2020.1846113","DOIUrl":"https://doi.org/10.1080/17686733.2020.1846113","url":null,"abstract":"ABSTRACT As a result of physiological thermoregulation, human eccrine sweat glands produce sessile droplets on the skin. This paper studies the thermal signatures associated with droplet dynamics on the face and the fingers of an individual using a mid-wave infrared camera. The thermal image analysis focuses on the morphology and evolution of individual droplets induced by various emotional stimuli. We follow the growth and evaporation rate of droplets with different lifetimes ranging from seconds to tens of seconds. The results are of interest in the fields of medicine and psychophysiology (in the studies of human emotional sweating) or physics (for the development of models of evaporative surface cooling).","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"19 1","pages":"115 - 125"},"PeriodicalIF":2.5,"publicationDate":"2021-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17686733.2020.1846113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45001570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-25DOI: 10.1080/17686733.2021.1877918
S. Yoon, J.C. Park, Y. J. Cho
ABSTRACT Blackbody radiation is standardised electromagnetic energy emitted into space at a given temperature and wavelength distribution. It is used as a reference model to compare radiation emitted from various objects. This reference performance of blackbody systems should be maintained through periodic inspection and calibration. In this study, temperature uniformity on the surface of the blackbody was evaluated using infrared cameras. To this end, we divided the blackbody system measurements into ‘before’ and ‘after’ calibration sets and examined the blackbody surface in different bands, using two infrared cameras with different measurement principles. To evaluate surface temperature uniformity, we calculated the signal transfer function, equivalent noise temperature difference, and 3D noise of the infrared detector, and then comparatively analysed them.
{"title":"An experimental study on the evaluation of temperature uniformity on the surface of a blackbody using infrared cameras","authors":"S. Yoon, J.C. Park, Y. J. Cho","doi":"10.1080/17686733.2021.1877918","DOIUrl":"https://doi.org/10.1080/17686733.2021.1877918","url":null,"abstract":"ABSTRACT Blackbody radiation is standardised electromagnetic energy emitted into space at a given temperature and wavelength distribution. It is used as a reference model to compare radiation emitted from various objects. This reference performance of blackbody systems should be maintained through periodic inspection and calibration. In this study, temperature uniformity on the surface of the blackbody was evaluated using infrared cameras. To this end, we divided the blackbody system measurements into ‘before’ and ‘after’ calibration sets and examined the blackbody surface in different bands, using two infrared cameras with different measurement principles. To evaluate surface temperature uniformity, we calculated the signal transfer function, equivalent noise temperature difference, and 3D noise of the infrared detector, and then comparatively analysed them.","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"19 1","pages":"172 - 186"},"PeriodicalIF":2.5,"publicationDate":"2021-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17686733.2021.1877918","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48260292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-15DOI: 10.1080/17686733.2020.1855390
Shawli Bardhan, Satyabrata Nath, Tathagata Debnath, D. Bhattacharjee, M. Bhowmik
ABSTRACT Limited application of thermography for inflammatory joint disease diagnosis is due to unavailability of joint thermogram dataset and formulated protocol of data acquisition. Focusing on the limitations, we aimed on creation and analysis of knee thermogram dataset by introducing standardized protocols of acquisition. The dataset named as “Infrared Knee Joint Dataset”, and includes healthy, and three different types of arthritis affected knee thermograms. Dataset validation and inflammation oriented ground truth generation procedures are also mentioned in this study. After data acquisition, thermograms are preprocessed and segmented. Finally, the system separates healthy and abnormal knee thermograms, and classifies those abnormal thermograms into three classes. For the classification, conventional feature-based techniques combined with shallow learning as well as deep learning have been used. The experimental results show the following: 1) classification of healthy and arthritis affected knee thermogram achieved 92% accuracy with SVM and 96% using VGG19; 2) In inter-arthritis classification VGG16 has shown the highest accuracy of 86% through ROI-based classification. Creation of standardized knee thermogram dataset and application of deep learning methodology diagnosis arthritis-oriented knee abnormality non-invasively. The described database acquisition protocol and classification strategies could contribute to the designing of accurate and robust image-based arthritis diagnosis systems.
{"title":"Designing of an inflammatory knee joint thermogram dataset for arthritis classification using deep convolution neural network.","authors":"Shawli Bardhan, Satyabrata Nath, Tathagata Debnath, D. Bhattacharjee, M. Bhowmik","doi":"10.1080/17686733.2020.1855390","DOIUrl":"https://doi.org/10.1080/17686733.2020.1855390","url":null,"abstract":"ABSTRACT Limited application of thermography for inflammatory joint disease diagnosis is due to unavailability of joint thermogram dataset and formulated protocol of data acquisition. Focusing on the limitations, we aimed on creation and analysis of knee thermogram dataset by introducing standardized protocols of acquisition. The dataset named as “Infrared Knee Joint Dataset”, and includes healthy, and three different types of arthritis affected knee thermograms. Dataset validation and inflammation oriented ground truth generation procedures are also mentioned in this study. After data acquisition, thermograms are preprocessed and segmented. Finally, the system separates healthy and abnormal knee thermograms, and classifies those abnormal thermograms into three classes. For the classification, conventional feature-based techniques combined with shallow learning as well as deep learning have been used. The experimental results show the following: 1) classification of healthy and arthritis affected knee thermogram achieved 92% accuracy with SVM and 96% using VGG19; 2) In inter-arthritis classification VGG16 has shown the highest accuracy of 86% through ROI-based classification. Creation of standardized knee thermogram dataset and application of deep learning methodology diagnosis arthritis-oriented knee abnormality non-invasively. The described database acquisition protocol and classification strategies could contribute to the designing of accurate and robust image-based arthritis diagnosis systems.","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"19 1","pages":"145 - 171"},"PeriodicalIF":2.5,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17686733.2020.1855390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43553607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-09DOI: 10.1080/17686733.2020.1841443
S. Gryś, S. Dudzik
ABSTRACT The paper presents the results of research on the data processing algorithm used to detect material defects using active thermography. The algorithm allows the analysis of thermogram sequences in both time and image domain. In the first stage of the algorithm operation, mathematical morphology or filtered contrast methods are used to remove the uneven heating from the sample, as well as to segment and detect defects using local and global thresholding methods. In the next stage, it is possible to determine the number of defects as well as automatically estimate their depth and characteristics (insulator/conductor) in relation to the background material (material without defect). The presented algorithm was tested on two material samples, i.e. PMMA and Expanded PVC, for two phases of the thermal process, i.e. heating and cooling. The study found that the best defect detection and characterisation results are obtained when processing thermographic data from the cooling phase in combination with a Top Hat morphological transformation, local thresholding (for defect detection), and relative incremental filtered contrast (for defect size estimation).
{"title":"Investigation on dual-domain data processing algorithm used in thermal non-destructive evaluation","authors":"S. Gryś, S. Dudzik","doi":"10.1080/17686733.2020.1841443","DOIUrl":"https://doi.org/10.1080/17686733.2020.1841443","url":null,"abstract":"ABSTRACT The paper presents the results of research on the data processing algorithm used to detect material defects using active thermography. The algorithm allows the analysis of thermogram sequences in both time and image domain. In the first stage of the algorithm operation, mathematical morphology or filtered contrast methods are used to remove the uneven heating from the sample, as well as to segment and detect defects using local and global thresholding methods. In the next stage, it is possible to determine the number of defects as well as automatically estimate their depth and characteristics (insulator/conductor) in relation to the background material (material without defect). The presented algorithm was tested on two material samples, i.e. PMMA and Expanded PVC, for two phases of the thermal process, i.e. heating and cooling. The study found that the best defect detection and characterisation results are obtained when processing thermographic data from the cooling phase in combination with a Top Hat morphological transformation, local thresholding (for defect detection), and relative incremental filtered contrast (for defect size estimation).","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"19 1","pages":"196 - 219"},"PeriodicalIF":2.5,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17686733.2020.1841443","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60453739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1080/17686733.2020.1829889
S. Altenburg, A. Strasse, A. Gumenyuk, C. Maierhofer
ABSTRACT Additive manufacturing offers a range of novel applications. However, the manufacturing process is complex and the production of almost defect-free parts with high reliability and durability is still a challenge. Thermography is a valuable tool for process surveillance, especially in metal additive manufacturing processes. The high process temperatures allow one to use cameras usually operating in the visible spectral range. Here, we compare the results of measurements during the manufacturing process of a commercial laser metal deposition setup using a mid wavelength infrared camera with those from a short wavelength infrared camera and those from a visual spectrum high-speed camera with band pass filter in the near infrared range.
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