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Pulsed thermography is a commonly used non-destructive testing method, and is increasingly studied for advanced materials such as carbon fiber-reinforced polymer (CFRP) evaluation. Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, we used a type of matrix decomposition using Robust-PCA via Inexact-ALM in our experiment. We investigate this method as a pre-and post-processing method on thermal data acquired by pulsed thermography. We employed state-of-the-art methods, i.e., PCT, PPT, and PLST, as the main process. The results indicate that pre-processing on thermal data can elevate the defect detectability while post-processing, in some cases, can deteriorate the results.
Samira Ebrahimi; Julien R Fleuret; Matthieu Klein; Louis-Daniel Théroux; Clemente Ibarra-Castanedo; Xavier P.V. Maldague. Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-fiber-Reinforced Polymers. 2021, 1 .
AMA StyleSamira Ebrahimi, Julien R Fleuret, Matthieu Klein, Louis-Daniel Théroux, Clemente Ibarra-Castanedo, Xavier P.V. Maldague. Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-fiber-Reinforced Polymers. . 2021; ():1.
Chicago/Turabian StyleSamira Ebrahimi; Julien R Fleuret; Matthieu Klein; Louis-Daniel Théroux; Clemente Ibarra-Castanedo; Xavier P.V. Maldague. 2021. "Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-fiber-Reinforced Polymers." , no. : 1.
The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson–Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4–74.4%) for multiclass and 89.6% (88.4–90.7%) for binary-class classification.
Bardia Yousefi; Satoru Kawakita; Arya Amini; Hamed Akbari; Shailesh Advani; Moulay Akhloufi; Xavier Maldague; Samad Ahadian. Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics. Journal of Clinical Medicine 2021, 10, 3100 .
AMA StyleBardia Yousefi, Satoru Kawakita, Arya Amini, Hamed Akbari, Shailesh Advani, Moulay Akhloufi, Xavier Maldague, Samad Ahadian. Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics. Journal of Clinical Medicine. 2021; 10 (14):3100.
Chicago/Turabian StyleBardia Yousefi; Satoru Kawakita; Arya Amini; Hamed Akbari; Shailesh Advani; Moulay Akhloufi; Xavier Maldague; Samad Ahadian. 2021. "Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics." Journal of Clinical Medicine 10, no. 14: 3100.
Thermography has been used extensively as a complementary diagnostic tool in breast cancer detection. Among thermographic methods, matrix factorization (MF) techniques show an unequivocal capability to detect thermal patterns corresponding to vasodilation in the cancer cases. One of the biggest challenges in such techniques is selecting the best representation of the thermal basis. In this study, an embedding method is proposed to address this problem and deep-semi-non-negative MF (Deep-SemiNMF) for thermography is introduced, then tested for 208 breast cancer screening cases. First, we apply Deep-SemiNMF to infrared images to extract low-rank thermal representations for each case. Then, we embed low-rank bases to obtain one basis for each patient. After that, we extract 300 thermal imaging features, called thermomics, to decode imaging information for the automatic diagnostic model. We reduced the dimensionality of thermomics by spanning them on to Hilbert space using radial-based function (RBF) kernel and select the three most efficient features using the block Hilbert–Schmidt Independence Criterion (HSIC) Lasso. The preserved thermal heterogeneity successfully classified asymptomatic versus symptomatic patients applying a random forest model [cross-validated accuracy of 71.36% (69.42%–73.3%)].
Bardia Yousefi; Hossein Memarzadeh Sharifipour; Xavier P. V. Maldague. A Diagnostic Biomarker for Breast Cancer Screening via Hilbert Embedded Deep Low-Rank Matrix Approximation. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -9.
AMA StyleBardia Yousefi, Hossein Memarzadeh Sharifipour, Xavier P. V. Maldague. A Diagnostic Biomarker for Breast Cancer Screening via Hilbert Embedded Deep Low-Rank Matrix Approximation. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-9.
Chicago/Turabian StyleBardia Yousefi; Hossein Memarzadeh Sharifipour; Xavier P. V. Maldague. 2021. "A Diagnostic Biomarker for Breast Cancer Screening via Hilbert Embedded Deep Low-Rank Matrix Approximation." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-9.
Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank
Bardia Yousefi; Clemente Ibarra-Castanedo; Martin Chamberland; Xavier Maldague; Georges Beaudoin. Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery. Remote Sensing 2021, 13, 2125 .
AMA StyleBardia Yousefi, Clemente Ibarra-Castanedo, Martin Chamberland, Xavier Maldague, Georges Beaudoin. Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery. Remote Sensing. 2021; 13 (11):2125.
Chicago/Turabian StyleBardia Yousefi; Clemente Ibarra-Castanedo; Martin Chamberland; Xavier Maldague; Georges Beaudoin. 2021. "Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery." Remote Sensing 13, no. 11: 2125.
Dimensional reduction methods have significantly improved the simplification of Pulsed Thermography (PT) data while improving the accuracy of the results. Such approaches reduce the quantity of data to analyze and improve the contrast of the main defects in the samples contributed to their popularity. Many works have been proposed in the literature mainly based on improving the Principal Component Thermography (PCT). Recently the Independent Component Analysis (ICA) has been a topic of attention. Many different approaches have been proposed in the literature to solve the ICA. In this paper, we investigated several recent ICA methods and evaluated their influence on PT data compared with the state-of-the-art methods. We conducted our evaluation on reference CFRP samples with known defects. We found that ICA outperform PCT for small and deep defects. For other defects ICA results are often not far from the results obtained by PCT. However, the frequency of acquisition and the ICA methods have a great influence on the results.
Julien Fleuret; Samira Ebrahimi; Clemente Ibarra-Castanedo; Xavier Maldague. Independent Component Analysis Applied on Pulsed Thermographic Data for Carbon Fiber Reinforced Plastic Inspection: A Comparative Study. Applied Sciences 2021, 11, 4377 .
AMA StyleJulien Fleuret, Samira Ebrahimi, Clemente Ibarra-Castanedo, Xavier Maldague. Independent Component Analysis Applied on Pulsed Thermographic Data for Carbon Fiber Reinforced Plastic Inspection: A Comparative Study. Applied Sciences. 2021; 11 (10):4377.
Chicago/Turabian StyleJulien Fleuret; Samira Ebrahimi; Clemente Ibarra-Castanedo; Xavier Maldague. 2021. "Independent Component Analysis Applied on Pulsed Thermographic Data for Carbon Fiber Reinforced Plastic Inspection: A Comparative Study." Applied Sciences 11, no. 10: 4377.
One of the concerns about the use of passive Infrared Thermography (IRT) for structural health monitoring (SHM) is the determination of a favorable period to conduct the inspections. This paper investigates the use of numerical simulations to find appropriate periods for IRT-based detection of subsurface damages in concrete bridge slabs under passive heating along a 1 year of time span. A model was built using the Finite Element Method (FEM) and calibrated using the results of a set of thermographic field inspections on a concrete slab sample. The results showed that the numerical simulation properly reproduced the experimental thermographic measurements of the concrete structure under passive heating, allowing the analysis to be extended for a longer testing period. The long-term FEM results demonstrated that the months of spring and summer are the most suitable for passive IRT inspections in this study, with around 17% more detections compared to the autumn and winter periods in Brazil. By enhancing the possibility of using FEM beyond the design stage, we demonstrate that this computation tool can provide support to long-term SHM.
Sandra Pozzer; Francisco Dalla Rosa; Zacarias Pravia; Ehsan Rezazadeh Azar; Xavier Maldague. Long-Term Numerical Analysis of Subsurface Delamination Detection in Concrete Slabs via Infrared Thermography. Applied Sciences 2021, 11, 4323 .
AMA StyleSandra Pozzer, Francisco Dalla Rosa, Zacarias Pravia, Ehsan Rezazadeh Azar, Xavier Maldague. Long-Term Numerical Analysis of Subsurface Delamination Detection in Concrete Slabs via Infrared Thermography. Applied Sciences. 2021; 11 (10):4323.
Chicago/Turabian StyleSandra Pozzer; Francisco Dalla Rosa; Zacarias Pravia; Ehsan Rezazadeh Azar; Xavier Maldague. 2021. "Long-Term Numerical Analysis of Subsurface Delamination Detection in Concrete Slabs via Infrared Thermography." Applied Sciences 11, no. 10: 4323.
Finding efficient and less expensive techniques for different aspects of culvert inspection is in great demand. This study assesses the potential of infrared thermography (IRT) to detect the presence of cavities in the soil around a culvert, specifically for cavities adjacent to the pipe of galvanized culverts. To identify cavities, we analyze thermograms, generated via long pulse thermography, using absolute thermal contrast, principal components thermography, and a statistical approach along with a combination of different pre- and post-processing algorithms. Using several experiments, we evaluate the performance of IRT for accomplishing the given task. Empirical results show a promising future for the application of this approach in culvert inspection. The size and location of cavities are among the aspects that can be extracted from analyzing thermograms. The key finding of this research is that the proposed approach can provide useful information about a certain type of problem around a culvert pipe which may indicate the early stage of the cavity formation. Becoming aware of this process in earlier stages will certainly help to prevent any costly incidents later.
Davood Kalhor; Samira Ebrahimi; Roger Tokime; Farima Mamoudan; Yohan Bélanger; Alexandra Mercier; Xavier Maldague. Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography. Applied Sciences 2021, 11, 4051 .
AMA StyleDavood Kalhor, Samira Ebrahimi, Roger Tokime, Farima Mamoudan, Yohan Bélanger, Alexandra Mercier, Xavier Maldague. Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography. Applied Sciences. 2021; 11 (9):4051.
Chicago/Turabian StyleDavood Kalhor; Samira Ebrahimi; Roger Tokime; Farima Mamoudan; Yohan Bélanger; Alexandra Mercier; Xavier Maldague. 2021. "Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography." Applied Sciences 11, no. 9: 4051.
It was recently demonstrated that a coplanar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, finite element (FE) modeling was employed to simulate the electric field distribution from a coplanar capacitive sensor and the way it interacts with a nonconducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with subsurface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.
Farima Abdollahi-Mamoudan; Sebastien Savard; Tobin Filleter; Clemente Ibarra-Castanedo; Xavier P. V. Maldague. Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Nondestructive Testing Method. Applied Sciences 2021, 11, 3804 .
AMA StyleFarima Abdollahi-Mamoudan, Sebastien Savard, Tobin Filleter, Clemente Ibarra-Castanedo, Xavier P. V. Maldague. Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Nondestructive Testing Method. Applied Sciences. 2021; 11 (9):3804.
Chicago/Turabian StyleFarima Abdollahi-Mamoudan; Sebastien Savard; Tobin Filleter; Clemente Ibarra-Castanedo; Xavier P. V. Maldague. 2021. "Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Nondestructive Testing Method." Applied Sciences 11, no. 9: 3804.
Infrared thermography has been widely adopted in many applications for material structure inspection, where data analysis methods are often implemented to elaborate raw thermal data and to characterize material structural properties. Herein, a multiscale thermographic data analysis framework is proposed and applied to building structure inspection. In detail, thermograms are first collected by conducting solar loading thermography, which are then decomposed into several intrinsic mode functions under different spatial scales by multidimensional ensemble empirical mode decomposition. At each scale, principal component analysis (PCA) is implemented for feature extraction. By visualizing the loading vectors of PCA, the important building structures are highlighted. Compared with principal component thermography that applies PCA directly to raw thermal data, the proposed multiscale analysis method is able to zoom in on different types of structural features.
Katherine Tu; Clemente Ibarra-Castanedo; Stefano Sfarra; Yuan Yao; Xavier Maldague. Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection. Sensors 2021, 21, 2806 .
AMA StyleKatherine Tu, Clemente Ibarra-Castanedo, Stefano Sfarra, Yuan Yao, Xavier Maldague. Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection. Sensors. 2021; 21 (8):2806.
Chicago/Turabian StyleKatherine Tu; Clemente Ibarra-Castanedo; Stefano Sfarra; Yuan Yao; Xavier Maldague. 2021. "Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection." Sensors 21, no. 8: 2806.
The authors wish to make the following corrections to this paper
Qiang Fang; Xavier. Maldague. Addendum: Fang, Q.; Maldague, X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. Appl. Sci. 2020, 10, 6819. Applied Sciences 2021, 11, 3451 .
AMA StyleQiang Fang, Xavier. Maldague. Addendum: Fang, Q.; Maldague, X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. Appl. Sci. 2020, 10, 6819. Applied Sciences. 2021; 11 (8):3451.
Chicago/Turabian StyleQiang Fang; Xavier. Maldague. 2021. "Addendum: Fang, Q.; Maldague, X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. Appl. Sci. 2020, 10, 6819." Applied Sciences 11, no. 8: 3451.
Pulsed Thermography (PT) data are usually affected by noise and as such most of the research effort in the last few years has been directed towards the development of advanced signal processing methods to improve defect detection. Among the numerous techniques that have been proposed, principal component thermography (PCT)—based on principal component analysis (PCA)—is one of the most effective in terms of defect contrast enhancement and data compression. However, it is well-known that PCA can be significantly affected in the presence of corrupted data (e.g., noise and outliers). Robust PCA (RPCA) has been recently proposed as an alternative statistical method that handles noisy data more properly by decomposing the input data into a low-rank matrix and a sparse matrix. We propose to process PT data by RPCA instead of PCA in order to improve defect detectability. The performance of the resulting approach, Robust Principal Component Thermography (RPCT)—based on RPCA, was evaluated with respect to PCT—based on PCA, using a CFRP sample containing artificially produced defects. We compared results quantitatively based on two metrics, Contrast-to-Noise Ratio (CNR), for defect detection capabilities, and the Jaccard similarity coefficient, for defect segmentation potential. CNR results were on average 40% higher for RPCT than for PCT, and the Jaccard index was slightly higher for RPCT (0.7395) than for PCT (0.7010). In terms of computational time, however, PCT was 11.5 times faster than RPCT. Further investigations are needed to assess RPCT performance on a wider range of materials and to optimize computational time.
Samira Ebrahimi; Julien Fleuret; Matthieu Klein; Louis-Daniel Théroux; Marc Georges; Clemente Ibarra-Castanedo; Xavier Maldague. Robust Principal Component Thermography for Defect Detection in Composites. Sensors 2021, 21, 2682 .
AMA StyleSamira Ebrahimi, Julien Fleuret, Matthieu Klein, Louis-Daniel Théroux, Marc Georges, Clemente Ibarra-Castanedo, Xavier Maldague. Robust Principal Component Thermography for Defect Detection in Composites. Sensors. 2021; 21 (8):2682.
Chicago/Turabian StyleSamira Ebrahimi; Julien Fleuret; Matthieu Klein; Louis-Daniel Théroux; Marc Georges; Clemente Ibarra-Castanedo; Xavier Maldague. 2021. "Robust Principal Component Thermography for Defect Detection in Composites." Sensors 21, no. 8: 2682.
Early diagnosis of breast cancer unequivocally improves the survival rate of patients and is crucial for disease treatment. With the current developments in infrared imaging, breast screening using dynamic thermography seems to be a great complementary method for clinical breast examination (CBE) prior to mammography. In this study, we propose a sparse deep convolutional autoencoder model named SPAER to extract low-dimensional deep thermomics to aid breast cancer diagnosis. The model receives multichannel, low-rank, approximated thermal bases as input images. SPAER provides a solution for high-dimensional deep learning features and selects the predominant basis matrix using matrix factorization techniques. The model has been evaluated using five state-of-the-art matrix factorization methods and 208 thermal breast cancer screening cases. The best accuracy was for non-negative matrix factorization (NMF)-SPAER + Clinical and NMF-SPAER for maintaining thermal heterogeneity, leading to finding symptomatic cases with accuracies of 78.2% (74.3–82.5%) and 77.7% (70.9–82.1%), respectively. SPAER showed significant robustness when tested for additive Gaussian noise cases (3–20% noise), evaluated by the signal-to-noise ratio (SNR). The results suggest high performance of SPAER for preserveing thermal heterogeneity, and it can be used as a noninvasive in vivo tool aiding CBE in the early detection of breast cancer.
Bardia Yousefi; Hamed Akbari; Michelle Hershman; Satoru Kawakita; Henrique Fernandes; Clemente Ibarra-Castanedo; Samad Ahadian; Xavier Maldague. SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography. Applied Sciences 2021, 11, 3248 .
AMA StyleBardia Yousefi, Hamed Akbari, Michelle Hershman, Satoru Kawakita, Henrique Fernandes, Clemente Ibarra-Castanedo, Samad Ahadian, Xavier Maldague. SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography. Applied Sciences. 2021; 11 (7):3248.
Chicago/Turabian StyleBardia Yousefi; Hamed Akbari; Michelle Hershman; Satoru Kawakita; Henrique Fernandes; Clemente Ibarra-Castanedo; Samad Ahadian; Xavier Maldague. 2021. "SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography." Applied Sciences 11, no. 7: 3248.
Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.
Qiang Fang; Farima Abdollahi-Mamoudan; Xavier Maldague. Defect Depth Estimation in Infrared Thermography with Deep Learning. 2021, 1 .
AMA StyleQiang Fang, Farima Abdollahi-Mamoudan, Xavier Maldague. Defect Depth Estimation in Infrared Thermography with Deep Learning. . 2021; ():1.
Chicago/Turabian StyleQiang Fang; Farima Abdollahi-Mamoudan; Xavier Maldague. 2021. "Defect Depth Estimation in Infrared Thermography with Deep Learning." , no. : 1.
It was recently demonstrated that a co-planar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, Finite Element (FE) modelling was employed to simulate the electric field distribution from a co-planar capacitive sensor and the way it interacts with a non-conducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with sub-surface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.
Farima Abdollahi Mamoudan; Sebastien Savard; Tobin Filleter; Clemente Ibarra-Castanedo; Xavier Maldague. Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Non-Destructive Testing Method. 2021, 1 .
AMA StyleFarima Abdollahi Mamoudan, Sebastien Savard, Tobin Filleter, Clemente Ibarra-Castanedo, Xavier Maldague. Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Non-Destructive Testing Method. . 2021; ():1.
Chicago/Turabian StyleFarima Abdollahi Mamoudan; Sebastien Savard; Tobin Filleter; Clemente Ibarra-Castanedo; Xavier Maldague. 2021. "Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Non-Destructive Testing Method." , no. : 1.
In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment.
Qiang Fang; Clemente Ibarra-Castanedo; Xavier Maldague. Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data. Big Data and Cognitive Computing 2021, 5, 9 .
AMA StyleQiang Fang, Clemente Ibarra-Castanedo, Xavier Maldague. Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data. Big Data and Cognitive Computing. 2021; 5 (1):9.
Chicago/Turabian StyleQiang Fang; Clemente Ibarra-Castanedo; Xavier Maldague. 2021. "Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data." Big Data and Cognitive Computing 5, no. 1: 9.
Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.
Shashank Pant; Parham Nooralishahi; Nicolas Avdelidis; Clemente Ibarra-Castanedo; Marc Genest; Shakeb Deane; Julio Valdes; Argyrios Zolotas; Xavier Maldague. Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application. Sensors 2021, 21, 1604 .
AMA StyleShashank Pant, Parham Nooralishahi, Nicolas Avdelidis, Clemente Ibarra-Castanedo, Marc Genest, Shakeb Deane, Julio Valdes, Argyrios Zolotas, Xavier Maldague. Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application. Sensors. 2021; 21 (5):1604.
Chicago/Turabian StyleShashank Pant; Parham Nooralishahi; Nicolas Avdelidis; Clemente Ibarra-Castanedo; Marc Genest; Shakeb Deane; Julio Valdes; Argyrios Zolotas; Xavier Maldague. 2021. "Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application." Sensors 21, no. 5: 1604.
Nowadays, performing dynamic line scan thermography (DLST) is very challenging, and therefore an expert is needed in order to predict the optimal set-up parameters. The parameters are mostly dependent on the material properties of the object to be inspected, but there are also correlations between the parameters themselves. The interrelationship is not always evident even for someone skilled in the art. Therefore, optimisation using response surface can give more insights in the interconnections between parameters, but also between the material properties and the variables. Performing inspections using an optimised parameter set will result in high contrast thermograms showing the size and shape of the defect accurately. Using response surfaces to predict the optimal parameter set enables to perform fast measurements without the need of extensive testing to find adequate measurement parameters. Differing from the optimal parameters will result in contrast loss or detail loss of the size and shape of the detected defect.
Simon Verspeek; Jona Gladines; Bart Ribbens; Xavier Maldague; Gunther Steenackers. Dynamic Line Scan Thermography Optimisation Using Response Surfaces Implemented on PVC Flat Bottom Hole Plates. Applied Sciences 2021, 11, 1538 .
AMA StyleSimon Verspeek, Jona Gladines, Bart Ribbens, Xavier Maldague, Gunther Steenackers. Dynamic Line Scan Thermography Optimisation Using Response Surfaces Implemented on PVC Flat Bottom Hole Plates. Applied Sciences. 2021; 11 (4):1538.
Chicago/Turabian StyleSimon Verspeek; Jona Gladines; Bart Ribbens; Xavier Maldague; Gunther Steenackers. 2021. "Dynamic Line Scan Thermography Optimisation Using Response Surfaces Implemented on PVC Flat Bottom Hole Plates." Applied Sciences 11, no. 4: 1538.
The recent development of gas imaging technologies has raised a growing interest for various applications. Gas imaging can significantly enhance functional safety by early detection of hazardous gas leaks. Moreover, optical gas imaging technologies can be used to identify possible gas leakages and to investigate the amount of gas emission in industrial sites, which is essential, primarily based on current efforts to decrease greenhouse gas emissions all around the world. Therefore, exploring the solutions for automating the inspection process can persuade industries for more regular tests and monitoring. One of the main challenges in gas imaging is the proximity condition required for data to be more reliable for analysis. Therefore, the use of unmanned aerial vehicles can be very advantageous as they can provide significant access due to their maneuver capabilities. Despite the advantages of using drones, their movements and sudden motions during hovering can diminish data usability. In this paper, we propose a method for gas leak detection and visually-enhancement of gas emanation involving stabilization and gas leak detection steps. In addition, a comparative analysis of candidate stabilization techniques is conducted to find the most suitable technique for the drone-based application. Moreover, the system is evaluated using three experiments respectively on an isolated environment, a real scenario, and a drone-based inspection.
Parham Nooralishahi; Fernando López; Xavier Maldague. A Drone-Enabled Approach For Gas Leak Detection Using Optical Flow Analysis. Applied Sciences 2021, 11, 1412 .
AMA StyleParham Nooralishahi, Fernando López, Xavier Maldague. A Drone-Enabled Approach For Gas Leak Detection Using Optical Flow Analysis. Applied Sciences. 2021; 11 (4):1412.
Chicago/Turabian StyleParham Nooralishahi; Fernando López; Xavier Maldague. 2021. "A Drone-Enabled Approach For Gas Leak Detection Using Optical Flow Analysis." Applied Sciences 11, no. 4: 1412.
The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.
Iván Garrido; Jorge Erazo-Aux; Susana Lagüela; Stefano Sfarra; Clemente Ibarra-Castanedo; Elena Pivarčiová; Gianfranco Gargiulo; Xavier Maldague; Pedro Arias. Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms. Sensors 2021, 21, 750 .
AMA StyleIván Garrido, Jorge Erazo-Aux, Susana Lagüela, Stefano Sfarra, Clemente Ibarra-Castanedo, Elena Pivarčiová, Gianfranco Gargiulo, Xavier Maldague, Pedro Arias. Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms. Sensors. 2021; 21 (3):750.
Chicago/Turabian StyleIván Garrido; Jorge Erazo-Aux; Susana Lagüela; Stefano Sfarra; Clemente Ibarra-Castanedo; Elena Pivarčiová; Gianfranco Gargiulo; Xavier Maldague; Pedro Arias. 2021. "Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms." Sensors 21, no. 3: 750.
Bardia Yousefi; Clemente Ibarra Castanedo; Xavier P. V. Maldague. Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -9.
AMA StyleBardia Yousefi, Clemente Ibarra Castanedo, Xavier P. V. Maldague. Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-9.
Chicago/Turabian StyleBardia Yousefi; Clemente Ibarra Castanedo; Xavier P. V. Maldague. 2021. "Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-9.