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Detecting defects on aerospace surfaces is critical to ensure safety and maintain the integrity of aircraft structures. Traditional methods often need more precision and efficiency for effective defect detection. This paper proposes an innovative approach that leverages deep learning and infrared imaging techniques to detect defects with high precision. The core contribution of our work lies in accurately detecting the size and depth of defects. Our method involves segmenting the size of the defect and calculating its centre to determine its depth. We achieve a more comprehensive and precise assessment of defects by integrating deep learning with infrared imaging based on the U-net model for segmentation and the CNN model for classification. The proposed model was rigorously tested on both a simulation dataset and an experimental dataset, demonstrating its robustness and effectiveness in accurately identifying and assessing defects on aerospace surfaces. The results indicate significant improvements in detection accuracy and computational efficiency, showing advancements over state-of-the-art methods and paving the way for enhanced maintenance protocols in the aerospace industry.

Process automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry has significantly gained from the implementation of process automation solutions in manufacturing, serving safety-critical sectors such as automotive and aerospace industries. However, this method of component fabrication is very susceptible to generating manufacturing flaws, hence necessitating adequate non-destructive testing (NDT) to ascertain the fitness for use of such components. Machine learning has taken the center stage in recent years as a tool for developing automated solutions for detecting and classifying flaws in digital X-ray radiography. These machine learning-based solutions have increasingly been developed and deployed for component inspection, to keep pace with the high production throughput in manufacturing industries. This work focuses on the development of a defect grading algorithm that assesses detected flaws to ascertain if they constitute a defect that could render a component unfit for use. Guided by ASTM 2973-15; Standard Digital Reference Images for Inspection of Aluminum and Magnesium Die Castings, a grading pipeline utilizing K-D (k-dimensional) trees was developed to effectively structure detected flaws, enabling the system to make decisions based on acceptable grading terms. This solution is dynamic in terms of its conformity to different grading criteria and offers the possibility to achieve automated decision making (Accept/Reject) in digital X-ray radiography applications.

Infrared thermography has been widely applied in real industrial inspection of aerospace, energy management systems, engines, and electric systems. However, two-dimensional imaging modality limits its development. Here, a technique named frequency multiplexed photothermal correlation tomography (FM-PCT) was developed to enable non-destructive and contactless cross-sectional imaging for manufactured material evaluation and characterization. By combining advantages of photothermal tomography and pulsed thermography, FM-PCT facilitates the generation of three-dimensional thermal images through temporal superposition (stacking) of two-dimensional images from sequential subsurface depths. FM-PCT image processing involves pulsed excitation signals to which frequency delay and matched filtering techniques are applied. Major features of FM-PCT are high-resolution three-dimensional tomographic imaging under low camera frame-rate conditions with self-correcting capability for diffusion (blurring) correction of subsurface images due to cross-correlation processing of individual frequencies in the Fourier decomposition spectrum of the excitation pulse. Furthermore, FM-PCT extends truncated-correlation photothermal coherence tomography from chirp and pulsed signals to more general linear heating sources. Lock-in thermography and x-ray computed tomography validation demonstrate that 3D FM-PCT imaging accurately reveals subsurface discontinuities/defects in solids despite the diffusive nature of thermal-wave imaging.

This study reviews advanced methods for corrosion detection and characterization in pipes using thermography, with a focus on addressing the limitations posed by small datasets. Thermography captures temperature distributions on the surface of pipes to identify subsurface defects. The challenges of sequential data processing, neural network performance, feature extraction, and dataset size are discussed, with proposed solutions such as advanced algorithms, feature selection techniques, and data augmentation. Given the significant gap in the current literature, there is a need for larger, more diverse datasets to train more robust and accurate machine learning models. A case study combining experimental data with Finite Element Method (FEM) simulations demonstrates that augmenting datasets with synthetic data significantly improves defect detection accuracy. These findings highlight the potential of integrating thermography with machine learning to enhance defect detection, providing insights for future research and practical applications.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford its versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.

Efficient multi-modal image fusion plays an important role in the non-destructive evaluation (NDE) of infrastructures, where an essential challenge is the precise visualizing of defects. While automatic defect detection represents a significant advancement, the determination of the precise location of both surface and subsurface defects simultaneously is crucial. Hence, visible and infrared data fusion strategies are essential for acquiring comprehensive and complementary information to detect defects across vast structures. This paper proposes an infrared and visible image registration method based on Euclidean evaluation together with a trade-off between key-point threshold and non-maximum suppression. Moreover, we employ a multi-modal fusion strategy to investigate the robustness of our image registration results.

Thermographic has proven to be effective for the early detection of breast cancer and with clinical breast examination (CBE). There are many matrix factorization methods developed for computational thermography that can be used to extract thermal variations across the acquisition time. These methods are often used to summarize thermographic sequences and simultaneously highlight predominant thermal patterns. Finding a single predominant infrared image capturing the prevalent patterns of changes remains a challenging task in the field. This study presents the applications of convex factor analysis combined with the bell-curve membership function embedding approach to tackle this task and generate one image to represent the entire sequence. This low-dimensional (LD) representation of a thermal sequence was then used to extract thermomics and train tuned hyperparameters random forest model for early breast cancer diagnosis. A comparative analysis of different embedding methods and factorization approaches is also provided. The results of the proposed method combining clinical information, and demographics yield 78.9% (75.7% and 85.9%), while the convex-nonnegative matrix factorization (NMF) alone gave 76.9% (73.7% and 86.1%). The result of the proposed method suggests that the embedding can help preserve important thermal patterns, which significantly aid CBE and early detection of breast cancer.

The thermographic inspection of buildings is a powerful and non-invasive method for monitoring and diagnosing building performance and structural integrity. It can effectively detect moisture, evaluate heat loss, and assess building’s roofs. The early detection of problems in a building allows owners to fix issues before they become more severe and costly. One of the challenges in automating thermal analysis for building inspections comes in the form of distinguishing between different surfaces. This paper presents an automated process pipeline using coupled thermal and visible images for building inspections to assist inspectors in adapting strategies and methods to discriminate the thermal signatures between different kinds of surfaces. A deep learning method is employed to segment visible images texturally. Thermal images are then analyzed based on the resulting segmentation. Moreover, a multi-modal dataset is introduced, presenting coupled thermal and visible images acquired during multiple building inspections.

Defects in in-service pipelines, including corrosion under insulation (CUI) and thickness loss, pose significant challenges to asset integrity in the oil and gas industry. These defects are particularly hazardous as they often remain unnoticed. The automation of defect detection processes can assist inspectors in reducing analysis time, costs, and human error. However, recent attempts to adopt machine learning for automated defect detection from thermal images have been hindered by limited data availability. This paper presents a novel approach to address this issue by utilizing thermal data augmentation, generating synthetic sub-surface defects via finite element modeling. The resulting synthetic thermal images, combined with real images, are then used to train a deep learning model for the automatic detection of potential defects. Additionally, this study explores the efficacy of synthetic thermal images in enhancing the generalization of the detection model.

Infrared thermography (IRT), is one of the most interesting techniques to identify different kinds of defects, such as delamination and damage existing for quality management of material. Objective detection and segmentation algorithms in deep learning have been widely applied in image processing, although very rarely in the IRT field. In this paper, spatial deep-learning image processing methods for defect detection and identification were discussed and investigated. The aim in this work is to integrate such deep-learning (DL) models to enable interpretations of thermal images automatically for quality management (QM). That requires achieving a high enough accuracy for each deep-learning method so that they can be used to assist human inspectors based on the training. There are several alternatives of deep Convolutional Neural Networks for detecting the images that were employed in this work. These included: 1. The instance segmentation methods Mask–RCNN (Mask Region-based Convolutional Neural Networks) and Center–Mask; 2. The independent semantic segmentation methods: U-net and Resnet–U-net; 3. The objective localization methods: You Only Look Once (YOLO-v3) and Faster Region-based Convolutional Neural Networks (Fast-er-RCNN). In addition, a regular infrared image segmentation processing combination method (Absolute thermal contrast (ATC) and global threshold) was introduced for comparison. A series of academic samples composed of different materials and containing artificial defects of different shapes and nature (flat-bottom holes, Teflon inserts) were evaluated, and all results were studied to evaluate the efficacy and performance of the proposed algorithms.

Nondestructive testing (NDT) of composite materials is of paramount importance to the aerospace industry. Several NDT methods have been adopted for the inspection of components during production and all through the aircraft service life, with infrared thermography (IRT) techniques, such as line scan thermography (LST) and pulsed thermography (PT), gaining popularity thanks to their rapidity and versatility. On one hand, LST is an attractive solution for the fast inspection of large and complex geometry composite parts during production. On the other hand, PT can be employed for the characterization of composite materials, e.g., the determination of thermal diffusivity and defect depth estimation. In this study, the use of LST with an uncooled microbolometer camera is explored for the identification of artificially produced porosity and barely visible impact damage (BVID) on academic samples. The performance of LST is quantitatively assessed with respect to PT (considered the gold standard in this case) using a high-definition cooled camera through the contrast-to-noise ratio (CNR) criterium. It is concluded that, although in most cases the measured CNR values were higher for PT than for LST (as expected since a high-definition camera and longer acquisition times were used), the majority of the defects were clearly detected (CNR ≥ 2.5) by LST without the need of advanced signal processing, proving the suitability of LST for the inspection of aerospace composite components. Furthermore, the deepest defect investigated herein (z ≈ 3 mm) was detected solely by LST combined with signal processing and spatial filtering (CNR = 3.6) and not by PT (since pulse heating was not long enough for this depth). In addition, PT was used for the determination of the thermal diffusivity of all samples and the subsequent depth estimation of porosity and damaged areas by pulsed phase thermography (PPT).

The drone-based multi-modal inspection of industrial structures is a relatively new field of research gaining interest among companies. Multi-modal inspection can significantly enhance data analysis and provide a more accurate assessment of the components’ operability and structural integrity, which can assist in avoiding data misinterpretation and providing a more comprehensive evaluation, which is one of the NDT4.0 objectives. This paper investigates the use of coupled thermal and visible images to enhance abnormality detection accuracy in drone-based multi-modal inspections. Four use cases are presented, introducing novel process pipelines for enhancing defect detection in different scenarios. The first use case presents a process pipeline to enhance the feature visibility on visible images using thermal images in pavement crack detection. The second use case proposes an abnormality classification method for surface and subsurface defects using both modalities and texture segmentation for piping inspections. The third use case introduces a process pipeline for road inspection using both modalities. A texture segmentation method is proposed to extract the pavement regions in thermal and visible images. Further, the combination of both modalities is used to detect surface and subsurface defects. The texture segmentation approach is employed for bridge inspection in the fourth use case to extract concrete surfaces in both modalities.

Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique.

In the present study, a relatively novel non-destructive testing (NDT) method called the coplanar capacitive sensing technique was applied in order to detect different sizes of rebars in a reinforced concrete (RC) structure. This technique effectively detects changes in the dielectric properties during scanning in various sections of concrete with and without rebars. Numerical simulations were carried out by three-dimensional (3D) finite element modelling (FEM) in COMSOL Multiphysics software to analyse the impact of the presence of rebars on the electric field generated by the coplanar capacitive probe. In addition, the effect of the presence of a surface defect on the rebar embedded in the concrete slab was demonstrated by the same software for the first time. Experiments were performed on a concrete slab containing rebars, and were compared with FEM results. The results showed that there is a good qualitative agreement between the numerical simulations and experimental results.

This paper presents a thermal imaging dataset from composite material samples (carbon and glass fiber reinforced plastic) that were inspected by pulsed thermography with the goal of detecting and characterizing subsurface defective zones (Teflon inserts representing delaminations between plies). The pulsed thermography experiment was applied to 6 academic plates (inspected from both sides) all having the dimensions of 300 mm x 300 mm x 2 mm and same distribution of defects but made of different materials: three plates on carbon fiber-reinforced plastic (CFRP) and three plates made on glass fiber reinforced plastic (GFRP) specimens with three different geometries: planar, curved and trapezoidal. Each plate contains 25 inserts having length/depth ratios between 1.7 and 75. Two FX60 BALCAR photographic flashes (6.2 kJ per flash) were used to generate the heat pulse (2 ms duration), an X6900 FLIR infrared camera using ResearchIR software to record the thermal images and a custom-built software/control unit to synchronize data recording with pulse generation. Finally, the dataset proposed consists of 12 sequences of approximately 2000 images of 512 × 512 pixels each.

Abstract Composite materials are being successfully used in aeronautics for more than two decades. This great success is mainly because this kind of materials is typically lighter when compared to metals and more resistant to corrosion. Resistance and stiffness of composite materials are usually related to their fiber orientation. The development of non-destructive methods is required to assess this kind of property without damaging the sample. In this work, a non-destructive method based on infrared thermography is presented for fiber orientation assessment. More specifically, two approaches are presented and results are compared for fiber orientation assessment of randomly oriented strand parts. The first one is a point-by-point inspection based on pulsed thermal ellipsometry and the second one is a line approach based on flying laser spot inspection combined with neural artificial networks. Results showed that the second approach provides similar accuracy when compared to the first one; however, it is much faster which makes the second approach very interesting for industrial applications.

Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). 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, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented.

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.

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 rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

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.

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.

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.

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.

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.

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.

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.

Nowadays, infrared thermography, as a widely used non-destructive testing method, is increasingly studied for impact evaluation of composite structures. Sparse pattern extraction is attracting increasing attention as an advanced post-processing method. In this paper, an enhanced sparse pattern extraction framework is presented for thermographic sequence processing and defect detection. This framework adapts cropping operator and typical component extraction as a preprocessing step to reduce the dimensions of raw data and applies sparse pattern extraction algorithms to enhance the contrast on the defect area. Different cases are studied involving several defects in four basalt-carbon hybrid fiber-reinforced polymer composite laminates. Finally, comparative analysis with intensity distribution is carried out to verify the effectiveness of contrast enhancement using this framework.

This work aims to address the effectiveness and challenges of non-destructive testing (NDT) by active infrared thermography (IRT) for the inspection of aerospace-grade composite samples and seeks to compare uncooled and cooled thermal cameras using the signal-to-noise ratio (SNR) as a performance parameter. It focuses on locating impact damages and optimising the results using several signal processing techniques. The work successfully compares both types of cameras using seven different SNR definitions, to understand if a lower-resolution uncooled IR camera can achieve an acceptable NDT standard. Due to most uncooled cameras being small, lightweight, and cheap, they are more accessible to use on an unmanned aerial vehicle (UAV). The concept of using a UAV for NDT on a composite wing is explored, and the UAV is also tracked using a localisation system to observe the exact movement in millimetres and how it affects the thermal data. It was observed that an NDT UAV can access difficult areas and, therefore, can be suggested for significant reduction of time and cost.

This work is focused on the use of line-scan thermography (LST) method for the inspection of an ancient book cover. Three widely used image post-processing techniques (i.e., pulsed phase thermography, partial least square thermography and principal component thermography) were applied to the acquired thermal sequences. Flash thermography (FT) anticipated the LST results in order to have a comparison of the results. It was concluded that LST is an effective technique for paper-based materials, and it can additionally provide a higher image contrast if compared to classical FT technique.

Detection of subsurface defects is undeniably a growing subfield of infrared non-destructive testing (IR-NDT). There are many algorithms used for this purpose, where non-negative matrix factorization (NMF) is considered to be an interesting alternative to principal component analysis (PCA) by having no negative basis in matrix decomposition. Here, an application of Semi non-negative matrix factorization (Semi-NMF) in IR-NDT is presented to determine the subsurface defects of an Aluminum plate specimen through active thermographic method. To benchmark, the defect detection accuracy and computational load of the Semi-NMF approach is compared to state-of-the-art thermography processing approaches such as: principal component thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT), Sparse PCT, Sparse NMF and standard NMF with gradient descend (GD) and non-negative least square (NNLS). The results show 86% accuracy for 27.5s computational time for SemiNMF, which conclusively indicate the promising performance of the approach in the field of IR-NDT.

Ceramic-coated materials used in different engineering sectors are the focus of world-wide interest and have generated a need for inspection techniques that detect very small structural anomalies. Non-destructive testing is increasingly being used to evaluate coating thickness and to test for coating flaws. The main pros of non-destructive testing is that the tested object remains intact and available for continued use afterward. This paper reports on an integrated, non-destructive testing approach that combines infrared thermography and acousto-ultrasonics to evaluate advanced aerospace sandwich structure materials with the aim of exploring any potential for detecting defects of more than one type. Combined, these two techniques successfully detected fabrication defects, including inclusions and material loss.

Natural fibers, including mineral and plant fibers, are increasingly used for polymer composite materials due to their low environmental impact. In this paper, thermographic non-destructive inspection techniques were used to evaluate and characterize basalt, jute/hemp and bagasse fibers composite panels. Different defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, homogeneous particleboards of sugarcane bagasse, a new plant fiber material, were studied. Pulsed phase thermography and principal component thermography were used as the post-processing methods. In addition, ultrasonic C-scan and continuous wave terahertz imaging were also carried out on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different methods was given.

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed.

In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided.

Bicycle frames made of carbon fibre are extremely popular for high-performance cycling due to the stiffness-to-weight ratio, which enables greater power transfer. However, products manufactured using carbon fibre are sensitive to impact damage. Therefore, intelligent nondestructive evaluation is a required step to prevent failures and ensure a secure usage of the bicycle. This work proposes an inspection method based on active thermography, a proven technique successfully applied to other materials. Different configurations for the inspection are tested, including power and heating time. Moreover, experiments are applied to a real bicycle frame with generated impact damage of different energies. Tests show excellent results, detecting the generated damage during the inspection. When the results are combined with advanced image post-processing methods, the SNR is greatly increased, and the size and localization of the defects are clearly visible in the images.