Browsing by Author "Fernandes, Henrique"
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Item Open Access Advanced diagnostics of aircraft structures using automated non-invasive imaging techniques: a comprehensive review(MDPI, 2025-04-01) Bardis, Kostas; Avdelidis, Nicolas P.; Ibarra-Castanedo, Clemente; Maldague, Xavier P. V.; Fernandes, HenriqueThe aviation industry currently faces several challenges in inspecting and diagnosing aircraft structures. Current aircraft inspection methods still need to be fully automated, making early detection and precise sizing of defects difficult. Researchers have expressed concerns about current aircraft inspections, citing safety, maintenance costs, and reliability issues. The next generation of aircraft inspection leverages semi-autonomous and fully autonomous systems integrating robotic technologies with advanced Non-Destructive Testing (NDT) methods. Active Thermography (AT) is an example of an NDT method widely used for non-invasive aircraft inspection to detect surface and near-surface defects, such as delamination, debonding, corrosion, impact damage, and cracks. It is suitable for both metallic and non-metallic materials and does not require a coupling agent or direct contact with the test piece, minimising contamination. Visual inspection using an RGB camera is another well-known non-contact NDT method capable of detecting surface defects. A newer option for NDT in aircraft maintenance is 3D scanning, which uses laser or LiDAR (Light Detection and Ranging) technologies. This method offers several advantages, including non-contact operation, high accuracy, and rapid data collection. It is effective across various materials and shapes, enabling the creation of detailed 3D models. An alternative approach to laser and LiDAR technologies is photogrammetry. Photogrammetry is cost-effective in comparison with laser and LiDAR technologies. It can acquire high-resolution texture and colour information, which is especially important in the field of maintenance inspection. In this proposed approach, an automated vision-based damage evaluation system will be developed capable of detecting and characterising defects in metallic and composite aircraft specimens by analysing 3D data acquired using an RGB camera and a IRT camera through photogrammetry. Such a combined approach is expected to improve defect detection accuracy, reduce aircraft downtime and operational costs, improve reliability and safety and minimise human error.Item Open Access Advanced thermal imaging processing and deep learning integration for enhanced defect detection in carbon fiber-reinforced polymer laminates(MDPI, 2025-03-25) Garcia Rosa, Renan; Pereira Barella, Bruno; Garcia Vargas, Iago; Tarpani, José Ricardo; Herrmann, Hans-Georg; Fernandes, HenriqueCarbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and first- and second-order derivatives were applied to refine thermographic signals, enhancing defect visibility and SNR. The U-Net architecture was used to assess segmentation performance on datasets with and without preprocessing. The results demonstrated that preprocessing significantly improved defect detection, achieving an Intersection over Union (IoU) of 95% and an F1-Score of 99%, outperforming approaches without preprocessing. These findings emphasize the importance of preprocessing in enhancing segmentation accuracy and reliability, highlighting its potential for advancing non-destructive testing techniques across various industries.Item Open Access Automatic defect detection in infrared thermal images of ancient polyptychs based on numerical simulation and a new efficient channel attention mechanism aided Faster R-CNN model(Springer, 2024-09-16) Wang, Xin; Jiang, Guimin; Hu, Jue; Sfarra, Stefano; Mostacci, Miranda; Kouis, Dimitrios; Yang, Dazhi; Fernandes, Henrique; Avdelidis, Nicolas P.; Maldague, Xavier; Gai, Yonggang; Zhang, HaiIn recent years, the preservation and conservation of ancient cultural heritage necessitate the advancement of sophisticated non-destructive testing methodologies to minimize potential damage to artworks. Therefore, this study aims to develop an advanced method for detecting defects in ancient polyptychs using infrared thermography. The test subjects are two polyptych samples replicating a 14th-century artwork by Pietro Lorenzetti (1280/85–1348) with varied pigments and artificially induced defects. To address these challenges, an automatic defect detection model is proposed, integrating numerical simulation and image processing within the Faster R-CNN architecture, utilizing VGG16 as the backbone network for feature extraction. Meanwhile, the model innovatively incorporates the efficient channel attention mechanism after the feature extraction stage, which significantly improves the feature characterization performance of the model in identifying small defects in ancient polyptychs. During training, numerical simulation is utilized to augment the infrared thermal image dataset, ensuring the accuracy of subsequent experimental sample testing. Empirical results demonstrate a substantial improvement in detection performance, compared with the original Faster R-CNN model, with the average precision at the intersection over union = 0.5 increasing to 87.3% and the average precision for small objects improving to 54.8%. These results highlight the practicality and effectiveness of the model, marking a significant progress in defect detection capability, providing a strong technical guarantee for the continuous conservation of cultural heritage, and offering directions for future studies.Item Open Access Brief review of vibrothermography and optical thermography for defect quantification in CFRP material(MDPI, 2025-03-16) Hidayat, Zulham; Avdelidis, Nicolas P.; Fernandes, HenriqueQuantifying defects in carbon-fiber-reinforced polymer (CFRP) composites is crucial for ensuring quality control and structural integrity. Among non-destructive evaluation techniques, thermography has emerged as a promising solution for defect detection and characterization. This literature review synthesizes current advancements in active thermography methods, with a particular focus on vibrothermography and optical thermography, in identifying defects such as delaminations and BVID in CFRP composites. The review evaluates state-of-the-art techniques, highlighting the advanced applications of optical thermography. It identifies a critical research gap in the integration of vibrothermography with advanced image-processing methods, such as computer vision, which is more commonly applied in optical thermography. Addressing this gap holds significant potential to enhance defect quantification accuracy, improve maintenance practices, and ensure the safety of composite structures.Item Open Access Composite material defect segmentation using deep learning models and infrared thermography(Universidade Federal do Rio Grande do Sul, 2025-02-20) Garcia Vargas, Iago; Fernandes, HenriqueFor non-destructive assessment, the segmentation of infrared thermographic images of carbon fiber composites is a critical task in material characterization and quality assessment. This study focuses on applying image processing techniques, particularly adaptive thresholding, alongside neural network models such as U-Net and DeepLabv3 for infrared image segmentation tasks. An experimental analysis was conducted on these networks to compare their performance in segmenting artificial defects from infrared images of a carbon-fibre reinforced polymer sample. The performance of these models was evaluated based on the F1-Score and Intersection over Union (IoU) metrics. The findings reveal that DeepLabv3 demonstrates superior results and efficiency in segmenting patterns of infrared images, achieving an F1-Score of 0.94 and an IoU of 0.74, showcasing its potential for advanced material analysis and quality control.Item Open Access Enhanced infrared image processing for impacted carbon/glass fiber-reinforced composite evaluation(MDPI, 2017-12-26) Zhang, Hai; Avdelidis, Nicolas Peter; Osman, Ahmad; Ibarra-Castanedo, Clemente; Sfarra, Stefano; Fernandes, Henrique; Matikas, Theodore E.; Maldague, Xavier P. V.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.Item Open Access Enhancing fault characterisation in composites using infrared thermography: a bee colony optimisation approach with self-organising maps(Taylor and Francis, 2024) Pereira Barella, Bruno; Garcia Rosa, Renan; Barbosa de Oliveira, Gina Maira; Fernandes, HenriqueThis work presents an innovative approach aimed at enhancing the characterisation of discontinuities through the processing of thermographic images. The proposed methodology combines self-organising maps (SOM) with bio-inspired parameter optimisation through bee colony optimisation technique. The primary focus is on improving the quality of the fault quantification metric known as the signal-to-noise ratio (SNR). The goal is to achieve a better fault visualisation, ultimately contributing to the advance of thermography as a non-destructive technique. To validate this novel approach, an experiment was conducted using pulsed thermography on a unidirectional carbon laminate piece measuring 33 ✕100 mm. This specimen was intentionally equipped with three artificial delaminations positioned at different depths on specific layers. The results were then compared against conventional approaches such as principal component analysis, partial least-squares regression and polynomial approximation. The findings from this experiment demonstrated the potential of the proposed approach, i.e. the bee colony optimisation coupled with SOM, on the characterisation of discontinuities using infrared thermography data. There was a 15% improvement on the SNR when using the proposed approach over the other tested approaches. This research makes a noteworthy contribution by offering a promising technique for both the detection and characterisation of faults in composite materials.Item Open Access Exploring the potentialities of thermal asymmetries in composite wind turbine blade structures via numerical and thermographic methods: a thermophysical perspective(Springer , 2024) Figueiredo, Alisson A. A.; D’Alessandro, G.; Perilli, S.; Sfarra, Stefano; Fernandes, HenriqueUsing composite materials in turbine blades has become common in the wind power industry due to their mechanical properties and low mass. This work aims to investigate the effectiveness of the active infrared thermography technique as a non-destructive inspection tool to identify defects in composite material structures of turbine blades. Experiments were carried out by heating the sample and capturing thermographic images using a thermal camera in four different scenarios, changing the heating strategy. Such a preliminary experiments are prodromic to build, in future, the so-called optimal experiment design for thermal property estimation. The experimental results using two heaters arranged symmetrically on the sample detected the presence of the defect through temperature curves extracted from thermal images, where temperature asymmetries of 25% between the regions with and without defect occurred. Moreover, when only a larger heater was used in transmission mode, the defect was detected based on differences between normalized excess temperatures on the side with and without the defect in the order of 20%. Additionally, numerical simulations were carried out to present solutions for improving defect detection. It was demonstrated that active infrared thermography is an efficient technique for detecting flaws in composite material structures of turbine blades. This research contributes to advancing knowledge in inspecting composite materials.Item Open Access Influence of thermal contrast and limitations of a deep-learning based estimation of early-stage tumour parameters in different breast shapes using simulated passive and dynamic thermography(Elsevier, 2025-04) Moraes, Mateus Felipe Benicio; Sfarra, Stefano; Fernandes, Henrique; Figueiredo, Alisson A. A.To enhance diagnostic sensitivity compared to passive thermography, thermal stress can be applied to the breast surface with the temperatures being measured in the thermal recovery phase, a process called dynamic thermography. This study aims to evaluate the limitations of both passive and dynamic thermography in estimating early-stage tumour parameters across different breast shapes and how to improve the results. Three breast models with thermoregulation were solved numerically using COMSOL Multiphysics®. A neural network developed in PyTorch was used to estimate breast tumour location and size. The estimates obtained using each approach were compared, and the effects of thermal contrast, noise, and tumour depth range were analysed. Dynamic thermography provided the most accurate estimates compared to passive thermography, with mean error reductions that reached up to 33.25%. Additionally, the number of estimates with errors higher than 10% was up to 48.42% lower. Tumour radius showed the lowest noise threshold, providing the highest estimations errors. Adding deeper tumours to the datasets caused mean error increases of up to 51.27%. Thus, this work contributes by comparing both types of thermography, analysing thermal aspects of the temperature data that influences the neural network's estimation process, and suggesting alternatives to improve its accuracy.Item Open Access Non-invasive inspection for a hand-bound book of the 19th century: numerical simulations and experimental analysis of infrared, terahertz, and ultrasonic methods(Elsevier, 2024-05-24) Jiang, Guimin; Zhu, Pengfei; Gai, Yonggang; Jiang, Tingyi; Yang, Dazhi; Sfarra, Stefano; Waschkies, Thomas; Osman, Ahmad; Fernandes, Henrique; Avdelidis, Nicolas P.; Maldague, Xavier; Zhang, HaiDue to fungal growth and mishandling in the book, there are various types of defects as they age such as foxing, tears, and creases. It is important to develop novel non-invasive inspection techniques and defect recognition algorithms. In this work, three non-invasive inspection techniques, including infrared thermography (IRT), terahertz time-domain spectroscopy (THz-TDS), and air-coupled ultrasound (ACU), were employed for the detection of defects in an ancient book cover. To improve the image quality and defect contrast, principal component analysis, fast Fourier transform, and partial least squares regression algorithms are used as the post-processing methods. Furthermore, the YOLOv7 network is deployed for defect automatic detection. Finite element analysis and finite-difference time-domain methods were employed for generating training dataset of YOLOv7 network. Experimental results demonstrate that IRT and THz-TDS has excellent detection capability for surface and subsurface defects, respectively. By employing YOLOv7 network with simulation datasets, defects can be effectively identified.Item Open Access Optical and mechanical excitation thermography for impact response in basalt-carbon hybrid fiber-reinforced composite laminates(IEEE, 2017-08-24) Zhang, Hai; Sfarra, Stefano; Sarasini, Fabrizio; Ibarra-Castanedo, Clemente; Perilli, Stefano; Fernandes, Henrique; Duan, Yuxia; Peeters, Jeroen; Avdelidis, Nicolas Peter; Maldague, Xavier P. V.In this paper, optical and mechanical excitation thermography were used to investigate basalt fiber reinforced polymer (BFRP), carbon fiber reinforced polymer (CFRP) and basalt-carbon fiber hybrid specimens subjected to impact loading. Interestingly, two different hybrid structures including sandwich-like and intercalated stacking sequence were used. Pulsed phase thermography (PPT), principal component thermography (PCT) and partial least squares thermography (PLST) were used to process the thermographic data. X-ray computed tomography (CT) was used for validation. In addition, signal-to-noise ratio (SNR) analysis was used as a means of quantitatively comparing the thermographic results. Of particular interest, the depth information linked to Loadings in PLST was estimated for the first time. Finally, a reference was provided for taking advantage of different hybrids in view of special industrial applications.Item Open Access Recognition of Brazilian vertical traffic signs and lights from a car using Single Shot Multi box Detector(Sociedade Brasileira de Computacao - SB, 2024-03-08) Pierre, Monhel Maudoony; Fernandes, HenriqueThis work presents an automated system for recognizing Brazilian vertical traffic signs and lights using artificial intelligence. The main objective of the system is to contribute to road safety by alerting drivers to potential risks such as speeding, alcohol consumption, and cell phone use, which could lead to severe accidents. The system’s core contribution lies in its ability to accurately recognize various traffic signs and lights, providing crucial warnings to drivers. To achieve this, the system utilizes a light version of the single shot multi box detector as its detection algorithm and experiments with three Mobilenet versions as base networks. The optimal Mobilenet version is selected based on a mean average precision higher than 80%, which guarantees reliable detection results. The dataset used for training and evaluation comprises images extracted from YouTube traffic videos, each annotated to create the necessary labels for training. Through this extensive experimentation, the system demonstrates its efficacy in achieving accurate and efficient detection. The results of the experiments are compared with other existing approaches and our work significantly advances the field by providing a tailored dataset, an optimized model, and also valuable insights into traffic sign and light recognition, collectively contributing to the improvement of road safety.Item Open Access Spatial and temporal deep learning algorithms for defect segmentation in infrared thermographic imaging of carbon fibre-reinforced polymers(Taylor and Francis, 2025-01-24) Garcia Vargas, Iago; Fernandes, HenriqueFor non-destructive evaluation, the segmentation of infrared thermographic images of carbon fibre composites is a critical task in material characterisation and quality assessment. This paper presents a study on the application of image processing techniques, particularly adaptive thresholding, and advanced neural network models, including U-Net, DeepLabv3, and BiLSTM, for the segmentation of infrared images. This work introduces the innovative combination of DeepLabv3 and BiLSTM applied in infrared images of carbon fibre-reinforced polymer samples for the first time, proposing it as a novel approach for enhancing the accuracy of segmentation tasks. An experimental comparison of these models was conducted to assess their effectiveness in identifying artificial defects in these images. The performance of each model was evaluated using the F1-Score and Intersection over Union (IoU) metrics. The results demonstrate that the proposed combination of DeepLabv3 and BiLSTM outperforms other methods, achieving an F1-Score of 0.96 and an IoU of 0.83, showcasing its potential for advanced material analysis and quality control.