Automatic defect detection and depth estimation using pulsed thermography

L’évaluation non-destructive (END) est une branche de la science qui s’intéresse à l’uniformité, la qualité et la conformité des matériaux et les composants qu’ils servent à construire. Les techniques de END visent à repérer et à mesurer les caractéristiques principales des matériaux sans en affecter ou à en détruire la structure ou la fonctionnalité. L’END permet d’observer les propriétés internes des pièces et de détecter les défauts sous leur surface. Cette approche est devenue graduellement une technologie importante pour garantir la sécurité et la fiabilité de plusieurs composantes de système en design, en fabrication et en développement de produits. La thermographie infrarouge est une approche d’END sans contact rapide qui utilise des caméras thermiques. Elle permet de détecter l’énergie thermique émise par les objets et à en afficher la distribution en température de la surface du spécimen sous observation. Dans ce projet, notre objectif est d’exploiter la thermographie infrarouge pour détecter les défauts sous la surface des objets. Plus spécialement, nous nous intéressons à la localisation des défauts et à l’estimation de leur profondeur sous la surface. Le manuscrit présente une investigation de différentes méthodes de localisation de défauts et de mesure de leur profondeur des défauts sous la surface pour différentes catégories de matériaux.Non-Destructive Testing (NDT) is an aspect of science concerning on uniformity, quality and serviceability of materials and their components. NDT techniques attempt to inspect and measure significant features of materials without changing or destroying their structure or functionality. NDT makes it possible to observe the internal properties of parts and detect the undersurface defects. NDT has progressively become an important technology to assure safety and reliability of many system components in the design, manufacturing and development areas. Infrared thermography is essentially a fast non-contact NDT inspection method that uses thermographic cameras. This technique detects the infrared energy emitted from objects and displays the corresponding temperature distributions on the specimen. In this project, we aim to use infrared thermography for detecting subsurface defects. Localizing the defects and estimating their depths are the important problems to be addressed in our research project. The manuscript investigates different methods related to these challenges

Dual mode imaging in mid infrared with thermal signal reconstruction for innovative diagnostics of the "Monocromo" by Leonardo da Vinci.

Dual mode imaging in the mid infrared band, a joint use of thermography and quasi-thermal reflectography, was recently proposed as a full field diagnostic tool in cultural heritage. Here we discuss for the first time, to the best of our knowledge, a detailed application of such non destructive technique to the diagnostics of frescoes, with an emphasis on the location of detachments. We also investigate the use of a thermographic method based on TSR (thermal signal reconstruction), in a long pulse stimulus scheme, as well as the spatial registration of thermal images after post-processing analysis to their visible counterpart, so as to obtain a fine resolution diagnostic map. As an exemplar case study, we report about the application of dual mode imaging with a 500 [Formula: see text] pixel size at object plane on the "Monocromo", a fresco by Leonardo da Vinci located in the Sforza Castle (Milan, Italy). Our technique was used to guide the conservators during the restoration works, opening new perspectives in artwork diagnostics

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

Imaging Cultural Heritage at Different Scales: Part I, the Micro-Scale (Manufacts)

Applications of non-invasive sensing techniques to investigate the internal structure and surface of precious and delicate objects represent a very important and consolidated research field in the scientific domain of cultural heritage knowledge and conservation. The present article is the first of three reviews focused on contact and non-contact imaging techniques applied to surveying cultural heritage at micro- (i.e., manufacts), meso- (sites) and macro-scales (landscapes). The capability to infer variations in geometrical and physical properties across the inspected surfaces or volumes is the unifying factor of these techniques, allowing scientists to discover new historical sites or to image their spatial extent and material features at different scales, from landscape to artifact. This first part concentrates on the micro-scale, i.e., inspection, study and characterization of small objects (ancient papers, paintings, statues, archaeological findings, architectural elements, etc.) from surface to internal properties

Cross-Spectral Full and Partial Face Recognition: Preprocessing, Feature Extraction and Matching

Cross-spectral face recognition remains a challenge in the area of biometrics. The problem arises from some real-world application scenarios such as surveillance at night time or in harsh environments, where traditional face recognition techniques are not suitable or limited due to usage of imagery obtained in the visible light spectrum. This motivates the study conducted in the dissertation which focuses on matching infrared facial images against visible light images. The study outspreads from aspects of face recognition such as preprocessing to feature extraction and to matching.;We address the problem of cross-spectral face recognition by proposing several new operators and algorithms based on advanced concepts such as composite operators, multi-level data fusion, image quality parity, and levels of measurement. To be specific, we experiment and fuse several popular individual operators to construct a higher-performed compound operator named GWLH which exhibits complementary advantages of involved individual operators. We also combine a Gaussian function with LBP, generalized LBP, WLD and/or HOG and modify them into multi-lobe operators with smoothed neighborhood to have a new type of operators named Composite Multi-Lobe Descriptors. We further design a novel operator termed Gabor Multi-Levels of Measurement based on the theory of levels of measurements, which benefits from taking into consideration the complementary edge and feature information at different levels of measurements.;The issue of image quality disparity is also studied in the dissertation due to its common occurrence in cross-spectral face recognition tasks. By bringing the quality of heterogeneous imagery closer to each other, we successfully achieve an improvement in the recognition performance. We further study the problem of cross-spectral recognition using partial face since it is also a common problem in practical usage. We begin with matching heterogeneous periocular regions and generalize the topic by considering all three facial regions defined in both a characteristic way and a mixture way.;In the experiments we employ datasets which include all the sub-bands within the infrared spectrum: near-infrared, short-wave infrared, mid-wave infrared, and long-wave infrared. Different standoff distances varying from short to intermediate and long are considered too. Our methods are compared with other popular or state-of-the-art methods and are proven to be advantageous

Infrared Thermography for Temperature Measurement and Non-Destructive Testing

The intensity of the infrared radiation emitted by objects is mainly a function of their temperature. In infrared thermography, this feature is used for multiple purposes: as a health indicator in medical applications, as a sign of malfunction in mechanical and electrical maintenance or as an indicator of heat loss in buildings. This paper presents a review of infrared thermography especially focused on two applications: temperature measurement and non-destructive testing, two of the main fields where infrared thermography-based sensors are used. A general introduction to infrared thermography and the common procedures for temperature measurement and non-destructive testing are presented. Furthermore, developments in these fields and recent advances are reviewed

Deep Learning Based Face Detection and Recognition in MWIR and Visible Bands

In non-favorable conditions for visible imaging like extreme illumination or nighttime, there is a need to collect images in other spectra, specifically infrared. Mid-Wave infrared (3-5 microm) images can be collected without giving away the location of the sensor in varying illumination conditions. There are many algorithms for face detection, face alignment, face recognition etc. proposed in visible band till date, while the research using MWIR images is highly limited. Face detection is an important pre-processing step for face recognition, which in turn is an important biometric modality. This thesis works towards bridging the gap between MWIR and visible spectrum through three contributions. First, a dual band based deep face detection model that works well in visible and MWIR spectrum is proposed using transfer learning. Different models are trained and tested extensively using visible and MWIR images and the one model that works well for this data is determined. For this model, experiments are conducted to learn the speed/accuracy trade-off. Following this, the available MWIR dataset is extended through augmentation using traditional methods and generative adversarial networks (GANs). Traditional methods used to augment the data are brightness adjustment, contrast enhancement, applying noise to and de-noising the images. A deep learning based GAN architecture is developed and is used to generate new face identities. The generated images are added to the original dataset and the face detection model developed earlier is once again trained and tested. The third contribution is the proposal of another GAN that converts given thermal ace images into their visible counterparts. A pre-trained model is used as discriminator for this purpose and is trained to classify the images as real and fake and an identity network is used to provide further feedback to the generator. The generated visible images are used as probe images and the original visible images are used as gallery images to perform face recognition experiments using a state-of-the-art visible-to-visible face recognition algorithm

Towards a Robust Thermal-Visible Heterogeneous Face Recognition Approach Based on a Cycle Generative Adversarial Network

Security is a sensitive area that concerns all authorities around the world due to the emerging terrorism phenomenon. Contactless biometric technologies such as face recognition have grown in interest for their capacity to identify probe subjects without any human interaction. Since traditional face recognition systems use visible spectrum sensors, their performances decrease rapidly when some visible imaging phenomena occur, mainly illumination changes. Unlike the visible spectrum, Infrared spectra are invariant to light changes, which makes them an alternative solution for face recognition. However, in infrared, the textural information is lost. We aim, in this paper, to benefit from visible and thermal spectra by proposing a new heterogeneous face recognition approach. This approach includes four scientific contributions. The first one is the annotation of a thermal face database, which has been shared via Github with all the scientific community. The second is the proposition of a multi-sensors face detector model based on the last YOLO v3 architecture, able to detect simultaneously faces captured in visible and thermal images. The third contribution takes up the challenge of modality gap reduction between visible and thermal spectra, by applying a new structure of CycleGAN, called TV-CycleGAN, which aims to synthesize visible-like face images from thermal face images. This new thermal-visible synthesis method includes all extreme poses and facial expressions in color space. To show the efficacy and the robustness of the proposed TV-CycleGAN, experiments have been applied on three challenging benchmark databases, including different real-world scenarios: TUFTS and its aligned version, NVIE and PUJ. The qualitative evaluation shows that our method generates more realistic faces. The quantitative one demonstrates that the proposed TV -CycleGAN gives the best improvement on face recognition rates. Therefore, instead of applying a direct matching from thermal to visible images which allows a recognition rate of 47,06% for TUFTS Database, a proposed TV-CycleGAN ensures accuracy of 57,56% for the same database. It contributes to a rate enhancement of 29,16%, and 15,71% for NVIE and PUJ databases, respectively. It reaches an accuracy enhancement of 18,5% for the aligned TUFTS database. It also outperforms some recent state of the art methods in terms of F1-Score, AUC/EER and other evaluation metrics. Furthermore, it should be mentioned that the obtained visible synthesized face images using TV-CycleGAN method are very promising for thermal facial landmark detection as a fourth contribution of this paper

Comparative study of infrared thermography, ultrasonic C-scan, X-ray computed tomography and terahertz imaging on composite materials

L’évaluation non destructive (NDT) des matériaux composites est compliquée en raison de la vaste gamme de défauts rencontrés (y compris délaminage, microfissuration, fracture de la fibre, retrait des fibres, fissuration matricielle, inclusions, vides et dommages aux chocs). La capacité de caractériser quantitativement le type, la géométrie et l’orientation des défauts est essentielle. La thermographie infrarouge (IRT), en tant que technique de diagnostic d’image, peut satisfaire le besoin industriel croissant de NDT&E. Dans la thèse, la thermographie par excitation optique et mécanique a été utilisée pour étudier différents matériaux composites, dont 1) des préformes sèches en fibres de carbone, 2) des composites de fibres naturelles, 3) des composites hybrides de basalte-fibres de carbone soumis à une charge d’impact (séquence de type sandwich et séquence d’empilement intercalé), 4) des défauts micro-dimensionnés dans un composite polymère renforcé de fibre de carbone (CFRP) en 3D avec une couture de type « joint en T », et 5) des peintures sur toile qui peuvent être considérées comme des matériaux composites. Une nouvelle technique IRT de thermographie de ligne par micro-laser (micro-LLT) a été proposée pour l’évaluation des porosités submillimétriques dans le CFRP. La microscopie de points par micro-laser (micro-LST) et la micro-vibrothermographie (micro-VT) ont également été présentées avec l’utilisation de microlentilles. La thermographie pulsée (PT) et la thermographie modulée « à verrouillage » (LT) ont été comparées à la tomographie par rayons X (TC) pour validation. Le C-scan ultrasonore (UT) et l’imagerie par ondes tera-hertziennes en onde continue (CW THz) ont également été réalisés à des fins comparatives. L’inspection par techniques thermographiques est une question ouverte à discuter pour le public scientifique. En fait, la thermographie par impulsions (PPT) basée sur la transformation de phase a été utilisée pour estimer la profondeur des dommages. Pour traiter les données thermographiques, on a également utilisé la reconstruction de signal thermographique de base (B-TSR), la thermographie des composants principaux (PCT) et la thermographie des moindres carrés partiels (PLST). Enfin, une analyse complète et comparative basée sur le diagnostic d’images thermographiques a été menée en vue d’applications industrielles potentielles.Non-destructive testing (NDT) of composite materials is complicated due to the wide range off laws encountered (including delamination, micro-cracking, fiber fracture, fiber pullout, matrix cracking, inclusions, voids, and impact damage). The ability to quantitatively characterize the type, geometry, and orientation of flaws is essential. Infrared thermography (IRT), as an image diagnostic technique, can satisfy the increasing industrial need for NDT&E. In the thesis, optical and mechanical excitation thermography were used to investigate different composite materials, including 1) carbon fiber dry preforms, 2) natural fiber composites, 3) basalt-carbon fiber hybrid composites subjected to impact loading (sandwich-like and intercalated stacking sequence), 4) micro-sized flaws in a stitched T-joint 3D carbon fiber reinforced polymer composite (CFRP), and 5) paintings on canvas which can be considered as composite materials. Of particular interest, a new IRT technique micro-laser line thermography (micro-LLT) was proposed for the evaluation of submillimeter porosities in CFRP. Micro-laser spot thermography (micro-LST) and micro-vibrothermography (micro-VT) were also presented with the usage of a micro-lens. Pulsed thermography (PT) and lock-in thermography (LT) were compared with x-ray computed tomography (CT) for validation. Ultrasonic C-scan (UT) and continuous wave terahertz imaging (CW THz) were also conducted for the comparative purpose. The inspection by thermographic techniques is an open matter to be discussed for the scientific audience. In fact, pulse phase thermography (PPT) based on phase transform was used to estimate the damage depth. Basic thermographic signal reconstruction (B-TSR), principal component thermography (PCT) and partial least squares thermography (PLST) (another more recent advanced image processing technique) were also used to pro-cess the thermographic data. Finally, a comprehensive and comparative analysis based on thermographic image diagnostics was conducted in view of potential industrial applications

Imaging cultural heritage at different scales : part I, the micro-scale (manufacts)

Applications of non-invasive sensing techniques to investigate the internal structure and surface of precious and delicate objects represent a very important and consolidated research field in the scientific domain of cultural heritage knowledge and conservation. The present article is the first of three reviews focused on contact and non-contact imaging techniques applied to surveying cultural heritage at micro- (i.e., manufacts), meso- (sites) and macro-scales (landscapes). The capability to infer variations in geometrical and physical properties across the inspected surfaces or volumes is the unifying factor of these techniques, allowing scientists to discover new historical sites or to image their spatial extent and material features at different scales, from landscape to artifact. This first part concentrates on the micro-scale, i.e., inspection, study and characterization of small objects (ancient papers, paintings, statues, archaeological findings, architectural elements, etc.) from surface to internal properties.peer-reviewe