Automatic Detection and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data.

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.

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms.

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.

IRT and GPR Techniques for Moisture Detection and Characterisation in Buildings.

The integrity, comfort, and energy demand of a building can be negatively affected by the presence of moisture in its walls. Therefore, it is essential to identify and characterise this building pathology with the most appropriate technologies to perform the required prevention and maintenance tasks. This paper proposes the joint application of InfraRed Thermography (IRT) and Ground-Penetrating Radar (GPR) for the detection and classification of moisture in interior walls of a building according to its severity level. The IRT method is based on the study of the temperature distribution of the thermal images acquired without an application of artificial thermal excitation for the detection of superficial moisture (less than 15 mm deep in plaster with passive IRT). Additionally, in order to characterise the level of moisture severity, the Evaporative Thermal Index (ETI) was obtained for each of the moisture areas. As for GPR, with measuring capacity from 10 mm up to 30 cm depth with a 2300 MHz antenna, several algorithms were developed based on the amplitude and spectrum of the received signals for the detection and classification of moisture through the inner layers of the wall. In this work, the complementarity of both methods has proven to be an effective approach to investigate both superficial and internal moisture and their severity. Specifically, IRT allowed estimating superficial water movement, whereas GPR allowed detecting points of internal water accumulation. Thus, through the combination of both techniques, it was possible to provide an interpretation of the water displacement from the exterior surface to the interior surface of the wall, and to give a relative depth of water inside the wall. Therefore, it was concluded that more information and greater reliability can be gained by using complementary IRT-GPR, showing the benefits of combining both techniques in the building sector.

Development of Thermal Principles for the Automation of the Thermographic Monitoring of Cultural Heritage.

The continuous deterioration of elements, with high patrimonial value over time, can only be mitigated or annulled through the application of techniques that facilitate the preventative detection of the possible agents of deterioration. InfraRed Thermography (IRT) is one of the most used techniques for this task. However, there are few IRT methodologies, which can automatically monitor the cultural heritage field, and are vitally important in eliminating the subjectivity in interpreting and accelerating the analysis process. In this work, a study is performed on a tessellatum layer of a mosaic to automatically: (i) Detect the first appearance of the thermal footprint of internal water, (ii) delimit the contours of the thermal footprint of internal water from its first appearance, and (iii) classify between harmful and non-harmful internal water. The study is based on the analysis of the temperature distribution of each thermal image. Five thermal images sequences are acquired during the simulation of different real situations, obtaining a set of promising results for the optimization of the thermographic inspection process, while discussing the following recommended steps to be taken in the study for future researches.

Zip6 Transporter Is an Essential Component of the Lymphocyte Activation Machinery.

Zinc deficiency causes immune dysfunction. In T lymphocytes, hypozincemia promotes thymus atrophy, polarization imbalance, and altered cytokine production. Zinc supplementation is commonly used to boost immune function to prevent infectious diseases in at-risk populations. However, the molecular players involved in zinc homeostasis in lymphocytes are poorly understood. In this paper, we wanted to determine the identity of the transporter responsible for zinc entry into lymphocytes. First, in human Jurkat cells, we characterized the effect of zinc on proliferation and activation and found that zinc supplementation enhances activation when T lymphocytes are stimulated using anti-CD3/anti-CD28 Abs. We show that zinc entry depends on specific pathways to correctly tune the NFAT, NF-κB, and AP-1 activation cascades. Second, we used various human and murine models to characterize the zinc transporter family, Zip, during T cell activation and found that Zip6 was strongly upregulated early during activation. Therefore, we generated a Jurkat Zip6 knockout (KO) line to study how the absence of this transporter affects lymphocyte physiology. We found that although Zip6KO cells showed no altered zinc transport or proliferation under basal conditions, under activation, these KO cells showed deficient zinc transport and a drastically impaired activation program. Our work shows that zinc entry into activated lymphocytes depends on Zip6 and that this transporter is essential for the correct function of the cellular activation machinery.

Combined Endodontic Therapy and Intentional Replantation for the Treatment of Palatogingival Groove.

A palatogingival groove is an anatomic malformation that predisposes the involved tooth to a severe periodontal defect. When the condition is complicated by pulpal necrosis, affected teeth often present a dilemma in terms of diagnosis and treatment planning. In this report, we describe the case of a patient with a maxillary lateral incisor with a deep palatogingival groove extending to the root apex and severe periodontal destruction (local pocketing). Suggested treatment modalities included curettage of the affected tissues, elimination of the groove by grinding and/or sealing with a variety of filling materials, and surgical procedures. In this case, a combined treatment approach, involving both endodontic therapy and intentional replantation after restoration with a self-etching flowable composite, resulted in periodontal healing and significant healing of the periradicular radiolucency at 12 months. In short, intentional replantation offers a predictable procedure and should be considered a viable treatment modality for the management of palatogingival grooves, especially for single-rooted teeth.