Defect-aware Super-resolution Thermography by Adversarial Learning

nfrared thermography is a valuable non-destructive tool for inspection of materials. It measures the surface temperature evolution, from which hidden defects may be detected. Yet, thermal cameras typically have a low native spatial resolution resulting in a blurry and low-quality thermal image sequence and videos. In this study, a novel adversarial deep learning framework, called Dual-IRT-GAN, is proposed for performing super-resolution tasks. The proposed Dual-IRT-GAN attempts to achieve the objective of improving local texture details, as well as highlighting defective regions. Technically speaking, the proposed model consists of two modules SEGnet and SRnet that carry out defect detection and super resolution tasks, respectively. By leveraging the defect information from SEGnet, SRnet is capable of generating plausible high-resolution thermal images with an enhanced focus on defect regions. The generated high-resolution images are then delivered to the discriminator for adversarial training using GAN's framework. The proposed Dual-IRT-GAN model, which is trained on an exclusive virtual dataset, is demonstrated on experimental thermographic data obtained from fiber reinforced polymers having a variety of defect types, sizes, and depths. The obtained results show its high performance in maintaining background color consistency and removing undesired noise, and in highlighting defect zones with finer detailed textures in high-resolution

Defect-aware Super-resolution Thermography by Adversarial Learning

nfrared thermography is a valuable non-destructive tool for inspection of materials. It measures the surface temperature evolution, from which hidden defects may be detected. Yet, thermal cameras typically have a low native spatial resolution resulting in a blurry and low-quality thermal image sequence and videos. In this study, a novel adversarial deep learning framework, called Dual-IRT-GAN, is proposed for performing super-resolution tasks. The proposed Dual-IRT-GAN attempts to achieve the objective of improving local texture details, as well as highlighting defective regions. Technically speaking, the proposed model consists of two modules SEGnet and SRnet that carry out defect detection and super resolution tasks, respectively. By leveraging the defect information from SEGnet, SRnet is capable of generating plausible high-resolution thermal images with an enhanced focus on defect regions. The generated high-resolution images are then delivered to the discriminator for adversarial training using GAN's framework. The proposed Dual-IRT-GAN model, which is trained on an exclusive virtual dataset, is demonstrated on experimental thermographic data obtained from fiber reinforced polymers having a variety of defect types, sizes, and depths. The obtained results show its high performance in maintaining background color consistency and removing undesired noise, and in highlighting defect zones with finer detailed textures in high-resolution

Defect-aware Super-resolution Thermography by Adversarial Learning

nfrared thermography is a valuable non-destructive tool for inspection of materials. It measures the surface temperature evolution, from which hidden defects may be detected. Yet, thermal cameras typically have a low native spatial resolution resulting in a blurry and low-quality thermal image sequence and videos. In this study, a novel adversarial deep learning framework, called Dual-IRT-GAN, is proposed for performing super-resolution tasks. The proposed Dual-IRT-GAN attempts to achieve the objective of improving local texture details, as well as highlighting defective regions. Technically speaking, the proposed model consists of two modules SEGnet and SRnet that carry out defect detection and super resolution tasks, respectively. By leveraging the defect information from SEGnet, SRnet is capable of generating plausible high-resolution thermal images with an enhanced focus on defect regions. The generated high-resolution images are then delivered to the discriminator for adversarial training using GAN's framework. The proposed Dual-IRT-GAN model, which is trained on an exclusive virtual dataset, is demonstrated on experimental thermographic data obtained from fiber reinforced polymers having a variety of defect types, sizes, and depths. The obtained results show its high performance in maintaining background color consistency and removing undesired noise, and in highlighting defect zones with finer detailed textures in high-resolution

Dual-IRT-GAN:A defect-aware deep adversarial network to perform super-resolution tasks in infrared thermographic inspection

InfraRed Thermography (IRT) is a valuable diagnostic tool for detecting defects in fiber-reinforced polymers in a non-destructive manner through the measurement of surface temperature distribution. Yet, thermal cameras typically have a low native spatial resolution resulting in a blurry and low-quality thermal image sequence. This study proposes a defect-aware Generative Adversarial Network (GAN) framework, termed Dual-IRT-GAN, in order to simultaneously perform Super-Resolution (SR) and defect detection tasks in infrared thermography. Furthermore, the visibility of defective regions in generated high-resolution images are enhanced by leveraging defect-aware attention maps from segmented defect images. Following a series of augmentation techniques and a second-order degradation process, the proposed Dual-IRT-GAN model is trained on an extensive numerically generated thermographic dataset of composite materials with various defect types, sizes and depts. The high inference performance of the virtually trained Dual-IRT-GAN is demonstrated on several experimental thermographic datasets which were obtained from composite coupon specimens with various defect types, sizes, and depths, as well as from aircraft stiffened composite panels having real (production) defects.</p

Effective Design and Implementation of Task-Driven Learning in High School Physics: Citing the Lesson on Composition and Resolution of Forces as a Case Study

One of the primary characteristics of the holistic module learning model is task-driven learning. In this learning model, a task is a distinct activity or collection of distinct activities designed to assist students in achieving learning objectives. The purpose of this paper is to examine the effective design and implementation of task-driven learning using the composition and resolution of forces lesson as a case study. On the basis of the lesson study, the principles of task-driven learning design are summarized and some recommendations for task-driven learning implementation are made

A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detection

Achieving a high prediction rate is a crucial task in fault detection. Although various classification procedures are available, none of them can give high accuracy in all applications. Therefore, in this paper, a novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. This is acquired by using Dempster-Shafer theory (DST). However, in cases with conflicting evidences, the DST may give counter-intuitive results. In this regard, a preprocessing technique based on a new metric is devised in order to measure and mitigate the conflict between the evidences. To evaluate and validate the effectiveness of the proposed approach, the method is applied to 15 benchmarks datasets from UCI and KEEL. Further, it is applied for classifying polycrystalline Nickel alloy first-stage turbine blades based on their broadband vibrational response. Through statistical analysis with different noise levels, and by comparing with four state-of-the-art fusion techniques, it is shown that that the proposed method improves the classification accuracy and outperforms the individual classifiers.Comment: arXiv admin note: text overlap with arXiv:2007.0878

On the influence of reference Mahalanobis distance space for quality classification of complex metal parts using vibrations

Mahalanobis distance (MD) is a well-known metric in multivariate analysis to separate groups or populations. In the context of the Mahalanobis-Taguchi system (MTS), a set of normal observations are used to obtain their MD values and construct a reference Mahalanobis distance space, for which a suitable classification threshold can then be introduced to classify new observations as normal/abnormal. Aiming at enhancing the performance of feature screening and threshold determination in MTS, the authors have recently proposed an integrated Mahalanobis classification system (IMCS) algorithm with robust classification performance. However, the reference MD space considered in either MTS or IMCS is only based on normal samples. In this paper, an investigation on the influence of the reference MD space based on a set of (i) normal samples, (ii) abnormal samples, and (iii) both normal and abnormal samples for classification is performed. The potential of using an alternative MD space is evaluated for sorting complex metallic parts, i.e., good/bad structural quality, based on their broadband vibrational spectra. Results are discussed for a sparse and imbalanced experimental case study of complex-shaped metallic turbine blades with various damage types; a rich and balanced numerical case study of dogbone-cylinders is also considered