Simultaneous measurement of temperature and strain in electronic packages using multi-frame super-resolution infrared thermography and digital image correlation

For microelectronic components and systems, reliability under thermomechanical stress is of critical importance. Experimental characterization of hotspots and temperature gradients, which can lead to deformation in the component, relies on accurate mapping of the surface temperature. One method of non-invasively acquiring this data is through infrared (IR) thermography. However, IR thermography is often limited by the typically low resolution of such cameras. Additionally, the unique surface finish preparations required to infer physical deformation using digital image correlation (DIC) generally interferes with the ability to measure the temperature with IR thermography, which prefers a uniform high emissivity. This work introduces a one-shot technique for the simultaneous measurement of surface temperature and deformation using multiframe super-resolution-enhanced IR imaging combined with digital image correlation (DIC) analysis. Multiframe super-resolution processing uses several sub-pixel shifted images, interpolating the image set to extract additional information and create a single higher-resolution image. Measurement of physical deformation is incorporated using a test sample with a black background and low-emissivity speckle features, heated in a manner that induces a non-uniform temperature field and stretched to induce physical deformation. Through processing and filtering, data from the black surface regions used for surface temperature mapping are separated from the speckle features used to track deformation with DIC. This method allows DIC to be performed on the IR images, yielding a deformation field consistent with the applied tensioning. While both the low- and super-resolution data sets can be successfully processed with DIC, super-resolution helps reduce noise in the extracted deformation fields. As for temperature measurement, using super-resolution is shown to allow for better removal of the speckle features and reduce noise, as quantified by a lower mean deviation from the spatial moving average

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

A Cost-Effective System for Aerial 3D Thermography of Buildings

Three-dimensional (3D) imaging and infrared (IR) thermography are powerful tools in many areas in engineering and sciences. Their joint use is of great interest in the buildings sector, allowing inspection and non-destructive testing of elements as well as an evaluation of the energy efficiency. When dealing with large and complex structures, as buildings (particularly historical) generally are, 3D thermography inspection is enhanced by Unmanned Aerial Vehicles (UAV-also known as drones). The aim of this paper is to propose a simple and cost-effective system for aerial 3D thermography of buildings. Special attention is thus payed to instrument and reconstruction software choice. After a very brief introduction to IR thermography for buildings and 3D thermography, the system is described. Some experimental results are given to validate the proposal

Twenty-five years of aerodynamic research with IR imaging: A survey

Infrared imaging used in aerodynamic research evolved during the last 25 years into a rewarding experimental technique for investigation of body-flow viscous interactions, such as heat flux determination and boundary layer transition. The technique of infrared imaging matched well its capability to produce useful results, with the expansion of testing conditions in the entire spectrum of wind tunnels, from hypersonic high-enthalpy facilities to cryogenic transonic wind tunnels. With unique achievements credited to its past, the current trend suggests a change in attitude towards this technique: from the perception as an exotic, project-oriented tool, to the status of a routine experimental procedure