Thermography Research Articles

Visualising delamination in CFRP: a combination of infrared thermography with image translation technique

ABSTRACT Delamination in carbon fibre reinforced polymer (CFRP) significantly degrades the performance of CFRP, and infrared thermography (IRT) is effective for detecting this type of internal defect. However, detection of some delaminations remains challenging due to either weak or absent indications in IRT results. This study proposed a novel approach to clearly visualise delamination by incorporating image translation technique into IRT. A strategy is constructed specifically for IRT to collect precisely aligned thermogram pairs, and a data augmentation method is developed considering both noise distribution in IRT results and delamination locations. Experiments on CFRP plates reveal that some delaminations, previously undetectable in raw IRT results, are distinctly visualised using the proposed method. Comparison analysis also demonstrates that the developed method exhibits superior performance compared to existing IRT methods. This study provides a new approach to effectively enhance the capability of IRT inspection technique, contributing to an accurate quality evaluation of CFRP material.

TOAD: Profiling and Evaluating 3D Printed IoT Rapid Prototype Designs

3D printing has revolutionized DIY IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and its effectiveness.

Can Infrared Thermal Imaging Reflect Exercise Load? An Incremental Cycling Exercise Study

Monitoring the training load is crucial in sports science research, as it provides scientific evidence for assessing training effects, optimizing athletic performance, and preventing overtraining by quantifying both external and internal loads. Although traditional monitoring methods have made significant progress, infrared thermography (IRT) technology, with its non-contact, real-time, and non-invasive characteristics, is gradually emerging as an effective tool for evaluating the relationship between the training load and physiological responses. This study evaluated 31 healthy male adults (age 21.9 ± 2.7 years, weight 75 ± 8.26 kg, and training duration 240 ± 65 min/week) performing incremental exhaustive exercise on a cycle ergometer (with a 60W starting load, increasing by 20W per minute). Entropy analysis was used to quantitatively assess the surface radiation patterns of regions of interest (forehead, chest, and abdomen) obtained through thermal imaging. Compared to baseline, significant differences in the surface radiation patterns of the regions of interest were observed at the point of exhaustion (p ≤ 0.01). Correlation analysis revealed strong associations between the external load, oxygen consumption, and chest temperature entropy (r = 0.973 and 0.980). Cluster analysis of the chest entropy, external load, and oxygen consumption showed a non-linear increasing trend in their inter-relationships. Further individual analysis demonstrated positive correlations between the percentage increase in the chest entropy and both the external load (r = 0.70–0.98) and oxygen consumption (r = 0.65–0.97). Entropy analysis offers a new approach for quantitatively assessing surface radiation patterns from infrared thermography, and reveals the coupling relationship between thermoregulation and metabolic responses during exercise.

The Temperature-Assisted Defect Detection in Rotating Machinery by Using Infrared Thermography Integrated Modified Artificial Neural Network-Based Image Processing: A Step Towards Futuristic Metrology

The Temperature-Assisted Defect Detection in Rotating Machinery by Using Infrared Thermography Integrated Modified Artificial Neural Network-Based Image Processing: A Step Towards Futuristic Metrology

Unique infrared thermographic profiles and altered hypothalamic neurochemistry associated with mortality in endotoxic shock.

Unique infrared thermographic profiles and altered hypothalamic neurochemistry associated with mortality in endotoxic shock.

Enhanced defect detection in thermography through temporal denoising and deep feature extraction using a shallow convolution autoencoder

Various stimulation mechanisms and supportive processing techniques promote infrared thermography (IRT) as a powerful tool for inspecting various industrial objects, such as composites, metals and coatings. Frequency-modulated thermography (FMT) is one stimulation scheme offering superior depth scanning and resolution capabilities. However, noise and non-uniform thermal backgrounds make defect detection challenging in infrared thermography. Recent post-processing advancements following NDT 4.0 adapt machine learning and deep learning-based techniques to automate and enhance defect detection procedures. This study offers a shallow convolution autoencoder (SCAE) in FMT with one-dimensional convolution layers to reduce noise in temporal thermal evolution and train high-level features, resulting in improved defect signatures. This method focuses on creating the denoised temporal thermal response at the decoder end and extracting high-level thermal features from the latent space. Experiments were carried out on a mild steel specimen and a carbon fibre-reinforced polymer (CFRP) specimen embedded with flaws of varying depths and sizes. Compared to the shallow autoencoder (SAE) and stacked deep autoencoder (SDAE), the suggested SCAE efficiently denoised thermal profiles, allowing for improved fault signatures. Furthermore, the latent features of the SCAE produced superior defect signatures compared to the above autoencoder models and traditional dimensionality reduction methods such as principal component analysis (PCA), random projection transform (RPT) and partial least-squares regression (PLSR). The thermal profile and defect signalto-noise ratio (SNR) demonstrate the advantages of the proposed method for improved defect detection.

Use of Thermography Method in Diagnosis of Horse Diseases

Abstract The paper is focused on the practical use of infrared thermography (IRT) in the diagnosis of clinical or subclinical forms of equine inflammation. The aim of the study was to verify the suitability of IRT for the early detection of areas with the potential existence of inflammation. The verification of findings was carried out through veterinary data (medical reports, radiographs). The study claimed the suitability of the IRT method for practical monitoring and detection of health problems, especially limb diseases – hooves, tendons, muscles (13 horses) as well as dermatological diseases (3 horses). The results showed that the most frequent occurrence of the disease, manifested by muscle and tendon overload and inflammatory processes, is on the limbs. The results claimed a significant difference between the measured temperatures and the typical temperatures of a healthy horse.

Optimal configuration strategies for multiple active infrared thermography in defect detection of silicone rubber-epoxy bonded composite materials

Optimal configuration strategies for multiple active infrared thermography in defect detection of silicone rubber-epoxy bonded composite materials

Full-field investigation of dissipative mechanisms and thermoelastic inversion effects within glass-fiber reinforced polyamides subjected to low-cycle fatigue

Full-field investigation of dissipative mechanisms and thermoelastic inversion effects within glass-fiber reinforced polyamides subjected to low-cycle fatigue

Advancing newborn care: Precise time of birth detection using ai-driven thermal imaging with adaptive normalization.

Advancing newborn care: Precise time of birth detection using ai-driven thermal imaging with adaptive normalization.

Thermal imaging in biomedical research: a non-invasive technology for animal models.

Thermal imaging has been used in animal models to non-invasively detect surface temperature changes after several pathologic and surgical processes. Infrared thermography (IRT) identifies increases or decreases in radiated heat according to blood circulation and microcirculation. The present review aims to discuss the most relevant aspects of IRT applied in biomedical research as a noninvasive technique in animal models, highlighting its importance in a clinical setting and for translational medicine. IRT provides an alternative to evaluate vascular anomalies where blood flow is interrupted. In surgical processes such as anastomosis and reconstructive techniques (e.g., grafts and flaps), thermal imaging can assess the viability of tissues. In burn injuries, IRT can predict and identify the areas of ischemia-necrosis and inflammation. Nonetheless, although IRT is a potential alternative to use in both animal models and human patients, the use of IRT and other imaging techniques is encouraged.

Clinical Assessment of Dairy Goats’ Udder Health Using Infrared Thermography

This study aimed to evaluate the capacity of infrared thermography (IRT) to be used for the in situ clinical assessment of udder health status across lactation in goats. Two epidemiological studies were designed, with 106 purebred Skopelos goats being prospectively monitored over one lactation in the first study and 132 goats of the same breed from a second farm being enrolled in the second, cross-sectional study. Goats were categorized based on the clinical status of each udder half (healthy, fibrotic, both fibrotic and asymmetric, asymmetric, swollen supra-mammary lymph nodes, and abscessed). Skin surface temperature values of teats (TSST) and udder halves (USST), as well as temperature variations between different types of abscesses, based on the stage of their development (superficial developed, superficial fully mature, drained), and the symmetrical sites of the non-affected udder halves, were evaluated. Mixed linear regression analyses indicated that fibrosis, particularly when accompanied by asymmetry, resulted in a decrease of 0.2, 0.3, and 0.3 °C (p < 0.05) and of 0.6, 0.9, and 1.4 °C (p < 0.001) in the maximum, mean, and minimum USST, respectively. Recording season significantly affected USST and TSST (p < 0.001), with both of them increasing between February and August. Additionally, TSST appeared to be an unreliable indicator of udder health status, while abscesses exhibited distinct temperature patterns based on their developmental stage. It is concluded that IRT has the potential to be used on site for the detection of temperature alterations associated with chronic udder health issues, yet future studies on a larger and more diverse population of goats with various udder health conditions is needed to confirm its extensive applicability under real-world conditions.

Accurate Core Body Temperature Prediction for Infrared Thermography Considering Ambient Temperature and Personal Features.

Accurate and timely core body temperature measurement is essential for identifying and preventing heat-related illnesses. Infrared thermography (IRT) provides a non-invasive, full-scale and efficient temperature path for body temperature screening. However, the complexity of environmental factors and personal features continuously affect the measured skin temperature, resulting in low accuracy and reliability of existing body temperature monitoring by IRT. To address this issue, this study proposed an innovative core temperature prediction model (CTPM) for IRT based on heat transfer mechanism between the human body and the ambient environment. Based on human body thermoregulation, the optimal facial thermal feature that can reflect the impact of ambient temperature on skin temperature is proposed. Combining it with personal features and distributed facial skin temperature features, a CTPM is established based on Random Forest algorithm. The proposed CTPM are evaluated using a publicly available PhysioNet facial and oral temperature dataset. The results demonstrate that the proposed optimal CTPM achieves the best accuracy and consistency in predicting core body temperature. The root-mean-square error of the optimal CTPM is 0.259°C, and the mean lower and upper 95% limits of agreement are -0.505 °C and 0.507°C, respectively. Variable importance analysis indicates that the proposed optimal facial thermal feature makes a dominant contribution to the prediction performance of the optimal CTPM. Our method enables accurate and stable core body temperature prediction in complex ambient environments over a wide range of temperatures, and has the potential to replace traditional contact measurements to meet clinical needs.

IR thermography & NN models for damaged component thickness detection

To achieve rapid detection of damage thickness in metal components using infrared thermography, a combination of heat transfer theory and image theory was employed. This involved theoretical analysis, finite element numerical simulation, a BP neural network prediction model, and infrared thermography experiments. Infrared thermal wave experiments were conducted under different heating temperatures. By analyzing the obtained temperature data, the response characteristics of surface temperature distribution to component thickness were investigated. The COMSOL numerical simulation software was used to simulate the surface temperature of the metal components. The bevel-cut metal components were heated to 80 °C, 105 °C, and 130 °C, and the fitted experimental temperature data were analyzed in conjunction with the simulated temperature data of the bevel-cut metal components. It was found that the fitted experimental temperature rise curve aligned with the simulated temperature rise curve trend. A comparative analysis of the simulation results and experimental values showed that the simulated temperature rise curve was basically consistent with the fitted experimental temperature curve, validating the feasibility of using numerical simulation as a substitute for experiments. The numerical simulation data were divided into a training set and a prediction set in an 8:2 ratio. Through training with the BP neural network, the predicted data were found to be basically consistent with the experimental data, verifying the feasibility of using the BP neural network for rapid detection of damage thickness in metal components. This laid the foundation for the subsequent promotion and application of BP neural network technology for rapid detection of damage thickness in metal components. This study holds significant importance for the application of neural network-based rapid detection technology for metal component thickness in the engineering field.

Liquid Crystal Thermography and Infrared Thermography Application in Heat Transfer Research on Flow Boiling in Minichannels

This study investigated FC-72 boiling heat transfer in minichannels using two non-contact temperature measurement techniques: liquid crystal thermography (LCT) and infrared thermography (IRT). These methods were applied simultaneously to measure temperature distributions on the heated wall surface of minichannels, formed by a thin metal foil. The temperature data facilitated the calculation of local heat transfer coefficients at the foil–working fluid contact surface. Calibration of the liquid crystal colour response to temperature was conducted prior to the use of LCT. According to a comparison of the heat transfer coefficients and Nusselt numbers determined using LCT and IRT measurements, comparable temperature distributions are provided, with the average relative differences in heat transfer coefficients determined using these techniques remaining below 15%. The findings highlight the advantages of non-contact temperature measurement in minimising system disturbances while providing precise data for understanding flow boiling heat transfer mechanisms. Such results can contribute to the design of minichannel heat exchangers.

Thermal Video Enhancement Mamba: A Novel Approach to Thermal Video Enhancement for Real-World Applications

Object tracking in thermal video is challenging due to noise, blur, and low contrast. We present TVEMamba, a Mamba-based enhancement framework with near-linear complexity that improves tracking in these conditions. Our approach uses a State Space 2D (SS2D) module integrated with Convolutional Neural Networks (CNNs) to filter, sharpen, and highlight important details. Key components include (i) a denoising module to reduce background noise and enhance image clarity, (ii) an optical flow attention module to handle complex motion and reduce blur, and (iii) entropy-based labeling to create a fully labeled thermal dataset for training and evaluation. TVEMamba outperforms existing methods (DCRGC, RLBHE, IE-CGAN, BBCNN) across multiple datasets (BIRDSAI, FLIR, CAMEL, Autonomous Vehicles, Solar Panels) and achieves higher scores on standard quality metrics (EME, BDIM, DMTE, MDIMTE, LGTA). Extensive tests, including ablation studies and convergence analysis, confirm its robustness. Real-world examples, such as tracking humans, animals, and moving objects for self-driving vehicles and remote sensing, demonstrate the practical value of TVEMamba.

Impact of claw trimming on surface temperature variations across claw regions in dairy cows: Insights from infrared thermography.

Impact of claw trimming on surface temperature variations across claw regions in dairy cows: Insights from infrared thermography.

Artificial Intelligence in IR Thermal Imaging and Sensing for Medical Applications.

The state of the art in IR thermal imaging methods for applications in medical diagnostics is discussed. A review of advances in IR thermal imaging technology in the years 1960-2024 is presented. Recently used artificial intelligence (AI) methods in the analysis of thermal images are the main interest. IR thermography is discussed in view of novel applications of machine learning methods for improved diagnostic analysis and medical treatment. The AI approach aims to improve image quality by denoising thermal images, using applications of AI super-resolution algorithms, removing artifacts, object detection, face and characteristic features localization, complex matching of diagnostic symptoms, etc.

Monitoring Mouse Surface Temperature During Stress with a Thermal Camera: A Low-Cost Infrared Videography System for Evaluating Murine Metabolism.

Energy is required for life, and organisms obtain their energy from fuel sources to enable both anabolic and catabolic processes. Some of this energy is radiated as heat, which can be quantified as a measure of metabolic rate. In some cases, environmental toxicants can alter metabolic energy in undesirable ways, and characterization of new pharmaceuticals can determine the efficacy of desirable metabolic rate manipulation or identify off-target adverse effects. Current methods to directly measure heat production in laboratory mice are expensive, can be laborious, and make it challenging to monitor animals in ways that are multiplexed, robust, and non-invasive. We present a set of protocols for assembling and deploying a simple, low-cost thermal camera to monitor and record thermogenic activity, modified from prior work. Parts used to build this system currently cost approximately $150 USD and, when assembled, can record mouse temperatures as well as ambient cage temperatures up to twice per second for extended periods. By using multiplexed cameras in a diurnal mouse incubator system, the thermogenic capacity of several mice can be simultaneously recorded and graphed. Exogenous agents and genotypes that alter metabolism can be readily identified with this technology. In this set of protocols, we describe the assembly of the thermal video camera device, its use, and related data capture and analysis methods. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Assembling thermal camera for thermogenic stress test Basic Protocol 2: In vivo measurement of mouse temperature under different ambient conditions.

Processing of Eddy Current Infrared Thermography and Magneto-Optical Imaging for Detecting Laser Welding Defects

Infrared (IR) magneto-optical (MO) bi-imaging is an innovative method for detecting weld defects, and it is important to process both IR thermography and MO imaging characteristics of weld defects. IR thermography and MO imaging can not only run simultaneously but can also run separately in special welding processes. This paper studies the sensing processing of eddy current IR thermography and MO imaging for detecting weld defects of laser spot welding and butt joint laser welding, respectively. To address the issues of high-level noise and low contrast in eddy current IR detection thermal images interfering with defect detection and recognition, a method based on least squares and Gaussian-adaptive bilateral filtering is proposed for denoising eddy current IR detection thermal images of laser spot welding cracks and improving the quality of eddy current IR detection thermal images. Meanwhile, the image gradient is processed by Gaussian-adaptive bilateral filtering, and then the filter is embedded in the least squares model to smooth and denoise the image while preserving defect information. Additionally, MO imaging for butt joint laser welding defects is researched. For the acquired MO images of welding cracks, pits, incomplete fusions, burn-outs, and weld bumps, the MO image processing method that includes median filtering, histogram equalization, and Wiener filtering was used, which could eliminate the noise in an image, enhance its contrast, and highlight the weld defect features. The experimental results show that the proposed image processing method can eliminate most of the noise while retaining the weld defect features, and the contrast between the welding defect area and the normal area is greatly improved. The denoising effect using the Natural Image Quality Evaluator (NIQE) and the Blind Image Quality Index (BIQI) has been evaluated, further demonstrating the effectiveness of the proposed method. The differences among weld defects could be obtained by analyzing the gray values of the weld defect MO images, which reflect the weld defect information. The MO imaging method can be used to investigate the magnetic distribution characteristics of welding defects, and its effectiveness has been verified by detecting various butt joint laser welding weldments.