Search Results (103)

This paper is about the design and fabrication of infrared lenses, which are the core components of thermal imaging cameras to be mounted on vehicles. To produce an athermalized optical system, chalcogenide glass (As₄₀Se₆₀) with a lower thermo-optic coefficient (dn/dT) than germanium was adopted as a lens material, and each lens was designed so that defocus occurs in opposite directions depending on temperature. The designed lens was fabricated using a compression molding method, and the molded lenses showed less than 1.5 μm of form error (PV) using a mold iteration process. Through evaluations of MTF and thermal images obtained from the lens module, it was judged that this optical design process is obtainable. Full article

Climate concerns are driving the automotive industry to adopt advanced manufacturing technologies that aim to improve energy efficiency and reduce vehicle weight. In this context, lightweight structural materials such as aluminium alloys have gained significant attention due to their favorable strength-to-weight ratio. Laser welding plays a crucial role in assembling such materials, offering high flexibility and fast joining capabilities for thin aluminium sheets. However, welding these materials presents specific challenges, particularly in controlling heat input to minimize distortions and ensure consistent weld quality. As a result, numerical simulations based on the Finite Element Method (FEM) are essential for predicting weld-induced phenomena and optimizing process performance. This study investigates welding-induced distortions in laser butt welding of 1.5 mm-thick Al 6061 samples through FEM simulations performed in the SYSWELD 2024.0 environment. The methodology provided by the software is based on the Moving Heat Source (MHS) model, which simulates the physical movement of the heat source and typically requires extensive calibration through destructive metallographic testing. This transient approach enables the detailed prediction of thermal, metallurgical, and mechanical behavior, but it is computationally demanding. To improve efficiency, the Imposed Thermal Cycle (ITC) model is often used. In this technique, a thermal cycle, extracted from an MHS simulation or experimental data, is imposed on predefined subregions of the model, allowing only mechanical behavior to be simulated while reducing computation time. To avoid MHS-based calibration, this work proposes using thermal cycles acquired in-line during welding via infrared thermography as direct input for the ITC model. The method was validated experimentally and numerically, showing good agreement in the prediction of distortions and a significant reduction in workflow time. The distortion values from simulations differ from the real experiment by less than 0.3%. Our method exhibits a slight decrease in performance, resulting in an increase in estimation error of 0.03% compared to classic approaches, but more than 85% saving in computation time. The integration of real process data into the simulation enables a virtual representation of the process, supporting future developments toward Digital Twin applications. Full article

Meat provides a favorable environment for the growth of microorganisms, so increasingly advanced methods are being sought to ensure the rapid detection of their presence and determine the degree of contamination. These measures are intended to ensure consumer health and reduce food losses. The aim of this study was to evaluate the suitability of a thermal imaging camera and FT-IR spectrophotometry for microbiological quality control of poultry meat. This study used poultry meat fillets packaged in a modified atmosphere and stored at 4 °C for 10 days. During the successive days of storage, the following were determined: the total number of microorganisms, the count of Enterobacteriaceae, the temperature of the samples tested using a thermal imaging camera, and the spectral data contained in the spectra recorded by the FT technique of IR spectroscopy. The results were analyzed using Tukey’s test in the STATISTICA 13.3 statistical program with an assumed significance level of α ≤ 0.05. Spectral data obtained by the FT-IR method were subjected to interpretation using the T.Q. Analyst 8 program. This study found that the number of microorganisms increased between the 2nd and 10th days of storage for the poultry meat samples of four log CFU/g, leading to a temperature increase of 2.61 °C, and also, the intensities and frequencies of selected IR bands generated by vibrations of various groups of atoms changed, including functional groups present in the compounds contained in the tested samples. It was shown that modern techniques such as FT-IR spectroscopy and thermal imaging cameras have significant potential applications in the food industry for assessing the microbiological quality of food. Full article

Overload detection is crucial in nuclear fusion experiments to prevent damage to plasma-facing components (PFCs) and ensure the safe operation of the reactor. At Wendelstein 7-X (W7-X), real-time monitoring and prediction of thermal events are essential for maintaining the integrity of PFCs. This paper proposes a machine learning approach for developing a real-time overload detector, trained and tested on OP1.2a experimental data. The analysis showed that Self-Organizing Maps (SOMs) are efficient in detecting the overload risk starting from a set of plasma parameters that describe the magnetic configuration, the energy behavior, and the power balance. This study aims to thoroughly evaluate the capabilities of the SOM in recognizing overload risk levels, defined by quantizing the maximum criticality across different IR cameras. The goal is to enable detailed monitoring for overload prevention while maintaining high-performance plasmas and sustaining long pulse operations. The SOM proves to be a highly effective overload risk detector. It correctly identifies the assigned overload risk level in 87.52% of the samples. The most frequent error in the test set, occurring in 10.46% of cases, involves assigning a risk level to each sample adjacent to the target one. The analysis of the results highlights the advantages and drawbacks of criticality discretization and opens new solutions to improve the SOM potential in this field. Full article

Early detection of plant water status is crucial for efficient crop management. In this research, proximal sensing tools (i.e., hyperspectral imaging HSI and thermal IR camera) were used to monitor changes in spectral and thermal profiles of a citrus orchard in Sicily (Italy), managed under five irrigation systems. The irrigation systems differ in the amount of water distribution and allow four different strategies of deficit irrigation to be obtained. The physiological traits, stem water potential, net photosynthetic rate, stomatal conductance and the amount of leaf chlorophyll were measured over the crop’s growing season for each treatment. The proximal sensing data consisted of thermal and hyperspectral imagery acquired in June–September during the irrigation seasons 2023–2024 and 2024–2025. Significant variation in physiological traits was observed in relation to the different irrigation strategies, highlighting the highest plant water stress in July, in particular for the partial root-zone drying irrigation system. The water-use efficiency (WUE) values in subsurface drip irrigation were similar to the moderate deficit irrigation treatment and more efficient (up to 50%) as compared to control. Proximal sensing measures confirmed a different plant water status in relation to the five different irrigations strategies. Moreover, four spectral indices (Normalized Difference Vegetation Index NDVI; Water Index WI; Photochemical Reflectance Index PRI; Transformed Chlorophyll Absorption Ratio Index TCARI), calculated from HSI spectra, highlighted strong correlations with physiological traits, especially with stem water potential and the amount of leaf chlorophyll (coefficient of correlation ranged between −0.4 and −0.5). This study demonstrated the effectiveness of using proximal sensing tools in precision agriculture and ecosystem monitoring, helping to ensure optimal plant health and water use efficiency. Full article

We present a thoughtfully curated collection of laboratory demonstrations, simulations, and straightforward experiments that explore the fundamental processes underlying greenhouse effect (GHE), climate, atmospheric physics, and Earth’s energy balance. The objective is to connect theory and practice in climate science education and address common student misconceptions. The activities are structured to guide students in constructing simple models of Earth’s radiative equilibrium. Experimental activities cover essential concepts such as the electromagnetic spectrum, radiation–matter interaction, thermal radiation, and energy balance. Physical experiments include visualizing the spectrum with a homemade spectroscope and an infrared (IR) thermal camera, studying absorption and selective transparency when light interacts with different materials, measuring the power emitted by a heated filament, and using simple models, such as black and white discs or a leaking bucket, to understand radiative equilibrium and steady states. This sequence was piloted in a physics education laboratory class with 85 university students enrolled in mathematics and physics courses for future teachers. To assess comprehension improvement, pre- and post-tests involving the production of drawings and explanations related to the GHE were administered to all students. These activities also aim to promote critical thinking and counter climate misinformation and denial. The results showed a significant improvement in understanding fundamental GHE concepts. Additionally, a small subset of students was interviewed to explore the psychological and social dimensions related to the climate crisis. Full article

The reflectance (R) of linear and circular micro-gratings on c-plane sapphire Al₂O₃ ablated by a femtosecond (fs) laser were spectrally characterised for thermal emission $\propto (1-R)$ in the mid-to-far infrared (IR) spectral range. An IR camera was used to determine the blackbody radiation temperature from laser-patterned regions, which showed (3–6)% larger emissivity dependent on the grating pattern. The azimuthal emission curve closely followed the Lambertian angular profile $\propto cos{\theta }_a$ at the 7.5–13 μm emission band. The back-side ablation method on transparent substrates was employed to prevent debris formation during energy deposition as it applies a forward pressure of >0.3 GPa to the debris and molten skin layer. The back-side ablation maximises energy deposition at the exit interface where the transition occurs from the high-to-low refractive index. Phononic absorption in the Reststrahlen region 20–30 μm can be tailored with the fs laser inscription of sensor structures/gratings. Full article

This study investigates the capabilities of accelerator-based Focused Ion Thermal Analysis (FITA), a remote nondestructive method developed for characterizing thermal properties using a proton beam as a localized heat source. Employing infrared (IR) imaging, FITA captures the evolution of temperature in material samples after the beam is deactivated, enabling precise extraction of thermal properties. However, the performance of FITA is inherently influenced by the IR camera’s resolution and frame rate, which imposes constraints on the types of materials that can be effectively analyzed. Here, a comprehensive series of finite element analysis (FEA) simulations were performed to evaluate the applicability of FITA for a wide range of materials. These simulations assess how variations in IR camera specifications impact the effectiveness of FITA in analyzing different materials. Our findings show that the current method can characterize a wide range of materials, including the majority of nuclear materials typically used in the nuclear industry. Full article

Deep learning and image processing technology continue to evolve, with YOLO models widely used for real-time object recognition. These YOLO models offer both blazing fast processing and high precision, making them super popular in fields like self-driving cars, security cameras, and medical support. Most YOLO models are optimized for RGB images, which creates some limitations. While RGB images are super sensitive to lighting conditions, infrared (IR) images using thermal data can detect objects consistently, even in low-light settings. However, infrared images present unique challenges like low resolution, tiny object sizes, and high amounts of noise, which makes direct application tricky in regard to the current YOLO models available. This situation requires the development of object detection models designed specifically for thermal images, especially for real-time recognition. Given the GPU and memory constraints in edge device environments, designing a lightweight model that maintains a high speed is crucial. Our research focused on training a YOLOv8 model using infrared image data to recognize humans. We proposed a YOLOv8s model that had unnecessary layers removed, which was better suited to infrared images and significantly reduced the weight of the model. We also integrated an improved Global Attention Mechanism (GAM) module to boost IR image precision and applied depth-wise convolution filtering to maintain the processing speed. The proposed model achieved a 2% precision improvement, 75% parameter reduction, and 12.8% processing speed increase, compared to the original YOLOv8s model. This method can be effectively used in thermal imaging applications like night surveillance cameras, cameras used in bad weather, and smart ventilation systems, particularly in environments requiring real-time processing with limited computational resources. Full article

To date, there is little publicly available scientific data on unidentified aerial phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project is designing, building, and commissioning a multi-modal, multi-spectral ground-based observatory to continuously monitor the sky and collect data for UAP studies via a rigorous long-term aerial census of all aerial phenomena, including natural and human-made. One of the key instruments is an all-sky infrared camera array using eight uncooled long-wave-infrared FLIR Boson 640 cameras. In addition to performing intrinsic and thermal calibrations, we implement a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance–Broadcast (ADS-B) data that we collect synchronously on site. Using a You Only Look Once (YOLO) machine learning model for object detection and the Simple Online and Realtime Tracking (SORT) algorithm for trajectory reconstruction, we establish a first baseline for the performance of the system over five months of field operation. Using an automatically generated real-world dataset derived from ADS-B data, a dataset of synthetic 3D trajectories, and a hand-labeled real-world dataset, we find an acceptance rate (fraction of in-range airplanes passing through the effective field of view of at least one camera that are recorded) of 41% for ADS-B-equipped aircraft, and a mean frame-by-frame aircraft detection efficiency (fraction of recorded airplanes in individual frames which are successfully detected) of 36%. The detection efficiency is heavily dependent on weather conditions, range, and aircraft size. Approximately 500,000 trajectories of various aerial objects are reconstructed from this five-month commissioning period. These trajectories are analyzed with a toy outlier search focused on the large sinuosity of apparent 2D reconstructed object trajectories. About 16% of the trajectories are flagged as outliers and manually examined in the IR images. From these ∼80,000 outliers and 144 trajectories remain ambiguous, which are likely mundane objects but cannot be further elucidated at this stage of development without information about distance and kinematics or other sensor modalities. We demonstrate the application of a likelihood-based statistical test to evaluate the significance of this toy outlier analysis. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers for the five-month interval at a 95% confidence level. This test is applicable to all of our future outlier searches. Full article

Infrared array sensor-based fall detection and activity recognition systems have gained momentum as promising solutions for enhancing healthcare monitoring and safety in various environments. Unlike camera-based systems, which can be privacy-intrusive, IR array sensors offer a non-invasive, reliable approach for fall detection and activity recognition while preserving privacy. This work proposes a novel method to distinguish between normal motion and fall incidents by analyzing thermal patterns captured by infrared array sensors. Data were collected from two subjects who performed a range of activities of daily living, including sitting, standing, walking, and falling. Data for each state were collected over multiple trials and extended periods to ensure robustness and variability in the measurements. The collected thermal data were compared with multiple statistical distributions using Earth Mover’s Distance. Experimental results showed that normal activities exhibited low EMD values with Beta and Normal distributions, suggesting that these distributions closely matched the thermal patterns associated with regular movements. Conversely, fall events exhibited high EMD values, indicating greater variability in thermal signatures. The system was implemented using a Raspberry Pi-based stand-alone device that provides a cost-effective solution without the need for additional computational devices. This study demonstrates the effectiveness of using IR array sensors for non-invasive, real-time fall detection and activity recognition, which offer significant potential for improving healthcare monitoring and ensuring the safety of fall-prone individuals. Full article

Multi-modal systems extract information about the environment using specialized sensors that are optimized based on the wavelength of the phenomenology and material interactions. To maximize the entropy, complementary systems operating in regions of non-overlapping wavelengths are optimal. VIS-IR (Visible-Infrared) systems have been at the forefront of multi-modal fusion research and are used extensively to represent information in all-day all-weather applications. Prior to image fusion, the image pairs have to be properly registered and mapped to a common resolution palette. However, due to differences in the device physics of image capture, information from VIS-IR sensors cannot be directly correlated, which is a major bottleneck for this area of research. In the absence of camera metadata, image registration is performed manually, which is not practical for large datasets. Most of the work published in this area assumes calibrated sensors and the availability of camera metadata providing registered image pairs, which limits the generalization capability of these systems. In this work, we propose a novel end-to-end pipeline termed DeepFusion for image registration and fusion. Firstly, we design a recursive crop and scale wavelet spectral decomposition (WSD) algorithm for automatically extracting the patch of visible data representing the thermal information. After data extraction, both the images are registered to a common resolution palette and forwarded to the DNN for image fusion. The fusion performance of the proposed pipeline is compared and quantified with state-of-the-art classical and DNN architectures for open-source and custom datasets demonstrating the efficacy of the pipeline. Furthermore, we also propose a novel keypoint-based metric for quantifying the quality of fused output. Full article

This study investigates the feasibility of using fiber Bragg grating (FBG) sensors for multipoint thermal monitoring of several power semiconductor devices (PSDs), such as insulated gate bipolar transistors (IGBTs), and rectifiers assembled on a common heatsink in a three-phase inverter. A novel approach is proposed to integrate FBG sensors beneath the baseplates of the IGBT modules, avoiding the need for invasive modifications to the device structure. By strategically positioning multiple FBG sensors, accurate temperature profiles of critical components can be obtained. The experimental results demonstrate the effectiveness of the proposed method, with the temperature measurements from FBG sensors closely matching those obtained using thermal infrared (IR) cameras within ±1.1 °C. This research highlights the potential of FBG sensors for reliable and precise thermal management in power electronic systems, contributing to improved performance and reliability. Full article

The integration of thermal imaging data with multimodal large language models (MLLMs) offers promising advancements for enhancing the safety and functionality of autonomous driving systems (ADS) and intelligent transportation systems (ITS). This study investigates the potential of MLLMs, specifically GPT-4 Vision Preview and Gemini 1.0 Pro Vision, for interpreting thermal images for applications in ADS and ITS. Two primary research questions are addressed: the capacity of these models to detect and enumerate objects within thermal images, and to determine whether pairs of image sources represent the same scene. Furthermore, we propose a framework for object detection and classification by integrating infrared (IR) and RGB images of the same scene without requiring localization data. This framework is particularly valuable for enhancing the detection and classification accuracy in environments where both IR and RGB cameras are essential. By employing zero-shot in-context learning for object detection and the chain-of-thought technique for scene discernment, this study demonstrates that MLLMs can recognize objects such as vehicles and individuals with promising results, even in the challenging domain of thermal imaging. The results indicate a high true positive rate for larger objects and moderate success in scene discernment, with a recall of 0.91 and a precision of 0.79 for similar scenes. The integration of IR and RGB images further enhances detection capabilities, achieving an average precision of 0.93 and an average recall of 0.56. This approach leverages the complementary strengths of each modality to compensate for individual limitations. This study highlights the potential of combining advanced AI methodologies with thermal imaging to enhance the accuracy and reliability of ADS, while identifying areas for improvement in model performance. Full article

This paper deals with the experimental procedures of lock-in thermography (LIT) for polyetheretherketone (PEEK), which is used as a lightweight material in various industrial fields. The LIT has limitations due to non-uniform heating by external optic sources and the non-uniformity correction (NUC) of the infrared (IR) camera. It is generating unintended contrast in the IR image in thermal imaging inspection, reducing detection performance. In this study, the non-uniformity effect was primarily improved by producing an equivalent array halogen lamp. Then, we presented absolute temperature compensation (ATC) and temperature ratio compensation (TRC) techniques, which can equalize the thermal contrast of the test samples by compensating for them using reference samples. By applying compensation techniques to data acquired from the test samples, defect detectability improvement was quantitatively presented. In addition, binarization was performed and detection performance was verified by evaluating the roundness of the detected defects. As a result, the contrast of the IR image was greatly improved by applying the compensation technique. In particular, raw data were enhanced by up to 54% using the ATC compensation technique. Additionally, due to improved contrast, the signal-to-noise ratio (SNR) was improved by 7.93%, and the R² value of the linear trend equation exceeded 0.99, demonstrating improved proportionality between the defect condition and SNR. Full article