Thermal Imaging System Research Articles

Compensation of temperature effects on imaging quality of thermal imaging objectives using deep learning techniques

Thermal imaging objectives are made from infrared materials with large thermal expansion coefficients, such as Ge, Si, and ZnSe. When the temperature changes, it leads to variations in the refractive index, curvature radius, and thickness of the lens, causing defocus shifts that degrade the image quality of the thermal imaging system. In this paper, we propose a novel method to compensate for the effects of temperature variations on the quality of thermal imaging objectives by using deep learning techniques. The temperature variations are measured using a thermal sensor. Subsequently, a U-Net network is employed to mitigate the impact of temperature on the imaging quality of the thermal imaging objectives without requiring any optical displacement or replacement of the lens. Simulation results show that the proposed method performs the effectively compensation for the influence of temperature changes on thermal imaging objective over a wide temperature range from -5 °C to 50 °C.

Multifunctional Thermal Imaging Complex for Medical Diagnostics

Introduction. Recent developments in the field of thermal imaging technology and the accumulated experience in instrumental analysis of thermal processes in the patient’s body indicate the prospects of developing new medical thermal imaging systems (thermal imagers).Aim. Creation of a low-budget domestic thermal imaging system for medical diagnostics with expanded functionality.Materials and methods. The distribution of temperature over the surface of the human body depends on its internal state and external environment. For each person, this distribution has its own physiological characteristics, the study and interpretation of which can be significant for diagnosing specific pathologies or assessing the general (psychophysical) state of the patient. Analysis of temperature fields on the surface of the human body makes it possible to diagnose various pathological processes manifested in the form of local temperature changes in individual areas. The proposed method for thermal diagnostics involves measuring the temperature at each point of such an area simultaneously (statically) or over a period of time (dynamically). In comparison with conventional approaches, two thermal imaging sensors are used for this purpose. These include a small-sized matrix (thus being low cost) and a point sensor. Such a combination of sensors provides for the necessary discretization of the temperature field image of sufficiently large areas of the surface of the human body, when shooting from a distance significantly reduced compared to the conventional approach.Results. A conceptual electrical circuit and a layout of a modern thermal imaging system are developed. The advantages of the proposed thermal imager over similar devices are assessed. A method for recording and analyzing temperature in a certain point and temperature fields of both separate areas and large areas on the surface of the human body is proposed and tested.Conclusion. The conducted testing of a material model of the proposed thermal imaging system using the facilities of large medical institutions in St. Petersburg showed its wide functionality along with simplicity and convenience.

Synthetic thermal image generation and processing for close proximity operations

The new scenarios foreseen in forthcoming space missions have increased interest towards optical-based relative navigation techniques, which have demonstrated efficacy in a variety of operational conditions. Although object detection methods have predominantly been used within the visible spectrum, optical payloads struggle in weak lighting conditions and are susceptible to overexposure. Consequently, thermal imaging systems are being investigated as a potential solution, as their integration into the current systems would greatly extend future mission capabilities. This study seeks to fill the gap in literature by assessing the performance of state-of-the-art object detection algorithms with images captured in the thermal spectrum. Given the scarcity of readily available thermal infrared (TIR) images captured in orbit, a novel rendering pipeline is implemented to generate physically accurate thermal images relevant to close-proximity scenarios. These synthetic representations feature a simplified target spacecraft against Earth and deep space backgrounds, including variations in illumination conditions, material properties, relative state, and scale. To ensure realistic outputs, the radiative field of the Earth is modelled based on satellite measurements collected in the cloud and Earth radiant energy system (CERES) database. To enrich the fidelity of the outputs, a thermal sensor model and the corresponding noise levels are introduced in the pipeline. The generated images are then used to test the performance of traditional object detection algorithms in discerning the region of interest (ROI) under different orbital scenarios. The results demonstrate the effectiveness of the selected methodologies in mitigating the influence of the Earth in the ROI extraction process, while also revealing a performance degradation due to the presence of multi-material targets.

Design of ultra-broadband long-wave to ultra-long-wave infrared absorber based on machine learning

Abstract long-wave infrared (LWIR) and ultra-long-wave infrared (ULWIR) radiation find extensive utility in atmospheric monitoring, night reconnaissance, deep space exploration, and various other fields. However, achieving ultra-wideband absorption within these spectral ranges has persistently posed a significant challenge for researchers. Metasurfaces have attracted great interest due to their ability to manipulate electromagnetic (EM) waves with unprecedented precision and efficiency. In this paper, a metasurface absorber with a simple structure is proposed. Combined with machine learning (ML) of Random Forest (RF) algorithm for design optimization, the absorber achieves ultra-wideband perfect absorption of 42.54 μm within the LWIR-ULWIR band. Furthermore, the stability and accuracy of the RF algorithm in absorber design are also evaluated and compared with other classical ML algorithms. The successful realization of this work can offer the advancement of thermal imaging, sensing, and communication systems operating within the LWIR and ULWIR spectra.

Optimization study on the compact thermal neutron imaging system based on portable D-T neutron generator

The distribution of lithium in lithium-ion batteries is critical to their performance and lifespan. Studies have shown that thermal neutron imaging can effectively perform non-destructive testing of this distribution. However, most current thermal neutron imaging systems rely on expensive, bulky, and immobile nuclear reactors or spallation neutron sources, significantly limiting their application scenarios. Therefore, this study adopts a portable D-T neutron generator as the neutron source and uses Monte Carlo simulation methods to optimize the design of key components in the thermal neutron imaging system. Simulation results indicate that the optimized thermal neutron imaging system achieves a thermal neutron flux of 8.36 × 104 n·cm−2·s−1 and a thermal neutron content (TNC) of 14.4% at a collimation ratio of 50. Within an imaging range of Φ100 mm, the non-uniformity of the thermal neutron fluence is approximately 2.07%. Simulation imaging results of lithium and iron step wedges demonstrate that the compact thermal neutron imaging system designed in this study can effectively distinguish between iron and lithium of the same thickness. This finding provides an important reference for subsequent detection of lithium distribution within lithium-ion batteries.

Methods to mitigate the impact of work environment on the measurement of building surface temperature by unmanned aerial vehicle onboard thermal imaging system

With the widespread adoption of UAVs and compact infrared cameras, UAV infrared remote sensing technology has extensive applications in qualitative research fields such as finding thermal bridges and air tightness detection in construction projects, but less in quantitative research, mainly due to its large temperature measurement error. When measuring temperature with UAV-mounted thermal imaging cameras, factors such as operational wind conditions and operation methods, in addition to the inherent accuracy of the instrument, can introduce measurement errors. To investigate the impact of operational wind conditions on temperature measurements by UAV-mounted thermal imaging cameras, both field and laboratory experiments were conducted to analyze the error characteristics caused by wind conditions. Devices were designed to mitigate the impact of wind on temperature measurements, and their effectiveness was validated through experiments. Based on these findings, methods were developed for reducing the influence of wind conditions on temperature measurements in UAV operations. The results indicate that wind condition errors primarily affect the stability of temperature data. Under the influence of wind conditions, the temperature data recorded by thermal infrared cameras exhibit periodic and significant fluctuations, with a temperature difference of approximately 4.7 °C between the highest and lowest points within a single cycle, constituting the main source of measurement error. However, by following the prescribed measurement procedures, the temperature measurement error can be reduced by 32 %, from ±5 °C to ±1.6 °C, approaching the accuracy of tripod-mounted thermal imaging cameras. This establishes a foundation for quantitative research in low-altitude infrared remote sensing.

Enhanced SWIR Absorption in PMMA‐Coated TiN‐Based Metamaterial

AbstractAchieving highly efficient absorption in the short wavelength infrared (SWIR) spectral region is crucial for advancing numerous technologies. SWIR absorption enhances energy harvesting in solar cells, improves the sensitivity of infrared sensors, and enables precise control of thermal radiative properties in thermal emission devices. These advancements are essential for applications such as thermal imaging, night vision, and thermophotovoltaic systems. The study presents a simple, cost‐effective design of a polymer‐coated multilayer perfect absorber (MPA) metamaterial optimized for the SWIR spectral region. The proposed MPA structure is composed of four distinct layers: a top layer made of polymethyl methacrylate (PMMA) polymer, a second layer featuring arrays of circular titanium nitride (TiN) disks, a dielectric middle layer consisting of a pure zinc oxide (ZnO) film, and a bottom metallic layer formed by a thin film of TiN. The results demonstrate that the MPA achieves near‐perfect absorption, with a remarkable 99% efficiency centered at a wavelength of 1.3 µm. The absorption properties are further investigated with respect to various structural parameters to ensure better performance of the absorber. These absorbers hold great promise for applications in energy absorption, infrared sensing, thermal emission, and related fields.

An efficient face and mask detection by embedded device via thermal imaging camera

Abstract Since 87.9% of COVID-19 patients develop a fever symptom, the mandatory wearing of masks, temperature screening, and isolation of individuals with fever are the most import methods for community epidemic prevention. This study develops a thermal imaging thermometer embedded system with face, face mask, and temperature detection capabilities using low-end CPU devices. The hardware includes a Raspberry Pi 4, FLIR Lepton 3.5 LWIR Micro Thermal Camera Module, display screen, and alarm. The system is characterized by its low cost and lightweight design, and it only needs to detect necessary areas around the face area during face mask and temperature detection, effectively avoiding external interference and further enhancing the effectiveness of epidemic prevention. The face detection model is based on the YOLO-Fastest architecture and has been improved, referred to as Pi-fast. In terms of performance on the training and testing sets, our proposed Pi-fast achieves Mean Average Precision (mAP) values of 99.64% and 93.18%, F1 score values of 0.94 and 0.87, and Intersection over Union (IoU)values of 78.07% and 65.98%, respectively. Regarding system performance, the frame rate of FLIR Lepton 3.5 Thermal Camera Module hardware specifications is 8.7 FPS. When using YOLO-Fastest and proposed Pi-fast model for detection on the proposed thermal imaging system, the frame rates are 6.58 FPS and 7.32FPS, respectively. The proposed Pi-fast model outperforms YOLO-Fastest about 0.74 FPS (11.2%). The system can detect objects at a distance of about 7 meters from the lens in thermographic camera with a resolution of only 160×120 pixels, making it suitable for multi-person screening and improving the screening process’s efficiency. The system can effectively perform temperature screening and face mask detection, making it suitable for office buildings, schools, medical clinics, and other public areas. During the severe epidemic period, the proposed thermal imaging embedded system that was actually used for epidemic prevention work at I-Shou University, improving screening efficiency and reducing labour costs.

Modernization of small-sized drying plant for searching optimal modes with thermovision control

The climatic features of the Murmansk region (low average annual temperature; insignificant number of sunny days) do not contribute to the effective use of natural methods of drying aquatic organisms and determine the use of artificial dehydration methods using appropriate equipment. The capabilities of ship fish processing complexes are limited; highly productive coastal fish processing plants are required to supply the population with fish products. The development and testing of new drying modes has been carried out using a small-sized drying unit UPOR-M2. In the course of the study, a drying unit modernization project has been developed, equipment has been selected (thermal imaging system; damper and fan control modules), the system has been modernized and a number of experiments has been conducted to determine the effectiveness of the work performed. The modernization of the drying unit has made it possible to increase the accuracy of the data obtained by 25 % (temperature), 20 % (relative humidity), 18 % (weight), to implement remote control of the processes and to ensure reliable operation of the drying system. Further research of different drying modes for aquatic organisms should include upgrading the air supply control system and improving communication equipment.

Detection of unmanned aerial vehicles using thermal imaging technology

The detection of UAVs can be carried out using their information features in various ranges: acoustic, radio, optical. Acoustic (ultrasonic) sensors, radar, radio frequency sensors, laser locators, electron-optical / IR sensors (direction finders) are used for their registration. This article will consider the possibilities of detecting UAVs using thermal imagers, pay attention to the characteristics of the resolution of thermal imaging systems, the signal-to-noise ratio, the possibilities of polarization processing of recorded thermal radiation, and the results of some foreign studies on the detection (recognition) of UAVs using thermal imaging cameras.

From sunburn detection to optimal cooling: A review of recent applications of thermal imaging to improve preharvest and postharvest handling of fruit and vegetables

Fruit and vegetables (FV) are susceptible to adverse physiological and biochemical changes due to temperature abuse during preharvest and postharvest handling. Rapid, reliable, and non-invasive technologies such as infrared (IR) cameras are new, cost-effective tools that can record temperature profiles of FV in real-time. In this article, the basic principles of thermal imaging (TI) technology and its recent applications in preharvest and postharvest handling of FV as a temperature monitoring tool are concisely highlighted. Detection of preharvest and postharvest quality of FV (wetness, immaturity, bruise, etc.) based on thermal images are also summarized. Additionally, different modeling and simulation methods, including statistical modeling, computer simulation, machine learning (MLR), and deep learning (DLR), are briefly introduced into the preharvest and postharvest handling of FV, and applications of TI technique as a temperature validation tool in these methods are also discussed. This review article summarized that non-contact TI technique exhibits great potential to monitor temperature profiles on the field and during the postharvest handling of FV in real-time. Quality attributes, such as sunburn, immaturity, and bruises in FV, can be detected by using thermal images. Therefore, yields can be increased in the field, and food losses reduced during the postharvest handling of FV using TI technology. Furthermore, TI technology is an effective validation tool for simulation studies of foods. Future research should focus on developing low-cost TI systems and integrating multiple imaging techniques to facilitate the decision-making process in the FV industry.

StarDICE II: Calibration of an Uncooled Infrared Thermal Camera for Atmospheric Gray Extinction Characterization.

The StarDICE experiment strives to establish an instrumental metrology chain with a targeted accuracy of 1 mmag in griz bandpasses to meet the calibration requirements of next-generation cosmological surveys. Atmospheric transmission is a significant source of systematic uncertainty. We propose a solution relying on an uncooled infrared thermal camera to evaluate gray extinction variations. However, achieving accurate measurements with thermal imaging systems necessitates prior calibration due to temperature-induced effects, compromising their spatial and temporal precision. Moreover, these systems cannot provide scene radiance in physical units by default. This study introduces a new calibration process utilizing a tailored forward modeling approach. The method incorporates sensor, housing, flat-field support, and ambient temperatures, along with raw digital response, as input data. Experimental measurements were conducted inside a climatic chamber, with a FLIR Tau2 camera imaging a thermoregulated blackbody source. The results demonstrate the calibration effectiveness, achieving precise radiance measurements with a temporal pixel dispersion of 0.09 W m-2 sr-1 and residual spatial noise of 0.03 W m-2 sr-1. We emphasize that the accuracy of scene radiance retrieval can be systematically affected by the camera's close thermal environment, especially when the ambient temperature exceeds that of the scene.

EXPERIMENT TO EVALUATE THE EFFECTIVENESS OF THERMAL REFLECTIVE COATING APPLIED TO CONSTRUCTIONS

Constructions are usually made of materials like reinforced concrete and steel. In hot weather conditions, the reflected thermal radiance in the daytime and the emitted thermal radiance of human activities inside the structure are significantly higher than in the background. Thanks to the temperature difference between them, thermal imaging systems can easily detect or recognize the target. In order to reduce the thermal contrast, it is vital to research and apply the specific coating that can be used to cover the construction and the outside door. On the other hand, there is yet to be a method to evaluate the effectiveness of thermal reflective coating for constructions and mobile targets in Vietnam. Hence, the article has focused on performing the experimental measurement of the thermal difference between two coating materials. The result will also be applied to evaluate and select the effective thermal paint for the protected system.

Single 5-centimeter-aperture metalens enabled intelligent lightweight mid-infrared thermographic camera.

Existing mid-infrared thermographic cameras rely on a stack of refractive lenses, resulting in bulky and heavy imaging systems that restrict their broader utility. Here, we demonstrate a lightweight metalens-based thermographic camera (MTC) enabled by a single 0.5-mm-thick, 3.7-g-weight, flat, and mass-producible metalens. The large aperture size (5 cm) of our metalens, when combined with an uncooled focal plane array, enables thermal imaging at distances of tens of meters. By computationally removing the veiling glare, our MTC realizes the temperature mapping with an inaccuracy of less than ±0.7% within the range of 35° to 700°C and shows exceptional environmental adaptability. Furthermore, by using intelligent algorithms and spectral filtering, our uncooled MTC enables visualization and quantification of the SF6 gas leakage at a long distance of 5 m, with a remarkable minimum detectable leak rate of 0.2 sccm. Our work opens the door to the lightweight and multifunctional intelligent thermal imaging systems.

Mini-infrared thermal imaging system image denoising with multi-head feature fusion and detail enhancement network

The mini-infrared thermal imaging system (MITIS) has been widely applied in outdoor photography, military observation, medical auxiliary diagnosis, and so on. However, due to the limitations of infrared sensor performance, imaging system size, and environment interference, the infrared images captured by the current MITIS generally encounter noise interference and low contrast issues. To solve these issues, we propose a multi-head feature fusion and detail enhancement network, namely MdNet, for complex features (texture and edges) retaining and denoising of infrared images obtained from the MITIS. Specifically, MdNet consists of a multi-head feature fusion block (MFFB), detail enhancement block (DEB), and reconstruction convolution. The MFFB is designed to extract and fuse multi-scale feature information, including multi-scale and feature fusion blocks. The DEB is developed to enhance the noise feature and reduce the loss of detailed information by three multi-level local feature attention blocks and residual learning. Quantitative and qualitative experimental results demonstrate that the MdNet achieves competitive performance in removing synthetic and natural noises compared with other state-of-the-art infrared image denoising methods and provides an effective solution for MITIS image denoising.

ALGORITHM FOR DETERMINING THE PROBABILITY OF TARGET DETECTION AND RECOGNITION BY AN AVIATION THERMAL VISION SYSTEM IN NOISE CONDITIONS

Thermal imaging equipment is widely used in modern military operations due to its ability to observe targets even in adverse weather conditions continuously. Thermal imaging surveillance systems (TISS) are primarily designed to detect, recognize, and identify targets. Despite the progress, existing methods do not allow quickly calculating the probability of detecting, recogniz- ing, or identifying a target at user-defined distances from the TISS to the target. The article aims to develop a more advanced method and algorithm for calculating the probability of detection, recognition, and identification of a target by a noise-limited thermal imaging surveillance system. A more advanced (convenient) algorithm and method for calculating the probability of detection, recognition, and identi- fication of an object (target) by a thermal imaging surveillance system at a given range have been developed. This is based on the minimum resolution temperature difference, Johnson’s criterion, as well as the TTPF and TRTPF functions. The proposed algorithm makes it relatively easy to calculate the probability of detecting, recognizing, and identifying a target by a thermal imaging system, which is limited by system noise.

Design and evaluation of an AR-based thermal imaging system for planning reconstructive surgeries.

Thermal imaging can be used for the non-invasive detection of blood vessels of the skin. However, mapping the results to the patient currently lacks user-friendliness. Augmented reality may provide a useful tool to superimpose thermal information on the patient. A system to support planning in reconstructive surgery using a thermal camera was designed. The obtained information was superimposed on the physical object using a Microsoft HoloLens. An RGB, depth, and thermal camera were combined to capture a scene of different modalities and reconstruct a virtual scene in real time. To register the different cameras and the AR device, an active calibration target was developed and evaluated. A Vuforia marker was used to register the hologram in the virtual space. The accuracy of the projected hologram was evaluated in a laboratory setting with participants by measuring the error between the physical object and the hologram. The AR-based system was evaluated by 21 participants in a laboratory setting. The mean projection error is 10.3 ± 9.4mm. The system is able to stream a three-dimensional scene with augmented thermal information in real time at 5 frames per second. The active calibration target can be used independently of the environment. The calibration target provides an easy-to-use method for the registration of cameras capturing the visible to long-infrared spectral range. The inside-out tracking of the HoloLens in combination with a Vuforia marker is not accurate enough for the intended clinical use.

Research on high-temperature mechanical behaviour of carbon/phenolic composites under ultra-high heating rates

Fibre-reinforced resin matrix composites exhibit excellent resistance to ablation, thermal insulation and mechanical properties at high temperatures, making them widely used in aerospace engineering. Needle-punched reinforced carbon/phenolic composites are the most commonly used structural material for solid rocket engine nozzles. However, during the working process of the engine, the mechanical behaviour under ultra-high heating rates is difficult to characterise due to extreme loads. Therefore, in this study, specimens were carbonised before testing, and an electric heating testing machine was combined with an infrared thermal imager and digital image correlation system to characterise the mechanical behaviour of carbon/phenolic at ultra-high heating rates. The results indicate that at a heating rate of 200 °C/s, the compressive modulus increases linearly with temperature, reaching 63.1 GPa at 1400 °C. The maximum compressive strength is 61.9 MPa at 1400 °C, while the minimum is 27.4 MPa at 600 °C. Referring to the high-temperature damage constitutive model, a pyrolysis damage high-temperature strengthening constitutive model was constructed, accurately describing the in-plane compression mechanical behaviour of carbon/phenolic composites.

Drones in Human Aid and Disaster Relief using Thermal Imaging Technology

Disaster Rescue is a very dangerous job to carry out the process without risking more lives in the process as the environment plays a major role. To find the people, requires the system to identify the movement and recognize the human by using the Thermal imaging system in the place of low-temperature areas such as forests, flood areas, Dark places, and so on. Not just thermal but also RGB cameras, stereo cameras and Infrared cameras are used to gain the surrounding situational inference, and by using the intelligent system to identify humans by their body shape from a distance and using the CV pipeline the movement can be identified and confirmed for victims condition in the disaster area. By mounting the optical systems in the Drone, the process makes it safe for the people, and a quick rescue response can be implemented. Using the heavy payload carrying Drone to supply emergency kit and food supplies to the Victims on time. It as well can also act as an emergency communication Network establishment with the GPS to gain the location of a specific point of Disaster regions.

Effects of main components on energy output characteristics of thermobaric explosive — A case study of typical formulations

As a kind of high-efficiency explosive with compound destructive capability, the energy output law of thermobaric explosives has been receiving great attention. In order to investigate the effects of main components on the explosive characteristics of thermobaric explosives, various high explosives and oxidants were selected to formulate five different types of thermobaric explosive. Then they were tested in both open space and closed space respectively. Pressure measurement system, high-speed camera, infrared thermal imager and multispectral temperature measurement system were used for pressure, temperature and fireball recording. The effects of different components on the explosive characteristics of thermobaric explosive were analyzed. The results showed that in open space, the overpressure is dominated by the high explosives content in the formulation. The addition of the oxidants will decrease the explosion overpressure but will increase the duration and overall brightness of the fireball. While in closed space, the quasi-static pressure formed after the explosion is positively correlated with the temperature and gas production. In addition, it was found that the differences in shell constraints can also alter the afterburning reaction of thermobaric explosives, thus affecting their energy output characteristics. PVC shell constraint obviously increases the overpressure and makes the fireball burn more violently.