Infrared Camera Research Articles

Predicting Rock Hardness and Abrasivity Using Hyperspectral Imaging Data and Random Forest Regressor Model

This study aimed to develop predictive models for rock hardness and abrasivity based on hyperspectral imaging data, providing valuable information without interrupting the mining processes. The data collection stage first involved scanning 159 rock samples collected from 6 different blasted rock piles using visible and near-infrared (VNIR) and short-wave infrared (SWIR) sensors. The hardness and abrasivity of the samples were then determined through Leeb rebound hardness (LRH) and Cerchar abrasivity index (CAI) tests, respectively. The data preprocessing involved radiometric correction, background removal, and staking VNIR and SWIR images. An integrated approach based on K-means clustering and the band ratio concept was employed for feature extraction, resulting in 28 band-ratio-based features. Afterward, the random forest regressor (RFR) algorithm was employed to develop predictive models for rock hardness and abrasivity separately. The performance assessment showed that the developed models can estimate rock hardness and abrasivity of unseen data with R2 scores of 0.74 and 0.79, respectively, with the most influential features located mainly within the SWIR region. The results indicate that integrated hyperspectral data and RFR technique have strong potential for practical and efficient rock hardness and abrasivity characterization during mining processes.

Automated image-based fire detection and alarm system using edge computing and cloud-based platform

To tackle the increasing wildfire challenges, this paper presents an automated image-based fire detection and alarm system utilizing edge computing and a cloud-based platform, specifically designed for urban building fire detection. The system captures both RGB and infrared images from thermal cameras and employs existing computer vision techniques to detect fire characteristics such as flames and smoke. By integrating edge computing, the system minimizes latency to enhance the accuracy of fire detection and alarm activation. The cloud platform supports extensive data storage, analysis, and remote monitoring, which can ensure data accessibility and scalable data management. The proposed system descriptions include a detailed system architecture design, data collection, and the selection and application of detection algorithms that leverage both RGB and thermal image data for fire detection. Using the campus building and surrounding risk-prone areas as a testbed, the proposed system demonstrated desired fire detection capabilities and a robust solution to quickly identify and respond to fire incidents within the urban area. The proposed system functionalities can be scaled and adapted to other fire risk-prone areas.

Segmenting hotspots from medical thermal images using Density-based modified FC-Pc FS with spatial information

ABSTRACT Thermal cameras complemented with robust and automated computational intelligence-aided diagnostic systems can be deployed at public gathering points and on smartphones to raise alarms about thriving diseases of their body due to inflammation in a timely manner, thereby strengthening human health systems. In this work, we have proposed two robust Density-based modified Picture Fuzzy Clustering methods with spatial information to segment hotspots from human’s abnormal thermal images. We formulated and solved the objective function by combining P c FS-based clustering method with modified Renyi’s Entropy. We demonstrated the robustness of our methods, DSIFC-Pc FS and DSIMFC-Pc FS, on three publicly available datasets and an artificially created noisy dataset of thermal images. We have obtained a ZC score of 0.933, 0.947 and 0.933 for DB-DMR-IR, DB-FOOT-IR and DB-THY-IR datasets, respectively. Also, our method converged to an optimal partition matrix in minimum iterations. The experimental performance shows that the proposed approaches, DSIFC- P c FS and DSIMFC- P c FS, outperformed other ten related approaches in segmenting the medical thermal images with and without noise, indicating their robustness and efficacy, in terms of various unsupervised evaluation metrics. Also, we statistically analysed their performance in contrast to other methods using the Friedman Test, which advocate their superiority.

Precision livestock farming in the 21st century: Challenges and opportunities for sustainable agriculture

Abstract This study aimed to review the recent development of different technologies in precision livestock farming (PLF), along with their scopes and challenges. PLF is an innovative, contemporary, and fast-expanding approach to agriculture that aims to improve sustainable livestock farming. The growing global population has increased the demand for animal products. To meet this demand, farmers have to increase their production, so without the integration of precision technology, this cannot be achieved. PLF currently employs a variety of technologies. Some of these methods include vision-based solutions, load cells, accelerometers, microphones, thermal cameras, photoelectric sensors, and radio-frequency identification (RFID). Despite the availability of different PLF technologies, their adoption by farmers varies widely on the basis of the cost of investment, ease of operation, availability, and accessibility. These technologies are used to track different activities of livestock farming, such as feeding, drinking, physical behavior, temperature regulation, tracking and identification, estrus detection, disease detection, and milking. PLF contributes significantly to technological advancement, human-animal relationships, environmental sustainability, and increased productivity. It does, however, present a number of obstacles and eventual advantages.

Thermographic Analysis of Green Wall and Green Roof Plant Types under Levels of Water Stress

Urban green infrastructure (UGI) plays a vital role in mitigating climate change risks, including urban development-induced warming. The effective maintenance and monitoring of UGI are essential for detecting early signs of water stress and preventing potential fire hazards. Recent research shows that plants close their stomata under limited soil moisture availability, leading to an increase in leaf temperature. Multi-spectral cameras can detect thermal differentiation during periods of water stress and well-watered conditions. This paper examines the thermography of five characteristic green wall and green roof plant types (Pachysandra terminalis, Lonicera nit. Hohenheimer, Rubus tricolor, Liriope muscari Big Blue, and Hedera algeriensis Bellecour) under different levels of water stress compared to a well-watered reference group measured by thermal cameras. The experiment consists of a (1) pre-test experiment identifying the suitable number of days to create three different levels of water stress, and (2) the main experiment tested the suitability of thermal imaging with a drone to detect water stress in plants across three different dehydration stages. The thermal images were captured analyzed from three different types of green infrastructure. The method was suitable to detect temperature differences between plant types, between levels of water stress, and between GI types. The results show that leaf temperatures were approximately 1–3 °C warmer for water-stressed plants on the green walls, and around 3–6 °C warmer on the green roof compared to reference plants with differences among plant types. These insights are particularly relevant for UGI maintenance strategies and regulations, offering valuable information for sustainable urban planning.

Indirect Thermographic Measurement of the Temperature of a Transistor Die during Pulse Operation

This paper presents aspects related to the indirect thermographic measurement of a C2M0280120D transistor in pulse mode. The tested transistor was made on the basis of silicon carbide and is commonly used in many applications. During the research, the pulse frequency was varied from 1 kHz to 800 kHz. The transistor case temperature was measured using a Flir E50 thermographic camera and a Pt1000 sensor. The transistor die temperature was determined based on the voltage drop on the body diode and the known characteristics between the voltage drop on the diode and the temperature of the die. The research was carried out in accordance with the presented measuring standards and maintaining the described conditions. The differences between the transistor case temperature and the transistor die temperature were also determined based on simulation work performed in Solidworks 2020 SP05. For this purpose, a three-dimensional model of the C2M0280120D transistor was created and the materials used in this model were selected; the methodology for selecting the model parameters is discussed. The largest recorded difference between the case temperature and the junction temperature was 27.3 °C. The use of a thermographic camera allows the transistor’s temperature to be determined without the risk of electric shock. As a result, it will be possible to control the C2M0280120D transistor in such a way so as not to damage it and to optimally select its operating point.

Methods for Assessing the Effectiveness of Modern Counter Unmanned Aircraft Systems

Given the growing threat posed by the widespread availability of unmanned aircraft systems (UASs), which can be utilised for various unlawful activities, the need for a standardised method to evaluate the effectiveness of systems capable of detecting, tracking, and identifying (DTI) these devices has become increasingly urgent. This article draws upon research conducted under the European project COURAGEOUS, where 260 existing drone detection systems were analysed, and a methodology was developed for assessing the suitability of C-UASs in relation to specific threat scenarios. The article provides an overview of the most commonly employed technologies in C-UASs, such as radars, visible light cameras, thermal imaging cameras, laser range finders (lidars), and acoustic sensors. It explores the advantages and limitations of each technology, highlighting their reliance on different physical principles, and also briefly touches upon the legal implications associated with their deployment. The article presents the research framework and provides a structural description, alongside the functional and performance requirements, as well as the defined metrics. Furthermore, the methodology for testing the usability and effectiveness of individual C-UAS technologies in addressing specific threat scenarios is elaborated. Lastly, the article offers a concise list of prospective research directions concerning the analysis and evaluation of these technologies.

Evaluation of aerial thermography for measuring the thermal transmittance (U-value) of a building façade

In efforts to mitigate greenhouse gas emissions in the construction sector, policies promoting energy efficiency improvements in existing buildings are being implemented. Accurately assessing the impact of energy retrofit strategies requires precise determination of thermal envelope performance through energy audits. Aerial thermography, employing unmanned aerial vehicles (UAVs) equipped with infrared thermal cameras (IRT), is an underused method overcoming limitations in energy audits of large buildings or complexes. This approach offers an elevated perspective, providing an overview of the building’s thermal patterns, aiding in the identification of thermal envelope defects. By measuring temperature distribution, even in inaccessible envelope areas, the severity of identified defects can be estimated. Beyond detecting thermal bridges, air leaks, and moisture, aerial thermography is valid for estimating thermal transmittance (U-value) of enclosures. The aim of this research is to evaluate this method to calculate the U-value of the façade of an university building located in a Subtropical Mediterranean climate, during a typical full day in different seasons of the year. Concurrently with aerial thermography, the thermometric method (THM) was applied to compare results and refine the operational conditions during U-value measurement using different statistical indices and uncertainty analysis. The results demonstrated the validation of airborne thermography as a method for U-value calculation under specific meteorological and operational conditions, overcoming the common physical limitations of traditional building audit methods.

Detection of anti-personnel mines of the “leg breakers” type by analyzing thermographic images captured from a drone at different heights

A method for the detection of plastic anti-personnel mines called “ leg breakers ” is presented from images of the ground acquired remotely by a drone at heights between 1 and 5 m. The proposal achieved an accuracy of 92.13 % and a sensitivity of 82.4 % when evaluating the performance on 260 test images, a number higher than that used in previous works, and with mines buried in real terrain with the presence of vegetation. The above on images acquired on different dates with different environmental conditions and soil moisture. In addition, it was found that the method was adapted to three different thermographic cameras, being necessary only to adjust the gamma parameter associated with the thermal contrast enhancement technique used. It was corroborated that mines are easier to detect to the extent that they are buried more superficially, and when the flight height of the drone is between 1 and 2 m. Finally, it is highlighted that the proposal has the potential to be executed in processing systems onboard the drone, thanks to its algorithmic simplicity.

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.

Automated wireless system for monitoring the technical condition of chimneys

The present paper considers the technology of monitoring the technical condition of chimneys in order to identify areas of increased heat losses. The technology involves an automated wireless monitoring system that includes an unmanned aircraft with additional equipment. Aim. To study the operation safety of reinforced concrete chimneys used in hazardous industrial facilities. The presented monitoring technology tackles the task of prompt identification of defects and damages to reduce the risks of potentially hazardous conditions. While developing technology for monitoring the technical condition, special attention is paid to its autonomy and intelligence. At the first stage, sensors, coordinators and remote thermometry system are installed with the transmission of data on the temperature of the chimney trunk to the control center. At the next stage, the outer surface of the chimney is examined using an unmanned aicraft equipped with a thermal imaging camera to verify information about heat losses. The final stage involves creating a thermogram of the object and a scheme of the possible locations of the identified defects in order to plan repair works. In addition, the paper introduces main advantages of the suggested technology.

Unlocking the secrets of Neapolitan pizza: A concise review of wood-fired, electric, and gas pizza ovens.

This review explores the Neapolitan pizza baking process in a traditional wood-fired oven, employing visual color analysis and IR thermal scanning to detail heat exchange mechanisms. During cooking, the oven floor temperature in the pizza area decreased proportionally to the pizza's mass, whereas the free area maintained a constant temperature of 439±3°C. An IR thermal camera indicated that the oven dome temperature reached approximately 480°C with a weak flame and 500°C with a strong flame. The pizza's bottom achieved a maximum temperature of 100±9°C, facilitated by the Pizzaiolo's skill in lifting and rotating the pizza for uniform cooking. Top temperatures varied: up to 180°C for white pizza and 84°C and 67°C for tomato and Margherita pizzas, respectively. IRIS electronic eye analysis revealed more browning and blackening on the pizza's top compared to its bottom, with peaks of about 26% and 8% for white pizza, respectively. Rapid baking is pivotal in Neapolitan pizza-making, necessitating precise heat and mass transfer management to influence sensory attributes. European Commission Regulation No. 97/2010 mandates wood-fired ovens for true Neapolitan pizza, but environmental concerns prompted the Associazione Verace Pizza Napoletana to certify gas or electric ovens when wood-fired ovens are impractical. Operating costs vary: liquefied petroleum gas ovens are the costliest, with costs ranging from €5.38 to €6.19/h, whereas natural gas and electric ovens have operating costs between €2.70 and €4.10/h. At €0.15/kg, firewood is the most economical, supporting traditionalist views. However, natural gas and electric ovens present competitive costs under stringent antipollution laws, making them viable alternatives.

Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks

Monitoring of the vital signs or environment of disabled people is currently very popular because it increases their safety, improves their quality of life and facilitates remote care. The article proposes a system for automatic protection against burns based on the detection of thermal threats intended for blind or visually impaired people. Deep learning methods and CNNs were used to analyze images recorded by mobile thermal cameras. The proposed algorithm analyses thermal images covering the field of view of a user for the presence of objects with high or very high temperatures. If the user’s hand appears in such an area, the procedure warning about the possibility of burns is activated and the algorithm generates an alarm. To achieve this effect, the thermal images were analyzed using the 15-layered convolutional neural network proposed in the article. The proposed solution provided the efficiency of detecting threat situations of over 99% for a set of more than 21,000 images. Tests were carried out for various network configurations, architecture and both the accuracy and precision of hand detection was 99.5%, whereas sensitivity reached 99.7%. The effectiveness of burn risk detection was 99.7%—a hot object—and the hand appeared simultaneously in the image. The presented method allows for quick, effective and automatic warning against thermal threats. The optimization of the model structure allows for its use with mobile devices such as smartphones and mobile thermal imaging cameras.

Using Unmanned Aerial Vehicles Equipped with Thermal Cameras to Survey a Known Population of White-Tailed Deer

Abstract New technologies are emerging that may have potential for yielding more reliable population estimates for white-tailed deer Odocoileus virginianus than traditional manned aerial surveys. In a study conducted in a 116.55-ha enclosure in the Edwards Plateau ecoregion of Texas, USA, we tested the accuracy and precision of population counts by using an unmanned aerial vehicle equipped with a dual thermal–red green blue (heat detection and color video) camera to conduct a complete area count of a known, closed population of white-tailed deer. We flew surveys during 2–4 December 2019 in preprogrammed transects and replicated them three times, in both the morning and afternoon. Detection was higher during morning surveys, when we detected almost all deer that were present, compared with afternoon surveys. The accuracy and precision of morning surveys reported herein justify further research into the development of a method using this equipment for surveying large mammal populations in certain habitats.

A Dataset of Visible Light and Thermal Infrared Images for Health Monitoring of Caged Laying Hens in Large-Scale Farming.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens' health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming.

Endonasal Thermal Imaging Before and After Nasal Airway Surgery.

Nasal airway surgery is often applied when treatment fails to relieve nasal obstruction. However, surgery that improves airflow does not always alleviate the symptoms of nasal obstruction. The perception of nasal breathing is likely more related to changes in mucosal temperature than the mechanical sensation of flow or pressure. This study aims to measure intranasal mucosal temperature pre-and post-surgery using endonasal thermal imaging, exploring its correlation with subjective nasal breathing and objective airflow measurements. A prospective study of adult patients with nasal obstruction managed with nasal airway surgery was performed. Intranasal mucosal temperatures were determined using the thermal endonasal image of the nasal passage produced by the infrared radiometric thermal camera (FILR VS290). A comparison was made between the mean values of mid-expiration (ExT) and mid-inspiration (InT) temperature data (internal nasal valve, nasal cavity, inferior turbinate, and overall airway [mean value]) and visual analog scale (VAS), Nasal Obstruction Symptom Evaluation (NOSE) scale and nasal airway resistance (NAR) before and after surgery. Seven patients (35.14 ± 16.45 years, 57.14% female) were included. All NOSE, VAS, and NAR improved after surgery (59.29 ± 10.89 vs 17.14 ± 14.64; P < .001, 64.50 ± 26.79 vs 18.57 ± 19.99; P < .001, 0.82 ± 0.48 vs 0.34 ± 0.11 Pa/cm3/s; P = .002, respectively). ExT, InT, and the difference between ExT and InT of three areas and overall airway were similar between pre-and post-surgery. No statistically significant correlations were found between intranasal mucosal temperature, VAS, NOSE, and NAR at pre-and post-surgery except for the difference between ExT and InT of overall airway and NOSE pre-operative (Pearson r = 0.57; 95% CI 0.06-1.09; P = .03). Endonasal thermal imaging can assess the intranasal mucosal temperature of a patient. However, more precise imaging of the nasal passages and data acquisition are required to establish mucosal temperature as an objective measure of nasal obstruction before and after nasal airway surgery in a clinical setting.

Integrated mid-infrared sensing and ultrashort lasers based on wafer-level Td-WTe2 Weyl semimetal

There is an urgent need for infrared (IR) detection systems with high-level miniaturization and room-temperature operation capability. The rising star of two-dimensional (2D) semimetals with extraordinary optoelectronic properties can fulfill these criteria. However, the formidable challenges with regard to large-scale patterning and substrate-selective requirements limit material deposition options for device fabrication. Here, we report a convenient and straightforward eutectic-tellurization transformation method for the wafer-level synthesis of 2D type-II Weyl semimetal WTe2. The non-cryogenic WTe2/Si Schottky junction device displays an ultrawide detection range covering 10.6 μm with a high detectivity of ∼109 Jones in the mid-infrared (MIR) region and a short response time of 1.3 μs. The detection performance has surpassed most reported IR sensors. On top of that, on-chip device arrays based on Schottky junction display an outstanding MIR imaging capability without cryogenic cooling, and 2D WTe2 Weyl semimetal can serve as a saturable absorber for stable Q-switched and mode-locked laser operation applications. Our work offers a viable route for wafer-scale vdW preparation of 2D semimetals, showcasing their intriguing potential in on-chip integrated MIR detection systems and ultrafast laser photonics.

Comparison of thermal cameras and human observers to estimate population density of fallow deer (Dama dama) from aerial surveys in Tasmania, Australia

Context The population of introduced fallow deer (Dama dama) is thought to have increased exponentially across much of the island of Tasmania, Australia, since 2000. Historically, deer management decisions have relied on population trend data from vehicular spotlight surveys. Renewed focus on the contemporary management of the species requires development of more robust and precise population estimation methodology. Aims This study demonstrates two aerial survey methods – conventional counts by trained human observers, and thermal imaging footage recorded during the same flights – to inform future survey practices. Methods Conventional counts were carried out by three observers, two seated on the left side of the helicopter, and one on the right. A high-resolution thermal camera was fitted to the helicopter and was orientated to meet the assumptions of distance sampling methodologies. Both survey methods were used to generate deer population density estimates. Spatial distribution of deer was also analysed in relation to patches of remnant native vegetation across an agricultural landscape. Mark–recapture distance sampling was used to estimate density from human observer counts and provide a comparison to the distance sampling estimates derived from the thermal camera. Key results Human observer counts gave a density estimate of 2.7 deer per km2, while thermal camera counts provided an estimate of 2.8 deer per km2. Deer population density estimates calculated via both methods were similar, but variability of the thermal camera estimate (coefficient of variation (CV) of 36%) was unacceptably high. Human observer data was within acceptable bounds of variability (CV, 19%). The estimated population size in central and north-eastern Tasmania for 2019 approximated 53,000 deer. Deer were primarily congregated within 200 metres of the interface between canopy cover and open pasture. Conclusions The population density estimate provides a baseline for monitoring and managing the Tasmanian deer population. Human observer data was more precise than thermal camera data in this study, but thermal counts could be improved by reducing sources of variability. Implications Improvements for the collection of thermal imagery are recommended. Future control efforts may be more efficient if they preferentially target habitat edges at this time of year, paired with random or grid-based searches where population density is lower.

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.

Increased accuracy and signal-to-noise ratio through recent improvements in infra-red video bolometer fabrication and calibration.

The infra-red video bolometer (IRVB) is a diagnostic equipped with an infra-red camera that measures the total radiated power in thousands of lines of sight within a large field of view. Recently validated in MAST-U [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)], it offers a high spatial resolution map of the radiated power in the divertor region, where large gradients are expected. The IRVB's sensing element comprises a thin layer of high Z absorbing material, typically platinum, usually coated with carbon to reduce reflections [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)].Here, the possibility of using a relatively inert material such as titanium, is explored that can be produced in layers up to 1 μm compared to 2.5 μm for Pt and then coat it with Pt of the desired thickness (0.3 μm per side here) and carbon. This leads to a higher temperature signal (about 3 times) and better spatial resolution (about 4 times), resulting in higher accuracy in the measured power [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)]. This assembly is also expected to improve foil uniformity, as the Pt layer is obtained via deposition rather than mechanical processes [Mukai et al., Rev. Sci. Instrum. 87, 2014 (2016)].Given its multi-material composition, measuring the thermal properties of the foil assembly is vital. Various methods using a calibrated laser as a heat source have been developed, analyzing the temperature profile shape [Sano et al., Plasma and Fusion Res. 7, 2405039 (2012)] and [Mukai et al., Rev. Sci. Instrum. 89, 10E114 (2018)] or fitting the calculated laser power for different intensities and frequencies [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)]. Here, a simpler approach is presented, which relies on analyzing the separate components of the foil heat equation for a single laser exposure in a given area. This can then be iterated over the entire foil to capture local deviations.