Thermal Imaging Technique Research Articles

Enhancing Preservation: Addressing Humidity Challenges in Indonesian Heritage Buildings Through Advanced Detection Methods Point Cloud Data

Heritage buildings are valuable assets that represent national cultural identity. Proper building maintenance is a major issue for preservation, as building monitoring aspects and preventive measures are often only taken after physical damage happens. In the context of Indonesian heritage buildings, high levels of humidity which may cause condensation and soil dampness are often overlooked. Early detection methods are urgently required to effectively detect potential risks of condensation. This study aims to investigate condensation risk for heritage building surfaces by calculating thermal properties (i.e., emissivity, albedo) and Blinn-Phong BRDF values through the integration of thermal imaging and 3D scanning techniques. This approach supports architects and conservators in making informed decisions to protect and maintain cultural heritage structures. The study also highlights gaps in current Indonesian regulations regarding moisture presence and condensation risk detection in heritage buildings.

Comparative analysis of deep neural network and artificial neural network for predicting wheat water content using UAV multispectral and thermal imagers

Water is a vital component for the growth of wheat and the quality of its grains. The measurement of plant water content (PWC) serves as a crucial indicator for assessing the water status of crops, thereby guiding effective irrigation management practices. Recent advancements in technology, particularly the use of multispectral and thermal imaging from unmanned aerial vehicles (UAVs), offer the capability to capture a comprehensive view of PWC variability across agricultural fields. This study aimed to create predictive models for PWC utilizing artificial neural networks (ANN), deep neural networks (DNN), and traditional stepwise regression techniques, all based on the analysis of multispectral and thermal imagery. To effectively evaluate the protein content in wheat, we combined high-resolution thermal and multispectral imaging techniques with machine learning approaches. This assessment was carried out through three distinct experiments, which included one conducted in a rainout shelter and two performed under rainfed conditions. The findings demonstrated that UAV-derived multispectral imagery, when coupled with machine learning models, can effectively predict wheat plant water content with remarkable precision. Notably by considering all the dataset, DNN model exhibited superior performance (R2=0.96, ENS=0.98, RMSE=1.37%, MAE=0.98%) compared to both the ANN (R2=0.95, ENS=0.95, RMSE=1.88%, MAE=1.46%,) and the stepwise regression model (SRM) (R2=0.67, ENS=0.51, RMSE=10.79%, MAE=9.03%). Across all machine learning approaches, both the DNN and ANN models significantly outperformed the stepwise regression model in predictive accuracy. The statistical outcomes derived from the calibration phase indicate that both the trained Artificial Neural Networks (ANN) and Deep Neural Networks (DNN) serve as effective instruments for accurate prediction of PWC. Notably, the DNN network demonstrates superior accuracy compared to ANN.

Data-driven AI for the automated classification of the isothermal heat-treated thermal barrier coatings using pulsed infrared thermography

Abstract Development of reliable age prediction models are crucial in monitoring the formation of oxide layer and degradation of TBC at regular intervals. This study proposes an automated classification of isothermal heat-treated TBC samples using temperature data, which helps in predicting the TBC life and monitoring the TBC degradation. TBC-coated samples are isothermal heat-treated at 1000 °C, and the initial growth of thermally grown oxide is monitored using a non-destructive thermal imaging technique. The proposed study integrates data-driven AI (DAI) models and feature extraction techniques to interpret complex thermal patterns measured from the TBC coating surface. The performance of the proposed classification framework is tested using deep learning and classical machine learning models with different types and window sizes of input data. Input data used for validation are raw experiment data, logarithmic of experiment data, polynomial fit data, and thermal signal reconstruction fit coefficients. The maximum classification performance is obtained with gated recurrent unit with accuracy and F1-score of 89.2% and 89.0%, respectively with raw temperature data as input of window 300. The study demonstrates that the proposed DAI approach effectively predicts the age of thermal barrier coatings under isothermal heat-treatment conditions by correlating the thermal response with coating degradation.

Comparisons of computer simulations and experimental data for capacitive hyperthermia using different split-phantoms

Introduction Several positive clinical trials have demonstrated that capacitive hyperthermia (CHT) improves the effectiveness of radiation therapy for the treatment of various cancer entities. However, the ability of CHT to induce significant heating throughout the body is under debate. Objectives To perform a pilot study involving comparisons of computer simulations and experimental data using different split-phantoms to validate hyperthermia treatment modeling for pre-planning for a clinical CHT system and to investigate the feasibility of split-phantom measurements in capacitive hyperthermia. Materials and methods The CHT system EHY-2030 (Oncotherm, Budapest, Hungary) was used. The system provides two electrode sizes, but only the smaller electrode, indicated as D200 electrode, was investigated in this pilot study. Horizontally and vertically splittable, different multi-slice phantoms with dielectric material properties simulating muscle and electrically low conductive fat were produced and heated. During the heating procedure, temperature-time curves were measured, and thermal images were captured. Specific absorption rate values were derived from the temperature rise (TR) values. Concomitantly, computer field simulations utilizing a detailed CAD-based model of the CHT system were performed using the simulation platform Sim4Life and compared with measurements. Results For the investigated electrode D200 the system power of 75 W was applied, which is half of the maximum power of 150 W and lies in the range of usual values for this electrode applied in patient treatments in our clinic. For 75 W, a heating of 3.6 °C in 6 min in a depth of 1 cm in an agar-based, muscle tissue-equivalent phantom was achieved. The addition of a 1 cm thick, synthetic, low dielectric fat layer reduced the TR up until a depth of 8.5 cm by on average around 38% (from 8.5 cm onwards the absolute local TR is similar, deviations are ≤0.1 °C). In terms of point-to-point absolute SAR comparison (without any normalization), up to a depth of 11 cm in the phantoms central vertical plot, the simulation differs from the measured TR points by on average 25% (ranging from 7% to 36%) for the homogeneous phantom and by on average 43% (ranging from 26% to 60%) for the inhomogeneous phantom. Conclusion Computer simulations and experimental data were compared for the CHT system EHY-2030 using the D200 electrode, applying a thermal imaging technique for different vertically splittable phantoms. This pilot study data can be used as a guidance regarding the expected heating for this commonly used electrode size but also to further elucidate the significance of non-thermal anticancer effects. Further studies are needed for different sizes and geometries of electrodes and phantoms.

Electrical Performance and Degradation Analysis of Field-Aged PV Modules in Tropical Climates: A Comparative Experimental Study

Performance degradation is a prevalent phenomenon in solar photovoltaic systems globally, with varying aging profiles and effects depending on environmental and climatic conditions. This paper presents an extensive investigation of the electrical performance, aging mechanisms, and degradation analysis of field-aged PV modules under the tropical climate of Malaysia. Utilizing a combination of visual inspection, I-V curve measurement, and thermal imaging techniques, this research provides a comprehensive assessment of the electrical and thermal properties of field-aged PV modules from two locations in Malaysia over extended periods (8 years at UMPSA and 10 years at Pasir Mas). The multi-faceted approach of the study allows for a deeper understanding of the degradation process, offering insights into the causes and effects of higher current and lower voltage in aged modules. The study found the average annual degradation rates at UMPSA were 0.3% for open circuit voltage (Voc), 0.23% for short circuit current (Isc), 0.81% for maximum power (Pmax), and 0.35% for fill factor (FF) and at Pasir Mas, the average annual degradation rates were 1.124% for Voc, −0.166% for Isc, 1.276% for Pmax, and 0.43% for FF. The study also found that monocrystalline silicon (m-Si) panels experienced an average power degradation of 6.48%, while polycrystalline silicon (p-Si) panels showed a higher degradation of 12.76%. However, Thermal imaging revealed significant temperature variations across the modules, with hotspots reaching up to 11.2 °C above cooler areas in UMPSA panels and an even more pronounced 26.1 °C difference in Pasir Mas modules. These temperature disparities highlight the uneven heat distribution and potential performance issues in the panels. This research contributes to the understanding of PV module degradation in tropical climates and aligns with sustainable development, climate change mitigation efforts, and SDG 7 by promoting sustainable energy solutions.

A MIL-based approach for welding defect classification

Welding quality plays a prominent role in many fabrication processes, such as those in the automotive, aerospace, shipbuilding, and electronics industries. Deep learning facilitates the automatic classification of welding quality using non-invasive techniques such as X-rays, thermal imaging, and ultrasonic techniques. In this study, we investigate the ability of deep learning to detect welding quality directly from the welded workpieces. Typically, good and bad weld samples can be seen together along with surrounding material as a background, which makes traditional classification based using computer vision difficult. Therefore, we propose a new framework based on Multiple Instance Learning (MIL) that reveals the distribution of each small piece of the image and serves as an effective guide for welding quality classification. Furthermore, a new collection of welding image datasets is developed to verify the baseline model and our proposed method. The results show that our proposed method is effective in classifying welding quality in welding workpiece images and identifying welding features in each small section of the welding area in workpiece images. This study demonstrates the effectiveness of incorporating MIL in weakly supervised welding quality classification and reveals the potential for performing quality control on welding in industrial pipelines automatically.

Decision Tree Clusters: Non-destructive detection of overheating defects in porcelain insulators using quantitative thermal imaging techniques

Porcelain insulators are subject to performance deterioration during operation under the influence of complex environments, thus increasing the occurrence of flashover accidents. Regular inspection of insulator condition is of great significance for the stable operation of power grids. Therefore, we propose a non-destructive detection method of insulator overheating defects using quantitative thermography. It can be used for defect detection of porcelain insulators with internal overheating defects. In this study, the relationship between the insulating properties of porcelain insulators and the heating anomalies was first analyzed. Then, Decision Tree Clusters (DTC) algorithm is used to extract the spatial temperature feature information to achieve the defective insulator location capture. Finally, Submodular-pick Local Interpretable Model-agnostic Explanations (SLIME) is used for model decision visualization to verify the feasibility of the technique. The experimental results show that DTC not only ensures the accuracy of insulator long-distance detection, but also realizes the quantitative detection of insulators.

Three-Dimensional Computed Tomography Scanning Study of the Superior Labial Artery in Chinese Individuals for Assessing Filler Injection Safety.

Lip filler injection is one of the most common minimally invasive cosmetic procedures involving the face; however, vascular complications are not uncommon. The aim of this study was to investigate the anatomy of the superior labial artery (SLA) and provide precise topographic information for dermal filler injection into the lips. Computed tomography (CT) scans of 52 cadaveric heads injected with lead oxide were obtained. We then used Mimics software to construct 3D images of the SLA described by a coordinate system based on the bilateral external auditory canal and the left orbit. This study aimed to classify the SLA in the Han Chinese population, measure its diameter at specific points, and determine the thickness of the lip at those points. Ultimately, we utilized a thermal imaging technique to illustrate the course and depth of the SLA within the lip. The objective of this study was to provide safe guidance for clinical injections. In this study, the SLA was successfully identified in all cadavers. The mean overall diameter of the superior labial arteries was 1.36±0.28 mm. The superior labial artery showed a general course from deep to shallow with an average depth of 5.68±1.68 mm from the oral commissure to the midline. There are anatomical differences in the superior labial arteries among Chinese people. Furthermore, 3D CT images can digitally elucidate the exact positions of the superior labial artery via a coordinate system, improving the safety of upper lip filler injections in clinical settings. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Measuring thermal contact resistance across solid and liquid interface mediums by thermal imaging technique: Applications on bearing steel

This study introduces a versatile method for measuring thermal contact resistance in bearing steel with solid–solid and liquid–solid interfaces using lock-in thermography (LIT) technique. The method relies on analyzing the lock-in thermal response across the layers of the bearing structure induced by periodic surface-laser heating. Thermal contact resistance is determined from the discontinuous behavior analysis of the thermal response at the solid–solid or liquid–solid contact interface, based on the resolved heat equation for the multilayered structure. The usability of the method is demonstrated through measurements conducted on a conventional bearing steel configuration, using air and lubricating oil as the interface mediums. Furthermore, the versatility of the method allows for investigating the impact of mechanical load on thermal contact resistance. This proposed approach represents an advanced tool for analyzing thermal contact resistance with applications beyond bearing steel to various multilayered materials. It has the potential to assist in enhancing thermal analysis, improving reliability, extending the lifetime, and reducing maintenance costs of different thermal engineering applications including bearings.

Thermal Imaging and Dimensionality Reduction Techniques for Subclinical Mastitis Detection in Dairy Sheep

Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive detection, but existing methodologies often rely on simplistic temperature differentials, potentially leading to inaccurate assessments. This study proposes an advanced algorithmic approach integrating thermal imaging processing with statistical texture analysis and t-distributed stochastic neighbor embedding (t-SNE). Our method achieves a high classification accuracy of 84% using the support vector machines (SVM) algorithm. Furthermore, we introduce another commonly employed evaluation metric, correlating thermal images with commercial California mastitis test (CMT) results after establishing threshold conditions on statistical features, yielding a sensitivity (the true positive rate) of 80% and a specificity (the true negative rate) of 92.5%. The evaluation metrics underscore the efficacy of our approach in detecting subclinical mastitis in dairy sheep, offering a robust tool for improved management practices.

Experimental investigation of metal foam embedded surface thermal characteristics using air-jet impingement with different orifice geometry

Experimental investigation of metal foam embedded surface thermal characteristics using air-jet impingement with different orifice geometry

Local Heat Transfer of a Smooth Flat Plate Impinged by Multiple Jets

A smooth flat plate impinged by multiple jets is investigated for the local heat transfer using a thermal imaging technique. An inline arrangement of circular jets with all side exit scheme is implemented. Jets are introduced from a 4 mm thick orifice plate. The jet diameter is 3 mm. Reynolds numbers (based on the jet diameter) range covered in this study are from 500 to 15,000. The variation of the local, spanwise average, and overall average Nusselt numbers is investigated for nozzle exit to targeted plate spacing of 0.5 d–6 d, where d is the jet diameter. A periodic pattern is witnessed in the local Nusselt number with a drop in its amplitude in the spanwise direction. For a given Reynolds number, the local and average Nusselt numbers are influenced by nozzle exit to targeted plate spacing. The influence of Reynolds number on the local and average Nusselt number is captured using Re n . The value of n is found to be 0.5 and 0.6 depending on the Reynolds number range. In comparison with a single jet, multiple jet impingement on a smooth flat plate deteriorates the local Nusselt number. A semi-empirical correlation that captures the periodic nature of local and average Nusselt numbers is suggested.

Automation of green roof plant cover measurements using machine learning and a comparison of digital and thermal imaging techniques

AbstractAimsPost‐analyses of digital red, green, blue (RGB) and thermal images have become increasingly popular as modern approaches to plant cover analysis. Image analyses are often coupled with semi‐automated or automated workflows to reduce the amount of human labor input compared with traditional manual procedures. This study aims to evaluate and compare different image segmentation methods for plant cover analysis using digital RGB and thermal images, focusing on the effectiveness of semi‐automated and manual segmentation techniques in monitoring plant cover on green roofs.LocationAn Extensive green roof in the City of Toronto.MethodsWe surveyed the plant cover of an extensive green roof using digital and thermal imagery. The plant cover values were obtained using three methods: traditional manual segmentation based on a visual examination (MS), ImageJ Color Threshold (CT) and Trainable Weka Segmentation (TWS), all performed within FIJI (a distribution of ImageJ). Manual segmentation based on visual examination was used as a reference standard.ResultsSignificant correlation was found between the cover estimation using the CT and TWS methods relative to MS, and between cover estimation using the thermal image and the RGB image. TWS overestimated plant cover on thermal images while producing an underestimation on RGB images. CT demonstrated a performance closer to MS than TWS, indicating that manually customized methods produced results more aligned with MS. The estimated cover values by MS were not significantly affected by the image type (digital RGB or thermal).ConclusionsResults suggest that RGB and thermal imaging techniques may provide complementary results and reveal unique information regarding the functioning of green roofs. The accuracy of supervised machine‐learning methods could be enhanced with site‐specific data to provide a more accurate and efficient estimation of plant cover, which might be beneficial for long‐term studies on green roofs and ecological sites in remote locations.

Experimental and Numerical Assessment of the Thermal Bridging Effect in a Reinforced Concrete Corner Pillar

This paper discusses experimental and simulated data regarding the thermal bridging effect in a reinforced concrete corner pillar, which belongs to a building dating back to the 1980s and located in Southern Italy. The thermal field determined by the concrete pillar corner has been evaluated, introducing an experimental procedure based on both direct measurements and indirect observations of the inner superficial temperature by means of thermal imaging techniques and surface temperature probes. Moreover, indoor and outdoor air temperature and relative humidity were measured to provide suitable boundary conditions in the numerical simulations, performed with a commercial software tool widely used in Italy based on 2D finite element techniques. The experimental measurements show that, at more than 50 cm from the corner, the surface temperatures become almost constant, meaning that the thermal bridging effect becomes less evident. However, the surface temperature in the corner is around 1.5 °C lower than in the undisturbed flanking walls. In terms of local heat flux, the discrepancy between simulations and measurements is below 3%. Finally, this paper verifies the effectiveness of External Thermal Insulation Composite System (ETICS) renovation in reducing the thermal bridging effect of the corner pillar. The results also include the calculation of the linear thermal transmittance with a series of relations available in well-known atlases for thermal bridges and show that these relations are more reliable in the case of uninsulated pillar than for the insulated one.

Effect of optimal rapid thermal alloying of ohmic contacts to microwave pseudomorphic HEMTs

Gallium Arsenide (GaAs) pseudomorphic High Electron Mobility Transistors (pHEMTs) are currently used as active devices in manufacturing Monolithic Microwave Integrated Circuits (MMICs) for microwave and millimetre wave applications like Transmit-Receive (T/R) modules in Radars and communication. Low contact resistances (Rc) of pHEMT devices are necessary to obtain low access resistances to the device channel, for enhancement of transconductance and cut-off frequency. Devices formed by optimal and suboptimal conditions of rapid thermal alloying by infra-red optical radiation are investigated using cross-section transmission electron microscopy (XTEM), backside secondary ion mass spectrometry (backside SIMS) and thermal imaging techniques to understand the role of optimal alloy formation and its role in enhancement of device parameters.

Correlating Light Intensity with Body Heat Changes: Implications for Screen Time and Well-being

This study investigates the correlation between light intensity and body heat changes during screen time activities, aiming to uncover potential implications for well-being. With the pervasive use of screens in modern society, understanding how light emitted from devices affects physiological responses is crucial. The research employs a multidisciplinary approach, combining thermal imaging techniques with light intensity measurements to explore the intricate relationship between these variables. The methodology involves recruiting participants to engage in screen-based activities, such as watching television or using laptops, under controlled conditions. Thermal imaging cameras are utilized to monitor changes in body heat, providing real-time data on thermal responses. Concurrently, light intensity meters measure the brightness emitted by the screens, allowing for the quantification of light exposure levels. Data analysis encompasses statistical techniques to identify correlations between light intensity and body heat changes. Factors such as screen type, content, and viewing distance are considered to elucidate potential moderating variables. The findings aim to provide insights into how different levels of light intensity impact physiological well-being during screen time activities. Implications of the study extend to various domains, including ergonomic design, health recommendations, and screen usage guidelines. Understanding the interplay between light exposure and body heat regulation can inform the development of anti-screen technologies or interventions aimed at mitigating potential negative effects of prolonged screen time on health and well-being. Overall, this research contributes to a comprehensive understanding of the complex relationship between screen-based activities, light exposure, and human physiology

Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review.

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

Nanoparticles based image-guided thermal therapy and temperature feedback.

Nanoparticles have emerged as versatile tools in the realm of thermal therapy, offering precise control and feedback mechanisms for targeted treatments. This review explores the intersection of nanotechnology and thermal therapy, focusing on the utilization of nanoparticles for image-guided interventions and temperature monitoring. Starting with an exploration of local temperature dynamics compared to whole-body responses, we delve into the landscape of nanomaterials and their pivotal role in nanomedicine. Various physical stimuli employed in therapy and imaging are scrutinized, laying the foundation for nanothermal therapies and the accompanying challenges. A comprehensive analysis of nanomaterial architecture ensues, delineating the functionalities of magnetic, plasmonic, and luminescent nanomaterials within the context of thermal therapies. Nano-design intricacies, including core-shell structures and monodisperse properties, are dissected for their impact on therapeutic efficacy. Furthermore, considerations in designing in vivo nanomaterials, such as hydrodynamic radii and core sizes at sub-tissue levels, are elucidated. The review then delves into specific modalities of thermally induced therapy, including magnetically induced hyperthermia and luminescent-based thermal treatments. Magnetic hyperthermia treatment is explored alongside its imaging and relaxometric properties, emphasizing the implications of imaging formulations on biotransformation and biodistribution. This review also provides an overview of the magnetic hyperthermia treatment using magnetic nanoparticles to induce localized heat in tissues. Similarly, optical and thermal imaging techniques utilizing luminescent nanomaterials are discussed, highlighting their potential for light-induced thermal therapy and cellular-level temperature monitoring. Finally, the application landscape of diagnosis and photothermal therapy (PTT) is surveyed, encompassing diverse areas such as cancer treatment, drug delivery, antibacterial therapy, and immunotherapy. The utility of nanothermometers in elucidating thermal relaxation dynamics as a diagnostic tool is underscored, alongside discussions on PTT hyperthermia protocols. Moreover, the advancements in nanoparticle magnetic imaging and implications of imaging formulations especially in creating positive MRI contrast agents are also presented. This comprehensive review offers insights into the evolving landscape of nanoparticle-based image-guided thermal therapies, promising advancements in precision medicine and targeted interventions, underscoring the importance of continued research in optimization for the full potential of magnetic hyperthermia to improve its efficacy and clinical translation.

Thermographic procedure for the assessment of Resistance Projection Welds (RPW): Investigating parameters and mechanical performances

This study presents a non-destructive testing (NDT) thermographic procedure for assessing the quality and mechanical strength of Resistance Projection Welded (RPW) joints with rectangular embossments. We analysed twelve RPW joints by systematically varying process parameters based on a factorial design. These joints underwent flash thermography followed by mechanical tests to evaluate the maximum breaking force (Fmax). Significant statistical correlations between process parameters (time and force) and Fmax were established. Furthermore, we found a correlation (p-value 0.86) between the optically measured fused region and Fmax. Subsequently, we developed a pulsed phase thermography-based procedure for non-destructively measuring the fused region, resulting in an average difference of approximately 4 % compared to optical measurements. An empirical linear relationship was derived to correlate the welded area obtained by thermal data with Fmax, enabling the estimation of mechanical joint strength through non-destructive pulsed thermography. This research offers a promising approach for assessing the mechanical integrity of RPW joints using thermal imaging techniques.

Predicting respiration rate in unrestrained dairy cows using image analysis and fast Fourier transform

Respiratory rate (RR) is commonly employed for identifying animals experiencing heat-stress conditions and respiratory diseases. Recent advancements in computer vision algorithms have enabled the estimation of the RR in dairy cows through image-based approaches, with a primary focus on standing positions, thermal imaging, and deep learning techniques. In this study, our objective was to develop a system capable of accurately predicting the RR of lying Holstein cows under unrestrained conditions using red, green, and blue (RGB) and infrared (IR) night vision images. Thirty lactating cows were continuously recorded for 12 h per day over a 3-d period, capturing at least one 30-s video segment of each cow during lying time. A total of 95 videos were manually annotated with rectangular bounding boxes encompassing the flank area (region of interest; ROI) of the lying cows. For future applications, we trained a model for ROI identification using YOLOv8 to avoid manual annotations. The observed RR was determined by visual counting of breaths in each video. To predict the RR, we devised an image processing pipeline involving (1) capturing the ROI for the entire video, (2) reshaping the pixel intensity of each image channel into a 2-dimensional object and calculating its per-frame mean, (3) applying fast Fourier transform (FFT) to the average pixel intensity vector, (4) filtering frequencies specifically associated with respiratory movements, and (5) executing inverse FFT on the denoized data and identifying peaks on the resulting plot, with the count of peaks serving as the predicted RR per minute. The evaluation metrics, root mean squared error of prediction (RMSEP) and R2, yielded values of 8.3 breaths/min (17.1% of the mean RR) and 0.77, respectively. To further validate the method, an additional dataset comprising preweaning dairy calves was used, consisting of 42 observations from 25 calves. The RMSEP and R2 values for this dataset were 13.0 breaths/min and 0.73, respectively. The model trained to identify the ROI exhibited a precision of 100%, a recall of 71.8%, and an F1 score of 83.6% for bounding box detection. These are promising results for the implementation of this pipeline in future studies. The application of FFT to signals acquired from both RGB and IR images proved to be an effective and accurate method for computing the RR of cows in unrestrained conditions.