Thermal Infrared Images Research Articles

Efficient thermal infrared tracking with cross-modal compress distillation

The key issue of thermal infrared tracking is to use neural networks to represent the target effectively and efficiently in the thermal infrared domain. The lack of thermal infrared trainable datasets makes it difficult to train a robust infrared object tracker from scratch, and the time-consuming convolution operations also make the tracking slow. To address the above problems, we proposed cross-modal compression distillation to represent thermal infrared objects for tracking, by leveraging an off-the-shelf RGB model with knowledge distillation. Specifically, cross-modal distillation is performed to effectively transfer knowledge from RGB modality to thermal infrared modality by inputting paired RGB and thermal infrared images into two branches of a Siamese network. Additionally, based on the teacher–student model architecture, the feature extractor is compressed into a lightweight model by model pruning and multi-level deep feature matching. Experimental results on LSOTB-TIR and PTB-TIR datasets show that the thermal infrared object tracking models distilled by our proposed method achieved faster tracking speed with better performance than the baseline RGB tracker by gaining an improvement of 1.5% Success Rate, 2.2% Precision, and 1.9% Normalized Precision, 58 frames per second (FPS) on LSOTB-TIR dataset, respectively.

Unmanned aerial vehicles (UAVs) with thermal infrared (TIR) sensors are effective for monitoring and counting threatened Vietnamese primates.

Monitoring the population size of threatened primate species with minimal disturbance is becoming an outstanding requirement for conservation and wildlife management. Drones with thermal infrared (TIR) and visible spectrum (RGB) imaging are increasingly used to survey arboreal primates, but ground-truthing is still required to assess the effectiveness of drone-based count estimates. Our pilot study aims to assess the ability of a drone with both TIR and RGB sensors to detect, count, and identify semi-wild population of four endangered species of langurs and gibbon in the Endangered Primate Rescue Center (EPRC) in northern Vietnam. We found that TIR imagery enabled higher detection rates compared to RGB imagery and obtained an accurate count with the TIR only after four drone flights. We could identify langurs species based on thermal signature at a flight height of 50m from the ground level (max tree height = 15m), via size and shape of the body. With TIR imagery, we were able to record inconspicuous behaviors such as foraging and play. While some individuals initially showed flight or avoidance behaviors when the drone was sighted, these behaviors decreased or were absent on following drone surveys. Our study suggests that monitoring and precisely counting langur and gibbon species populations could be successful with the use of thermal drones only.

Power Cable Terminal Fault Detection Method Based on Infrared Thermal Imaging

According to the analysis results, most conventional power cable terminal fault detection methods use the time-domain joint analysis principle to determine whether the terminal has abnormal and fault-hidden dangers. Then, it carries out fault detection. The detection process is complex and time-consuming, and the cable terminal fault information cannot be obtained the first time. Based on this, infrared thermal imaging technology is introduced, and a new fault detection method is proposed. The method comprises the following steps. Firstly, acquire operation data of a power cable terminal and preliminarily judge whether the cable terminal is abnormal according to dynamic changes of conductive thermal resistance and convective thermal resistance of the terminal. Secondly, introduce an infrared thermal imaging technology on the basis and capture infrared rays radiated by the power cable terminal. Lastly, acquire temperature information of the surface of the terminal, combine the temperature with infrared ray quantity results in two directions, and make all-round detection of cable terminal faults. The experimental analysis indicates that the proposed cable terminal fault detection method can complete the fault detection in a shorter time. The detection time is shorter, and the efficiency is higher.

Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques.

Within aerospace and automotive manufacturing, the majority of quality assurance is through inspection or tests at various steps during manufacturing and assembly. Such tests do not tend to capture or make use of process data for in-process inspection and certification at the point of manufacture. Inspection of the product during manufacturing can potentially detect defects, thus allowing consistent product quality and reducing scrappage. However, a review of the literature has revealed a lack of any significant research in the area of inspection during the manufacturing of terminations. This work utilises infrared thermal imaging and machine learning techniques for inspection of the enamel removal process on Litz wire, typically used for aerospace and automotive applications. Infrared thermal imaging was utilised to inspect bundles of Litz wire containing those with and without enamel. The temperature profiles of the wires with or without enamel were recorded and then machine learning techniques were utilised for automated inspection of enamel removal. The feasibility of various classifier models for identifying the remaining enamel on a set of enamelled copper wires was evaluated. A comparison of the performance of classifier models in terms of classification accuracy is presented. The best model for enamel classification accuracy was the Gaussian Mixture Model with expectation maximisation; it achieved a training accuracy of 85% and enamel classification accuracy of 100% with the fastest evaluation time of 1.05 s. The support vector classification model achieved both the training and enamel classification accuracy of more than 82%; however, it suffered the drawback of a higher evaluation time of 134 s.

A Method Based on Blackbody to Estimate Actual Radiation of Measured Cooperative Target Using an Infrared Thermal Imager

Infrared signature of targets is one important approach for target detection and recognition. When measuring the infrared signature of a target in the atmosphere, it is necessary to take the atmospheric transmittance and atmospheric radiation between the measured target and the observer into account. In this study, a blackbody-based approach for estimating atmospheric transmittance and atmospheric radiation is proposed to improve accuracy. Radiometric calibration is first carried out in the laboratory for the infrared thermal imager to determine the slope and offset used in the linear regression. With a set of different temperatures, radiance of the blackbody and digital number value of images are calculated. Finally, according to the analytical expressions derived, the atmospheric transmittance and atmospheric radiation are determined, and actual radiance for the cooperative target is calculated. Results demonstrate that the uncertainty of the actual radiance of measured cooperative target calculated via the proposed method is lower than that by MODTRAN, from MODTRAN at 5.7% and 16.7%, from proposed method at 2.56% and 10.2% in two experiments.

A practical method for angular normalization on land surface temperature using space between thermal radiance and fraction of vegetation cover

Land surface temperature (LST) retrieved from thermal infrared (TIR) remote sensing data displays strong directional anisotropy. This characteristic makes the LST at different angles incomparable and limits its application. Multi-angle data are usually required to model or retrieve the angular anisotropy, but most of the TIR images are observed from one single angle. This study investigates the relationship between fractional vegetation coverage (FVC) and thermal radiance, and proposes a practical method for angular normalization of LST using an FVC-radiance space LST angular normalization (called the FRAN method) to perform LST angular normalization for two-component (leaf and soil) vegetated canopy using only single-angle, single-temporal TIR data. In this method, the single-angle data are used to construct the space and build a linear relationship between FVC and radiance. Nadir radiance and LST are obtained by using FVC data at nadir. The validity and accuracy of the FRAN method are tested by using simulated dataset. Results show that 60%–70% of the angular effect of LST can be corrected, and the maximum LST correction reaches 2.0 K. The LST biased error distribution changes to unbiased distribution after angular normalization. The method sensitivity to the vertex of FVC-radiance space and the parameter in FVC calculation is analyzed. The FRAN method is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data, and obtains the maximum correction of LST as 1.2 K. Viewing zenith angle and land cover affect the corrected LST. The FRAN method has the potential for practical angular normalization of LST with single-angle data.

UAV-based RGB and TIR imaging for geothermal monitoring: a case study at Kestanbol geothermal field, Northwestern Turkey.

Kestanbol is one of the most important geothermal fields in NW Turkey. This study conducted the first-ever surveys over a 10ha reach of the Kestanbol geothermal field using an unmanned aerial vehicle (UAV) equipped with visible (RGB) and thermal infrared (TIR) cameras. Low-altitude flights below 40m above the ground were operated above the Kestanbol geothermal field. Approximately 3500 RGB and TIR images were captured using the UAV. We recorded high-resolution RGB and TIR data of the Kestanbol geothermal field and applied the structure from motion (SfM) algorithm to identify the distribution of geothermal springs and seeps. The Kestanbol geothermal field was monitored to create a georeferenced RGB orthophoto, RGB 3D surface model, thermal anomaly map, and digital surface model (DSM) of the area with centimeter-level accuracy. In the TIR orthophoto, the surface temperature in the geothermal field was found to be between 15 and 75°C. All the thermal anomalies revealed by the survey were verified by field observations. The geothermal springs and seeps were parallel to the NE-SW regional tectonic trends. The results of this study demonstrate an effective technique for monitoring and assessing geothermal water using UAV-based RGB and TIR imaging and provide an accurate basis for geothermal development projects. RGB and TIR imaging using UAVs are considered promising methods for improving the assessment of the effects of geothermal water on the environment.

Automated leakage detection method of pipeline networks under complicated backgrounds by combining infrared thermography and Faster R-CNN technique

Leakage detection is essential to process safety and loss prevention of pipeline networks. As one of the attractive methods for detecting leakages in single pipelines, infrared thermography (IRT) faces new challenges for leakage detection of pipeline networks. Compared with the single pipeline, the pipeline network has a complicated structure with higher topology features and diverse material composition. In addition, the environment of the pipeline network is complex and changeable. These factors increase the complexity of the infrared (IR) thermal images of pipeline networks, which makes the leakage features too weak to be detected via naked-eye observation in an efficient and precise manner. To address this issue, this study develops an automated detection method for pipeline network leakages under complicated background, which is achieved by combining IRT with the Faster Region-based Convolutional Neural Network (Faster R-CNN) technique. First, the pipeline network system in the complicated background is designed for IR thermal image acquisition and data analysis. Then, an automated leakage detection model is built based on Faster R-CNN, which uses a modified VGG16 network for feature extraction for its outstanding performance in the feature extraction of small objects under complicated backgrounds. Finally, the high efficiency and precision of the proposed method for automated detection of pipeline network leakages in complicated backgrounds are verified by extensive experiments. Meanwhile, the generalization ability of the proposed method is verified by conducting experiments under various experimental conditions (different moments all-day, different viewing angles of IR camera, pedestrian disturbing). In general, the proposed method provides a promising way to detect pipeline network leakages in complicated backgrounds.

MUGAN: Thermal Infrared Image Colorization Using Mixed-Skipping UNet and Generative Adversarial Network

Common cameras cannot capture high quality image in the night or extreme weather conditions that without enough light, while thermal infrared(TIR) cameras are not limited in this situation. Hence, TIR imaging technique is widely used in military, surveillance, nighttime traffic and other scenarios. However, TIR images are monochromatic, and the majority of details of such images are lost, which are difficult for human or computer system to analyze. Translating TIR images into visible images is beneficial to subsequent observation or further processing. Though there are some advances to realize the transformation from TIR images to color visible images, edge distortion and semantic confusion remain to be solved. Therefore, we propose a Mixed-Skipping UNet(MS-UNet) based image colorization model joint Generative Adversarial Network, which is denoted by MUGAN. Firstly, the dense skip connections of UNet++ and full-scale skip connections of UNet 3+ are combined to form the MS-UNet, which is regarded as the generator. In addition, we design a feature extraction module in MS-UNet to effectively capture the multi-scale features in source image. Then, a novel attention mechanism module is designed for decoding stage which can help decoder of MS-UNet to focus on the important information. Moreover, we explore the effect of different loss functions in TIR image colorization task, and the loss function with excellent performance is selected to further optimize the training process of the model. Extensive experiments prove the superiority of our method in the task of TIR image colorization. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HangyingLiao/MUGAN</uri> .

Near-Surface Soil Moisture Characterization in Mississippi’s Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were &gt;0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.

Possibility of infrared (IR) thermography camera to investigate properties of injected plastic product and optimisation injection process parameter using a design of experiment

ABSTRACT The injection processing parameters have an important role in product quality. The purpose of this research is to estimate the usage of infrared (IR) thermal camera in automatic quality control of polymer parts in the production lines compared to simulation. The evaluation of injected part properties monitored from an IR camera is studied in terms of the defect and mechanical properties. The optimisation of the processing parameters to increase mechanical properties and decrease injection defects is examined. There are four processing factors with four levels namely, melt temperature, mould temperature, injection pressure, and flow rate designed by using Taguchi’s experimental design. The effect of average temperature of injection moulded parts monitored by IR camera on defect and mechanical properties is determined. The variance (ANOVA) method is used to evaluate the influence of each factors. The parameter factors of all experiments are definitely different because of P-values less than 0.05. The optimum processing parameters of PP were a melting temperature of 193°C, mould temperature of 30°C, injection pressure of 50 bar, and flow rate of 40 cm3/s. The suitable processing conditions for ABS are melting temperature, mould temperature, injection pressure, and flow rate of 216°C, 50°C, 90 bar, and 20 cm3/s respectively. IR thermograph accurately monitors short shot, sink mark, and large flash in moulding part; moreover, IR camera can estimate the mechanical properties of ABS by average temperature but cannot accurately measure the mechanical properties of high glossy PP. It is meant that IR thermal imaging technique can automatically control the product quality in the injection moulding process.

Attention and Pixel Matching in RGB-T Object Tracking

Visual object tracking using visible light images and thermal infrared images, named RGB-T tracking, has recently attracted increasing attention in the tracking community. Deep neural network-based methods becoming the most popular RGB-T trackers, still have to balance the robustness and the speed of calculation. A novel tracker with Siamese architecture is proposed to obtain the accurate object location and meet the real-time requirements. Firstly, a multi-modal weight penalty module is designed to assign different weights to the RGB and thermal infrared features. Secondly, a new pixel matching module is proposed to calculate the similarity between each pixel on the search and the template features, which can avoid bringing excessive background information versus the regular cross-correlation operation. Finally, an improved anchor-free bounding box prediction network is put forward to further reduce the interference of the background information. The experimental results on the standard RGB-T tracking benchmark datasets show that the proposed method achieves better precision and success rate with a speed of over 34 frames per second which satisfies the real-time tracking.

Generation of fused visible and thermal-infrared images for uncooperative spacecraft proximity navigation

State-of-the-art techniques for vision-based relative navigation rely on images acquired in the visible spectral band. Consequently, the accuracy and robustness of the navigation is strongly influenced by the illumination conditions. The exploitation of thermal-infrared images for navigation purposes is studied in the present paper, as a possible solution to improve navigation in close proximity with a target spacecraft. Thermal-infrared images depend on the thermal radiation emitted by the target, hence, they are independent from light conditions; however, they suffer from a poorer texture and a lower contrast with respect to visible ones. This paper proposes pixel-level image fusion to overcome the limitations of the two types of images. The two source images are merged into a more informative one, retaining the strengths of the distinguished sensing modalities. The contribution of this work is twofold: firstly, a realistic thermal infrared images rendering tool for artificial targets is implemented; secondly, different pixel-level visible-thermal infrared images fusion techniques are assessed through qualitative and quantitative performance metrics to ease and improve the subsequent image processing step. The work presents a comprehensive evaluation of the best fusion techniques for on-board implementation, paving the way to the development of a multispectral end-to-end navigation chain.

Digital infrared thermal imaging system based breast cancer diagnosis using 4D U-Net segmentation

Medical Research field has been taken continuous efforts to develop an efficient method for detecting breast cancer, but the goal has still not yet achieved. To overcome this issue, a 4D U-Net segmentation using digital infrared (IR) thermal imaging system is proposed in this manuscript for the diagnosis of breast cancer (DBC-4D U-Net-DITI). Initially, the digital infrared thermal images are taken from DMR-IR data set as input, and the imageries are pre-processed to maintain local features and compress the dynamic range of image based upon Altered Phase Preserving Dynamic Range Compression (APPDRC) approach by removing the speckle noise. Then, the image segmentation is carried out with the help of 4D U-Net for obtaining the segmented digital infrared thermal image. The 4D U-Net weight parameters are optimized with Glowworm Swarm Optimization Algorithm (GSOA). The segmented regions of digital infrared thermal images are fed to Binarized Spiking Neural Network (BSNN) for classifying the pathology stage as No spread, Early Stage, Localized, Regional and Distant. The proposed approach is executed in MATLAB. The performance of proposed approach attains better accuracy of 39.01%, 28.34%, and 37.45%, better precision of 17.12%, 24.12% and 32.07% when compared to existing approaches like chaotic salp swarm algorithm (CSSA) based segmentation of thermal images for breast cancer identification (DBC-CSSA-DITI), marine-predators-algorithm based segmentation of thermal images for the diagnosis of breast cancer (DBC-MPA-DITI) and diagnosis of breast cancer based upon CNN using thermal imageries (DBC-CNN-DITI) respectively.

π‐Conjugated Carbon‐Based Materials for Infrared Thermal Imaging

AbstractInfrared (IR) thermal imaging is receiving a great deal of attention due to its wide range of applications. Given multiple issues (like cost and availability) with the inorganic materials currently exploited for IR imaging, there is nowadays a great push of developing organic imaging materials. Carbon‐based materials are known to have significant transparency in the visible and IR regions and some are used as transparent conductors. Here, whether π‐conjugated carbon‐based materials are suitable for long‐wave (LW) and mid‐wave (MW) IR imaging applications is computationally assessed. Using density functional theory calculations, the IR‐vibrational properties of molecules from acenes to coronenes and fullerenes, and of periodic systems like graphene and carbon nanotubes are characterized. Fullerenes, graphenes, and double‐walled carbon nanotubes are found to be very attractive as they are transparent in both the LWIR and MWIR regions, a feature resulting from the absence of hydrogen atoms. Also, it is found that replacing hydrogen atoms in a molecule with deuterium or sulfur atoms can be an efficient way to improve their LWIR or MWIR transparency, respectively. For fused‐ring systems having hydrogen atoms on the periphery, designing molecules with trio CH‐units is another way to enhance the transparency in the LWIR region.

LANDSAT 8 LST Pan sharpening using novel principal component based downscaling model

The panchromatic (PAN) band is accessible at a much higher spatial resolution than the thermal infrared (TIR) band in remote sensing satellites. To mitigate this issue, current research proposes a fusion method based on principal components of PAN and TIR band pairs to enhance the spatial resolution of Land surface temperature (LST) images. In this research, LANDSAT-8 LST images of 100 m resolution have been downscaled to 15 m resolution by the Novel principal component (NPC) based downscaling model. The downscaled LST image's thermal values have been cross-verified by in situ thermal points collected by the thermal data logger and image quality indices. The results have shown the root mean square error (RMSE) of 0.51–0.92 for downscaled LST image from 100m to 15m resolution. The correlation between downscaled LST and ground truth points is between 0.54 and 0.83. The proposed NPC-based LST-PAN combination technique collects spatial details of the Earth's surface from PAN data as well as indulges thermal information of TIR image. The proposed fusion technique has upgraded the spatial goal and peculiarity of TIR images data set. The higher spatial resolution of thermal images can increase the level of precision in various remote sensing applications. These improved data sets can be used in a variety of applications, including urban heat island mapping, agricultural crop monitoring, forest fire detection, and hydrological modeling, to name a few. The increased spatial resolution provided by NPC fusion results in more accurate and consistent temperature readings, leading to improved decision-making and management in these and other applications.

Classification of Micromobility Vehicles in Thermal-Infrared Images Based on Combined Image and Contour Features Using Neuromorphic Processing

Trends of environmental awareness, combined with a focus on personal fitness and health, motivate many people to switch from cars and public transport to micromobility solutions, namely bicycles, electric bicycles, cargo bikes, or scooters. To accommodate urban planning for these changes, cities and communities need to know how many micromobility vehicles are on the road. In a previous work, we proposed a concept for a compact, mobile, and energy-efficient system to classify and count micromobility vehicles utilizing uncooled long-wave infrared (LWIR) image sensors and a neuromorphic co-processor. In this work, we elaborate on this concept by focusing on the feature extraction process with the goal to increase the classification accuracy. We demonstrate that even with a reduced feature list compared with our early concept, we manage to increase the detection precision to more than 90%. This is achieved by reducing the images of 160 × 120 pixels to only 12 × 18 pixels and combining them with contour moments to a feature vector of only 247 bytes.

Machine Learning-Based Pain Severity Classification of Lumbosacral Radiculopathy Using Infrared Thermal Imaging

Pain is subjective and varies among individuals. Doctors determine pain severity based on a patient’s self-reported symptoms. In such situations, a language barrier may prevent patients from expressing their pain accurately, which may cause doctors to underestimate their pain degree. Moreover, patients’ subjective descriptions of pain can determine their eligibility for secondary benefits, as in the case of compensation for traffic or industrial accidents. Therefore, to perform a multiclass prediction of the severity of lumbar radiculopathy, the authors applied digital infrared thermographic imaging (DITI) to a machine-learning (ML) algorithm. The DITI dataset included data from a healthy population and patients with radiculopathy with herniated lumbar discs at the L3/4, L4/5, and L5/S1 levels. The dataset of 1000 patients was split into training and test datasets in a 7:3 ratio to evaluate the model’s performance. For the training dataset, the average accuracy, precision, recall, and F1 score were 0.82, 0.76, 0.72, and 0.74, respectively. For the test dataset, these values were 0.77, 0.71, 0.75, and 0.73, respectively. Applying the ML algorithm to a pain-severity classification using thermographic images will aid in the treatment of lumbosacral radiculopathy and allow providers to monitor the therapeutic effect of interventions through an assessment of physiological evidence.

Clinical Retrospective Analysis of Cold and Heat Diagnosis of Traditional Chinese Medicine by Application of Infrared Thermal Imaging Technology

Objective: Exploring the application of infrared thermal imaging technology in Traditional Chinese Medicine (TCM) diagnosis of cold and heat by comparing the cold and heat of TCM clinical dialectics with the cold and heat objectively measured by infrared thermal imaging technology. Methods: First, there were 77 patients from the Prevention and Treatment Center of Guangdong Hospital of TCM selected to analyze their clinical information retrospectively. Then, transforming the temperature index measured by infrared thermal imaging technology into the cold and heat deviation index, which can reflect the cold and heat of traditional Chinese medicine. Finally, a Correlation analysis was made between patients' cold and heat deviation index, and clinical symptoms, the type of cold and heat syndrome diagnosed by doctors. Results: The cold and heat deviation index of the outer, upper outer, and lower outer parts of the patients was significantly lower than that of the inner, upper inner, and lower inner parts of the patients and the difference in the mean values of the three groups was 0.06, 0.07 and 0.03, respectively. In all parts of the subjects, the coincidence rate of clinical dialectics of cold and cold and heat deviation index less than 0 (belonging to cold) was 70.17%. In patients with fatigue symptoms, the cold and heat deviation index of the lower inner part, the upper inner part, and the inner part was significantly higher than that of the lower outer part, the upper outer part, and the outer part. Conclusions: The pattern of human torso region division combined with the cold and heat deviation index can be used to reflect the quantitative understanding of cold and heat in the TCM clinic. Quantifying the variation of cold and heat according to the trend of torso regional temperature deviation can explain the pathogenesis of some elementary symptoms closely related to cold and heat. As a quantitative index of TCM cold and heat, the offset index can be proof for visualization, objectification, and quantification, combined with doctors' judgment, which can an effective auxiliary diagnostic means of TCM to a certain extent.

Fusion of UAV-based infrared and visible images for thermal leakage map generation of building facades

To make the best use of available energy resources and reduce costs, improving the energy efficiency of buildings has become a critical issue for the construction industry. Today, developing a three-dimensional model of the energy consumption rates in buildings based on thermal infrared images is essential to visualize, identify and increase energy efficiency. The purpose of this study is to suggest a methodology for generating a thermal leakage map of building facades utilizing the fusion of thermal infrared and visible images captured by Unmanned Aerial Vehicles (UAVs). In general, the proposed method involves three basic steps: the generation of thermal infrared and visible dense point clouds from the building’s facade using Structure from Motion (SfM) and Multi-View Stereo (MVS) algorithms; the fusion of visible and thermal infrared dense point clouds using the Iterative Closest Point (ICP) algorithm to overcome thermal infrared point cloud constraints; the use of edge extraction and region-based segmentation methods to determine the location of the thermal leakage of building facade’s. To that end, two datasets obtained for separate building facades are used to assess the proposed strategy. The results of the data analyses for the extraction of the desired components and determination of thermal leakage locations on the building facets provided a Precision and Recall score of 87 and 90% for the first dataset and 87 and 88 for the second dataset. Examining the outcomes of calculating thermal leakage zones indicates improving Precision and Recall.