Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time realtime.Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems.T...Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time realtime.Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems.The low-cost thermal imaging software produces low-resolution thermal images in grayscale format,hence necessitating methods for improving the resolution and colorizing the images.The objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images,followed by a sparse autoencoder for colorization of thermal images and amultimodal convolutional neural network for gas detection using electronic nose and thermal images.The dataset used comprises 6400 thermal images and electronic nose measurements for four classes.A multimodal Convolutional Neural Network(CNN)comprising an EfficientNetB2 pre-trainedmodel was developed using both early and late feature fusion.The Super Resolution Generative Adversarial Network(SRGAN)model was developed and trained on low and high-resolution thermal images.Asparse autoencoder was trained on the grayscale and colorized thermal images.The SRGAN was trained on lowand high-resolution thermal images,achieving a Structural Similarity Index(SSIM)of 90.28,a Peak Signal-to-Noise Ratio(PSNR)of 68.74,and a Mean Absolute Error(MAE)of 0.066.The autoencoder model produced an MAE of 0.035,a Mean Squared Error(MSE)of 0.006,and a Root Mean Squared Error(RMSE)of 0.0705.The multimodal CNN,trained on these images and electronic nose measurements using both early and late fusion techniques,achieved accuracies of 97.89% and 98.55%,respectively.Hence,the proposed framework can be of great aid for the integration with low-cost software to generate high quality thermal camera images and highly accurate detection of gases in real-time.展开更多
The visual features of continuous pseudocolor encoding is discussed and the optimiz- ing design algorithm of continuous pseudocolor scale is derived.The algorithm is restricting the varying range and direction of ligh...The visual features of continuous pseudocolor encoding is discussed and the optimiz- ing design algorithm of continuous pseudocolor scale is derived.The algorithm is restricting the varying range and direction of lightness,hue and saturation according to correlation and naturalness,automatically calculating the chromaticity coordinates of nodes in uniform color space to get the longest length of scale path,then interpolating points between nodes in equal color differences to obtain continuous pseudocolor scale with visual uniformity.When it was applied to the pseudocolor encoding of thermal image displays,the results showed that the correlation and the naturalness of original images and cognitive characteristics of target pattern were reserved well;the dynamic range of visual perception and the amount of visual information increased obviously;the contrast sensitivity of target identification improved;and the blindness of scale design were avoided.展开更多
In the experimental study, AGE-782 thermal instrument was used to detect the infrared radiation variation of coal and sandstone (wave-length range 3.6～5.5 μm was used). It's discovered that coal and sandstone fa...In the experimental study, AGE-782 thermal instrument was used to detect the infrared radiation variation of coal and sandstone (wave-length range 3.6～5.5 μm was used). It's discovered that coal and sandstone failure under load have three kinds of infrared thermal features as well as infrared forewarning messages. That are: (1) temperature rises gradually but drops before failure ; (2) temperature rises gradually but quickly rises before failure; (3) first rises,then drops and lower temperature emerges before failure. The further researches and the prospect of micro-wave remote sensing detection .on ground pressure is also discussed.展开更多
An efficient multi-threshold approach to segment thermal image is given based on wavelet transform. The gray-level histogram of original image is obtained. In order to reduce the effect of noise, the gray-level histog...An efficient multi-threshold approach to segment thermal image is given based on wavelet transform. The gray-level histogram of original image is obtained. In order to reduce the effect of noise, the gray-level histogram is smoothed by Bezier curve and Bezier histogram is obtained. One dimension stationary wavelet transform is done to the curvature curve of Bezier histogram. Positions of peak values of curvature curve in wavelet domain are adjusted from 'fine-to-coarse' at all scales. The gray level values, which are located in adjusted peak values at all scales, are considered as segmentation thresholds. The gray level values of valley between peaks are considered as quantity gray levels. Optimal segmentation scale is obtained by a cost criterion. The results of experiment show that a target can be segmented effectively from complex background in thermal image by new approach.展开更多
Induction motors(IMs)typically fail due to the rate of stator short-circuits.Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between...Induction motors(IMs)typically fail due to the rate of stator short-circuits.Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories,non-destructive fault detection is universally perceived as a difficult issue.This paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of IMs.Based on the publicly available thermal images(IRT)dataset related to condition monitoring of electrical equipment-IMs,the proposed approach outperforms the highest training accuracy,validation accuracy,and testing accuracy,i.e.,99%,100%,and 94%,respectively,compared with 8 benchmark approaches based on deep learning models and 3 existing approaches in the literature for 11-class IMs faults.Even the training loss,validation loss,and testing loss of the eleven deployed deep learning models meet industry standards.展开更多
Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual conta...Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual contact)type fault identification technique which may give good precision in all aspects.The soiling issue,which is major productivity affecting factor may import from several reasons such as dust on the wind,bird mucks,etc.The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7%in the county,such as India,to more than 25%in middle-east countries country,such as Dubai,Kuwait,etc.This research offers a solar panel soiling detection system built on thermal imaging which powers the inspection method and mitigates the requirement for physical panel inspection in a large solar production place.Hence,in this method,solar panels can be verified by working without disturbing production operation and it will save time and price of recognition.India ranks 3rd worldwide in the usage use age of Photovoltaic(PV)panels now and it is supported about 8.6%of the Nation’s electricity need in the year 2020.In the meantime,the installed PV production areas in India are aged 4–5 years old.Hence the need for inspection and maintenance of installed PV is growing fast day by day.As a result,this research focuses on finding the soiling hotspot exactly of the working solar panels with the help of Principal Components Thermal Analysis(PCTA)on MATLAB Environment.展开更多
In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance syst...In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance system for snowplows, which intends to detect and estimate the distance of trailing vehicles. Due to the operational conditions of snowplows, which include heavy-blowing snow, traditional optical sensors like LiDAR and visible spectrum cameras have reduced effectiveness in detecting objects in such environments. Thus, we propose using a thermal infrared camera as the primary sensor along with machine learning algorithms. First, we curate a large dataset of thermal images of vehicles in heavy snow conditions. Using the curated dataset, two machine-learning models based on the modified ResNet architectures were trained to detect and estimate the trailing vehicle distance using real-time thermal images. The trained detection network was capable of detecting trailing vehicles 99.0% of the time at 1500.0 ft distance from the snowplow. The trained trailing distance network was capable of estimating distance with an average estimation error of 10.70 ft. The inference performance of the trained models is discussed, along with the interpretation of the performance.展开更多
Rockfalls are among the frequent hazards in underground mines worldwide,requiring effective methods for detecting unstable rock blocks to ensure miners’and equipment’s safety.This study proposes a novel approach for...Rockfalls are among the frequent hazards in underground mines worldwide,requiring effective methods for detecting unstable rock blocks to ensure miners’and equipment’s safety.This study proposes a novel approach for identifying potential rockfall zones using infrared thermal imaging and image segmentation techniques.Infrared images of rock blocks were captured at the Draa Sfar deep underground mine in Morocco using the FLUKE TI401 PRO thermal camera.Two segmentation methods were applied to locate the potential unstable areas:the classical thresholding and the K-means clustering model.The results show that while thresholding allows a binary distinction between stable and unstable areas,K-means clustering is more accurate,especially when using multiple clusters to show different risk levels.The close match between the clustering masks of unstable blocks and their corresponding visible light images further validated this.The findings confirm that thermal image segmentation can serve as an alternative method for predicting rockfalls and monitoring geotechnical issues in underground mines.Underground operators worldwide can apply this approach to monitor rock mass stability.However,further research is recommended to enhance these results,particularly through deep learning-based segmentation and object detection models.展开更多
Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatur...Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatures of sows in existing studies are obtained manually from infrared thermal images,posing an obstacle to the automatic prediction of ovulation time.In this study,an improved YOLO-V5s detector based on feature fusion and dilated convolution(FDYOLOV5s)was proposed for the automatic extraction of the vulva temperature of sows based on infrared thermal images.For the purpose of reducing the model complexity,the depthwise separable convolution and the modified lightweight ShuffleNet-V2 module were introduced in the backbone.Meanwhile,the feature fusion network structure of the model was simplified for efficiency,and a mixed dilated convolutional module was designed to obtain global features.The experimental results show that FD-YOLOV5s outperformed the other nine methods,with a mean average precision(mAP)of 99.1%,an average frame rate of 156.25 fps,and a model size of only 3.86 MB,indicating that the method effectively simplifies the model while ensuring detection accuracy.Using a linear regression between manual extraction and the results extracted using this method in randomly selected thermal images,the coefficients of determination for maximum and average vulvar temperatures reached 99.5%and 99.3%,respectively.The continuous vulva temperature of sows was obtained by the target detection algorithm,and the sow estrus detection was performed by the temperature trend and compared with the manually detected estrus results.The results showed that the sensitivity,specificity,and error rate of the estrus detection algorithm were 89.3%,94.5%,and 5.8%,respectively.The method achieves real-time and accurate extraction of sow vulva temperature and can be used for the automatic detection of sow estrus,which could be helpful for the automatic prediction of ovulation time.展开更多
In order to reduce the temperature measurement error with the uncooled infrared thermal imager, experiments were conducted to evaluate the effects of environment temperature and measurement distance on the measurement...In order to reduce the temperature measurement error with the uncooled infrared thermal imager, experiments were conducted to evaluate the effects of environment temperature and measurement distance on the measurement error of human eye temperature. First, the forehead temperature was used as an intermediate variable to obtain the actual temperature of human eyes. Then, the effects of environment temperature and measurement distance on the temperature measurement were separately analyzed. Finally, an empirical model was established to correlate actual eye temperature with the measured temperature, environment temperature, and measurement distance. To verify the formula, three different environment temperatures were tested at different distances. The measurement errors were substantially reduced using the empirical model for temperature correction. The results show that this method can effectively improve the accuracy of temperature measurement using the infrared thermal imager.展开更多
The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation(drip irrigation,sprinkler irrigation,flood irrigation).It is the first time tha...The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation(drip irrigation,sprinkler irrigation,flood irrigation).It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass.The temperature of crop and background was measured by thermal infrared image.It is necessary to get the crop background separation index(CBSIL,CBSIH),which can be used for distinguishing the crop value from the image.CBSIL and CBSIH(the temperature when the leaves are wet adequately;the temperature when the stomata of leaf is closed completely) are the threshold values.The temperature of crop ranged from CBSIL to CBSIH.Then the ICWSI was calculated based on relevant theoretical method.The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI.In order to construct the high accuracy simulation model,the samples were divided into two parts.One was used for constructing the simulation model,the other for checking the accuracy of the model.Such result of the model was concluded as:(1) As for the simulation model of soil moisture,the correlation coefficient(R2) is larger than 0.887 6,the average of relative error(Er) ranges from 13.33% to 16.88%;(2) As for the simulation model of winter wheat yield,drip irrigation(0.887 6,16.89%,-0.12),sprinkler irrigation(0.970 0,14.85%,-0.12),flood irrigation(0.969 0,18.87%,0.18),with the values of R2,Er and CRM listed in the parentheses followed by the individual term.(3) As for winter wheat biomass,drip irrigation(0.980 0,13.70%,0.13),sprinkler irrigation(0.95,13.15%,-0.14),flood irrigation(0.970 0,14.48%,-0.13),and the values in the parentheses are demonstrated the same as above.Both the CRM and Er are shown to be very low values,which points to the accuracy and reliability of the model investigated.The accuracy of model is high and reliable.The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.展开更多
Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectr...Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the ac- tive appearance model AAM parameters and three defined head motion features are extracted from visible spectrum im- ages, and several thermal statistical features are extracted from infrared （IR） images. Second, feature selection is per- formed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression （NVIE） spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal 1R images＇ supplementary role for visible facial expression recognition.展开更多
The purpose of this study is to evaluate the Spectral Angle Mapper （SAM） classification method for determining the optimum threshold （maximum spectral angle） to unveil the hydrothermal mineral assemblages related ...The purpose of this study is to evaluate the Spectral Angle Mapper （SAM） classification method for determining the optimum threshold （maximum spectral angle） to unveil the hydrothermal mineral assemblages related to mineral deposits. The study area indicates good potential for Cu-Au porphyry, epithermal gold deposits and hydrothermal alteration well developed in arid and semiarid climates, which makes this region significant for Advanced Spaceborne Thermal Emission and Reflection Radiometer （ASTER） image processing analysis. Given that achieving an acceptable mineral mapping requires knowing the alteration patterns, petrochemistry and petrogenesis of the igneous rocks while considering the effect of weathering, overprinting of supergene alteration, overprinting of hypogene alteration and host rock spectral mixing, SAM classification was implemented for argillic, sericitic, propylitic, alunitization, silicification and iron oxide zones of six previously known mineral deposits： Maherabad, a Cu-Au porphyry system; Sheikhabad, an upper part of Cu-Au porphyry system; Khoonik, an Intrusion related Au system; Barmazid, a low sulfidation epithermal system; Khopik, a Cu-Au porphyry system; and Hanish, an epithermal Au system. Thus, the investigation showed that although the whole alteration zones are affected by mixing, it is also possible to produce a favorable hydrothermal mineral map by such complementary data as petrology, petrochemistry and alteration patterns.展开更多
Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for th...Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for thermal imaging and transport research.Here,we introduce an approach to measure nanoscale thermal resistance using in situ inelastic scanning transmission electron microscopy.By constructing unidirectional heating flux with controlled temperature gradients and analyzing electron energy-loss/gain signals under optimized acquisition conditions,nanometer-resolution in mapping phonon apparent temperature is achieved.Thus,interfacial thermal resistance is determined by calculating the ratio of interfacial temperature difference to bulk temperature gradient.This methodology enables direct measurement of thermal transport properties for atomic-scale structural features(e.g.,defects and heterointerfaces),resolving critical structure-performance relationships,providing a useful tool for investigating thermal phenomena at the(sub-)nanoscale.展开更多
0 INTRODUCTION Geohazards in mountainous regions pose significant risks to the construction and safe operation of transportation,water conservancy,and other critical infrastructure projects.Engineering geological inve...0 INTRODUCTION Geohazards in mountainous regions pose significant risks to the construction and safe operation of transportation,water conservancy,and other critical infrastructure projects.Engineering geological investigations are crucial for disaster prevention and mitigation.展开更多
Sleeping site selection is essential for understanding primate behavioral ecology and survival.Identifying where species sleep helps determine priority areas and critical resources for targeted conservation efforts.Ho...Sleeping site selection is essential for understanding primate behavioral ecology and survival.Identifying where species sleep helps determine priority areas and critical resources for targeted conservation efforts.However,observing sleeping sites at night is challenging,especially for species sensitive to human disturbance.Thermal infrared imaging(TIR)with drones is increasingly used for detecting and counting primates,yet it has not been utilized to investigate ecological strategies.This study investigates the sleeping site selection of the Critically Endangered black-shanked douc langur(Pygathrix nigripes)in Cát Tiên National Park,Vietnam.Our aim is to assess the feasibility of using a TIR drone to test sleeping site selection strategies in non-nesting primates,specifically examining hypotheses related to predation avoidance and food proximity.Between January and April 2023,we conducted 120 drone flights along 22 transects(~1-km long)and identified 114 sleeping sites via thermal imaging.We established 116 forest structure plots along 29 transects in non-selected sites and 65 plots within douc langur sleeping sites.Our observations reveal that douc langurs selected tall and large trees that may provide protection against predators.Additionally,they selected sleeping sites with increased access to food,such as Afzelia xylocarpa,which serves as a preferred food source during the dry season.These results highlight the effective use of TIR drones for studying douc langur sleeping site selection with minimal disturbance.Besides offering valuable insights into habitat selection and behavioral ecology for conservation,TIR drones hold great promise for the noninvasive and long-term monitoring of large-bodied arboreal species.展开更多
OBJECTIVE:To study the effects of temperature on different facial areas by suspended moxibustion at two points,Zusanli(ST 36) and Hegu(LI 4),and probe the phenomena underlying self-regulation in the human body after t...OBJECTIVE:To study the effects of temperature on different facial areas by suspended moxibustion at two points,Zusanli(ST 36) and Hegu(LI 4),and probe the phenomena underlying self-regulation in the human body after thermal stimulation.METHODS:Thirty healthy volunteers accepted moxibustion over Zusanli(ST 36) and Hegu(LI 4),and the order of moxibustion points was randomly determined.Moxibustion method:suspension of moxibustion over Zusanli(ST 36) and Hegu(LI 4) on both sides was performed using an ignited moxa stick stuck in a support for 20 min.Observation method:An infrared thermal image of the face was taken before and after suspended moxibustion using a CK350 medical infrared thermal imaging instrument.Data analysis:A thermal microscopic section view system(TMTSys) was used to analyze the change in temperature in special facial areas.Statistical analysis was carried out using SPSS 14.0 software.RESULTS:Before moxibustion was suspended,the facial thermal image showed a T-shaped thermal area related to the vascular distribution with even temperature and good symmetry on both sides.Suspended moxibustion over Zusanli(ST 36) have a very significant increase in temperature at the forehead,around the nose,at the corners of the mouth,and at the cheeks and lips(P<0.01).Suspended moxibustion over Hegu(LI 4) also have a significant(P<0.05) increase in temperature around the nose,the corners of the mouth,the cheeks,and lips,where has a new high temperature area was formed(P<0.01).Suspended moxibustion over Hegu(LI 4) raised the temperature at the middle point of the lips more obviously than did Zusanli(ST 36) in the same person,(P<0.05).After 10 min of moxibustion over Zusanli(ST 36) and Hegu(LI 4),the change in temperature in the facial area reached its peak value.CONCLUSIONS:Facial infrared thermography of healthy people revealed a T-shaped thermal area reflecting a physiological thermal area.Moxibustion over Zusanli(ST 36) or Hegu(LI 4) raised the temperature in this facial T-shaped thermal area.Hegu(LI 4) led to the formation of a new thermal area in the lips.The time required for moxibustion to regulate human body temperature was 10 min.展开更多
Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentic...Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D masks.In order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images.The normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal images.Therefore,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing faces.In order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed.Firstly,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands.Then,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency subbands.Finally,the DTCWT inverse transform is used to obtain the fused image containing the facial texture features.Face recognition is carried out on the fused image to realize identity authentication.Experimental results show that this algorithm can effectively resist attacks from photos,videos or masks.Compared with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.展开更多
OBJECTIVE： To examine ginseng for improving people. the efficacy of Korean red blood flow in healthy METHODS： Participants were randomized and treated with 1500 mg of Korean red ginseng extract or placebo for 8 wee...OBJECTIVE： To examine ginseng for improving people. the efficacy of Korean red blood flow in healthy METHODS： Participants were randomized and treated with 1500 mg of Korean red ginseng extract or placebo for 8 weeks. The effect of Korean red ginseng was evaluated by digital infrared thermal images, and Doppler sonography, and blood test. RESULTS： Imbalance Forty subjects completed the protoco n local thermal distribution was significantly decreased in the Korean red ginseng group confirmed by digital infrared thermal images. Doppler sonography showed no significant change in maximum and average rates of blood circulation in single or complex areas. Blood analyses for coagulation and lipid metabolism factors revealed no significant changes. No abnormal reactions to the Korean red ginseng were observed. CONCLUSION： Digital infrared thermal imaging showed that the temperature deviation in the whole body decreased safely in the Korean red ginseng group, which mitigated the body- temperature imbalance. This result suggests that the Korean red ginseng improves blood circulation in the human body.展开更多
Thermal conducting materials may be damaged during long-term use,resulting in the increase of thermal resistance and therefore inefficient heat dissipation.The introduction of self-healing ability may solve this probl...Thermal conducting materials may be damaged during long-term use,resulting in the increase of thermal resistance and therefore inefficient heat dissipation.The introduction of self-healing ability may solve this problem,but the realization of fast and room-temperature self-healing in thermal conducting composites is quite challenging.Herein,we choose a flexible poly(dimethylsiloxane)polymer material(PDMS-COOH)as the matrix and graphene nanosheets as the thermal conductive filler to prepare a new kind of thermal conductive polymer composite(PDMS-COOH-CG)that can quickly self-heal at room temperature.The thermal conductivity of PDMS-COOH-CG10 with 10%of graphene content is 0.48 W·m^(-1)·K^(-1),which is 16 times that of PDMS-COOH(0.03 W·m^(-1)·K^(-1)).At room temperature,self-healing efficiency of PDMS-COOH-CG10 based on tensile strength can be 53.8%for 30 s and 84.6%for 24 h.Dynamic infrared thermal imaging dipicted that after 2 min of self-healing at room temperature,the thermal conduction temperature near the damage was basically restored to the level of the pristine sample.展开更多
基金funded by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydneysupported by the Researchers Supporting Project,King Saud University,Riyadh,Saudi Arabia,under Project RSP2025 R14.
文摘Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time realtime.Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems.The low-cost thermal imaging software produces low-resolution thermal images in grayscale format,hence necessitating methods for improving the resolution and colorizing the images.The objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images,followed by a sparse autoencoder for colorization of thermal images and amultimodal convolutional neural network for gas detection using electronic nose and thermal images.The dataset used comprises 6400 thermal images and electronic nose measurements for four classes.A multimodal Convolutional Neural Network(CNN)comprising an EfficientNetB2 pre-trainedmodel was developed using both early and late feature fusion.The Super Resolution Generative Adversarial Network(SRGAN)model was developed and trained on low and high-resolution thermal images.Asparse autoencoder was trained on the grayscale and colorized thermal images.The SRGAN was trained on lowand high-resolution thermal images,achieving a Structural Similarity Index(SSIM)of 90.28,a Peak Signal-to-Noise Ratio(PSNR)of 68.74,and a Mean Absolute Error(MAE)of 0.066.The autoencoder model produced an MAE of 0.035,a Mean Squared Error(MSE)of 0.006,and a Root Mean Squared Error(RMSE)of 0.0705.The multimodal CNN,trained on these images and electronic nose measurements using both early and late fusion techniques,achieved accuracies of 97.89% and 98.55%,respectively.Hence,the proposed framework can be of great aid for the integration with low-cost software to generate high quality thermal camera images and highly accurate detection of gases in real-time.
文摘The visual features of continuous pseudocolor encoding is discussed and the optimiz- ing design algorithm of continuous pseudocolor scale is derived.The algorithm is restricting the varying range and direction of lightness,hue and saturation according to correlation and naturalness,automatically calculating the chromaticity coordinates of nodes in uniform color space to get the longest length of scale path,then interpolating points between nodes in equal color differences to obtain continuous pseudocolor scale with visual uniformity.When it was applied to the pseudocolor encoding of thermal image displays,the results showed that the correlation and the naturalness of original images and cognitive characteristics of target pattern were reserved well;the dynamic range of visual perception and the amount of visual information increased obviously;the contrast sensitivity of target identification improved;and the blindness of scale design were avoided.
文摘In the experimental study, AGE-782 thermal instrument was used to detect the infrared radiation variation of coal and sandstone (wave-length range 3.6～5.5 μm was used). It's discovered that coal and sandstone failure under load have three kinds of infrared thermal features as well as infrared forewarning messages. That are: (1) temperature rises gradually but drops before failure ; (2) temperature rises gradually but quickly rises before failure; (3) first rises,then drops and lower temperature emerges before failure. The further researches and the prospect of micro-wave remote sensing detection .on ground pressure is also discussed.
文摘An efficient multi-threshold approach to segment thermal image is given based on wavelet transform. The gray-level histogram of original image is obtained. In order to reduce the effect of noise, the gray-level histogram is smoothed by Bezier curve and Bezier histogram is obtained. One dimension stationary wavelet transform is done to the curvature curve of Bezier histogram. Positions of peak values of curvature curve in wavelet domain are adjusted from 'fine-to-coarse' at all scales. The gray level values, which are located in adjusted peak values at all scales, are considered as segmentation thresholds. The gray level values of valley between peaks are considered as quantity gray levels. Optimal segmentation scale is obtained by a cost criterion. The results of experiment show that a target can be segmented effectively from complex background in thermal image by new approach.
基金supported by the National Natural Science Foundation of China(No.62001197)National High Technology Research and Development Program(863 Program)(2011AA05A107)+1 种基金Natural Sciences Research Grant for Colleges and Universities of Jiangsu Province(No.22KJD470002)Jiangsu Provincial Postgraduate Research and Practice Innovation Program(No.XSJCX21_58).
文摘Induction motors(IMs)typically fail due to the rate of stator short-circuits.Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories,non-destructive fault detection is universally perceived as a difficult issue.This paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of IMs.Based on the publicly available thermal images(IRT)dataset related to condition monitoring of electrical equipment-IMs,the proposed approach outperforms the highest training accuracy,validation accuracy,and testing accuracy,i.e.,99%,100%,and 94%,respectively,compared with 8 benchmark approaches based on deep learning models and 3 existing approaches in the literature for 11-class IMs faults.Even the training loss,validation loss,and testing loss of the eleven deployed deep learning models meet industry standards.
文摘Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual contact)type fault identification technique which may give good precision in all aspects.The soiling issue,which is major productivity affecting factor may import from several reasons such as dust on the wind,bird mucks,etc.The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7%in the county,such as India,to more than 25%in middle-east countries country,such as Dubai,Kuwait,etc.This research offers a solar panel soiling detection system built on thermal imaging which powers the inspection method and mitigates the requirement for physical panel inspection in a large solar production place.Hence,in this method,solar panels can be verified by working without disturbing production operation and it will save time and price of recognition.India ranks 3rd worldwide in the usage use age of Photovoltaic(PV)panels now and it is supported about 8.6%of the Nation’s electricity need in the year 2020.In the meantime,the installed PV production areas in India are aged 4–5 years old.Hence the need for inspection and maintenance of installed PV is growing fast day by day.As a result,this research focuses on finding the soiling hotspot exactly of the working solar panels with the help of Principal Components Thermal Analysis(PCTA)on MATLAB Environment.
文摘In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance system for snowplows, which intends to detect and estimate the distance of trailing vehicles. Due to the operational conditions of snowplows, which include heavy-blowing snow, traditional optical sensors like LiDAR and visible spectrum cameras have reduced effectiveness in detecting objects in such environments. Thus, we propose using a thermal infrared camera as the primary sensor along with machine learning algorithms. First, we curate a large dataset of thermal images of vehicles in heavy snow conditions. Using the curated dataset, two machine-learning models based on the modified ResNet architectures were trained to detect and estimate the trailing vehicle distance using real-time thermal images. The trained detection network was capable of detecting trailing vehicles 99.0% of the time at 1500.0 ft distance from the snowplow. The trained trailing distance network was capable of estimating distance with an average estimation error of 10.70 ft. The inference performance of the trained models is discussed, along with the interpretation of the performance.
基金supported by the Moroccan Ministry of Higher Education,Scientific Research,and Innovationthe Moroccan Digital Development Agency(DDA)+2 种基金the National Center for Scientific and Technical Research of Morocco(CNRST)through the Al-Khawarizmi projectthe MANAGEM groupMASCIR supporting this project.
文摘Rockfalls are among the frequent hazards in underground mines worldwide,requiring effective methods for detecting unstable rock blocks to ensure miners’and equipment’s safety.This study proposes a novel approach for identifying potential rockfall zones using infrared thermal imaging and image segmentation techniques.Infrared images of rock blocks were captured at the Draa Sfar deep underground mine in Morocco using the FLUKE TI401 PRO thermal camera.Two segmentation methods were applied to locate the potential unstable areas:the classical thresholding and the K-means clustering model.The results show that while thresholding allows a binary distinction between stable and unstable areas,K-means clustering is more accurate,especially when using multiple clusters to show different risk levels.The close match between the clustering masks of unstable blocks and their corresponding visible light images further validated this.The findings confirm that thermal image segmentation can serve as an alternative method for predicting rockfalls and monitoring geotechnical issues in underground mines.Underground operators worldwide can apply this approach to monitor rock mass stability.However,further research is recommended to enhance these results,particularly through deep learning-based segmentation and object detection models.
基金This work was financially supported by the Tianjin Key Research and Development Program Science and Technology Support Key Project(Grant No.20YFZCSN00220)the Central Government Leading Local Science and Technology Development Special Project(Grant No.21ZYCGSN00590)the Inner Mongolia Autonomous Region Science and Technology Department Project(Grant No.2020GG0068).
文摘Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatures of sows in existing studies are obtained manually from infrared thermal images,posing an obstacle to the automatic prediction of ovulation time.In this study,an improved YOLO-V5s detector based on feature fusion and dilated convolution(FDYOLOV5s)was proposed for the automatic extraction of the vulva temperature of sows based on infrared thermal images.For the purpose of reducing the model complexity,the depthwise separable convolution and the modified lightweight ShuffleNet-V2 module were introduced in the backbone.Meanwhile,the feature fusion network structure of the model was simplified for efficiency,and a mixed dilated convolutional module was designed to obtain global features.The experimental results show that FD-YOLOV5s outperformed the other nine methods,with a mean average precision(mAP)of 99.1%,an average frame rate of 156.25 fps,and a model size of only 3.86 MB,indicating that the method effectively simplifies the model while ensuring detection accuracy.Using a linear regression between manual extraction and the results extracted using this method in randomly selected thermal images,the coefficients of determination for maximum and average vulvar temperatures reached 99.5%and 99.3%,respectively.The continuous vulva temperature of sows was obtained by the target detection algorithm,and the sow estrus detection was performed by the temperature trend and compared with the manually detected estrus results.The results showed that the sensitivity,specificity,and error rate of the estrus detection algorithm were 89.3%,94.5%,and 5.8%,respectively.The method achieves real-time and accurate extraction of sow vulva temperature and can be used for the automatic detection of sow estrus,which could be helpful for the automatic prediction of ovulation time.
基金supported by the National Key Research and Development Program of China(No.2016YFD0500903)the National Natural Science Foundation of China(Nos.61501422,61705218)
文摘In order to reduce the temperature measurement error with the uncooled infrared thermal imager, experiments were conducted to evaluate the effects of environment temperature and measurement distance on the measurement error of human eye temperature. First, the forehead temperature was used as an intermediate variable to obtain the actual temperature of human eyes. Then, the effects of environment temperature and measurement distance on the temperature measurement were separately analyzed. Finally, an empirical model was established to correlate actual eye temperature with the measured temperature, environment temperature, and measurement distance. To verify the formula, three different environment temperatures were tested at different distances. The measurement errors were substantially reduced using the empirical model for temperature correction. The results show that this method can effectively improve the accuracy of temperature measurement using the infrared thermal imager.
基金China-Germany international cooperation project(IRTG1070)National Natural Science Foundation of China(Item number:0971940)
文摘The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation(drip irrigation,sprinkler irrigation,flood irrigation).It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass.The temperature of crop and background was measured by thermal infrared image.It is necessary to get the crop background separation index(CBSIL,CBSIH),which can be used for distinguishing the crop value from the image.CBSIL and CBSIH(the temperature when the leaves are wet adequately;the temperature when the stomata of leaf is closed completely) are the threshold values.The temperature of crop ranged from CBSIL to CBSIH.Then the ICWSI was calculated based on relevant theoretical method.The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI.In order to construct the high accuracy simulation model,the samples were divided into two parts.One was used for constructing the simulation model,the other for checking the accuracy of the model.Such result of the model was concluded as:(1) As for the simulation model of soil moisture,the correlation coefficient(R2) is larger than 0.887 6,the average of relative error(Er) ranges from 13.33% to 16.88%;(2) As for the simulation model of winter wheat yield,drip irrigation(0.887 6,16.89%,-0.12),sprinkler irrigation(0.970 0,14.85%,-0.12),flood irrigation(0.969 0,18.87%,0.18),with the values of R2,Er and CRM listed in the parentheses followed by the individual term.(3) As for winter wheat biomass,drip irrigation(0.980 0,13.70%,0.13),sprinkler irrigation(0.95,13.15%,-0.14),flood irrigation(0.970 0,14.48%,-0.13),and the values in the parentheses are demonstrated the same as above.Both the CRM and Er are shown to be very low values,which points to the accuracy and reliability of the model investigated.The accuracy of model is high and reliable.The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.
文摘Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the ac- tive appearance model AAM parameters and three defined head motion features are extracted from visible spectrum im- ages, and several thermal statistical features are extracted from infrared （IR） images. Second, feature selection is per- formed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression （NVIE） spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal 1R images＇ supplementary role for visible facial expression recognition.
基金supported by National Geoscience Database and Geological Survey of Iran
文摘The purpose of this study is to evaluate the Spectral Angle Mapper （SAM） classification method for determining the optimum threshold （maximum spectral angle） to unveil the hydrothermal mineral assemblages related to mineral deposits. The study area indicates good potential for Cu-Au porphyry, epithermal gold deposits and hydrothermal alteration well developed in arid and semiarid climates, which makes this region significant for Advanced Spaceborne Thermal Emission and Reflection Radiometer （ASTER） image processing analysis. Given that achieving an acceptable mineral mapping requires knowing the alteration patterns, petrochemistry and petrogenesis of the igneous rocks while considering the effect of weathering, overprinting of supergene alteration, overprinting of hypogene alteration and host rock spectral mixing, SAM classification was implemented for argillic, sericitic, propylitic, alunitization, silicification and iron oxide zones of six previously known mineral deposits： Maherabad, a Cu-Au porphyry system; Sheikhabad, an upper part of Cu-Au porphyry system; Khoonik, an Intrusion related Au system; Barmazid, a low sulfidation epithermal system; Khopik, a Cu-Au porphyry system; and Hanish, an epithermal Au system. Thus, the investigation showed that although the whole alteration zones are affected by mixing, it is also possible to produce a favorable hydrothermal mineral map by such complementary data as petrology, petrochemistry and alteration patterns.
基金supported by the National Natural Science Foundation of China(Grant No.52125307)the National Key R&D Program of China(Grant No.2021YFB3501500)the support from the New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for thermal imaging and transport research.Here,we introduce an approach to measure nanoscale thermal resistance using in situ inelastic scanning transmission electron microscopy.By constructing unidirectional heating flux with controlled temperature gradients and analyzing electron energy-loss/gain signals under optimized acquisition conditions,nanometer-resolution in mapping phonon apparent temperature is achieved.Thus,interfacial thermal resistance is determined by calculating the ratio of interfacial temperature difference to bulk temperature gradient.This methodology enables direct measurement of thermal transport properties for atomic-scale structural features(e.g.,defects and heterointerfaces),resolving critical structure-performance relationships,providing a useful tool for investigating thermal phenomena at the(sub-)nanoscale.
基金financially supported by the National Key R&D Program of China(No.2022YFC3080200)。
文摘0 INTRODUCTION Geohazards in mountainous regions pose significant risks to the construction and safe operation of transportation,water conservancy,and other critical infrastructure projects.Engineering geological investigations are crucial for disaster prevention and mitigation.
基金financial support of the Belgian National Fund for Scientific Research(FNRS)the Duesberg Foundation,and the University of Liège.
文摘Sleeping site selection is essential for understanding primate behavioral ecology and survival.Identifying where species sleep helps determine priority areas and critical resources for targeted conservation efforts.However,observing sleeping sites at night is challenging,especially for species sensitive to human disturbance.Thermal infrared imaging(TIR)with drones is increasingly used for detecting and counting primates,yet it has not been utilized to investigate ecological strategies.This study investigates the sleeping site selection of the Critically Endangered black-shanked douc langur(Pygathrix nigripes)in Cát Tiên National Park,Vietnam.Our aim is to assess the feasibility of using a TIR drone to test sleeping site selection strategies in non-nesting primates,specifically examining hypotheses related to predation avoidance and food proximity.Between January and April 2023,we conducted 120 drone flights along 22 transects(~1-km long)and identified 114 sleeping sites via thermal imaging.We established 116 forest structure plots along 29 transects in non-selected sites and 65 plots within douc langur sleeping sites.Our observations reveal that douc langurs selected tall and large trees that may provide protection against predators.Additionally,they selected sleeping sites with increased access to food,such as Afzelia xylocarpa,which serves as a preferred food source during the dry season.These results highlight the effective use of TIR drones for studying douc langur sleeping site selection with minimal disturbance.Besides offering valuable insights into habitat selection and behavioral ecology for conservation,TIR drones hold great promise for the noninvasive and long-term monitoring of large-bodied arboreal species.
基金Supported by the National Natural Science Foundation (No.30973795)
文摘OBJECTIVE:To study the effects of temperature on different facial areas by suspended moxibustion at two points,Zusanli(ST 36) and Hegu(LI 4),and probe the phenomena underlying self-regulation in the human body after thermal stimulation.METHODS:Thirty healthy volunteers accepted moxibustion over Zusanli(ST 36) and Hegu(LI 4),and the order of moxibustion points was randomly determined.Moxibustion method:suspension of moxibustion over Zusanli(ST 36) and Hegu(LI 4) on both sides was performed using an ignited moxa stick stuck in a support for 20 min.Observation method:An infrared thermal image of the face was taken before and after suspended moxibustion using a CK350 medical infrared thermal imaging instrument.Data analysis:A thermal microscopic section view system(TMTSys) was used to analyze the change in temperature in special facial areas.Statistical analysis was carried out using SPSS 14.0 software.RESULTS:Before moxibustion was suspended,the facial thermal image showed a T-shaped thermal area related to the vascular distribution with even temperature and good symmetry on both sides.Suspended moxibustion over Zusanli(ST 36) have a very significant increase in temperature at the forehead,around the nose,at the corners of the mouth,and at the cheeks and lips(P<0.01).Suspended moxibustion over Hegu(LI 4) also have a significant(P<0.05) increase in temperature around the nose,the corners of the mouth,the cheeks,and lips,where has a new high temperature area was formed(P<0.01).Suspended moxibustion over Hegu(LI 4) raised the temperature at the middle point of the lips more obviously than did Zusanli(ST 36) in the same person,(P<0.05).After 10 min of moxibustion over Zusanli(ST 36) and Hegu(LI 4),the change in temperature in the facial area reached its peak value.CONCLUSIONS:Facial infrared thermography of healthy people revealed a T-shaped thermal area reflecting a physiological thermal area.Moxibustion over Zusanli(ST 36) or Hegu(LI 4) raised the temperature in this facial T-shaped thermal area.Hegu(LI 4) led to the formation of a new thermal area in the lips.The time required for moxibustion to regulate human body temperature was 10 min.
基金This research was funded by the Hebei Science and Technology Support Program Project(Grant No.19273703D)the Hebei Higher Education Science and Technology Research Project(Grant No.ZD2020318).
文摘Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D masks.In order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images.The normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal images.Therefore,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing faces.In order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed.Firstly,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands.Then,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency subbands.Finally,the DTCWT inverse transform is used to obtain the fused image containing the facial texture features.Face recognition is carried out on the fused image to realize identity authentication.Experimental results show that this algorithm can effectively resist attacks from photos,videos or masks.Compared with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.
基金supported by the 2010 grant from the Korean Society of Ginseng funded by Korea Ginseng Corporation
文摘OBJECTIVE： To examine ginseng for improving people. the efficacy of Korean red blood flow in healthy METHODS： Participants were randomized and treated with 1500 mg of Korean red ginseng extract or placebo for 8 weeks. The effect of Korean red ginseng was evaluated by digital infrared thermal images, and Doppler sonography, and blood test. RESULTS： Imbalance Forty subjects completed the protoco n local thermal distribution was significantly decreased in the Korean red ginseng group confirmed by digital infrared thermal images. Doppler sonography showed no significant change in maximum and average rates of blood circulation in single or complex areas. Blood analyses for coagulation and lipid metabolism factors revealed no significant changes. No abnormal reactions to the Korean red ginseng were observed. CONCLUSION： Digital infrared thermal imaging showed that the temperature deviation in the whole body decreased safely in the Korean red ginseng group, which mitigated the body- temperature imbalance. This result suggests that the Korean red ginseng improves blood circulation in the human body.
基金supported by the National Natural Science Foundation of China(Nos.21631006 and 21771100)the Fun dame ntal Research Funds for the Cen tral Universities(No.020514380212).
文摘Thermal conducting materials may be damaged during long-term use,resulting in the increase of thermal resistance and therefore inefficient heat dissipation.The introduction of self-healing ability may solve this problem,but the realization of fast and room-temperature self-healing in thermal conducting composites is quite challenging.Herein,we choose a flexible poly(dimethylsiloxane)polymer material(PDMS-COOH)as the matrix and graphene nanosheets as the thermal conductive filler to prepare a new kind of thermal conductive polymer composite(PDMS-COOH-CG)that can quickly self-heal at room temperature.The thermal conductivity of PDMS-COOH-CG10 with 10%of graphene content is 0.48 W·m^(-1)·K^(-1),which is 16 times that of PDMS-COOH(0.03 W·m^(-1)·K^(-1)).At room temperature,self-healing efficiency of PDMS-COOH-CG10 based on tensile strength can be 53.8%for 30 s and 84.6%for 24 h.Dynamic infrared thermal imaging dipicted that after 2 min of self-healing at room temperature,the thermal conduction temperature near the damage was basically restored to the level of the pristine sample.
