Infrared thermal imaging technology has become a versatile and transformative tool in geotechnical engineering due to its non-contact,high-sensitivity,and real-time monitoring capabilities.This review explores the pri...Infrared thermal imaging technology has become a versatile and transformative tool in geotechnical engineering due to its non-contact,high-sensitivity,and real-time monitoring capabilities.This review explores the principles,applications,and future potential of infrared thermal imaging technology in the field.Key applications include measuring soil and rock properties,conducting geotechnical surveys,and monitoring geological hazards.Infrared thermal imaging technology has proven effective in detecting thermal anomalies,assessing geotechnical material characteristics,and monitoring hazards such as landslides and rockfalls.Despite its broad applications,challenges persist,including thermal interference,limitations in data processing,and complexities in technology integration.This review outlines advancements needed in algorithm optimization,integration with complementary technologies,and the expansion of applications into emerging areas such as ecological geotechnical engineering and heritage preservation.Addressing these challenges will unlock the full potential of infrared thermal imaging technology,positioning it as an essential tool for enhancing the safety,efficiency,and sustainability of geotechnical engineering practices.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 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
The reasons why thermal imaging systems consume power are analyzed,and a low power consumption design scheme is presented for the thermal imaging systems operating at multiple temperatures. The relation between the re...The reasons why thermal imaging systems consume power are analyzed,and a low power consumption design scheme is presented for the thermal imaging systems operating at multiple temperatures. The relation between the response performance of α-Si microbolometer detector and its operating temperature is studied by means of formulas of microbolometer detector's noise equivalent temperature difference(NETD) and detectivity. Numerical analysis based on true parameters demonstrates that the detectivity decreases slightly and NETD increases slightly when operating temperature rises,which indicates that α-Si microbolometer detector has approximately uniform response in a wide operating temperature range. According to these analyses,a thermal imaging system operating at multiple temperatures is designed. The power of thermoelectric stabilizer(TEC) is less than 350 mW and NETD is less than 120 mK in the ambient temperature range of-40 ℃-60 ℃,which shows that this system not only outputs high-quality images but consumes low power.展开更多
Objective:To investigate the differences between meditation and resting states using infrared thermal imaging(IRTI)to determine facial temperature distribution features during meditation and annotate the patterns of f...Objective:To investigate the differences between meditation and resting states using infrared thermal imaging(IRTI)to determine facial temperature distribution features during meditation and annotate the patterns of facial temperature changes during meditation from the perspective of traditional Chinese medicine facial diagnosis.Methods:Each participant performed 10 min meditation and 10 min resting but in different sequences.A concentration test was set as the task load,followed by a meditation/resting or resting/meditation session,during which the participants'facial temperatures were observed using IRTI.Participants were scored on the Big Five Inventory(BFI)and Mindful Attention Awareness Scale(MAAS).Results:Forehead temperatures decreased more during meditation than during the resting state.The chin temperature increased only during meditation(P<.0001).For the subjects with meditation experience,there were significant differences in the temperatures of the left forehead(P<.01),right forehead(P<.01)and chin(P<.05)between the meditation and resting state at the 10~(th)min.In the nontask state,the BFI-Extraversion showed a negative correlation with the temperature of the left forehead(R=-0.41,P=.03).In the post-task state,the temperature of the left forehead was negatively correlated with scores on the MAAS(R=-0.42,P=.02).Conclusion:Using IRTI to study meditation offers a practical solution to the challenges in meditation research.The results indicate that an increase in chin temperature may be a representative feature of a meditation state,and forehead temperature is also a potential indicator.展开更多
A joint green-edge computing idea is now realized in practice with the help of intelligent infrastructure for modern sport venues,based on Internet of Things(IoT)platforms and Cyber-Physical Systems(CPS).To monitor th...A joint green-edge computing idea is now realized in practice with the help of intelligent infrastructure for modern sport venues,based on Internet of Things(IoT)platforms and Cyber-Physical Systems(CPS).To monitor their sports actions,athletes need smart environments.Using edge-enabled low-cost and low-power sensors,such as infrared monitoring systems that analyze thermal information,this environment should alert to possible physical damages.Early recognition of sports injuries and joint injuries can usually prevent athletes from pain and missing exercise.One of the most efficient methods for identifying pain and movement problems is to monitor the energy emitted by lower limb injuries.By analyzing thermal images of the lower body parts,this research attempts to automatically identify sports injuries.The thermal image is first isolated from the region of interest.Convolutional structures are applied to identify lesions using a newly developed and optimized method.The performance of the classifier is performed with the possibility of deep learning by pruning the features,to reduce the computational complexity and improve the accuracy,and a model has been developed based on the classification of sports injuries in binary mode(i.e.,whether the lesions are present or not)and multiclass mode(i.e.,the severity of sports injuries)resulted in optimal results.Thermal images show the different states of joints,including lesions caused by various sports in the lower limbs.This model could provide the ability of solving uncertainty of answers,repeatability,and convergence towards minimum error.As compared to conventional feature extraction and classification approaches,the outputs are more acceptable.By taking advantage of the K-fold cross-validation method,the average error of the proposed method to detect the severity of damage is less than 2.22%.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
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
A plant protection unmanned aerial vehicle(UAV)applied for spraying pesticide has the advantages of low cost,high efficiency and environmental protection.However,the complex and changeable farmland environment is not ...A plant protection unmanned aerial vehicle(UAV)applied for spraying pesticide has the advantages of low cost,high efficiency and environmental protection.However,the complex and changeable farmland environment is not conductive to perform spray test effectively.It is therefore necessary to carry out spray test under controlled conditions.The current study aimed to illuminate the variation law of droplet deposition characteristics under different UAV flight speeds,and to verify the feasibility for applying infrared thermal imaging in detection of droplet deposition effects.A UAV simulation platform with an airborne spray system was established and an analysis program Droplet Analysis for dealing with water-sensitive paper was developed.The results showed that,when the flight speed was set at 0.3 m/s,0.5 m/s,0.7 m/s,0.9 m/s and 1 m/s,respectively,the droplet deposition density,droplet deposition coverage and arithmetic mean of droplet size D0 decreased as the UAV flight speed increased.On the contrary,the droplet diameter variation coefficient CV increased with the increase of UAV flight speed,resulting in the worse uniformity of sprayed droplet distribution as well.The results can provide a theoretical support for optimizing the spraying parameters of plant protection UAV,and demonstrate the practicability of infrared thermal imaging in evaluating the droplet deposition in the field of aerial spraying.展开更多
Objective: By observing body surface temperature variation of the intermediate structures of the Lung(Fei) and Large Intestine(Dachang) exterior-interior relationship in asthmatic patients, to investigate the patholog...Objective: By observing body surface temperature variation of the intermediate structures of the Lung(Fei) and Large Intestine(Dachang) exterior-interior relationship in asthmatic patients, to investigate the pathological response on the pathway of channels and to substantiate the objective existence of the intermediary structures. Methods: The study included 60 subjects meeting the bronchial asthma inclusion criteria(experimental group) and 60 healthy subjects(normal control group). ATIR-M301 infrared thermal imaging device was used for detecting body surface temperature of the subjects and collecting the infrared thermal images. The temperature values of the intermediate structures of Lung and Large Intestine exterior-interior relationship [throat, Quepen, elbow, nose, Lieque(LU 7), Pianli(LI 6)], control areas(0.2 cm lateral to the above structures) and Yintang(EX-HN 3) were measured on the infrared thermal image by infrared imaging system. Then, the above temperature values were compared and analyzed within and between two groups. Results: There were insignificant differences between the temperature on the left and right sides of the intermediate structures(Quepen, elbow, LU 7, LI 6) in normal control group(P>0.05). Except for that of Quepen, there were insignificant differences between the temperature of the intermediate structures and their corresponding control areas in normal control group(P>0.05). In the experimental group, the temperature on the left and right sides of the intermediate structures(Quepen, elbow, LU 7, LI 6) showed statistically significant differences(P<0.05 or P<0.01); the temperature difference between intermediate structure(throat, Quepen, elbow, nose, LI 6) and their respective control areas were also significant(P<0.05 or P<0.01). The temperature of the intermediate structures(throat, Quepen, elbow, LU7, LI 6) between the experimental group and normal control group showed significant differences(P<0.05 or P<0.01). Conclusions: This study is an initial step to validate the objective existence of Lung and Large Intestine exterior-interior relationship intermediate structures, as described in the Chinese classical medical literatures, through the functional imaging angle. The intermediate structures are the pathological reaction areas of the bronchial asthmatic patients.展开更多
Unmanned aerial vehicle(UAV)technology,artificial intelligence,and the relevant hardware can be used for monitoring wild animals.However,existing methods have several limitations.Therefore,this study explored the monit...Unmanned aerial vehicle(UAV)technology,artificial intelligence,and the relevant hardware can be used for monitoring wild animals.However,existing methods have several limitations.Therefore,this study explored the monitoring and protection of Amur tigers and their main prey species using images from UAVs by optimizing the algorithm models with respect to accuracy,model size,recognition speed,and elimination of environmental inter-ference.Thermal imaging data were collected from 2000 pictures with a thermal imaging lens on a DJI M300RTK UAV at the Hanma National Nature Reserve in the Greater Khingan Mountains in Inner Mongolia,Wangqing National Nature Reserve in Jilin Province,and Siberian Tiger Park in Heilongjiang Province.The YOLO V5s al-gorithm was applied to recognize the animals in the pictures.The accuracy rate was 94.1%,and the size of the model weight(total weight of each model layer trained with the training set)was 14.8 MB.The authors improved the structures and parameters of the YOLO V5s algorithm.As a result,the recognition accuracy rate became 96%,and the model weight was 9.3 MB.The accuracy rate increased by 1.9%,the model weight decreased by 37.2%from 14.8 MB to 9.3 MB,and the recognition time of a single picture was shortened by 34.4%from 0.032 to 0.021 s.This not only increases the recognition accuracy but also effectively lowers the hardware requirements that the algorithm relies on,which provides a lightweight fast recognition method for UAV-based edge computing and online investigation of wild animals.展开更多
The air conditioning(A/C)of cabins allows for customized control,but manual adjustments may distract drivers,as well as result in energy inefficiency.Several existing thermal sensation models require complex inputs,wh...The air conditioning(A/C)of cabins allows for customized control,but manual adjustments may distract drivers,as well as result in energy inefficiency.Several existing thermal sensation models require complex inputs,which are challenging to gather whilst driving.To address this issue,this study developed a non-contact thermal sensation model for cabin occupants based on thermal imaging sensor.To collect actual data used for modeling,an outdoor subject experiment was conducted.In this study,initial training was conducted to compare the performance of six algorithms in building the model,with random forests algorithm showing the best performance.Besides,this study employed the recursive feature elimination(RFE)method with cross-validation algorithm for identifying the key features.In the end,the model was retrained using the selected features.The model that incorporated both environmental parameters and facial-temperature features demonstrated the best performance,with an R2 of 0.659 on the test set.Eliminating the hard-to-measure windshield surface temperature resulted in a slight reduction in accuracy,yielding an R2 of 0.651.To verify the generalizability of the model,this study further conducted independent validation experiments.The selected model,which exhibited a mean absolute error(MAE)of less than 0.4 in thermal sensation units,was proven to be highly applicable.The results can offer new solutions for automatic control of cabin A/C.展开更多
The thermal imaging technique relies on the usage of infrared signal to detect the temperature field.Using temperature as a flow tracer,thermography is used to investigate the scalar transport in the shallow-water wak...The thermal imaging technique relies on the usage of infrared signal to detect the temperature field.Using temperature as a flow tracer,thermography is used to investigate the scalar transport in the shallow-water wake generated by an emergent circular cylinder.Thermal imaging is demonstrated to be a good quantitative flow visualization technique for studying turbulent mixing phenomena in shallow waters.A key advantage of the thermal imaging method over other scalar measurement techniques,such as the Laser Induced Fluorescence（LIF）and Planar Concentration Analysis（PCA）methods,is that it involves a very simple experimental setup.The dispersion characteristics captured with this technique are found to be similar to past studies with traditional measurement techniques.展开更多
To accurately and efficiently detect dead caged laying ducks,thereby reducing reliance on manual inspection,this study proposes a method that integrates infrared thermography with deep learning technology.A lightweigh...To accurately and efficiently detect dead caged laying ducks,thereby reducing reliance on manual inspection,this study proposes a method that integrates infrared thermography with deep learning technology.A lightweight object detection algorithm is developed,utilizing YOLO v8n as the baseline model.The backbone network is replaced with StarNet,which is based on“Star Operate”.Additionally,the C2f-Star structure is designed by combining the Star Block from StarNet with the C2f module,and it is inserted into the Neck structure of the baseline model.Lightweight module L-SPPF replaces the SPPF module in the baseline model to enhance feature augmentation.Furthermore,a lightweight shared convolutional detection head,termed SCSB-Head,is introduced to reduce computational complexity.These improvements collectively form a lightweight object detection algorithm named SLSS-YOLO.Experimental results show that SLSS-YOLO achieves mAP@50%-95%,precision,and recall scores of 80.50%,99.44%,and 98.46%,respectively.Compared to the baseline model,these metrics improve by 1%,1.98%,and 0.26%,respectively.In terms of model size and detection speed,SLSS-YOLO has 1.44 M parameters and 4.6 G FLOPs,achieving an FPS rate of 134.9 f/s.This represents a reduction of 52.16%and 43.90%in parameters and FLOPs,respectively,while increasing FPS by 5.4 f/s compared to the baseline model.Moreover,an object tracking model is constructed using SLSS-YOLO and Hybrid-SORT.Tracking tests demonstrate that Hybrid-SORT achieves zero ID-Switches,with a detection speed of 10.9 ms/f.It outperforms Bot-SORT,ByteTrack,Deep OC-SORT,and OC-SORT in terms of tracking performance.Therefore,the proposed thermal infrared detection method can effectively identify and track dead ducks in complex cage environments,providing a reference for automated inspection in caged duck farms.展开更多
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
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
Aim:The versatile application of perforator free flaps for coverage of any extremity has been well proven.Often,a"freestyle"-like approach is used to design these flaps,as conventional imaging techniques for...Aim:The versatile application of perforator free flaps for coverage of any extremity has been well proven.Often,a"freestyle"-like approach is used to design these flaps,as conventional imaging techniques for perforator identification may be too expensive or unavailable.As will be demonstrated,the recent application of a thermal imaging camera using a smartphone is a cheaper and therefore more universal means to better identify the requisite perforators upon which a free flap can be designed and then monitored.Methods:Smartphone thermography can be used on any patient preoperatively to identify preferable perforators or vascular network"hot spots"within the desired donor site territory.Intraoperative management of the choice of perforators and subsequent flap dissection can be similarly facilitated.Intermittent postoperative monitoring based on changes of the thermal image color palette will provide a comparison that can be used to determine if perfusion across the microanastomosis is sustained.Results:An overview of how to use a smartphone in concert with a thermal imaging camera is outlined.Dynamic infrared thermography represents a thermal stress necessary with a smartphone to better identify donor site"hot spots".Conclusion:Smartphone thermography is an inexpensive and expeditious means for identification of"hot spots"that correlate with perforators that would suffice to insure perfusion to a free perforator flap.However,since perforator caliber and course cannot be determined,this should be considered to be only a complementary adjunct for conventional methods.Nevertheless,its simplicity will overall improve the safer design,harvest,and subsequent monitoring of free flaps.展开更多
A method of micro-scanning location adaptive calibration was proposed, which was real- ized by the digital image micro-displacement estimation. With geometric calculation, this calibration method used the displacement...A method of micro-scanning location adaptive calibration was proposed, which was real- ized by the digital image micro-displacement estimation. With geometric calculation, this calibration method used the displacement estimation of two thermal microscope images to get the size and direc- tion of each scanning location calibration angle. And each location calibration process was repeated according to the offset given by the system beforehand. The comparison experiments of sequence oversampling reconstruction before and after the micro-scanning location calibration were done. The results showed that the calibration method effectively improved the thermal microscope imaging qual- ity.展开更多
Based on a strong inter-diagonal matrix and Taylor series expansions,an oversample reconstruction method was proposed to calibrate the optical micro-scanning error. The technique can obtain regular 2 ×2 microscan...Based on a strong inter-diagonal matrix and Taylor series expansions,an oversample reconstruction method was proposed to calibrate the optical micro-scanning error. The technique can obtain regular 2 ×2 microscanning undersampling images from the real irregular undersampling images,and can then obtain a high spatial oversample resolution image. Simulations and experiments show that the proposed technique can reduce optical micro-scanning error and improve the system＇s spatial resolution. The algorithm is simple,fast and has low computational complexity. It can also be applied to other electro-optical imaging systems to improve their spatial resolution and has a widespread application prospect.展开更多
Gait is an essential biomedical feature that distinguishes individuals through walking.This feature automatically stimulates the need for remote human recognition in security-sensitive visual monitoring applications.H...Gait is an essential biomedical feature that distinguishes individuals through walking.This feature automatically stimulates the need for remote human recognition in security-sensitive visual monitoring applications.However,there is still a lack of sufficient accuracy of gait recognition at night,in addition to taking some critical factors that affect the performances of the recognition algorithm.Therefore,a novel approach is proposed to automatically identify individuals from thermal infrared(TIR)images according to their gaits captured at night.This approach uses a new night gait network(NGaitNet)based on similarity deep convolutional neural networks(CNNs)method to enhance gait recognition at night.First,the TIR image is represented via personal movements and enhanced body skeleton segments.Then,the state-space method with a Hough transform is used to extract gait features to obtain skeletal joints′angles.These features are trained to identify the most discriminating gait patterns that indicate a change in human identity.To verify the proposed method,the experimental results are performed by using learning and validation curves via being connected by the Visdom website.The proposed thermal infrared imaging night gait recognition(TIRNGaitNet)approach has achieved the highest gait recognition accuracy rates(99.5%,97.0%),especially under normal walking conditions on the Chinese Academy of Sciences Institute of Automation infrared night gait dataset(CASIA C)and Donghua University thermal infrared night gait database(DHU night gait dataset).On the same dataset,the results of the TIRNGaitNet approach provide the record scores of(98.0%,87.0%)under the slow walking condition and(94.0%,86.0%)for the quick walking condition.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42461160293 and 42230710)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20221250).
文摘Infrared thermal imaging technology has become a versatile and transformative tool in geotechnical engineering due to its non-contact,high-sensitivity,and real-time monitoring capabilities.This review explores the principles,applications,and future potential of infrared thermal imaging technology in the field.Key applications include measuring soil and rock properties,conducting geotechnical surveys,and monitoring geological hazards.Infrared thermal imaging technology has proven effective in detecting thermal anomalies,assessing geotechnical material characteristics,and monitoring hazards such as landslides and rockfalls.Despite its broad applications,challenges persist,including thermal interference,limitations in data processing,and complexities in technology integration.This review outlines advancements needed in algorithm optimization,integration with complementary technologies,and the expansion of applications into emerging areas such as ecological geotechnical engineering and heritage preservation.Addressing these challenges will unlock the full potential of infrared thermal imaging technology,positioning it as an essential tool for enhancing the safety,efficiency,and sustainability of geotechnical engineering practices.
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 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.
文摘The reasons why thermal imaging systems consume power are analyzed,and a low power consumption design scheme is presented for the thermal imaging systems operating at multiple temperatures. The relation between the response performance of α-Si microbolometer detector and its operating temperature is studied by means of formulas of microbolometer detector's noise equivalent temperature difference(NETD) and detectivity. Numerical analysis based on true parameters demonstrates that the detectivity decreases slightly and NETD increases slightly when operating temperature rises,which indicates that α-Si microbolometer detector has approximately uniform response in a wide operating temperature range. According to these analyses,a thermal imaging system operating at multiple temperatures is designed. The power of thermoelectric stabilizer(TEC) is less than 350 mW and NETD is less than 120 mK in the ambient temperature range of-40 ℃-60 ℃,which shows that this system not only outputs high-quality images but consumes low power.
基金supported by the Fundamental Research Funds for the Central Universities(x2021-JYB-XJSJJ-032)Beijing Municipal Commission of Education,Double First-class,High-caliber Talents Grant(1000041510156)。
文摘Objective:To investigate the differences between meditation and resting states using infrared thermal imaging(IRTI)to determine facial temperature distribution features during meditation and annotate the patterns of facial temperature changes during meditation from the perspective of traditional Chinese medicine facial diagnosis.Methods:Each participant performed 10 min meditation and 10 min resting but in different sequences.A concentration test was set as the task load,followed by a meditation/resting or resting/meditation session,during which the participants'facial temperatures were observed using IRTI.Participants were scored on the Big Five Inventory(BFI)and Mindful Attention Awareness Scale(MAAS).Results:Forehead temperatures decreased more during meditation than during the resting state.The chin temperature increased only during meditation(P<.0001).For the subjects with meditation experience,there were significant differences in the temperatures of the left forehead(P<.01),right forehead(P<.01)and chin(P<.05)between the meditation and resting state at the 10~(th)min.In the nontask state,the BFI-Extraversion showed a negative correlation with the temperature of the left forehead(R=-0.41,P=.03).In the post-task state,the temperature of the left forehead was negatively correlated with scores on the MAAS(R=-0.42,P=.02).Conclusion:Using IRTI to study meditation offers a practical solution to the challenges in meditation research.The results indicate that an increase in chin temperature may be a representative feature of a meditation state,and forehead temperature is also a potential indicator.
文摘A joint green-edge computing idea is now realized in practice with the help of intelligent infrastructure for modern sport venues,based on Internet of Things(IoT)platforms and Cyber-Physical Systems(CPS).To monitor their sports actions,athletes need smart environments.Using edge-enabled low-cost and low-power sensors,such as infrared monitoring systems that analyze thermal information,this environment should alert to possible physical damages.Early recognition of sports injuries and joint injuries can usually prevent athletes from pain and missing exercise.One of the most efficient methods for identifying pain and movement problems is to monitor the energy emitted by lower limb injuries.By analyzing thermal images of the lower body parts,this research attempts to automatically identify sports injuries.The thermal image is first isolated from the region of interest.Convolutional structures are applied to identify lesions using a newly developed and optimized method.The performance of the classifier is performed with the possibility of deep learning by pruning the features,to reduce the computational complexity and improve the accuracy,and a model has been developed based on the classification of sports injuries in binary mode(i.e.,whether the lesions are present or not)and multiclass mode(i.e.,the severity of sports injuries)resulted in optimal results.Thermal images show the different states of joints,including lesions caused by various sports in the lower limbs.This model could provide the ability of solving uncertainty of answers,repeatability,and convergence towards minimum error.As compared to conventional feature extraction and classification approaches,the outputs are more acceptable.By taking advantage of the K-fold cross-validation method,the average error of the proposed method to detect the severity of damage is less than 2.22%.
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This research was financially support by Major Science and Technology Projects of Zhejiang Province(2015C02007).
文摘A plant protection unmanned aerial vehicle(UAV)applied for spraying pesticide has the advantages of low cost,high efficiency and environmental protection.However,the complex and changeable farmland environment is not conductive to perform spray test effectively.It is therefore necessary to carry out spray test under controlled conditions.The current study aimed to illuminate the variation law of droplet deposition characteristics under different UAV flight speeds,and to verify the feasibility for applying infrared thermal imaging in detection of droplet deposition effects.A UAV simulation platform with an airborne spray system was established and an analysis program Droplet Analysis for dealing with water-sensitive paper was developed.The results showed that,when the flight speed was set at 0.3 m/s,0.5 m/s,0.7 m/s,0.9 m/s and 1 m/s,respectively,the droplet deposition density,droplet deposition coverage and arithmetic mean of droplet size D0 decreased as the UAV flight speed increased.On the contrary,the droplet diameter variation coefficient CV increased with the increase of UAV flight speed,resulting in the worse uniformity of sprayed droplet distribution as well.The results can provide a theoretical support for optimizing the spraying parameters of plant protection UAV,and demonstrate the practicability of infrared thermal imaging in evaluating the droplet deposition in the field of aerial spraying.
基金Supported by National Basic Research and Development Program(973 Program,No.2009CB522708)
文摘Objective: By observing body surface temperature variation of the intermediate structures of the Lung(Fei) and Large Intestine(Dachang) exterior-interior relationship in asthmatic patients, to investigate the pathological response on the pathway of channels and to substantiate the objective existence of the intermediary structures. Methods: The study included 60 subjects meeting the bronchial asthma inclusion criteria(experimental group) and 60 healthy subjects(normal control group). ATIR-M301 infrared thermal imaging device was used for detecting body surface temperature of the subjects and collecting the infrared thermal images. The temperature values of the intermediate structures of Lung and Large Intestine exterior-interior relationship [throat, Quepen, elbow, nose, Lieque(LU 7), Pianli(LI 6)], control areas(0.2 cm lateral to the above structures) and Yintang(EX-HN 3) were measured on the infrared thermal image by infrared imaging system. Then, the above temperature values were compared and analyzed within and between two groups. Results: There were insignificant differences between the temperature on the left and right sides of the intermediate structures(Quepen, elbow, LU 7, LI 6) in normal control group(P>0.05). Except for that of Quepen, there were insignificant differences between the temperature of the intermediate structures and their corresponding control areas in normal control group(P>0.05). In the experimental group, the temperature on the left and right sides of the intermediate structures(Quepen, elbow, LU 7, LI 6) showed statistically significant differences(P<0.05 or P<0.01); the temperature difference between intermediate structure(throat, Quepen, elbow, nose, LI 6) and their respective control areas were also significant(P<0.05 or P<0.01). The temperature of the intermediate structures(throat, Quepen, elbow, LU7, LI 6) between the experimental group and normal control group showed significant differences(P<0.05 or P<0.01). Conclusions: This study is an initial step to validate the objective existence of Lung and Large Intestine exterior-interior relationship intermediate structures, as described in the Chinese classical medical literatures, through the functional imaging angle. The intermediate structures are the pathological reaction areas of the bronchial asthmatic patients.
基金funded by a program of the Natural Science Foundation of Heilongjiang Province,Research on Key Technologies of Wildlife Intelligent Monitoring(LH2020C034)the National Natural Science Foundation of China(NSFC31872241,32100392)the Fundamental Research Funds for the Central Universities(2572022DS04).
文摘Unmanned aerial vehicle(UAV)technology,artificial intelligence,and the relevant hardware can be used for monitoring wild animals.However,existing methods have several limitations.Therefore,this study explored the monitoring and protection of Amur tigers and their main prey species using images from UAVs by optimizing the algorithm models with respect to accuracy,model size,recognition speed,and elimination of environmental inter-ference.Thermal imaging data were collected from 2000 pictures with a thermal imaging lens on a DJI M300RTK UAV at the Hanma National Nature Reserve in the Greater Khingan Mountains in Inner Mongolia,Wangqing National Nature Reserve in Jilin Province,and Siberian Tiger Park in Heilongjiang Province.The YOLO V5s al-gorithm was applied to recognize the animals in the pictures.The accuracy rate was 94.1%,and the size of the model weight(total weight of each model layer trained with the training set)was 14.8 MB.The authors improved the structures and parameters of the YOLO V5s algorithm.As a result,the recognition accuracy rate became 96%,and the model weight was 9.3 MB.The accuracy rate increased by 1.9%,the model weight decreased by 37.2%from 14.8 MB to 9.3 MB,and the recognition time of a single picture was shortened by 34.4%from 0.032 to 0.021 s.This not only increases the recognition accuracy but also effectively lowers the hardware requirements that the algorithm relies on,which provides a lightweight fast recognition method for UAV-based edge computing and online investigation of wild animals.
基金supported by the National Key R&D Program of China(2022YFC3803201).
文摘The air conditioning(A/C)of cabins allows for customized control,but manual adjustments may distract drivers,as well as result in energy inefficiency.Several existing thermal sensation models require complex inputs,which are challenging to gather whilst driving.To address this issue,this study developed a non-contact thermal sensation model for cabin occupants based on thermal imaging sensor.To collect actual data used for modeling,an outdoor subject experiment was conducted.In this study,initial training was conducted to compare the performance of six algorithms in building the model,with random forests algorithm showing the best performance.Besides,this study employed the recursive feature elimination(RFE)method with cross-validation algorithm for identifying the key features.In the end,the model was retrained using the selected features.The model that incorporated both environmental parameters and facial-temperature features demonstrated the best performance,with an R2 of 0.659 on the test set.Eliminating the hard-to-measure windshield surface temperature resulted in a slight reduction in accuracy,yielding an R2 of 0.651.To verify the generalizability of the model,this study further conducted independent validation experiments.The selected model,which exhibited a mean absolute error(MAE)of less than 0.4 in thermal sensation units,was proven to be highly applicable.The results can offer new solutions for automatic control of cabin A/C.
基金supported by the Non-profit Public Research Project of Ministry of Water Resources(Grant No.200901005)the National Natural Science Foundation of China(Grant No.50879019)the Research Fund for Doctoral Program of Higher Education(Grant No.200802940001)
文摘The thermal imaging technique relies on the usage of infrared signal to detect the temperature field.Using temperature as a flow tracer,thermography is used to investigate the scalar transport in the shallow-water wake generated by an emergent circular cylinder.Thermal imaging is demonstrated to be a good quantitative flow visualization technique for studying turbulent mixing phenomena in shallow waters.A key advantage of the thermal imaging method over other scalar measurement techniques,such as the Laser Induced Fluorescence（LIF）and Planar Concentration Analysis（PCA）methods,is that it involves a very simple experimental setup.The dispersion characteristics captured with this technique are found to be similar to past studies with traditional measurement techniques.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2662022GXYJ004).
文摘To accurately and efficiently detect dead caged laying ducks,thereby reducing reliance on manual inspection,this study proposes a method that integrates infrared thermography with deep learning technology.A lightweight object detection algorithm is developed,utilizing YOLO v8n as the baseline model.The backbone network is replaced with StarNet,which is based on“Star Operate”.Additionally,the C2f-Star structure is designed by combining the Star Block from StarNet with the C2f module,and it is inserted into the Neck structure of the baseline model.Lightweight module L-SPPF replaces the SPPF module in the baseline model to enhance feature augmentation.Furthermore,a lightweight shared convolutional detection head,termed SCSB-Head,is introduced to reduce computational complexity.These improvements collectively form a lightweight object detection algorithm named SLSS-YOLO.Experimental results show that SLSS-YOLO achieves mAP@50%-95%,precision,and recall scores of 80.50%,99.44%,and 98.46%,respectively.Compared to the baseline model,these metrics improve by 1%,1.98%,and 0.26%,respectively.In terms of model size and detection speed,SLSS-YOLO has 1.44 M parameters and 4.6 G FLOPs,achieving an FPS rate of 134.9 f/s.This represents a reduction of 52.16%and 43.90%in parameters and FLOPs,respectively,while increasing FPS by 5.4 f/s compared to the baseline model.Moreover,an object tracking model is constructed using SLSS-YOLO and Hybrid-SORT.Tracking tests demonstrate that Hybrid-SORT achieves zero ID-Switches,with a detection speed of 10.9 ms/f.It outperforms Bot-SORT,ByteTrack,Deep OC-SORT,and OC-SORT in terms of tracking performance.Therefore,the proposed thermal infrared detection method can effectively identify and track dead ducks in complex cage environments,providing a reference for automated inspection in caged duck farms.
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.
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.
文摘Aim:The versatile application of perforator free flaps for coverage of any extremity has been well proven.Often,a"freestyle"-like approach is used to design these flaps,as conventional imaging techniques for perforator identification may be too expensive or unavailable.As will be demonstrated,the recent application of a thermal imaging camera using a smartphone is a cheaper and therefore more universal means to better identify the requisite perforators upon which a free flap can be designed and then monitored.Methods:Smartphone thermography can be used on any patient preoperatively to identify preferable perforators or vascular network"hot spots"within the desired donor site territory.Intraoperative management of the choice of perforators and subsequent flap dissection can be similarly facilitated.Intermittent postoperative monitoring based on changes of the thermal image color palette will provide a comparison that can be used to determine if perfusion across the microanastomosis is sustained.Results:An overview of how to use a smartphone in concert with a thermal imaging camera is outlined.Dynamic infrared thermography represents a thermal stress necessary with a smartphone to better identify donor site"hot spots".Conclusion:Smartphone thermography is an inexpensive and expeditious means for identification of"hot spots"that correlate with perforators that would suffice to insure perfusion to a free perforator flap.However,since perforator caliber and course cannot be determined,this should be considered to be only a complementary adjunct for conventional methods.Nevertheless,its simplicity will overall improve the safer design,harvest,and subsequent monitoring of free flaps.
基金Supported by Beijing Natural Science Foundation(4062029)Ministry of Science and Technology Innovation Foundation for Small and Medium-sized Enterprises (06KW1051)North China University of Technology Dr. Start-up Fund for 2013
文摘A method of micro-scanning location adaptive calibration was proposed, which was real- ized by the digital image micro-displacement estimation. With geometric calculation, this calibration method used the displacement estimation of two thermal microscope images to get the size and direc- tion of each scanning location calibration angle. And each location calibration process was repeated according to the offset given by the system beforehand. The comparison experiments of sequence oversampling reconstruction before and after the micro-scanning location calibration were done. The results showed that the calibration method effectively improved the thermal microscope imaging qual- ity.
基金Supported by the National Natural Science Foundation of China(NSFC 61501396)the Colleges and Universities under the Science and Technology Research Projects of Hebei Province(QN2015021)
文摘Based on a strong inter-diagonal matrix and Taylor series expansions,an oversample reconstruction method was proposed to calibrate the optical micro-scanning error. The technique can obtain regular 2 ×2 microscanning undersampling images from the real irregular undersampling images,and can then obtain a high spatial oversample resolution image. Simulations and experiments show that the proposed technique can reduce optical micro-scanning error and improve the system＇s spatial resolution. The algorithm is simple,fast and has low computational complexity. It can also be applied to other electro-optical imaging systems to improve their spatial resolution and has a widespread application prospect.
文摘Gait is an essential biomedical feature that distinguishes individuals through walking.This feature automatically stimulates the need for remote human recognition in security-sensitive visual monitoring applications.However,there is still a lack of sufficient accuracy of gait recognition at night,in addition to taking some critical factors that affect the performances of the recognition algorithm.Therefore,a novel approach is proposed to automatically identify individuals from thermal infrared(TIR)images according to their gaits captured at night.This approach uses a new night gait network(NGaitNet)based on similarity deep convolutional neural networks(CNNs)method to enhance gait recognition at night.First,the TIR image is represented via personal movements and enhanced body skeleton segments.Then,the state-space method with a Hough transform is used to extract gait features to obtain skeletal joints′angles.These features are trained to identify the most discriminating gait patterns that indicate a change in human identity.To verify the proposed method,the experimental results are performed by using learning and validation curves via being connected by the Visdom website.The proposed thermal infrared imaging night gait recognition(TIRNGaitNet)approach has achieved the highest gait recognition accuracy rates(99.5%,97.0%),especially under normal walking conditions on the Chinese Academy of Sciences Institute of Automation infrared night gait dataset(CASIA C)and Donghua University thermal infrared night gait database(DHU night gait dataset).On the same dataset,the results of the TIRNGaitNet approach provide the record scores of(98.0%,87.0%)under the slow walking condition and(94.0%,86.0%)for the quick walking condition.
