Real Time Thermal Image Based Machine Learning Approach for Early Collision Avoidance System of Snowplows

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.

Evaluation of facial temperature distribution changes during meditation using infrared thermal imaging:An experimental,cross-over study

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.

Resolution and noise in ghost imaging with classical thermal light

The resolution and classical noise in ghost imaging with a classical thermal light are investigated theoretically. For ghost imaging with a Gaussian Schell model source, the dependences of the resolution and noise on the spatial coherence of the source and the aperture in the imaging system are discussed and demonstrated by using numerical simulations. The results show that an incoherent source and a large aperture will lead to a good image quality and small noise.

High-order ghost imaging with N-colour thermal light

High-order ghost imaging with thermal light consisting of N different frequencies is investigated. The high-order intensity correlation and intrinsic correlation functions are derived for such N-colour light. It is found that they are similar in form to those for the monochromatic case, thus most of the conclusions we obtained previously for monochromatic Nth-order ghost imaging are still applicable. However, we find that the visibility of the N-colour ghost image depends strongly on the wavelength used to illuminate the object, and increases as this wavelength increases when the test arm is fixed. On the contrary, changes of wavelength in the reference arms do not lead to any change of the visibility.

Applications of Thermal Imaging in Agriculture—A Review

In thermal remote sensing the invisible radiation patterns of objects are converted into visible images and these images are called thermograms or thermal images. Thermal images can be acquired using portable, hand-held or thermal sensors that are coupled with optical systems mounted on an airplane or satellite. This technology is a non-invasive, non-contact and non-destructive technique used to determine thermal properties and features of any object of interest and therefore it can be used in many fields, where heat is generated or lost in space and time. Potential use of thermal remote sensing in agriculture includes nursery and greenhouse monitoring, irrigation scheduling, plants disease detection, estimating fruit yield, evaluating maturity of fruits and bruise detection in fruits and vegetables. This paper reviews the application of thermal imaging in agriculture and its potential use in various agricultural practices.

Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches

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.

Image Recognition Based on Deep Learning with Thermal Camera Sensing

As the COVID-19 epidemic spread across the globe,people around the world were advised or mandated to wear masks in public places to prevent its spreading further.In some cases,not wearing a mask could result in a fine.To monitor mask wearing,and to prevent the spread of future epidemics,this study proposes an image recognition system consisting of a camera,an infrared thermal array sensor,and a convolutional neural network trained in mask recognition.The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen.The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4(YOLOv4)object detection technology,which experiments show has more efficient training parameters and a higher level of accuracy in object recognition.All datasets are uploaded to the cloud for storage using Google Colaboratory,saving human resources and achieving a high level of efficiency at a low cost.

Photovoltaic Cell Panels Soiling Inspection Using Principal Component Thermal Image Processing

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.

Identification of thermal front dynamics in the northern Malacca Strait using ROMS 3D-model

The thermal front in the oceanic system is believed to have a significant effect on biological activity.During an era of climate change,changes in heat regulation between the atmosphere and oceanic interior can alter the characteristics of this important feature.Using the simulation results of the 3D Regional Ocean Modelling System(ROMS),we identified the location of thermal fronts and determined their dynamic variability in the area between the southern Andaman Sea and northern Malacca Strait.The Single Image Edge Detection(SIED)algorithm was used to detect the thermal front from model-derived temperature.Results show that a thermal front occurred every year from 2002 to 2012 with the temperature gradient at the location of the front was 0.3°C/km.Compared to the years affected by El Ni?o and negative Indian Ocean Dipole(IOD),the normal years(e.g.,May 2003)show the presence of the thermal front at every selected depth(10,25,50,and 75 m),whereas El Ni?o and negative IOD during 2010 show the presence of the thermal front only at depth of 75 m due to greater warming,leading to the thermocline deepening and enhanced stratification.During May 2003,the thermal front was separated by cooler SST in the southern Andaman Sea and warmer SST in the northern Malacca Strait.The higher SST in the northern Malacca Strait was believed due to the besieged Malacca Strait,which trapped the heat and make it difficult to release while higher chlorophyll a in Malacca Strait is due to the freshwater conduit from nearby rivers(Klang,Langat,Perak,and Selangor).Furthermore,compared to the southern Andaman Sea,the chlorophyll a in the northern Malacca Strait is easier to reach the surface area due to the shallower thermocline,which allows nutrients in the area to reach the surface faster.

Visibility enhancement in two-dimensional lensless ghost imaging with true thermal light

We report an experimental demonstration of two-dimensional（2D） lensless ghost imaging with true thermal light. An electrodeless discharge lamp with a higher light intensity than the hollow cathode lamp used before is employed as a light source. The main problem encountered by the 2D lensless ghost imaging with true thermal light is that its coherence time is much shorter than the resolution time of the detection system. To overcome this difficulty we derive a method based on the relationship between the true and measured values of the second-order optical intensity correlation, by which means the visibility of the ghost image can be dramatically enhanced. This method would also be suitable for ghost imaging with natural sunlight.

Micro-Scanning Error Correction Technique for an Optical Micro-Scanning Thermal Microscope Imaging System

An error correction technique for the micro-scanning instrument of the optical micro-scanning thermal microscope imaging system is proposed. The technique is based on micro-scanning technology combined with the proposed second-order oversampling reconstruction algorithm and local gradient image reconstruction algorithm. In this paper, we describe the local gradient image reconstruction model, the error correction technique, down-sampling model and the error correction principle. In this paper, we use a Lena original image and four low-resolution images obtained from the standard half-pixel displacement to simulate and verify the effectiveness of the proposed technique. In order to verify the effectiveness of the proposed technique, two groups of low-resolution thermal microscope images are collected by the actual thermal microscope imaging system for experimental study. Simulations and experiments show that the proposed technique can reduce the optical micro-scanning errors, improve the imaging effect of the system and improve the system's spatial resolution. It can be applied to other electro-optical imaging systems to improve their resolution.

Design of Uncooled Thermal Imaging Systems Operating at Multiple Temperatures

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.

Optical micro-scanning location calibration of thermal microscope imaging system

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.

Investigation of automatic segmentation on digital infrared thermal imaging of breast

Objective: To scan all the possibly diseased areas of an organ, a new method of digital infrared thermal imaging (DITI) system was designed on the basis of medical theory. Methods: This new method of DITT is operated in 2 steps; the image is sharpened with wavelet transformation and then the image is divided into normal and possibly diseased areas with Fuzzy clustering. Results: It was found for a comparison between the old and new methods that the new method is more reliable in clinical practice and takes less time to complete a computation. Conclusion : The new model of DITI system can be used clinically to improve the diagnostic accuracy of breast disease.

The relationship between thermal imaging and waist circumference in young adults

Technologies such as 3-dimensional body scanners and thermal cameras are currently being investigated to eliminate the traditional means of assessing anthropometrics in the overweight and obese population. The purpose of this study was to determine the potential for thermal imaging to assess the relationship between thermal patterning and anthropometrics in young adults. Participants were 18 - 24 year old men (n = 176) and women (n = 260) with different Body Mass Indices (BMI), somatotypes, and activity levels. Participants were weighed, body scanned and thermally imaged. Statistical treatment included descriptive statistics and ANOVA. Statistically significant differences between mean thermal ratings were found between the normal and abnormal groups as categorized by waist circumference for both males (p < 0.003) and females (p < 0.001). The mean ratings of the contour regions between normal and overweight/ obese groups were also found to be statistically different for both males

Analysis of rotor eddy current loss and thermal deformation of magnetic liquid double suspension bearing

Magnetic-liquid double suspension bearing(MLDSB)is a new type of suspension bearing based on electromagnetic suspension and supplemented by hydrostatic supporting.Without affecting the electromagnetic suspension force,the hydrostatic supporting effect is increased,and the real-time coupling of magnetic and liquid supporting can be realized.However,due to the high rotation speed,the rotor part produces eddy current loss,resulting in a large temperature rise and large ther-mal deformation,which makes the oil film thickness deviate from the initial design.The support and bearing characteristics are seriously affected.Therefore,this paper intends to explore the internal effects of eddy current loss of the rotor on the temperature rise and thermal deformation of MLDSB.Firstly,the 2D magnetic flow coupling mathematical model of MLDSB is established,and the eddy current loss distribution characteristics of the rotor are numerically simulated by Maxwell software.Secondly,the internal influence of mapping relationship of structural operating parameters such as input current,coil turns and rotor speed on rotor eddy current loss is revealed,and the changing trend of rotor eddy current loss under different design parameters is explored.Thirdly,the eddy cur-rent loss is loaded into the heat transfer finite element calculation model as a heat source,and the temperature rise of the rotor and its thermal deformation are simulated and analyzed,and the influ-ence of eddy current loss on rotor temperature rise and thermal deformation is revealed.Finally,the pressure-flow curve and the distribution law of the internal flow field are tested by the particle image velocimetry(PIV)system.The results show that eddy current loss increases linearly with the in-crease of coil current,coil turns and rotor speed.The effect of rotational speed on eddy current loss is much higher than that of coil current and coil turns.The maximum temperature rise,minimum temperature rise and maximum thermal deformation of the rotor increase with the increase of eddy current loss.The test results of flow-pressure and internal trace curves are basically consistent with the theoretical simulation,which effectively verifies the correctness of the theoretical simulation.The research results can provide theoretical basis for the design and safe and stable operation of magnetic fluid double suspension bearings.

Infra-red Thermal Imaging of the Inner Canthus: Correlates with the Temperature of the Injured Human Brain

Introduction: Infra-red (IR) thermometry is a safe and valid method to determine internal and surface temperature in human subjects. Under conditions of brain damage (head injury or stroke) knowledge of changes in the temperature of intracranial tissue is justified because of the vulnerability of neurons to accelerated damage at temperatures at the upper end of the febrile range. Aim: To determine the temperature at the inner canthus (IC) of the eye as a potential surrogate for brain temperature. Methods: Invasive monitoring of deep brain structures, lateral ventricle and deep white matter. IR temperature readings obtained at right and left IC. Results: ?Strong correlations were evident between R and L IC and brain. Close, as well as poor, agreement between?? sites was shown in some patients and at some times. For right hemispheric lesions four had a better correlation between TbrV and TRIC when compared to TLIC.? When the correlation between TbrV and TLIC was better compared to TbrV and TRIC, four had a predominant right hemispheric lesion. Conclusions: Improved techniques for IR thermal imaging accuracy at the bedside has the potential to improve temperature measurement agreement. The predominant lesion side may have a bearing on maximum ipsilateral IC temperature Further studies are ongoing in this pilot study population.

Oversample Reconstruction Based on a Strong Inter-Diagonal Matrix for an Optical Microscanning Thermal Microscope Imaging System

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.

Gait Recognition System in Thermal Infrared Night Imaging by Using Deep Convolutional Neural Networks

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.

Experimental Detection of Depth of Field for a Thermal Light Lensless Ghost Imaging System

We propose optical experiments to study the depth of field for a thermal light lensless ghost imaging system. It is proved that the diaphragm is an important factor to influence the depth of field, and the ghost images of two detected objects with longitudinal distance less than the depth of field can be achieved simultaneously. The longitudinal coherence scale of the thermal light lensless ghost imaging determines the depth of field. Theoretical analysis can well explain the experimental results.