Thermal Infrared Camera Research Articles

Thermal infrared imaging for the detection of relatively warm lacustrine groundwater discharge at the surface of freshwater bodies

Thermal infrared (TIR) imaging has been previously applied to survey relatively large thermal footprints in coastal zones, lakes, reservoirs and rivers. In freshwater systems, the buoyancy of relatively warm groundwater during the winter months allows for the surface identification of groundwater discharge or thermal pollution using TIR imaging. However, information regarding the performance of TIR for resolving this warm groundwater upwelling is limited, particularly at fine spatial scales and variable discharge rates. In order to evaluate the suitability of TIR to trace warm groundwater upwelling at the water surface of lakes, ponds and reservoirs (e.g. lacustrine groundwater discharge (LGD) in shallow near-shore zones) we conducted a mesocosm experiment with a TIR camera situated 4 m above the water surface to capture thermal patterns in response to different groundwater discharge rates, weather conditions and the diurnal cycle. A fiber optic distributed temperature sensing system (FO-DTS) installed at 2 cm below the water surface was used to ground-truth spatial patterns observed in TIR images. Results show the impacts of both the diurnal cycle of net radiation and prevailing weather conditions on the accuracy of TIR imaging for resolving warm groundwater discharge. Most reliable results were obtained under overcast weather conditions and during the night. The results of our study provide guidance for those looking to use TIR for conducting thermal tracing of LGD at the surface of freshwater bodies during winter.

Superimposing Thermal-Infrared Data on 3D Structure Reconstructed by RGB Visual Odometry

In this paper, we propose a method to generate a three-dimensional (3D) thermal map and RGB + thermal (RGB-T) images of a scene from thermal-infrared and RGB images. The scene images are acquired by moving both a RGB camera and an thermal-infrared camera mounted on a stereo rig. Before capturing the scene with those cameras, we estimate their respective intrinsic parameters and their relative pose. Then, we reconstruct the 3D structures of the scene by using Direct Sparse Odometry (DSO) using the RGB images. In order to superimpose thermal information onto each point generated from DSO, we propose a method for estimating the scale of the point cloud corresponding to the ex-trinsic parameters between both cameras by matching depth images recovered from the RGB camera and the thermal-infrared camera based on mutual information. We also generate RGB-T images using the 3D structure of the scene and Delaunay triangulation. We do not rely on depth cameras and, therefore, our technique is not limited to scenes within the measurement range of the depth cameras. To demonstrate this technique, we generate 3D thermal maps and RGB-T images for both indoor and outdoor scenes.

Deep learning approach for human action recognition in infrared images

Human action recognition based Ambient assisted living (AAL) systems, targeted towards providing assistance for the elderly and persons with disabilities, have been of interest to researchers from various disciplines. The research primarily focuses on development of automatic, minimally intrusive and privacy preserving systems. Although popular in the strategic sector, thermal infrared (IR) cameras haven’t been explored much in AAL. This work demonstrates the use of IR cameras in the field of AAL and discusses its performance in human action recognition (HAR). Particular attention is drawn towards one of the most critical actions - falling. In this reference, a dataset of IR images was generated comprising of 6 action classes – walking, standing, sitting on a chair, sitting on a chair with a desk in front, fallen on the desk in front and fallen/lying on the ground. The dataset comprises of 5278 image samples which have been randomly sampled from thermal videos, each of about 30 s, representing the six action classes. To achieve robust action recognition, we have designed the supervised Convolution Neural Network (CNN) architecture with two convolution layers to classify the 6 action classes. Classification accuracy of 87.44% has been achieved on the manually selected complex test data.

Research on Geometric Calibration of Spaceborne Linear Array Whiskbroom Camera.

The geometric calibration of a spaceborne thermal-infrared camera with a high spatial resolution and wide coverage can set benchmarks for providing an accurate geographical coordinate for the retrieval of land surface temperature. The practice of using linear array whiskbroom Charge-Coupled Device (CCD) arrays to image the Earth can help get thermal-infrared images of a large breadth with high spatial resolutions. Focusing on the whiskbroom characteristics of equal time intervals and unequal angles, the present study proposes a spaceborne linear-array-scanning imaging geometric model, whilst calibrating temporal system parameters and whiskbroom angle parameters. With the help of the YG-14—China’s first satellite equipped with thermal-infrared cameras of high spatial resolution—China’s Anyang Imaging and Taiyuan Imaging are used to conduct an experiment of geometric calibration and a verification test, respectively. Results have shown that the plane positioning accuracy without ground control points (GCPs) is better than 30 pixels and the plane positioning accuracy with GCPs is better than 1 pixel.

Non-destructive Detection and Screening of Non-uniformity in Microwave Sterilization Using Hyperspectral Imaging Analysis

The non-uniformity of microwave sterilization usually causes incomplete inactivation of microorganism (especially foodborne pathogens) and potential food safety issue. Detecting the non-uniformity of load distribution using hyperspectral imaging (HSI) (400–1050 nm) in tandem with multivariate analysis was investigated and infrared (IR) thermal imaging technique was used to assist the evaluation. The best simplified model of least-squares support vector machines (LS-SVM) obtained based on ten optimal wavelengths showed an R2p of 0.905, RMSEP of 0.404 log CFU/mL and RPD of 2.62, while the analysis of temperatures detected by IR thermal camera showed that the temperatures of mid-way along each edge were obviously higher than the center, and heating time of microwave had a significant effect on mean temperature (p < 0.01) and temperature coefficient of variation (COV) values (p < 0.01). Moreover, the visualization of L. monocytogenes load distribution was acquired by transferring the quantitative model of LS-SVM to each pixel in the image at different heating times, with the results being well consistent with those of temperature distribution by IR thermal camera. Therefore HSI technique could be used to monitor the non-uniformity of microwave sterilization processing.

Analysis of Heat Generated in the Shear Zone during Machining of Titanium Alloy Ti6Al4V Using Infrared Thermal Camera

Machining represents a thermo-mechanical reaction based on factors such as plasticity, wear and friction. The experimental design consists of conducting a series of turning trials on a titanium alloy Ti6Al4V using combinations of cutting parameters namely spindle speed (n) of 770 rpm; feed rate (f) of 0.2 and 0.4 mm/rev; and depth of cut (d) of 1 and 2 mm. The length of cut (L) of 10 mm remains constant for all trials. All trails are done under dry conditions. In this paper, a detailed investigation has been carried out to study the temperature at shear zone and the effect of cutting variables on temperature has been evaluated. The temperature in shear zone for each trial was measured using an infrared thermal camera.

Multiple-Model Fully Convolutional Neural Networks for Single Object Tracking on Thermal Infrared Video

The availability of affordable thermal infrared (TIR) camera has instigated its usage in various research fields, especially for the cases that require images to be captured in dark surroundings. One of the low-level tasks required by most TIR-based researches is the need to track an object throughout a video sequence. The main challenge posed by TIR camera usage is the lack of texture to differentiate two nearby objects of the same class. According to the VOT-TIR 2016 challenge, the best fully convolutional neural network (FCNN)-based tracker has only managed to obtain the third place. The discriminative ability of the FCNN tracker is not fully utilized because of the homogenous appearance pattern of the tracked object. This paper aims to improve FCNN-based tracker ability to predict object location through comprehensive sampling approach as well as better scoring scheme. Hence, a multiple-model FCNN is proposed, in which a small set of fully connected layers is updated on the top of pre-trained convolutional neural networks. The possible object locations are generated based on a two-stage sampling that combines stochastically distributed samples and clustered foreground contour information. The best sample is selected according to a combined score of appearance similarity, predicted location, and model reliability. The small set of appearance models is updated by using positive and negative training samples, accumulated from two periods of time which are the recent and parent node intervals. To further improve training accuracy, the samples are generated according to a set of adaptive variances that depends on the trustworthiness of the tracker output. The results show an improvement over TCNN, an FCNN-based tracker that won the VOT 2016 challenge with the expected average overlap increasing from 0.248 to 0.257. The performance enhancement is attributed to the better robustness with a 20% reduction in tracking failure rate compared to the TCNN.

PERFORMANCE EVALUATION OF DRY EYE DETECTION SYSTEM USING HIGHER-ORDER SPECTRA FEATURES FOR DIFFERENT NOISE LEVELS IN IR THERMAL IMAGES

The enhanced tear film evaporation and diminished tear production causes a dry eye (DE) condition. A non-invasive infrared (IR) thermography is most commonly used as a diagnostic tool for diagnosis of DE. However, the availability of high-quality IR thermal camera at low cost is difficult. Hence, an efficient DE detection system which can perform efficiently by using low-cost and low-quality images instead of conventional IR images would be a significant contribution. Therefore, in this work, we have evaluated the performance of automated non-invasive DE detection system using low-quality images obtained by adding different levels of noise to high-quality IR images. In this work, the performances of two non-linear higher-order spectra (HOS) cumulants and bispectrum features are compared. These features are extracted from the IR images with different levels of Gaussian noise. Principal component analysis (PCA) is performed on these extracted features and they are ranked using [Formula: see text]-value and later fed to different classifiers. We have achieved the accuracies, sensitivities and specificities of: (i) 86.90%, 85.71% and 88.10%, with noise level 0 using 24 bispectrum features, and (ii) 80.95%, 85.71% and 76.19%, with noise level 10 using 15 bispectrum features for right eye IR images. This study exhibits that even in the presence of high levels of noise, the detection of DE is possible and our proposed method performs efficiently using HOS bispectrum features. Thus, our proposed method can be used to detect DE using low-quality and inexpensive cameras instead of high-cost IR camera.

Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests - preliminary results

ABSTRACTEffective wildlife management and conservation require reliable assessments of animal abundance. However, no ungulate monitoring methods is entirely satisfying in terms of cost-effectiveness and accuracy. A new method combining unmanned aerial vehicles (drones) and thermal infrared (TIR) imaging may have great potential as a tool for ungulate surveys. Drones enable safe operations at low flying altitudes, and at night – a time that often offers the optimal conditions for wildlife monitoring. To assess the feasibility of the proposed method we used fixed-wing drones with TIR cameras to conduct test surveys in Drawieński National Park, Poland. We demonstrated that ungulate thermal signatures are visible both in leafless deciduous and in pine-dominated coniferous forests. Survey timing highly influenced the results – the best quality thermal images were obtained at sunrise, late evening, and at night. Our preliminary results indicated that thermal surveys from drones are a promising method for ungulate enumeration. We demonstrated that with ground resolution of ~10 cm it is possible to visibly distinguish large species (i.e. red deer) and achieve a good level of area coverage. The main challenges of the method are difficulties in species identification due to relatively low resolution of TIR cameras, regulations limiting drone operations to visual line of sight, and high dependence on weather.

Detection of Oil Product on the Water Surface with Thermal Infrared Camera

From all existing remote detectors infrared sensors are the cheapest and most widely used. In this article described experiment was done to determine if it is possible to detected oil products on the water surface using thermal infrared camera. This hypothesis was confirmed – thickest layer of used oil product appeared hotter than water. Also, it was found that temperatures of oil product on the surface directly depend on the air temperature. However, clouds have a significant effect on efficiency of this remote sensing method.

Finite element modeling of aluminum alloy AA5083-O friction stir welding process

The temperature distribution and the residual thermal stresses generated from friction stir welding process were predicted by using finite element analysis (FEA). The predicted temperature was validated experimentally by using infra-red thermal image camera. Different AA5083-O joints were friction stir welded using cylindrical threaded pin profile and tapered smooth one at different rotational and welding speeds. The increase of welding speed values resulted in lower peak temperature during the welding process and the increase of rotational speed caused higher welding peak temperature. Defect-free welds were obtained by using a threaded pin at lower rotational speed (i.e.400rpm). All the welded joints by the tapered tool pin profile had internal defects at all welding conditions. The maximum tensile strength of 230MPa was obtained by using the threaded tool at 400rpm, 50mm/min while; the minimum one of 85MPa was obtained by using the tapered tool at 630rpm, 100mm/min.

Detection of Small Wildfire by Thermal Infrared Camera With the Uncooled Microbolometer Array for 50-kg Class Satellite

The thermal infrared camera with the uncooled microbolometer array based on commercial products has been developed in a laboratory of a Japanese university and mounted to a 50-kg class small satellite specialized for discovering wildfire. It has been launched in 2014 and successfully detected considerable hotspots not only wildfire but also volcanoes. Brightness temperature derived from observation has been verified, and the scale of observed wildfire has been provisionally presumed; the smallest wildfire ever detected has a flame zone less than $\sim 300~\text{m}^{\mathrm {\mathbf {2}}}$ and the fire radiative power = ~35.4 mW. It is 1/30th the size of the initial requirement estimated in the design process. Our thermal infrared camera developed in a short time with low cost has attained enough ability to discover small wildfire which is suppressive at initial attack.

In-vitro Thermal Maps to Characterize Human Dental Enamel and Dentin.

The crown of a human tooth has an outer layer of highly-mineralized tissue called enamel, beneath which is dentin, a less-mineralized tissue which forms the bulk of the tooth-crown and root. The composition and structure of enamel and dentin are different, resulting in different thermal properties. This gives an opportunity to characterize enamel and dentin from their thermal properties and to visually present the findings as a thermal map. The thermal properties of demineralized enamel and dentin may also be sufficiently different from sound tissue to be seen on a thermal map, underpinning future thermal assessment of caries. The primary aim of this novel study was to produce a thermal map of a sound, human tooth-slice to visually characterize enamel and dentin. The secondary aim was to map a human tooth-slice with demineralized enamel and dentin to consider future diagnostic potential of thermal maps for caries-detection. Two human slices of teeth, one sound and one demineralized from a natural carious lesion, were cooled on ice, then transferred to a hotplate at 30°C where the rewarming-sequence was captured by an infra-red thermal camera. Calculation of thermal diffusivity and thermal conductivity was undertaken, and two methods of data-processing used customized software to produce thermal maps from the thermal characteristic-time-to-relaxation and heat-exchange. The two types of thermal maps characterized enamel and dentin. In addition, sound and demineralized enamel and dentin were distinguishable within both maps. This supports thermal assessment of caries and requires further investigation on a whole tooth.

비 가시 환경에서의 적외선 열화상 카메라 가시도

비 가시 환경에서의 적외선 열화상 카메라 가시도

Exploring Streamwater Mixing Dynamics via Handheld Thermal Infrared Imagery

Stream confluences are important hotspots of aquatic ecological processes. Water mixing dynamics at stream confluences influence physio-chemical characteristics of the stream as well as sediment mobilisation and pollutant dispersal. In this study, we investigated the potential for handheld thermal infrared (TIR) imagery to provide rapid information on stream water mixing dynamics at small scales. In-situ visualisation of water mixing patterns can help reduce analytical errors related to stream water sampling locations and improve our understanding of how confluences and tributaries influence aquatic ecological communities. We compared TIR-inferred stream temperature distributions with water electrical conductivity and temperature (measured with a submerged probe) data from cross-channel transects. We show that the use of a portable TIR camera can enhance the visualisation of mixing dynamics taking place at stream confluences, identify the location of the mixing front between two different water sources and the degree of mixing. Interpretation of handheld TIR observations also provided information on how stream morphology and discharge can influence mixing dynamics in small streams. Overall, this study shows that TIR imagery is a valuable support technique for eco-hydrological investigation at small stream confluences.

Thermal Infrared Polarimetric Sensor for Automated Detection of Oil Spills

One of the most effective ways of minimizing oil spill impact is early detection. Effective early detection requires automated detection that relies as little as possible on an operator and can operate 24/7. A new and innovative optical detection system exploits the polarization of light, the same physics used to reduce glare through the use of polarized glasses but in the thermal infrared (TIR) portion of the optical spectrum. Measuring the polarization of thermally emitted radiation from an oil spill enhances the detection over conventional thermal cameras and has the potential to provide automated day / night monitoring and surveillance. The sensors developed thus far are relatively small and inexpensive and can be easily mounted in areas that need monitoring and installed in unmanned aerial systems (UAS). Since the sensor is adapted from a conventional TIR camera, thermal imagery as currently used is collected in addition to the polarimetric imagery to further improve the detection performance. Lens options enable wide area coverage at shorter ranges and higher resolution at longer ranges from the camera position. A TIR Polarimetric camera was tested at Ohmsett to establish performance under a variety of conditions. The Polarimetric camera was tested during the day and at night, under several different wave conditions generated in the wave tank, and with oil of different compositions and thicknesses. The imagery collected was analyzed to establish the contrast improvement through the polarimetric properties of the oil and to assess the automation of the detection process. In this poster, the sensor and test setup will be briefly described with detailed description of the results and the potential of this detection approach for automated detection.

Distributive Analysis of Temperature Variation According to Fire Patterns through Burn Test

본 연구는 열전대 29개와 데이터록, 그리고 열화상 카메라 등 첨단장비를 활용한 연소실험을 통하여 화재패턴의 영향요인/변수 중 하나인 플래시오버(flashover)를 중심으로 플래시오버 이전단계(preflashover)와 플래시오버 이후단계(post-flashover) 상황으로 구분하여 실험을 전개하였다. 열전대 실험에서 U패턴과 역콘형 패턴을 위시하여 V패턴, 완전 연소, 포어패턴(pour pattern), 엘리게이터 탄화패턴(alligator char pattern), 보호구역 및 열 그림자 그리고 폴다운패턴(fall down pattern)에 대하여 각각 관찰하였다. 또한, 과학적인 데이터 수집과 그 분석 자료에 기초하여 실제 화재조사 현장실무에 적합한 새로운 조사이론과 아이디어를 개발하고자 열전대 및 열화상 데이터 자료에서 수집한 화재패턴과 온도변화의 분포 비교를 통하여 화재패턴 형성과정에 따른 온도의 변화를 검토하였다. 특히, 열전대 데이터에서는 화재기둥과 공기유입의 상관관계 이론을 기초로 컨테이너 구획실의 코너와 벽에서의 화재기둥온도 900℃ 및 1,300℃를 비교 분석한 결과, 내부에서 방화에 의한 비정상 연소를 입증할 수 있었다.This research paper is to study on an analysis for fire pattern’s change in temperature and use of their tests data through combustion experiments using state-of-the art device such as 29 Thermocouples, a Data Logger, and Infrared Thermal Camera. Burn pattern test have been conducted in the phase of pre-flashover fire and post-flashover fire. In the thermocouple experiment, the U pattern and the inverted cone pattern are used and the V pattern, the clean burn, the pour pattern, the alligator char pattern, the protection zone and the heat shadow and the fall down pattern were observed. In addition, to develop new research theories and ideas that are appropriate for practical fire investigations on the basis of scientific data collection and analysis data, the change of temperature according to the process was examined by comparing fire patterns and temperature variations collected from thermocouples and thermal image data. In particular, from thermocouple data and intensity patterns from fires, these tests have determined that the burn have become intense and have the highest temperature (1300℃) when near a floor against wall at a main gate, compared to that of corner (900℃) in experiment sets.

Obstacle Detection System for Small UAVs using ADS-B and Thermal Imaging

Thermal Infrared (TIR) imaging is a promising technology which can provide enhanced capabilities to current vision-based Sense-and-Avoid (SAA) systems. It allows operation under extreme illumination conditions, such as direct sun exposure and during nighttime. This paper presents a lightweight obstacle detection system for small UAVs that integrates a TIR camera and an Automatic Dependent Surveillance Broadcast (ADS-B) receiver. Algorithms for the detection of flying obstacles in TIR images were developed and TIR images were experimentally compared with synchronized RGB images for validation. Matching between aircraft detected in TIR images and those reported by an ADS-B receiver was performed to obtain distance information to the visually detected aircraft. We experimentally proved that our system is able to enhance individual ADS-B and TIR detection capabilities by detecting aircraft under challenging illumination conditions at real-time frame rates while providing distance estimations to visual detections.

Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems

ABSTRACTIn this article, we describe an unmanned aerial system equipped with a thermal-infrared camera and software pipeline that we have developed to monitor animal populations for conservation purposes. Taking a multi-disciplinary approach to tackle this problem, we use freely available astronomical source detection software and the associated expertise of astronomers, to efficiently and reliably detect humans and animals in aerial thermal-infrared footage. Combining this astronomical detection software with existing machine learning algorithms into a single, automated, end-to-end pipeline, we test the software using aerial video footage taken in a controlled, field-like environment. We demonstrate that the pipeline works reliably and describe how it can be used to estimate the completeness of different observational datasets to objects of a given type as a function of height, observing conditions, etc. – a crucial step in converting video footage to scientifically useful information such as the spatial distribution and density of different animal species. Finally, having demonstrated the potential utility of the system, we describe the steps we are taking to adapt the system for work in the field, in particular systematic monitoring of endangered species at National Parks around the world.

Explorative study of using infrared imaging for temperature measurement of pallet of fresh produce

Food industry is currently using thermal imaging to measure temperature of fresh produce, but there is no comparative study between temperatures measured traditionally by temperature probes and the temperatures measured by thermal imaging reported in the literature. This paper is exploring the possibility to use surface temperature measured by infrared (IR) thermal imaging versus temperatures measured by a network of temperature sensors. Two different types of vegetables (chard and cucumber) were covered with three different types of covers (Tyvek® material, Metalized PET and Metalized PET with bubble wrap layer) which are designed to protect shipments of produce against temperature abuses. Each cover/produce combination was exposed to a temperature warm up regime in order to simulate a break in the cold chain during distribution, and it was monitored with the thermal IR camera and temperature sensors for comparisons. Results of temperature measured by thermal imaging showed a differential between 1.9 and 6 °C compared to temperature measured by thermal sensors. These found differences are unacceptable for fresh produce temperature control. The high emissivity of metalized PET produced significant reading errors when using the IR camera. Tyvek® cover had more consistent results due to a higher emissivity value that is closer to the value used by the IR camera. This study is of great value since it is giving instructions of how the thermal camera should be used for temperature measurements for covered pallets.