Environmental Sensing Using Land-Based Spectrally-Selective Cameras and a Quadcopter

The melting characteristics of cheese play a pivotal role in determining functional performance in various applications. Measuring the meltability and melting point of cheese is a challenge and requires sophisticated equipment, a laboratory setup, and personnel training, and the cost can be prohibitive. Over the years, many tests have been developed to determine the meltability or melting point of cheese. Currently, the melting point of cheese is measured using a specialized instrument, a rheometer for dynamic stress rheology values, to calculate the transition temperature (tan δ). It measures the transition of cheese from the solid to the viscous phase as the cheese is heated. However, the test is time-consuming. A thermal infrared (IR) image camera and software analysis can be used to quickly measure cheese's meltability and melting point. The study was designed to develop a method to estimate the melting point of cheese using a thermal imaging camera, providing a more practical and cost-effective alternative to conventional rheological analysis. Commercial samples of cheeses, including natural Cheddar and various processed cheese slices, were procured from the market. The samples were analyzed for meltability using the tube melt, Schreiber melt, and rheological (G', G″, and tan δ) tests. The thermal IR image camera test method was developed, and the data were processed using software to determine the melting point. The experiment was conducted in triplicate using different lots of cheese. The collected data were statistically analyzed using RStudio. The melt temperature points from the thermal image camera after 1 min were then correlated with the rheological analysis, and the R2 was calculated. The melt characteristics differed significantly according to cheese type. The melting points (tan δ) obtained using a rheometer for cheeses varied between 46.66°C and 64.33°C, and the corresponding values from image analysis were between 48.07°C and 64.61°C. The R2 values for the correlations ranged from 0.804 to 0.813, demonstrating a strong relationship between the rheological and image analysis methods. Additionally, thermal images identify the cheese's melting point and heat distribution. The detailed interpretation aids researchers in understanding the thermal properties and structural changes during melting, while also providing sales professionals with informative visual representations to effectively showcase product quality and performance. In conclusion, the study demonstrated that a thermal imaging method provides a practical, quick, and cost-effective approach for estimating the melting point in cheese compared with conventional rheological analysis and can be used on-site.

Police, firefighters, and emergency medical personnel are first responders that use thermal imaging cameras (TICs) every day. However, few performance metrics have been developed to assist first responders in evaluating thermal imaging technology. This paper describes a metric for evaluating the nonuniformity of commercial TICs. Three TICs, each employing a different thermal detection technology, were examined. Each TIC was considered a ‘black box’ while being tested. An extended area blackbody (178 mm square) was used as the thermal target, and was placed very close to the objective lens of the TIC under test. The resulting National Television Systems Committee (NTSC) video output signal of the TICs was collected, digitized, and processed. The nonuniformity was calculated using the standard deviation of the digitized image pixel intensities divided by the mean of those pixel intensities. This procedure was repeated for each TIC at several blackbody temperatures in the range from 30 °C to 260 °C. It was observed that the nonuniformity increased in a roughly linear manner with temperature. The testing procedure described herein was developed as part of a suite of tests to be incorporated into a performance standard addressing TICs used by the fire service.

Skin temperature assessment is essential for the diagnosis of cellulitis and monitoring treatment response, but is currently subjective and can contribute to overdiagnosis. We aimed to characterise skin temperature changes over time in cellulitis and compare two objective measurement approaches, a thermal imaging camera (TIC) and a non-contact infrared thermometer (NCIT). A device comparison study nested within a prospective cohort. We measured limb temperatures daily for 4 days using a TIC and two NCITs. Two acute hospitals in the UK's National Health Service. 202 adults (age ≥18 years) diagnosed with lower limb cellulitis who attended hospital for antibiotic treatment. We used linear mixed-effects models to quantify changes in temperature over time and intraclass correlation coefficients (ICC) to assess reliability. We compared temperature measurements between devices using Lin's concordance coefficients and Bland-Altman plots with estimated 95% limits of agreement. 202 patients were included: 95% white ethnicity. Baseline limb temperature differences varied between 2.4°C and 3.4°C, depending on the device. All devices showed significant reductions in affected limb temperature per day, with the largest decrease recorded by the TIC (-0.34°C per day, 95% CI -0.48°C to -0.19°C, p<0.001). Only the TIC and NCIT-1 showed significant reductions in limb temperature difference per day. All devices had excellent reliability (ICCs ≥0.98). However, the magnitude of daily temperature change estimates was small relative to the repeatability coefficients of each device. The NCIT-2 consistently recorded the smallest differences in limb temperatures and demonstrated evidence of proportional bias. Daily temperature changes may be too small for reliable monitoring at the individual patient level, but cumulative changes from day 0 to day 3 may be sufficient for clinical interpretation, despite limitations in the precision of device measurements. NCITs' measurement capabilities differ widely, so these devices cannot be used interchangeably. Due to this and the potential benefits of advanced thermal image analysis, TICs should be prioritised for further study in cellulitis. Future research should confirm our findings in different skin tones and explore the clinical utility of thermal imaging in enabling earlier diagnosis or detecting signs of therapeutic failure.

The paper proposes a hierarchical fault detection method for the high voltage equipment using a microphone array which detects the location of fault and the thermal imaging and CCD cameras which verifies the fault and stores the image, respectively. There are partial arc discharges on the faulty insulators, which generates a specific pattern of sound. Detecting the signal using the microphone array, the location of the faulty insulator can be estimated. The 6th band-pass filter was applied to remove noise signal from wind or external influence. When the mobile robot carries the thermal and CCD cameras to the possible place of the fault insulator, the fault insulators or power transmission wires can be detected by the thermal images, which are caused by the aging or natural erosion. Finally, the CCD camera captures the image of the fault insulator for the record. The detection scheme of fault location using the microphone array and the thermal images have been proved to be effective through the real experiments. As a result of this research, it becomes possible to use a mobile robot with the integrated sensors to detect the fault insulators instead of a human being.

The acquisition of the body temperature of animals kept in captivity in biology laboratories is crucial for several studies in the field of animal biology. Traditionally, the acquisition process was carried out manually, which does not guarantee much accuracy or consistency in the acquired data and was painful for the animal. The process was then switched to a semi-manual process using a thermal camera, but it still involved manually clicking on each part of the animal's body every 20 s of the video to obtain temperature values, making it a time-consuming, non-automatic, and difficult process. This project aims to automate this acquisition process through the automatic recognition of parts of a lizard's body, reading the temperature in these parts based on a video taken with two cameras simultaneously: an RGB camera and a thermal camera. The first camera detects the location of the lizard's various body parts using artificial intelligence techniques, and the second camera allows reading of the respective temperature of each part. Due to the lack of lizard datasets, either in the biology laboratory or online, a dataset had to be created from scratch, containing the identification of the lizard and six of its body parts. YOLOv5 was used to detect the lizard and its body parts in RGB images, achieving a precision of 90.00% and a recall of 98.80%. After initial calibration, the RGB and thermal camera images are properly localised, making it possible to know the lizard's position, even when the lizard is at the same temperature as its surrounding environment, through a coordinate conversion from the RGB image to the thermal image. The thermal image has a colour temperature scale with the respective maximum and minimum temperature values, which is used to read each pixel of the thermal image, thus allowing the correct temperature to be read in each part of the lizard.

Simple SummaryLameness has high economic and welfare cost to the U.K. dairy industry; accurate and early detection of lameness minimises this cost. Thermal imaging devices can be used as a method of detecting lameness; however, these devices are typically high-cost and fragile, limiting their usefulness in a farm setting. This study looked at the effectiveness of low-cost thermal imaging devices when used as lameness detection aids, by comparing one to a research-specification thermal imaging device. Thermal images were taken of cattle feet, and each cow was assessed for lameness. Both devices tested were able to determine whether the cattle were lame; however, the research-specification device performed marginally better at this function. This minimal difference in effectiveness between these devices suggests that low-cost thermal imaging devices could be used as a lameness detection aid; increased use of these devices by farmers may increase lameness detection rates and benefit animal welfare.Lameness has a high economic cost to the U.K. dairy industry; accurate and early detection of lameness minimises this cost. Infrared thermal imaging (IRT) devices have shown promising results for use as a lameness detection aid in cattle when used in research settings; these devices are typically high-cost, limiting their adoption. This study analysed the effectiveness of low-cost IRT devices (LCDs) as lameness detection aids, by comparing both maximum environmentally adjusted temperature values and hindfeet temperature difference collected by an LCD to the mobility score of the cow; this test was repeated for data collected by a research-specification device. Data collection occurred during routine milking of 83 cattle; each cow’s mobility was scored afterwards. Significant differences were found between lame and sound cows with the LCD, upon analysis of both methods. There was no significant difference between the data captured by differing devices. The maximum sensitivity and specificity values for the LCD were calculated as 66.95 and 64.53, respectively, compared with 70.34 and 70.94, respectively, for the research-specification device; optimum threshold values for these were equivalent for both devices, suggesting IRT lameness identification is not device-dependent. It was concluded that a minimal difference in effectiveness between tested devices suggests that LCDs could be used as a lameness detection aid; consequently, there is potential for widespread adoption as on-farm detection aids.

To determine the diagnostic accuracy of thermal imaging camera in the identification of perforators using peroperative visual inspection as gold standard. Descriptive study. Place of Study: Jinnah Burn and Reconstructive Surgery Centre / Allama Iqbal Medical College, Lahore, from October 2017 to September 2018. Patients undergoing pedicled or free perforator flap for reconstructive surgery of upper and lower limb, abdomen, groin, and head and neck region, where soft tissue defect was caused by trauma (road traffic accidents and electric burns), were included in this study. All patients underwent the same treatment protocol of identification of perforator location with Flir One camera preoperatively. Later, it was confirmed peroperatively by visual inspection. Diagnostic accuracy was calculated in terms of sensitivity, specificity, positive predictive value, and negative predictive value. Diagnostic accuracy in identification of perforators in pedicled (n=154) and free flaps (n=30) by thermal imaging camera was calculated as sensitivity of 86.2%, specificity of 80.0% with the positive and negative predictive values of 98.7%, and 25.0%, respectively. Flir One, a smartphone-compatible miniature thermal imaging camera, is useful for the detection of perforators and is highly sensitive and specific. Key Words: Flir One, Perforators, Thermal imaging, Thermal imaging camera.

To determine the accuracy of the thermal imaging camera for the detection of vascular insult before the onset of permanent tissue damage. Descriptive study. Department of Burn and Plastic Surgery, from July to December 2019. The thermal image was used as an adjunct to clinical assessment in 84 flaps. Both pedicled and free flaps were performed in the study with a frequency of 68 (81%) and 16 (19%), respectively. Flap monitoring was performed regularly for five days by same team of surgeons and nurses. Flap vascularity was assessed by temperature gradient colour coding of thermal image taking the clinical assessment like colour, temperature, capillary refill, as a gold standard. Sensitivity and specificity of the thermal camera were calculated. The age range of patients was from 5 years to 55 years. The diagnostic accuracy of thermal imaging camera was calculated to be 96.43% with a sensitivity of 98.7%, specificity of 75%. The positive predictive value was found to be 97.4%, negative predictive value was 85.7%, the false-negative value was 14.3%, and the false-positive value was 2.6%. Thermal imaging camera is an effective and useful adjunct for clinical assessment, as it is an easy and contactless technique. Key Words: Flir one, Flap monitoring, Thermal imaging camera.

The SARS-CoV-2 virus pandemic has shown that the use of a contact thermometer to verify the elevated body temperature of a suspected person carries a risk of spreading disease. The perfect solution seems to be the use of thermal imaging as a diagnostic method in fever evaluation. The aim of the research is to develop an algorithm for thermovision measurements in fever screening standards in the context of the impact of various weather conditions on the temperature of people entering the public institution. Each examined person had two thermal images of the face-AP and lateral projection. Using a T1020 FLIR thermal camera with a resolution of 1024 × 768 pixels; the mean temperature was measured from the area of the forehead, the maximum forehead, the corners of the eyes, the inside of the mouth and the external auditory canal temperature. On the other hand, using classic contact thermometers, the temperature in the armpit and ear was measured. The obtained preliminary results showed very strong and positive correlations between the temperature in the ear measured with an ear thermometer and the maximum, minimum and average forehead temperature. These correlations oscillate at approximately r = 0.6, but the highest value of Spearman coefficient was obtained for the mean temperature of the forehead. Moreover, high correlations were also obtained between the temperature in the ear, measured with an ear thermometer, and the maximum temperature in the corners of the eyes and in the ear, measured with a thermal imaging camera. These values were, respectively, r = 0.54, r = 0.65. In summarizing, remote body temperature measurement taken with a thermal camera can be useful in the assessment of the body's core temperature.

In recent years, thermal imaging cameras are widely used in the field of intelligent surveillance because of their special imaging characteristics and better privacy protection properties. However, due to the low resolution and fixed location for current thermal imaging cameras, it is difficult to effectively identify human behavior using a single detection method based on skeletal keypoints. Therefore, a self-update learning method is proposed for fixed thermal imaging camera scenes, called the behavioral parameter field (BPF). This method can express the regularity of human behavior patterns concisely and directly. Firstly, the detection accuracy of small targets under low-resolution video is improved by optimizing the YOLOv4 network to obtain a human detection model under thermal imaging video. Secondly, the BPF model is designed to learn the human normal behavior features at each position. Finally, based on the learned BPF model, we propose to use metric modules, such as cosine similarity and intersection over union matching, to accomplish the classification of human abnormal behaviors. In the experimental stage, the living scene of the indoor elderly living alone is applied as our experimental case, and a variety of detection models are compared to the proposed method for verifying the effectiveness and practicability of the proposed behavioral parameter field in the self-collected thermal imaging dataset for the indoor elderly living alone.

The variation of air temperature (T_air) over a spatial area is usually hard to detect. This is because it requires a high-spatial network of weather stations distributed over the study area which might be impractical to implement in large studies. Nevertheless, remotely sensed images could provide a low-scale land surface temperature (LST) map for large areas where one of the renowned satellites that provide LST is Landsat. This research tries to understand the accuracy of these LST maps using records from weather stations as well as thermal camera images. To do so, remotely sensed images between April 2021 and June 2022 were retrieved from Landsat 8 to estimate the LST over the University City of Sharjah in the UAE to validate them using 14 weather stations distributed on different land uses. The weather stations recorded T_air every 30 minutes at 0.5 meters and 1.5 meters above the ground. In addition, thermal images from the thermal camera were taken at the same time as Landsat 8. The correlation analysis shows that the relationship between LST and the thermal camera is weaker during higher temperatures, while strong at lower temperatures (R² of 0.878) with an absolute difference of about 8°C. On the other hand, the relationship between LST and T_air at 0.5 meters is slightly stronger than the one at 1.5 meters (R² of 0.877) and (R² of 0.870) respectively.

Fault detection and classification of an electrical equipment is a significant subject concerning the continuity of efficient working and necessary tasks. The heat concept creates a stimulating effect in case of failure among the electrical equipment. For this reason, thermal camera images can be functional and are used to detect the malfunctions. In this paper, thermal camera images are utilized to detect 11 different conditions of induction motors that are 8 different short-circuit faults of stator windings, rotor failure, cooling fan failure, and no-load. First-order statistics (FOS) are considered to obtain the discriminative information among the thermal images. The classification unit of model is specified examining five efficient algorithms that are neural networks (NN), k-nearest neighbors (k-NN), random forest (RF), logistic regression (LR), and support vector machines (SVM). In the experiments, 10-fold cross validation is chosen as the test method, and four metrics (accuracy, specificity, sensitivity, AUC) are considered to evaluate the performance. Consequently, the best accuracy of 97.29% is observed by k-NN and RF techniques. In a detailed examination, it is revealed that the most qualified technique rises as RF for the proposed model by considering the accuracy and AUC rates.

The microclimate of a residential landscape can affect both the energy use in your home and the human thermal comfort in your garden, ultimately affecting the heat in the neighbourhood or precinct. A thermal imaging camera provides information about the temperature of surfaces. By using Stefan–Boltzmann’s law and the surface properties, these temperatures can be used to calculate the emission of longwave radiation (radiant exitance) in W m−2. A thermal camera was used to determine the amount of radiant exitance from a range of residential landscape elements. A standard procedure for capturing these images was developed, taking into account factors which affect the quality of the radiometric data. A quantitative database comparing this radiation has been compiled for different times of day and different seasons. The sky view factor of these elements was chosen such that it was as close to 1 as possible. For a particular landscape design, areas of each landscape element can be measured and the amount of radiation reduced or emitted at different times can be calculated. This data can be used to improve landscape designs to reduce home energy use and human thermal comfort through shading and reduction of surfaces which emit longwave radiation close to the house.

Book Review| December 01 2021 Night on Earth Jeffrey D. Sack, Jeffrey D. Sack Department Editor Search for other works by this author on: This Site PubMed Google Scholar Karen Cruse Suder Karen Cruse Suder Science Faculty, The Summit Country Day School, Cincinnati, Ohio 45208, suder_k@summitcds.org Search for other works by this author on: This Site PubMed Google Scholar The American Biology Teacher (2021) 83 (9): 613. https://doi.org/10.1525/abt.2021.83.9.613 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Karen Cruse Suder; Night on Earth. The American Biology Teacher 1 December 2021; 83 (9): 613. doi: https://doi.org/10.1525/abt.2021.83.9.613 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentThe American Biology Teacher Search What happens when the sun sets? As the light dims, the biological world does not slow. It awakens with a flurry of activity not usually visible to the world. Netflix offers a documentary series entitled Night on Earth that dives into the unseen biological world with sophisticated night and thermal imaging cameras. The organisms featured range from large savanna mammals to pitcher plants to bioluminescent dinoflagellates. Organisms do not just go into hiding when the sun sets—reproduction, feeding, migration, and general survival continue under the veil of darkness. Night on Earth lifts that veil, allowing a glimpse into a previously unknown living world while crisscrossing the globe. This British nature documentary was produced by Plimsoll Productions and released by Netflix in January 2020 as six episodes with a rating of TV-PG for fear. Narrated by Samina Wiley, the 44- to 52-minute episodes are divided into the following categories: Moonlit Plains,... You do not currently have access to this content.

Handheld thermal imaging cameras are an important tool for the first responder community. As their use becomes more prevalent, it will become important for a set of standard test metrics to be available to characterize the performance of these cameras. A major factor in the performance of the imagers is the quality of the image on a display screen. An imager may employ any type of display screen, but the results of this paper will focus on those using liquid crystal displays. First responders, especially firefighters, in the field rely on the performance of this screen to relay vital information during critical situations. Current research on thermal imaging camera performance metrics for first responder applications uses trained observer tests or camera composite output signal measurements. Trained observer tests are subjective and composite output tests do not evaluate the performance of the complete imaging system. It is the goal of this work to develop a non-nondestructive, objective method that tests the performance of the entire thermal imaging camera system, from the infrared destructive, sensor to the display screen. Application of existing display screen performance metrics to thermal imaging cameras requires additional consideration. Most display screen test metrics require a well defined electronic input, with either full black or white pixel input, often encompassing detailed spatial patterns and resolution. Well characterized thermal inputs must be used to obtain accurate, repeatable, and non-destructive display screen measurements for infrared cameras. For this work, a thermal target is used to correlate the measured camera output with the actual display luminance. A test method was developed to determine display screen luminance. A well characterized CCD camera and digital recording device were used to determine an electro-optical transfer function for thermal imaging cameras. This value directly relates the composite output signal to the luminance of the display screen, providing a realistic characterization of system performance.