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Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 298
Author(s):  
César Melo ◽  
Sandra Dixe ◽  
Jaime C. Fonseca ◽  
António H. J. Moreira ◽  
João Borges

COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people’s behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body temperature (using thermal cameras). For this task, synthetic data generation techniques were used to create hybrid datasets from public ones to train state-of-the-art algorithms, such as YOLOv5 object detector and a keypoint detector based on Resnet-50. For RGB mask detection, YOLOv5 achieved an average precision of 82.4%. For thermal masks, glasses, and caruncle detection, YOLOv5 and keypoint detector achieved an average precision of 96.65% and 78.7%, respectively. Moreover, RGB and thermal datasets were made publicly available.


Animals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 9
Author(s):  
Paul M. Cryan ◽  
Paulo M. Gorresen ◽  
Bethany R. Straw ◽  
Syhoune (Simon) Thao ◽  
Elise DeGeorge

Wind energy producers need deployable devices for wind turbines that prevent bat fatalities. Based on the speculation that bats approach turbines after visually mistaking them for trees, we tested a potential light-based deterrence method. It is likely that the affected bats see ultraviolet (UV) light at low intensities. Here, we present the results of a multi-month experiment to cast dim, flickering UV light across wind turbine surfaces at night. Our objectives were to refine and test a practical system for dimly UV-illuminating turbines while testing whether the experimental UV treatment influenced the activity of bats, birds, and insects. We mounted upward-facing UV light arrays on turbines and used thermal-imaging cameras to quantify the presence and activity of night-flying animals. The results demonstrated that the turbines can be lit to the highest reaches of the blades with “invisible” UV light, and the animal responses to such experimental treatment can be concurrently monitored. The UV treatment did not significantly change nighttime bat, insect, or bird activity at the wind turbine. Our findings show how observing flying animals with thermal cameras at night can help test emerging technologies intended to variably affect their behaviors around wind turbines.


Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8455
Author(s):  
Diana Queirós Pokee ◽  
Carina Barbosa Pereira ◽  
Lucas Mösch ◽  
Andreas Follmann ◽  
Michael Czaplik

In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient’s consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims’ mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies’ longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night.


2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Qiong Chen ◽  
Yalin Wang ◽  
Xiangyu Liu ◽  
Xi Long ◽  
Bin Yin ◽  
...  

Abstract Background Heart rate (HR) is an important vital sign for evaluating the physiological condition of a newborn infant. Recently, for measuring HR, novel RGB camera-based non-contact techniques have demonstrated their specific superiority compared with other techniques, such as dopplers and thermal cameras. However, they still suffered poor robustness in infants’ HR measurements due to frequent body movement. Methods This paper introduces a framework to improve the robustness of infants’ HR measurements by solving motion artifact problems. Our solution is based on the following steps: morphology-based filtering, region-of-interest (ROI) dividing, Eulerian video magnification and majority voting. In particular, ROI dividing improves ROI information utilization. The majority voting scheme improves the statistical robustness by choosing the HR with the highest probability. Additionally, we determined the dividing parameter that leads to the most accurate HR measurements. In order to examine the performance of the proposed method, we collected 4 hours of videos and recorded the corresponding electrocardiogram (ECG) of 9 hospitalized neonates under two different conditions—rest still and visible movements. Results Experimental results indicate a promising performance: the mean absolute error during rest still and visible movements are 3.39 beats per minute (BPM) and 4.34 BPM, respectively, which improves at least 2.00 and 1.88 BPM compared with previous works. The Bland-Altman plots also show the remarkable consistency of our results and the HR derived from the ground-truth ECG. Conclusions To the best of our knowledge, this is the first study aimed at improving the robustness of neonatal HR measurement under motion artifacts using an RGB camera. The preliminary results have shown the promising prospects of the proposed method, which hopefully reduce neonatal mortality in hospitals.


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7777
Author(s):  
Martin Clinton Tosima Manullang ◽  
Yuan-Hsiang Lin ◽  
Sheng-Jie Lai ◽  
Nai-Kuan Chou

Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.


2021 ◽  
Author(s):  
Askat Kuzdeuov ◽  
Dana Aubakirova ◽  
Darina Koishigarina ◽  
Hüseyin Atakan Varol

Face detection and localization of facial landmarks are the primary steps in building many face applications in computer vision. Numerous algorithms and benchmark datasets have been proposed to develop accurate face and facial landmark detection models in the visual domain. However, varying illumination conditions still pose challenging problems. Thermal cameras can address this problem because of their operation in longer wavelengths. However, thermal face detection and localization of facial landmarks in the wild condition are overlooked. The main reason is that most of the existing thermal face datasets have been collected in controlled environments. In addition, many of them contain no annotations of face bounding boxes and facial landmarks. In this work, we present a thermal face dataset with manually labeled bounding boxes and facial landmarks to address these problems. The dataset contains 9,202 images of 145 subjects, collected in both controlled and wild conditions. As a baseline, we trained the YOLOv5 object detection model and its adaptation for face detection, YOLO5Face, on our dataset. To show the efficacy of our dataset, we evaluated these models on the RWTH-Aachen thermal face dataset in addition to our test set. We have made the dataset, source code, and pretrained models publicly available at https://github.com/IS2AI/TFW to bolster research in thermal face analysis. <br>


2021 ◽  
Author(s):  
Askat Kuzdeuov ◽  
Dana Aubakirova ◽  
Darina Koishigarina ◽  
Hüseyin Atakan Varol

Face detection and localization of facial landmarks are the primary steps in building many face applications in computer vision. Numerous algorithms and benchmark datasets have been proposed to develop accurate face and facial landmark detection models in the visual domain. However, varying illumination conditions still pose challenging problems. Thermal cameras can address this problem because of their operation in longer wavelengths. However, thermal face detection and localization of facial landmarks in the wild condition are overlooked. The main reason is that most of the existing thermal face datasets have been collected in controlled environments. In addition, many of them contain no annotations of face bounding boxes and facial landmarks. In this work, we present a thermal face dataset with manually labeled bounding boxes and facial landmarks to address these problems. The dataset contains 9,202 images of 145 subjects, collected in both controlled and wild conditions. As a baseline, we trained the YOLOv5 object detection model and its adaptation for face detection, YOLO5Face, on our dataset. To show the efficacy of our dataset, we evaluated these models on the RWTH-Aachen thermal face dataset in addition to our test set. We have made the dataset, source code, and pretrained models publicly available at https://github.com/IS2AI/TFW to bolster research in thermal face analysis. <br>


2021 ◽  
Author(s):  
Prayushi Mathur ◽  
Ashish Kumar Singh ◽  
Syed Azeemuddin ◽  
Jayram Adoni ◽  
Prasad Adireddy

2021 ◽  
Author(s):  
Morsy Ismail ◽  
Osama Galal ◽  
Waleed Saad

Abstract Given the circumstances the world is going through due to the novel coronavirus (Covid-19); this paper proposes a new smart system that aims to reduce the spread of the virus. The proposed Covid-19 containment system is designed to be installed outside hospitals and medical centers. Additionally, it works at night as well as at daylight. The system is based on Deep Learning applied to pedestrian temperature data sets that are collected using thermal cameras. The data set is primarily of temperature of pedestrians around medical centers. The thermal cameras are paired with conventional cameras for image capturing and cross referencing the target pedestrian with an existing central database (Big Data). If target is positive, the system sends a text message to the potentially infected person's cell phone upon recognition. The advisory sent text may contain useful information such as the nearest testing or isolation facility. This proposed system is assumed to be linked with the bigger network of the country’s Covid-19 response efforts. The simulation results reveal that the system can achieve an average precision of 90% fever detection among pedestrians.


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