Coronavirus Spread Limitation Using Detective Smart System (original) (raw)
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The COVID-19 pandemic has had catastrophic consequences all over the world since the detection of the first case in December 2019. Currently, exponential growth is expected. In order to stop the spread of this pandemic, it is necessary to respect sanitary protocols such as the mandatory wearing of masks. In this research paper, we present an affordable artificial intelligence-based solution to increase the protection against COVID-19, covering several relevant aspects to facilitate the detection and prevention of this pandemic: noncontact temperature measurement, mask detection, automatic gel-dispensing, and automatic sterilization. Our main contribution is to provide high-quality, real-time learning and analysis. To achieve this goal, we used a deep convolutional neural network (CNN) based on MobileNetV2 architecture as the learning algorithm and Advanced Encryption Standard (AES) as an encryption protocol for sending secure data to notify hospital staff. The experimental results show the effectiveness of our model by providing 99.7% accuracy in detecting masks with a runtime of 1.54 s.
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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 de...
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Since the end of 2019, a respiratory disease called COVID-19 caused by SARS- COV2 has been spread around the world. The disease has similar symptoms with influenza. The common symptoms are cough, fever and fatigue. The human-to human transmission occurs primarily through droplets spread by coughing or sneezing from infected people directly and indirectly. In this paper a system based on embedded devices that can be used to help prevent the spread of COVID-19 between humans through face mask detection and a contactless thermal sensor is proposed. CNN based deep learning for the facemask detection and IR thermal sensor for non-contact human temperature measurement are used. The system is implemented locally on the Raspberry Pi platform. The training result shows the accuracy of the face mask detector is higher than 90% and stable after epoch 2. The thermal sensor shows the stable input with 0.25 deviation.