ABSTRACT
The pandemic of the coronavirus disease 2019 has shown weakness and threats in various fields of human activity. In turn, the World Health Organization has recommended different preventive measures to decrease the spreading of coronavirus. Nonetheless, the world community ought to be ready for worldwide pandemics in the closest future. One of the most productive approaches to prevent spreading the virus is still using a face mask. This case has required staff who would verify visitors in public areas to wear masks. The aim of this paper was to identify persons remotely who wore masks or not, and also inform the personnel about the status through the message queuing telemetry transport as soon as possible using the edge computing paradigm. To solve this problem, we proposed to use the Raspberry Pi with a camera as an edge device, as well as the TensorFlow framework for pre-processing data at the edge. The offered system is developed as a system that could be introduced into the entrance of public areas. Experimental results have shown that the proposed approach was able to optimize network traffic and detect persons without masks. This study can be applied to various closed and public areas for monitoring situations. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
ABSTRACT
The recent COVID-19 pandemic has necessitated the need to develop effective COVID-19 pandemic control strategies. One of the crucial steps for individual protection is to stop the virus spread by the wearing face masks. The proposed method is developed to monitor the infected people in the crowded public areas like shopping centers, wedding hall, workplace, school or college. The abnormal temperature is detected by using sensor and the obtained signal will then be sent to the Arduino device connected to the controller. In order to stop the spread of COVID 19 viruses, this study intends to design and develop a novel system to automatically limit the room capacity based on temperature. The proposed Atmega328 microcontroller-based body temperature detection and a room capacity measuring device is connected with the android smart phone of the user. © 2022 IEEE.
ABSTRACT
Although droplets and aerosol are considered the main transmission routes of SARS-CoV-2, indirect contact has been indicated to play a critical role in transmission. The aim of this study is to evaluate the presence of SARS-CoV-2 RNA on different environmental surfaces in public areas in Cyprus. Using RT-qPCR, samples from 50 swab specimens collected from high-touch surfaces were analyzed for viral RNA. Six surfaces (12.0%) in all were positive for SARS-CoV-2. Among the examined surfaces within supermarkets, SARS-CoV-2 was detected in 22.2% (n=4/18) of the sampling points, with shopping trolley handles and POS keyboards being the most frequently contaminated items. In the hospital setting, two (n=2/5, 40%) samples were positive for SARS-CoV-2. Our results indicate that, at the current stage of the pandemic, viral contamination of public spaces exists in the community. Lifting protective measures may have contributed to fomite transmission in public spaces. [ FROM AUTHOR] Copyright of Erciyes Medical Journal / Erciyes Tip Dergisi is the property of KARE Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
At the outbreak of COVID-19, researchers worldwide are seeking approaches to containing this disease. It is necessary to monitor social distance in enclosed public areas, such as subways or shopping malls. Passive localization, such as surveillance cameras, is a natural candidate for this issue, which is meaningful for rapid response to finding the infected suspect. However, the latest surveillance camera system is rotatable, even movable. And it is impossible for professionals to regularly calibrate the extrinsic parameters in a large-scale application, like COVID-19 suspect monitoring. We propose an inertial-aided passive localization method using surveillance camera for social distance measurement without the necessity to obtain extrinsic parameters. Moreover, the hardware modification cost of the off-the-shelf commercial camera is low, which suits the immediate application. The method uses SGBM (Semi-Global Block Matching) for 3D reconstruction and combines YOLOv3 and Gaussian Mixture Model (GMM) clustering algorithm to extract pedestrian point clouds in real time. Combining the 2D DNN-based and model-based methods makes a better balance between the computational load and the detection accuracy than end-to-end 3D DNN-based method. The inertial sensor provides an extra observation for the coordinate transformation from the camera frame into the world ground frame. Results show we can get a decimeter-level social distancing accuracy under noisy background and foreground environments at a low cost, which is promising for urgent COVID-19 public area monitoring. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Notwithstanding the religious intention of billions of devotees, the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most attendees ignored preventive measures, namely maintaining physical distance, practising hand hygiene, and wearing facemasks. Wearing a face mask in public areas protects people from spreading COVID-19. Artificial intelligence (AI) based on deep learning (DL) and machine learning (ML) could assist in fighting covid-19 in several ways. This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering (DLFMD-RMG) technique during the COVID-19 pandemic. The DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass gathering. To accomplish this, the presented DLFMD-RMG technique undergoes two pre-processing levels: Bilateral Filtering (BF) and Contrast Enhancement. For face detection, the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector. In addition, the face detection performance can be improved by the seeker optimization algorithm (SOA) for tuning the hyperparameter of the ResNet-50 module, showing the novelty of the work. At last, the faces with and without masks are classified using the Fuzzy Neural Network (FNN) model. The stimulation study of the DLFMD-RMG algorithm is examined on a benchmark dataset. The results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches. © 2023 CRL Publishing. All rights reserved.
ABSTRACT
In the special period of new corona virus pneumonia epidemic prevention and control, while taking safety protection measures such as disinfection of elevator-related public areas and passenger flow restriction, the application of elevator Internet of Things technology can provide the public with a more intelligent and safe elevator ride guarantee, and further promote the intelligence and safety of elevator operation. To this end, an automatic ultraviolet germicidal lamp system is designed, which can set the required disinfection time and disinfection frequency according to the actual situation, and can achieve disinfection only in the elevator when there is no one in the elevator and when it is not running. At the same time, combined with the actual situation of people's social life, the intelligent appointment, intelligent security system and other special functions combined with the Internet of Things have been added[1]. © 2022 SPIE.
ABSTRACT
COVID-19 has been affecting human mobility to avoid the risk of infection. Movement restriction was one of the government policies to reduce the rate of infection. However, the mobility was still occurred to be recorded during the policy. This action has led to the problem of the number of beds on hospital have to be prepared for the peak of infection. This study developed a model using Multilayer perceptron as a useful theorem in regression analysis to see the fitness approximation over this problem. Five layers neural networks combination have been used to see the performance of the model to reach the best fit of the model. The process of the study includes data acquisition of the influence of community mobility over the positive number of COVID-19, managed hyperparameters, and calculate the results of prediction in the form of the length of time the patient would be infected with COVID-19 from 2020 to 2021. This study found that the infection was happening mostly after 12 days of human mobility activity in public area such as ATM, market, park, and any public area recorded by Google mobility data. It was also showed the number of infections after 12 days in order to prepare the number of beds on hospital. Furthermore, this study found the best model with smallest loss value on 0.01452617616472448 with the gap number of infection from public area as much as 77 persons. © 2022 IEEE.
ABSTRACT
Under the COVID-19 and other terrible environments workers are constrained to sweep campus and public area. Intelligent and driverless sanitation robot can solve the problem. Obstacle avoidance and garbage cleanup are its important functions. Based on the driverless sanitation robot project introduced by Sanda University, this paper carries out recognition of campus vehicles and improves its obstacle avoidance function. Through image processing, the object features of different environment and climate conditions are extracted, analyzed and recognized, so as to achieve more accurate recognition of campus vehicles. And opencv and python language are used to complete the implementation of vehicle detection. © 2022 IEEE.
ABSTRACT
Corona is one of the most destructive viruses that has ever produced a pandemic in human life, not only in terms of direct victims but also in terms of the socio-economic consequences of the virus' transmission. The 2nd anniversary of the global coronavirus pandemic passed away in 2021. However, it's still impossible to say how long the epidemic will last. After reviewing a study by the World Health Organization on COVID-19, the country's national government urged residents to use facemask in order to reduce the incidence of COVID-19 transmission. As a result of COVID-19, there are presently no facemask detection app that are in great demand for ensuring safety in public area. In the context of the outbreak of COVID-19, A facemask detection model based on deep learning approach of state-of-the-art YOLOv5 may be useful in real-time applications. In this paper, we propose a web app for detecting if the people wears facemask or not in real-time via webcam or public camera. In the app, we deployed and persisted many different YOLOv5-based models that the users can switch between them to guarantee the performance and timing trade-off. Furthermore, our system is able to detect if an individual person captured by surveillance cameras is wearing facemask in acceptable counting time at staging level. In our opinion, this kind of system is extremely efficient for use in airports, train stations, offices, and other public areas, as well as in military. © 2022 IEEE.
ABSTRACT
Because of the pandemic of COVID-19 since 2020, it seriously affects people's daily life and causes huge economic loss. Recently, the international community has mostly adopted an attitude of coexisting with Covid-19. We cannot ignore the harm the virus can bring to us. In order to effectively protect everyone from the virus, the most basic and effective way is to wear a mask to keep you away from exposure to the virus when going to public areas. Vision intelligence can play an important role in public health issues. In this paper, we utilize the object detection method to implement an actual mask wearing recognition system which can detect if people have a face mask on their face, and send a warning message if not wearing a mask. YOLOv3 is the basic framework for our implementation. After training and fine-tuning processes, the implemented model can perform effectively and correctly. © 2022 IEEE.
ABSTRACT
To control the spread of the virus, mask detection is crucial in public areas, especially after the outbreak of Covid-19 pneumonia. This paper aims to improve the accuracy and precision of mask detection. This study improves mask-wearing detection by adding data augmentation, using the smooth label to replace the one-hot vector, and customizing the network connection of the YOLOv3 network. Through these targeted improvements, the average precision of face with mask detection has been increased by 0.9%, and the average precision of face without mask detection has been increased by 2.9%, which implies that it is a better strategy to do mask detection based on YOLOv3. By inputting photographs, the network can check, with high accuracy, whether the pedestrians in the picture wear masks or not, which will be a good supplementary to epidemic prevention and control. © 2022 SPIE.
ABSTRACT
The COVID-19 Pandemic brought the whole society to a standstill, which has more significant psychological pressure on children and adolescents. Governments, companies, and social groups are trying to confront COVID-19 and social distancing in a gamified way. However, due to fear of the virus and uncertainty about the future, even after the Pandemic is well controlled in physical space, people are still reluctant to stop and play in public areas and are afraid to engage with others because of their internal sense of alienation. From the perspective of urban renewal and environmental design, creating a series of micro-scale design interventions in public spaces to relieve psychological pressure has urgency and relevant significance. This paper analyzes the symbiotic relationship between public art installations and communities. Then discovers the characteristics of public installations based on emotional healing. Furthermore, create two design prototypes to demonstrate more vividly how gamified interactive experience could relieve the mental pressure of the surrounding residents and help them gradually adapt to the new normal life. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Corona virus had arrived in a new way of life, one in which social isolation and the uses of face masks are crucial in preventing the transmission of the illness. However, most individuals do not use face masks in public areas, allowing infections to spread more easily. This might leadto a significant situation with increased spread. Here, created a covid smart route for identifying persons without masks and temperature screening in this paper. If the temperature is too high, the alarm will make a beep and prevent the user from going any farther. It keeps a constant eye on the walking path. According to recent results gained employing radiology imaging techniques, such images offer important information about the COVID-19 virus, which will be valuable in detecting the virus. © 2022 IEEE.
ABSTRACT
COVID-19 Pandemic affects daily life and the global economy. The COVID-19 virus can be spread by small liquid particles, which can be filtered using a face mask. Wearing masks in public areas is an excellent approach to preventing illness. As a result, mask detection is necessary to stop the spread of the disease before a person enters the facility. Regarding Single Shot Multibox Detector-MobileNetV2 (SSD-MobileNetV2) was used in this research to build tools to detect and monitor unmasked people in the facility or working rooms that consist of many people. In this paper, we showed the experimental performance of SSDMobileNetv2 based on an application that runs on an edge device to detect unmasked people in the room and compromise with very high accuracy of 97% in rooms smaller than 16 square meters, which is sufficient to detect the wearing of masks in public places or various locations. © 2022 IEEE.
ABSTRACT
As today a disease called COVID-19 is causing health crisis and deaths, it became most essential to wear a mask for protecting ourselves from Corona virus. Even in public areas, where is more rush we should wear mask as no virus can spread from person to person if any one of person from public is Corona positive. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID19. So basically in this project we are going to use Python, Keras, OpenCV alongwith MobileNet for this Face Mask Detection System. This includes some steps like data preprocessing, training and testing the model, run and view the accuracy and applying model in the camera. The inputs has provided here are 1000+ images of people with mask and without mask. First the data get processed and then by checking features of each image it will train all the models and the persons with and without mask get separated to two categories: with mask and without mask. If person is wearing mask with 90 or more percent of accuracy, then he will get added to with mask category and person not wearing mask get added to without mask category, so that we can permit with mask person to public areas. © 2022 IEEE.
ABSTRACT
Several authorities require individuals to be vaccinated with WHO-approved vaccines to allow cross-border travel or access to public areas. Recently vaccination certificates have become crucial in curbing the spread of the corona virus. Therefore, it is crucial to ensure the integrity and authenticity of vaccination records. A secure mechanism is required to maintain the immunization records and issue an immutable vaccination certificate to individuals that may be used as a passport for uninhibited movement. We propose a blockchain-based solution that issues verifiable and trustworthy vaccination certificates for not just COVID-19 vaccines but also other diseases. Our proposed model reduces the time required to retrieve vaccination status. The solution is scalable due to its simplicity as it does not require multiple participants on the network for its operation. Moreover, the system can be easily integrated with existing enterprise applications for its functionality. © 2022 IEEE.
ABSTRACT
The transmission of Covid-19 continues to be a serious concern of many institutions in industrialized countries. However, circumstances dictate that on-site work must continue in education and industry. How authorities address the pandemic is a subject of controversy, and enforcement of Covid-19 protocols locally by everyday workers and educators can be uncomfortable, if not dangerous. The Maskbot project aims to automate two of these protocols: people should wear facemasks in public areas to minimize exhalation of water droplets in the air, and people should quarantine if they are running a body temperature above nominal values. With Maskbot, enforcement of these protocols take the shape of indoor traffic control of incoming and outgoing movement. Incoming traffic is audited with a Raspberry Pi B+ loaded with a model that allows it to analyze faces for masks with the feed coming from a USB camera. Additionally, incoming traffic is checked for high body temperature with an infrared thermometer. If an unmasked or irregularly high temperature status is received, the entrance gate will block incoming traffic with barrier arms attached to two parallax servos, controlled by a linked Arduino Uno. Furthermore, these undesired states will result in an alarm going off, an LCD informing the passerby that they are not to proceed, and a picture being taken of the passerby to be sent to a building manager email. Desired states will result in a prompt to proceed and raise barrier arms. Outgoing traffic is managed by an exit gate, which raises its barrier arms on command when a leaving pedestrian waves their hand in front of an ultrasonic sensor. © American Society for Engineering Education, 2022.
ABSTRACT
Coronavirus pandemics have influenced people's daily life seriously since 2019. Authorized organizations suggested people wear a mask in public areas can significantly reduce the probability of getting infected. Thus, we proposed a method based on a simple convolutional neural network (CNN) to perform mask detection. The whole developing process was divided into two stages and mainly used three datasets (dataset_l, dataset_2 and dataset_3). Dataset_1 has images of people with and without masks. Dataset_2 and dataset_3 have one more category-images of people wearing masks incorrectly. The first stage was to train the model based on dataset_1 and it achieved 100% accuracy on validation set. It could also be applied to another two similar datasets without any training on them with accuracy 73.55% and 66.80% respectively. In the second stage, to detect people wearing masks incorrectly, the same model was trained based on dataset_2. The accuracy of this model reached 99.34%. However, when applying it directly to dataset_3, only 44.50% accuracy was achieved. To improve the accuracy, the distribution of dataset_2 and dataset_3 was rearranged. Finally, the accuracy of the model for dataset_3 was nearly 80%. We concluded that generally deep learning models would have better generalization on mask-detection tasks and our model was good at handling two-label mask dataset. © 2022 IEEE.
ABSTRACT
COVID-19 is an infectious disease caused by a newly discovered coronavirus called SARSCoV-2. There are two ways of contamination risk, namely spreading through droplets or aerosol-type spreading into the air with people's speech in crowded environments. The best way to prevent the spread of COVID-19 in a crowd public area is to follow social distance rules. Violation of the social distance is a common situation in areas where people frequently visit such as hospitals, schools and shopping centers. In this study, an artificial intelligence-based social distance determination study was developed in order to detect social distance violations in crowded areas. Within the scope of the study, a new dataset was proposed to determine social distance between pedestrians. The YOLOv3 algorithm, which is very successful in object detection, was compared with the SSD-MobileNET, which is considered to be a light weighted model, and the traditionally handcrafted methods Haar-like cascade and HOG methods. Inability to obtain depth information, which is one of the biggest problems encountered in monocular cameras, has been tried to be eliminated by perspective transformation. In this way, the social distance violation detected in specific area is notified by the system to the relevant people with a warning. © 2022 Lavoisier. All rights reserved.
ABSTRACT
The contagious Corona Virus (COVID-19) transmission can be reduced by following and maintaining physical distancing (also known as COVID-19 social distance). The World Health Organisation (WHO) recommends it to prevent COVID-19 from spreading in public areas. On the other hand, people may not be maintaining the required 2-m physical distance as a mandated safety precaution in shopping malls and public places. The spread of the fatal disease may be slowed by an active monitoring system suitable for identifying distances between people and alerting them. This paper introduced a deep learning-based system for automatically detecting physical distance using video from security cameras. The proposed system employed the fine-tuning YOLO v4 for object detection and classification and Deepsort for tracking the detected people using bounding boxes from the video. Pairwise L2 vectorized normalization was utilized to generate a three-dimensional feature space for tracking physical distances and the violation index, determining the number of individuals who follow the distance rules. For training and testing, we use the MS COCO and Oxford Town Centre (OTC) datasets. We compared the proposed system to two well-known object detection models, YOLO v3 and Faster RCNN. Our method obtained a weighted mAP score of 87.8% and an FPS score of 28;both are computationally comparable. © 2022, Springer Nature Switzerland AG.