Efficient Face Mask Detection Method Using YOLOX: An Approach to Reduce Coronavirus Spread

The advent of the COVID-19 pandemic has brought several never-before-seen changes in the daily lives of people around the globe. As a way to curb the spreading of the disease, wearing face masks has become mandatory in the majority of public places. To solve the necessity of face mask detection in such situations, there have been only a handful of research endeavors up to this date. Computer vision has advanced multi-fold with the advent of AlexNet architecture. With a motivation to go deeper with the neural network architecture, the concept of Depthwise Separable Convolutions and projection layer was developed in MobileNetV1. In this work, a novel lightweight deep learning model based on Single Shot Detector (SSD) MobileNetV2 architecture is proposed for face mask detection using images and video streams of crowds aiming its utilization on low compute re-source environment. An open benchmark face mask dataset, with 4095 images including masked and no mask images, is utilized to train the model for detection. The model is initialized using transfer learning with the freezing of base layers. The proposed methodology can efficiently aid in tracking and enforcing social distancing rules in crowded places with the use of surveillance cameras. On the different benchmarks that we have tested, the model proved to be highly successful and has achieved an accuracy rate of 99.39% and an F1 score of 0.995.

A new and deadly virus known as SARS-CoV-2, which is responsible for the coronavirus disease (COVID-19), is spreading rapidly around the world causing more than 3 million deaths. Hence, there is an urgent need to find new and innovative ways to reduce the likelihood of infection. One of the most common ways of catching the virus is by being in contact with droplets delivered by a sick person. The risk can be reduced by wearing a face mask as suggested by the World Health Organization (WHO), especially in closed environments such as classrooms, hospitals, and supermarkets. However, people hesitate to use a face mask leading to an increase in the risk of spreading the disease, moreover when the face mask is used, sometimes it is worn in the wrong way. In this work, an autonomic face mask detection system with deep learning and powered by the image tracking technique used for the augmented reality development is proposed as a mechanism to request the correct use of face masks to grant access to people to critical areas. To achieve this, a machine learning model based on Convolutional Neural Networks was built on top of an IoT framework to enforce the correct use of the face mask in required areas as it is requested by law in some regions.

The spread of the coronavirus has been the focus the world's attention. The government has issued a new regulation on the coronavirus that obligates the entire community to always wear a face mask and implement social distancing to prevent the spread of viruses. These issues have been impacted by an increase in the use of face masks throughout the world and in Indonesia. Moreover, the World Health Organization (WHO) suggested using medical face masks. Furthermore, in the health care sector, the possible loading of pathogens in sub-micrometer sizes and properties such as splash resistance and the ability to prevent infection by reducing the concentration of inhaled particulates has created limitations on the types of face masks that can be used. Therefore it is necessary to design a face mask sterilizer device that can extend the life span of masks, thereby decreasing the masks consumption. An investigation on mask sterilizer devices based on heat pipes and thermoelectrics was conducted in this study. The objective of this study was to investigate the thermal performance of the mask sterilizer device. The method used was an experimental method using variations in the input voltage on the thermoelectric, namely 9 V, 10 V, and 11 V, and the microcontroller was also adjusted to control the temperature by 70 ℃, 80 ℃, and 90 ℃. The results showed that the thermoelectrics can generate heat with a temperature difference between the hot side and the cold side that can reach 80.11 ℃. In terms of voltage, the greater voltage that was given led to a greater resulting temperature of the sterilization device and reduced the amount of time to achieve the minimum sterilization temperature.KeywordsSterilizationFace maskHeat pipeThermoelectric

In December 2019, a disease known as COVID-19 outbroke in some part of China which killed a bunch of people in that area. Till Mid-March, the disease spread all over the world killing more people. Due to this, World Health Organization (WHO) had to declare this decease as a pandemic. Therefore, scientists from all over the world were working very hard, giving their best everyday in order to make an anti-dote of this disease but it was expected to take alot of time and it even took. Now, government allowed the citizens to continue their normal way of living but still they made some protocols which was to be followed such as sanitization of hands on a regular interval, maintaining two-yard distance from each other, wearing masks, etc. Considering those protocols, we have evolved a Face-masks Detection System with a view to be useful in figuring out whether or not a person is wearing a mask or not inside the public locations which include Temples, Airports, and so on. The face masks detection database contains a mask and further to the facial snap shots, we've used OpenCV to carry out actual-time face detection from live streaming thru webcam. We have used the database to create a COVID-19 face mask detector from a computer view the use of Python, OpenCV, and Tensor Flow and Cameras. We present an in-intensity reading software that could come across conditions where a face mask isn't always used well. Our software incorporates a two-section configuration of the Convolutional Neural Network (CNN) that could hit upon hidden and unidentified faces and may point-out with pre-hooked up CCTV cameras. This allows in monitoring safety violations, promote using face mask, and ensures a safe running environment.

Deep Learning Models for Multiple Face Mask Detection under a Complex Big Data Environment

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdown to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77% and 94.58% respectively on two different datasets. We explore optimized values of parameters using the Sequential Convolutional Neural Network of Deep Learning model to detect the presence of masks correctly without causing over-fitting.

Is Africa prepared for tackling the COVID-19 (SARS-CoV-2) epidemic. Lessons from past outbreaks, ongoing pan-African public health efforts, and implications for the future

The Covid-19 epidemic was declared a pandemic by the World Health Organization in March 2020. It is difficult to foresee the future length and severity; it may extend to weeks, months, or even years to deplete the energy and resources of the health care facilities and the providers as there is marginal to no pharmacological medication available to treat the Covid-19. Unless an effective pharmacological treatment such as medicines and vaccines is developed and released publicly, wearing protective face masks and protecting personal health and hygiene is merely a choice to avoid the Covid-19 spread. This review summarizes the background knowledge on the Covid-19 disease and currently available face masks for highly infectious disease primary prevention. According to recent studies of Covid-19 prevention, diagnosis, and treatment, nanotechnologists have provided a revolutionary approach that involves both pharmacological and non-pharmacological steps, one of which is the use of nanofibers in facemasks and respirators. Various researches carried out in the field of nanomask and patented reports based on the application of nanomask were reviewed. The most recent developments of nanofibers, including research publications, patents and commercial products in Covid-19 prevention, are extensively reviewed from scientific literature and appropriately represented in this study.

Community face masking is possibly of great value in reducing COVID-19 transmission, especially when universally adopted with high compliance. The aim of this study is to investigate the knowledge, common misconceptions, barriers, and the compliance of the community with the use of face masks for the prevention of COVID-19. A validated questionnaire was administered to the participants through a web link by using various social media. The collected data were statistically analyzed for significant differences according to demographic variables. The average knowledge of face masks and their role in preventing COVID-19 transmission was 95.64%, with no differences among most of the demographical factors. Older groups and females demonstrated a better attitude towards wearing face masks than other groups did (p<0.001). Another significant difference in the participant’s attitude was noticed between the various educational levels, employment, and nationality (p<0.001). Of the total respondents, 88.2% encouraged wearing face masks. Misconceptions about wearing face masks were very low. The frequency of wearing face masks at public places, workplaces, or social gatherings was 87.2%, 80.5%, and 47.5% respectively. There was a significant variation in the compliance with wearing face masks between the various groups based on age, gender, nationality, and employment status (p<0.001). The inconvenience in wearing face masks was reported by 36.3%. Face irritation and ear pain were reported by 70.2% and 43.5%, respectively. The inconvenience of wearing face masks with eyeglasses was reported by 44.3% of those wearing eyeglasses. In general, the study demonstrated a good attitude among participants towards wearing face masks. Although the respondents in the study were aware of the benefits of wearing face masks, the barriers may have decreased their desire to do so. These barriers include difficulty in breathing, discomfort, face irritation, and ear pain.

Background: In spite of the vaccination available for COVID-19, the importance of non-pharmacological interventions (NPls) methods became very significant in control and prevention of COVID-19 infection. Since the government of India issued phase-wise nationwide unlock, a drastic change in growth rate of the cases was observed. This study was conducted to ascertain the practice and attitude towards the use of face mask, social distancing and safe measures by people in public with their understanding to have informeds insight regarding the growth of COVID-19. Methods: This observational qualitative study was carried out at public places in Pilkhuwa, Hapur, UP, India from 31st March 2020 to 1st April 2021. A total of 300 people were observed. Data were collected from different public (crowded) areas. Collected data were segregated into the segment based on research question and analyzed using qualitative content analysis. People were not engaged in designing the study, research questions, setting understanding or evaluating results, or research reporting. Results: People have a different attitude towards the use of face mask, social distancing, safe measure and different knowledge or understanding regarding COVID-19 and prevention. Conclusion: The confusion is due to information pollution, new unfamiliar practices, serious disease (high mortality), attack on freedom and diversion of the real issue in understanding the ongoing health crisis and disobedient attitude towards the preventive measures.

The World Health Organization (WHO) considers the COVID-19 Coronavirus to be a global pandemic. The most effective form of protection is to wear a face mask in public places. Moreover, the COVID-19 pandemic prompted all the countries to set up a lockdown to prevent viral transmission. According to a survey study, the use of facemasks at work decreases the chances of fast transmission. If the facemasks are not used or are worn incorrectly, it contributes to the third and fourth waves of the corona virus spreading throughout the world. This motivates us to conduct an efficient investigation of the face mask identification system and monitor people, who use suitable face mask in public places. Deep learning is the most effective approach for detecting whether or not a person is wearing a face mask in a crowded area. Using a multiclass deep learning technique, this research study proposes an efficient two stage identification (ETSI) for face mask detection. Whereas, the binary classification does not offer information about face mask detection and error. The proposed approach employs CNN's "ReLU" activation function to detect the face mask. Furthermore, in the current pandemic crisis, this research article offers a very efficient and precise approach for identifying COVID-19. Precision has increased as a result of the employment of a multi-class abbreviation in the final output.

Reputed Packet Delivery Using Efficient Audit Misbehaviour Detection and Monitoring Method in Mobile Ad Hoc Networks

SARS-CoV-2's origin should be investigated worldwide for pandemic prevention

Responding to COVID-19 pandemic: Why a strong health system is required.

The new coronavirus, initially detected in Wuhan, China, has spread worldwide, wreaking devastation. The speed with which it spreads and the severity of the epidemic have made this a global emergency. COVID-19 is a very concerning disease from a public health perspective, making it critical to take precautions against its transmission, such as limiting close personal contact and using protective gear. The primary objective of this research is to create a system that can recognise human face masks and determine whether individuals are attempting to maintain social distance. Alterations to everyone's daily routines. In such stages, everyone must always keep their identity concealed. Because the massive number of populations has changed since the Outbreak of the Coronavirus pandemic, finding people who are not wearing veils is a challenge. The global spread of COVID-19 has altered society. Many of us are staying in our homes, avoiding contact with city dwellers, and adjusting our routines-such as when and where we go to work or school-in ways we never would have imagined. We need updated timetables as we transition away from outmoded procedures. What stands out the most is the widespread practise of hiding one's face behind a veil or other kind of covering whenever we enter a public building. Wearing a veil or covering one's face may provide some comfort while also preventing the spread of the COVID-19 virus. By preventing anybody, even the unwitting carriers, from spreading the virus, widespread usage of protective clothing has the potential to significantly reduce the rate of disease spread in a given area. Thus, the importance of the veil and its identification are made very plain. There has been a rise in the importance of face recognition frameworks, which are especially useful in hospitals and medical clinics where privacy of patients is a concern. They're also vital in places like airports, sports stadiums, warehouses, and other such crowded areas where heavy foot traffic necessitates strict security measures to ensure everyone's safety. The framework of face veil recognition can ensure our safety and the safety of those around us. This assignment may serve as a digitally administered test anywhere from a classroom to a hospital to a bank to an airport terminal. Through the use of photo processing and extensive learning, we are able to recognise human faces and separate them into two groups, those with and without head coverings. The assignment will let a person who is responsible for screening people to do so even if they are located at a remote location, while still being able to effectively screen and provide guidance. Open CV, Tensor Flow, and Keras are some of the Python libraries used. With Deep Learning, As part of their model preparation, these activities make use of Convolution Neural Networks, a subset of Deep Neural Networks.