Significance of Face Mask detection

Facemask wearing is becoming a norm in our daily lives to curb the spread of Covid-19. Ensuring facemasks are worn correctly is a topic of concern worldwide. It could go beyond manual human control and enforcement, leading to the spread of this deadly virus and many cases globally. The main aim of wearing a facemask is to curtail the spread of the covid-19 virus, but the biggest concern of most deep learning research is about who is wearing the mask or not, and not who is incorrectly wearing the facemask while the main objective of mask wearing is to prevent the spread of the covid-19 virus. This paper compares three state-of-the- art object detection approaches: Haarcascade, Multi-task Cascaded Convolutional Networks (MTCNN), and You Only Look Once version 4 (YOLOv4) to classify who is wearing a mask, who is not wearing a mask, and most importantly, who is incorrectly wearing the mask in a real-time video stream using FPS as a benchmark to select the best model. Yolov4 got about 40 Frame Per Seconds (FPS), outperforming Haarcascade with 16 and MTCNN with 1.4. YOLOv4 was later used to compare the two datasets using Intersection over Union (IoU) and mean Average Precision (mAP) as a comparative measure; dataset2 (balanced dataset) performed better than dataset1 (unbalanced dataset). Yolov4 model on dataset2 mapped and detected images of masks worn incorrectly with one correct class label rather than giving them two label classes with uncertainty in dataset1, this work shows the advantage of having a balanced dataset for accuracy. This work would help decrease human interference in enforcing the COVID-19 face mask rules and create awareness for people who do not comply with the facemask policy of wearing it correctly. Hence, significantly reducing the spread of COVID-19.

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.

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.

The use of cold atmospheric plasmas (CAPs) to decontaminate sensitive surfaces is an interesting field of applied plasma physics. Motivated by the shortages of face masks and safety clothing in the beginning of the corona pandemic, we conducted studies on the decontamination of FF3 face masks with CAP and the resulting material effects. Therefore, the bactericidal and sporicidal efficacy of CAP afterglow decontamination of FFP3 mask material was investigated by inoculating fabric samples with test germs <i>Escherichia coli</i> (<i>E. coli</i>) and <i>Bacillus atrophaeus</i> (<i>B. atrophaeus</i>) and subsequent CAP afterglow treatment in a surface micro discharge (SMD) plasma device. In addition, a detailed analysis of the changes in long-term plasma treated (15 h) mask material and its individual components&#x2014;ethylene vinyl acetate (EVA) and polypropylene (PP)&#x2014;was carried out using surface analysis methods, such as laser microscopy, contact angle measurements, X-ray photoelectron spectroscopy (XPS) as well as fabric permeability and resistance measurements. The experiments showed that <i>E. coli</i> and <i>B. atrophaeus</i> could both be effectively inactivated by plasma treatment in nitrogen mode (12 kV_pp, 5 kHz). <i>For B. atrophaeus</i> inactivation of more than 4-log was achieved after 30 min. <i>E. coli</i> population could be reduced by 5-log within 1 min of CAP treatment and after 5 min a complete inactivation (&#x003E; 6 log) was achieved. Material analysis showed that long-term (&#x003E; 5 h) plasma treatment affects the electrostatic properties of the fabric. From this, it can be deduced that the plasma treatment of FFP3 face masks with the CAP afterglow of an SMD device effectively inactivates microorganisms on the fabric. FFP3 masks can be plasma decontaminated and reused multiple times (up to 5 h of CAP treatment time) but only to a limited extent, as otherwise the permeability levels no longer meet the DIN EN 149 specifications.

The face mask is the first line of defense against infectious particulates and droplets that may cause illness. Currently in the Philippines, the wearing of face mask is compulsory whenever citizens leave their residences as mandated by the government to mitigate the spread of COVID-19. The wearing of face masks has become a new normal among Filipinos. This created market opportunities for different types which became commonly and immediately available for purchase. This study aimed to differentiate the effectiveness of locally available face masks in terms of electrostatic filtration capability. Twelve different types of face masks grouped into five categories – surgical, fabric, N95 variants, foam type, and novelty type – were evaluated. Electrostatic fields were measured from each face mask including pore sizes via scanning electron microscopy. Moreover, by utilizing the estimated charge and mass of the SARS-CoV-2 virion, the transmission rate was simulated using COMSOL Multiphysics®. It was observed that face masks with negatively charged materials combined with small pore sizes afforded less particle transmission. The results of this study are of timely significance in potentially laying out public awareness in the selection and utilization of face masks that can provide foremost shielding against viral transmission.

Uncovering the release of micro/nanoplastics from disposable face masks at times of COVID-19

Many lives have been put in danger since the start of the COVID-19 outbreak. According to WHO (World Health Organization)’s statements, breathing without a mask is highly dangerous in public and crowded places. Wearing a mask is essential as long as the vaccination is not widely used and does not fully protect everyone. Wearing masks does minimize the chances of becoming infected and the transmission of Coronavirus. Due to this, many public service providers may mandate customers to wear masks in order to get service.However, manually monitoring whether people are wearing a mask or not in public is inefficient and difficult. This paper proposes replacing manual inspection with a deep learning-based method using YOLOX, the most powerful objection detection algorithm. The results of the experiments show that the algorithm described in this work can efficiently detect face masks and enable more effective personnel monitoring than other YOLO series models. In addition, as compared to other models, our technique has significant benefits in terms of speed and accuracy in small and crowded areas.

COVID-19 since its outbreak has affected the daily life of humans throughout the world. Virus causing COVID-19 transmission happens through droplets from one to another person with airflow. COVID-19 spread can be limited by taking into account simple as well as effective precautionary measures as well as adopting habits of using sanitizers and covering face with masks. Facemasks along with sanitizers help the humans to come back to their routine work during COVID-19 to control the harm at source by limiting the spread of the virus. This chapter has highlighted the use of different types of facemasks and hand sanitizers, their effect on the human health and environment, recommendations by WHO as well as formulation of more safe and efficient sanitizers.

With the number of positive cases of Covid-19 infection is increasing, it is essential for everyone to wear a face mask and prevent the spread of Covid. As people are gathering in a large number at different locations, it is quite important for everyone to wear a face mask and prevent the covid spread. With the increase in the crowd gathering, it is often hard to see who is not wearing a mask. Although various techniques have been proposed earlier for face mask detection, the results have not been effective. This paper proposes region-based deep learning detection techniques for face mask detection using Faster R-CNN. The proposed model uses ResNet-50 as RPN which generates anchors and output region proposals. Later, ROI pooling is used to map the feature map in proposal to target dimensions. Finally, a classifier is used to output the final class and bounding box around the face. The proposed work attained a final mean average precision (mAP) of 45% over 30 epochs and achieved satisfactory performance.

A recent update to the Geneva Declaration’s ‘Physician Pledge’ involves the ethical requirement of physicians to share medical knowledge for the benefit of patients and healthcare. With the spread of...

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.

A face mask is a vital component of personal protective equipment to prevent potentially contagious respiratory infections. There was a lack of evidence showing the proportion and determinants of face mask use in Ethiopia. Therefore, this study aimed to identify face mask utilization determinants to prevent spread of the Covid-19 pandemic among quarantined adults in Tigrai region, northern Ethiopia. A total of 331 participants selected using a systematic random sampling method were included in the study. An interviewer-administered questionnaire was employed. After describing the variables using frequencies, means, and standard deviations, multivariable logistic regression determined factors associated with face mask utilization to prevent COVID-19 spread. The study participants were primarily males (70%) and mean age was 30.5 (SD = 11) years. Nearly half of the participants reported they did not wear a face mask when leaving home. Face mask utilization was significantly associated with knowledge score, employment status, gender, age, and educational status of the study participants.

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 usage of mask is necessary for the prevention and control of COVID-19 which is a respiratory disease that passes from person to person by contact and droplets from the respiratory tract. It is an important task to identify people who do not wear face mask in the community. In this study, performance comparison of the automated deep learning based models including the ones that use transfer learning for face mask detection on images was performed. Before training deep models, faces were detected within images using multi-task cascaded convolutional network (MTCNN). Images obtained from face mask detection dataset, COVID face mask detection dataset, mask detection dataset, and with/without mask dataset were used for training and testing the models. Face areas that are detected with MTCNN were used as input for convolutional neural network (CNN), MobileNetV2, VGG16 and ResNet50. VGG16 showed best performance with 97.82% accuracy. MobileNetV2 showed the worst performance for detecting faces without mask with 72.44% accuracy. Comparison results show that VGG16 can be used effectively to detect faces without mask. This system can be used in crowded public areas to warn people without mask that may help the reduce the risk of pandemic.

Head injury models are notoriously time consuming and resource demanding in simulations, which prevents routine application. Here, we extend a convolutional neural network (CNN) to instantly estimate element-wise distribution of peak maximum principal strain (MPS) of the entire brain (>36 k speedup accomplished on a low-end computing platform). To achieve this, head impact rotational velocity and acceleration temporal profiles are combined into two-dimensional images to serve as CNN input for training and prediction of MPS. Compared with the directly simulated counterparts, the CNN-estimated responses (magnitude and distribution) are sufficiently accurate for 92.1% of the cases via 10-fold cross-validation using impacts drawn from the real world (n = 5661; range of peak rotational velocity in augmented data extended to 2-40 rad/sec). The success rate further improves to 97.1% for "in-range" impacts (n = 4298). When using the same CNN architecture to train (n = 3064) and test on an independent, reconstructed National Football League (NFL) impact dataset (n = 53; 20 concussions and 33 non-injuries), 51 out of 53, or 96.2% of the cases, are sufficiently accurate. The estimated responses also achieve virtually identical concussion prediction performances relative to the directly simulated counterparts, and they often outperform peak MPS of the whole brain (e.g., accuracy of 0.83 vs. 0.77 via leave-one-out cross-validation). These findings support the use of CNN for accurate and efficient estimation of spatially detailed brain strains across the vast majority of head impacts in contact sports. Our technique may hold the potential to transform traumatic brain injury (TBI) research and the design and testing standards of head protective gears by facilitating the transition from acceleration-based approximation to strain-based design and analysis. This would have broad implications in the TBI biomechanics field to accelerate new scientific discoveries. The pre-trained CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains.