Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

An IoT based Smart Robot that Aids in the Prevention of COVID19 Spread

The Internet of Things revolution is transforming current healthcare practices by combining technological, economic, and social aspects. Since December 2019, the global spread of COVID19 has influenced the global economy. The COVID19 epidemic has forced governments all around the world to implement lockdowns to prevent viral infections. Wearing a face mask in a public location, according to survey results, greatly minimizes the risk of infection. The suggested robotics design includes an IoT solution for facemask detection, body temperature detection, an automatic dispenser for hand sanitizing, and a social distance monitoring system that can be used in any public space as a single IoT solution. Our goal was to use IoT-enabled technology to help prevent the spread of COVID19, with encouraging results and a future Smart Robot that Aids in COVID19 Prevention. Arduino NANO, MCU unit, ultrasonic sensor, IR sensor, temperature sensor, and buzzer are all part of our suggested implementation system. Our system's processing components, the Arduino UNO and MCU modules are all employed to process and output data. Countries with large populations, such as India and Bangladesh, as well as any other developing country, will benefit from using our cost-effective, trustworthy, and portable smart robots to effectively reduce COVID-19 viral transmission. © 2022 IEEE.

Masked Face Detection and Recognition System Based on Deep Learning Algorithms

Coronavirus (COVID-19) pandemic and its several variants have developed new habits in our daily lives. For instance, people have begun covering their faces in public areas and tight quarters to restrict the spread of the disease. However, the usage of face masks has hampered the ability of facial recognition systems to determine people's identities for registration authentication and dependability purpose. This study proposes a new deep-learning-based system for detecting and recognizing masked faces and determining the identity and whether the face is properly masked or not using several face image datasets. The proposed system was trained using a Convolutional Neural Network (CNN) with cross-validation and early stopping. First, a binary classification model was trained to discriminate between masked and unmasked faces, with the top model achieving a 99.77% accuracy. Then, a multi-class model was trained to classify the masked face images into three labels, i.e., correctly, incorrectly, and non-masked faces. The proposed model has achieved a high accuracy of 99.5%. Finally, the system recognizes the person's identity with an average accuracy of 97.98%. The visual assessment has proved that the proposed system succeeds in locating and matching faces. © 2023 by the authors.

Towards smart surveillance as an aftereffect of COVID-19 outbreak for recognition of face masked individuals using YOLOv3 algorithm.

The eruption of COVID-19 pandemic has led to the blossoming usage of face masks among individuals in the communal settings. To prevent the transmission of the virus, a mandatory mask-wearing rule in public areas has been enforced. Owing to the use of face masks in communities at different workplaces, an effective surveillance seems essential because several security analyses indicate that face masks may be used as a tool to hide the identity. Therefore, this work proposes a framework for the development of a smart surveillance system as an aftereffect of COVID-19 for recognition of individuals behind the face mask. For this purpose, transfer learning approach has been employed to train the custom dataset by YOLOv3 algorithm in the Darknet neural network framework. Moreover, to demonstrate the competence of YOLOv3 algorithm, a comparative analysis with YOLOv3-tiny has been presented. The simulated results verify the robustness of YOLOv3 algorithm in the recognition of individuals behind the face mask. Also, YOLOv3 algorithm achieves a mAP of 98.73% on custom dataset, outperforming YOLOv3-tiny by approximately 62%. Moreover, YOLOv3 algorithm provides adequate speed and accuracy on small faces.

AI-app development for Yolov5-based face mask wearing detection

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.

Effective face mask and social distance detection with alert system for covid-19 using YOLOv5 model

Globally, numerous preventive measures were taken to treat the COVID-19 epidemic. Face masks and social distancing were two of the most crucial practices for limiting the spread of novel viruses. With YOLOv5 and a pre-Trained framework, we present a novel method of complex mask detection. The primary objective is to detect complex different face masks at higher rates and obtain accuracy of about 94% to 99% on real-Time video feeds. The proposed methodology also aims to implement a structure to detect social distance based on a YOLOv5 architecture for controlling, monitoring, accomplishing, and reducing the interaction of physical communication among people in the day-To-day environment. In order for the framework to be trained for the different crowd datasets from the top, it was trained for the human contrasts. Based on the pixel information and the violation threshold, the Euclidean distance between peoples is determined as soon as the people in the video are spotted. In the results, this social distance architecture is described as providing effective monitoring and alerting. © 2022 The authors and IOS Press.

A Framework to Combat COVID19 like Pandemic in Future

This paper outlines a framework to prevent the COVID19 like pandemics for visitors to buildings or sites that receive many visitors. The proposed system is used to detect visitors who have not worn a facemask, or visitors with high body temperature, communicate daily visitor data to the security officer, sound an alarm to notify the officer, and screen the visitors with the results of the measurements. Also, the proposed solution uses deep learning and computer vision techniques to detect the facemask. Further, a testbed is designed based on an Arduino microcontroller connected to a PC for collecting, processing, and storing the data. Furthermore, the proposed system used a contactless infrared temperature sensor to avoid any chance to transfer the COVID-like disease to normal visitors. Finally, we tested the system by passing many subjects with and without face masks and high temperatures. The accuracy of the system shows that the system accurately detects each subject with and without a face mask and with high temperatures. © 2022 IEEE.

Face Mask Detection by Using Deep Learning with the Generative Adversarial Network-Based Feature

COVID-19 is one of the diseases that causes a lot of trouble in social communication. To prevent the spread of the Covid-19, people must wear a mask during conversation, shopping, or studying. Therefore, it is necessary to develop an application that helps to verify whether people wear a face mask or not. Many approaches have been proposed to detect a facemask based on using machine learning-based methods, such as support vector machines, and convolutional neural networks. However, the performance of existing systems still has limitations under difficult deployment environments where the camera's quality is not good enough for detection. Therefore, in this research, we study the benefits of generative adversarial networks to produce a stable feature for robust deep learning model-based facemask detection. First, the generative adversarial network is used to learn and discover stable features from the input dataset. Second, the Yolo is then employed to learn the stable feature to effectively detect and recognize facemasks under various testing conditions. With comprehensive experimental results, we found that our proposed method achieved a detection rate of 84.45% by using the MaskedFace-Net dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Detection of a Facemask Using Convolution Neural Network

Due to the COVID-19 pandemic, wearing the mask has become obligatory in public locations as it gives a most preventive impact in opposition to viral transmission. It has affected our day-to-day life to a greater extent. Though people had got vaccinated, mask wearing, social distance maintenance and sanitization need to be practiced probably till the pandemic gets vanished. Proposed work layout a real-time deep learning version to satisfy current demand for detection of facemask wearing position of someone earlier than he or she enters a public place. This paper provides a simplified method for achieving the intended goal in machine learning applications such as TensorFlow, Keras, OpenCV, and MobileNet. The proposed approach determines how the face mask is worn in real time;it leverages live image captures that provide accurate information about whether a person is wearing the mask appropriately. The parameters of the convolution neural network model are used to detect the presence of facial mask(s). The proposed approach attains the accuracy that is almost nearer to 99.75%. © 2022 IEEE.

Real-Time Face Mask Detection Using Streamlit,TensorFlow, Keras and Open-CV

This paper is based on the protection of our health from coronavirus officially known as COVID-19. Real-time detection of a face mask can help to prevent of the coronavirus, detecting the mask with the help of machine learning and data science algorithms such as Streamlit, MoblieNetV2, OpenCV, etc., are widely used in this ideal methodology. This paper is about the method that provides an accuracy of 99.78% in detecting the mask with live video stream. The method proposes building accurate model and integrating the model with a graphical interface which can improve the experience of the user. © 2022 IEEE.

An Integrated Facemask Detection with Face Recognition and Alert System Using MobileNetV2

COVID-19 pandemic has impacted the lives of individuals, organizations, markets, and the whole world in a way that has changed the functioning of all the systems. To get going, some try to adapt to working online, children started studying online and people started ordering food online. While this is still going on, there are many people whose jobs demand physical presence at workplaces and they have no choice but to be exposed to the virus while keeping our society functioning. People are needed to adapt to the new “normal” by practicing social distancing and wearing masks. Wearing masks is the most effective means of prevention of Covid-19. To ensure this, we built a web application that aims at keeping people advised to wear masks constantly with the help of an integrated facemask detection and face-recognition system. The proposed system initially detects whether the person in the real-time video feed is wearing a mask or not and then recognizes the face of the person if they are not wearing a mask. Finally, the proposed system alerts that specific violator to wear a mask through an auto-generated email to his personal email id. The application also allows the admin and the violators to log in and access the list of fines levied along with photo evidence. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Facemask Detection to Prevent COVID-19 Using YOLOv4 Deep Learning Model

The on-going global Covid-19 pandemic has impacted everyone's life. World Health organization (WHO) and Governments all over world have found that social distancing and donning a mask in public places has been instrumental in reducing the rate of COVID-19 transmission. Stepping out of homes in a face mask is a social obligation and a law mandate that is often violated by people and hence a face mask detection model that is accessible and efficient will aid in curbing the spread of disease. Detecting and identifying a face mask on an individual in real time can be a daunting and challenging task but using deep learning and computer vision, establish tech-based solutions that can help combat COVID-19 pandemic. In this paper, YOLOv4 deep learning model is designed and applied deep transfer learning approach to create a face mask detector which can be used in real time. GPU used was Google Collab to run the simulations and to draw inferences. Proposed implementation considered three types of data as input such as image dataset, video dataset and real time data for face mask detection. Performance parameters are tabulated and obtained mean average precision of 0.86, F1 score 0.77 for image dataset, 90 % accuracy for video dataset. And real time face mask detector with accuracy of 95%, it is successfully able to identify a person with and without facemask and report if they are wearing a face mask or not. © 2022 IEEE.

Social Distancing and Face Mask Monitoring System Using Deep Learning Based on COVID-19 Directive Measures

We, the entire world is in the lock of a micro size virus named Corona we are in the urge of saving our life rather than the money. This virus had changed the attitude of people from generations together, in this two years people realized that their health worth more than their net worth. We are in an uncertain situation but, we can bring the world back to normal so, we need to follow the guidelines issued by the health organizations so our government insisted people wear the mask and maintain social distance to control the spread of the disease but 90% percent of people not following covid guidelines. The main motive in this paper, mask detection on face with social distancing which helps to overcome this pandemic situation. Our proposed system comprises of data processing, data augmentation, image classification using mobilenetv2 and object detection plays a vital role in this paper. The modules are developed using TensorFlow and open-cv python programming to detect the faces with mask. If a person wears a mask they will be in a safe zone and the system shows a green box where if the person doesn't wear a mask, then it will be shown in a red box and with the message of alert as well. Social distancing detection will detect that two or more person in a single frame are walking with maintaining social distancing with at least 2 meters of range with each other using the Euclidean distance method, it will work in a Reliable manner with accurate results during this current situation which will easily help to track the person and collect fine if they violate any government directive guidelines so our system, will prevent the spread of the disease. Every Automation process reduces manual inspection to inspect the people which can be used in public places to control the spread of the virus and this prototype could be used in many places like park, hospital, airports, temples, railway station etc.To control this pandemic situation © 2021 IEEE.

A Deep Learning-based Approach for Real-time Facemask Detection

The COVID-19 pandemic is causing a global health crisis. Public spaces need to be safeguarded from the adverse effects of this pandemic. Wearing a facemask becomes one of the effective protection solutions adopted by many governments. Manual real-time monitoring of facemask wearing for a large group of people is becoming a difficult task. The goal of this paper is to use deep learning (DL), which has shown excellent results in many real-life applications, to ensure efficient real-time facemask detection. The proposed approach is based on two steps. An off-line step aiming to create a DL model that is able to detect and locate facemasks and whether they are appropriately worn. An online step that deploys the DL model at edge computing in order to detect masks in real-time. In this study, we propose to use MobileNetV2 to detect facemask in real-time. Several experiments are conducted and show good performances of the proposed approach (99% for training and testing accuracy). In addition, several comparisons with many state-of-the-art models namely ResNet50, DenseNet, and VGG16 show good performance of the MobileNetV2 in terms of training time and accuracy. © 2021 IEEE.

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

The COVID-19 pandemic is a virus that has disastrous effects on human lives globally;still spreading like wildfire causing huge losses to humanity and economies. There is a need to follow few constraints like social distancing norms, personal hygiene, and masking up to effectively control the virus spread. The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtained for mask detection are found to be effective. The system is trained using 4500 images to accurately judge and justify its accuracy. The aim is to develop an algorithm to automatically detect a mask, but the approach does not facilitate the percentage of improper usage. Accuracy levels are as low as 50% if the mask is improperly covered and an alert is raised for improper placement. It can be used at traffic places and social gatherings for the prevention of virus transmission. It works by first locating the region of interest by creating a frame boundary, then facial points are picked up to detect and concentrate on specific features. The training on the input images is performed using different epochs until the artificial face mask detection dataset is created. The system is implemented using Tensor-Flow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-source datasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier is used to load and pre-process the image dataset for building a fully connected head. The objective is to assess the accuracy of the identification, measuring the efficiency and effectiveness of algorithms for precision, recall, and F1 score. © 2022 CRL Publishing. All rights reserved.

Face Mask Detection Using Optimized CNN

COVID-19 has had a rapid impact on people's lives, affecting global trade and transportation. Protecting against COVID-19 by wearing a face mask has become the new normal. Many public service providers will need clients to wear masks to access their services in the near future. As a result, in today's culture, face mask detection is essential. This study proposes attaining the aim by utilizing some basic platforms such as Machine Learning packages such as TensorFlow, Keras, and OpenCV libraries. The goal of this project is to reliably detect the face in an image and then determine whether or not the individual is wearing a mask. In addition, the model can detect the existence of a mask in real time. The mask detection dataset was compiled using Internet resources, and a Google form was constructed to collect photographs with and without masks. We examine optimum parameter values for the Sequential Convolutional Neural Network model in order to correctly detect the presence of masks without causing over-fitting. On camera or in real time, we want to see if a person wearing a face mask is actually wearing one. © 2021 IEEE.

An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic.

The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people's routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus's spread. In this paper, we developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, we collected and annotated 1000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1853 images and increased the dataset to 3300 images by data augmentation. Then, we trained and tested multiple Tensorflow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, we employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle's effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. We also used transfer learning for training the model. The final model was applied on four videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers' safety in the pandemic situation.

Comparison of Convolutional Neural Network Architectures for Face Mask Detection

In 2020 World Health Organization (WHO) has declared that the Coronaviruses (COVID-19) pandemic is causing a worldwide health disaster. One of the most effective protections for reducing the spread of COVID-19 is by wearing a face mask in densely and close populated areas. In various countries, it has become mandatory to wear a face mask in public areas. The process of monitoring large numbers of individuals to comply with the new rule can be a challenging task. A cost-effective method to monitor a large number of individuals to comply with this new law is through computer vision and Convolution Neural Network (CNN). This paper demonstrates the application of transfer learning on pre-trained CNN architectures namely;AlexNet, GoogleNet ResNet-18, ResNet-50, ResNet-101, to classify whether or not a person in the image is wearing a facemask. The number of training images are varied in order to compare the performance of these networks. It is found that AlexNet performed the worst and requires 400 training images to achieve Specificity, Accuracy, Precision, and F-score of more than 95%. Whereas, GoogleNet and Resnet can achieve the same level of performance with 10 times fewer number of training images.