Mask Detection Research Articles

FACE MASK DETECTION

This project intends to develop a Face Mask Detection system using OpenCV, Keras/TensorFlow and Deep Learning. The system can be easily integrated to various embedded devices with limited computational capacity as it uses MobileNetV2 architecture. It will detect face masks in images as well as in real-time videos. In recent times, where Covid-19 has impacted a domino effect on manufacturing, travel, tourism, hospitality, crippling the global economy. In addition to it, is the growing curve of human deaths across the globe due to the pandemic, this project which relies on computer vision and deep learning, intends to make an impact and solve the real-world problem of safety measures at some significant level. This project can be used at airports, offices, hospitals and many more public places to ensure that the safety standards are maintained and people are abiding by the rules and regulations to wear protective masks at public places. If the detection system classifies as ‘No Mask’, reminders can be given as well as actions can be taken against such individuals.

Effective Face Mask Detection by Deep ConvNeuralNets Learning for Covid-19 Prevention

In Recent years, COVID-19 is the buzzword in our society, since it is too much dangerous, believed as it is originated from China from the place of Wuhan in December 2019. This Disease is spreading from humans to humans through droplets and airborne. A methodology has to be developed recognize whether the people are wearing mask or not. Therefore, this paper proposes a framework to recognize the mask. Based on the features proposes a machine learning basis system which recognizes the mask from the inputted image. Existing system Only detecting the person who is not using a mask apart from that system will not predict whether the used mask safe or not. A better deep learning framework which predicts the people with mask or not so it will be helping the society is discussed and apart from that this, it extends the mean of finding what type of mask they are wearing also predicts the efficiency of mask so user can protect themselves from the dreadful corona virus.

Automated Social Distancing and Face Mask Detection System

Abstract: COVID-19 epidemic has fleetly affected our day-to- day life dismembering the world trade and movements. Wearing a defensive face mask has become a new normal. In the near future, numerous public service providers will ask the guests to wear masks rightly to benefit of their services. Thus, face mask recognition has turn out to be a pivotal task to help global society. This paper presents a simplified approach to achieve this purpose using some introductory Machine Learning packages like TensorFlow, Keras, OpenCV and Scikit- Learn. The projected system detects the face from the image appropriately and then identifies if it has a mask on it or not. As a surveillance task executor, it can also distinguish a face along with a mask in motion. The system attains precision up to95.77 and 94.58 independently on two different datasets. We discover optimized values of parameters using the Convolutional Neural Network model to spot the presence of masks rightly without causing over-fitting. Keywords: Coronavirus, Covid-19, Machine Learning, Face Mask Recognition, Convolutional Neural Network, TensorFlow

Real Time Face Mask Detection and Email Alert

“Coronaviruses, a large family of distinct viruses, have recently become exceedingly prevalent, communicable, and harmful to the whole human race. It transmits from person to person by exhaling infected breath, which deposits virus droplets on various surfaces, which are subsequently breathed by another person, who contracts the illness. As a result, it is critical that we safeguard ourselves and others around us from this predicament. We may take safeguards like as keeping social distance, washing hands every two hours, using sanitizer, and, most importantly, wearing a mask. Wearing a mask in public has grown quite widespread in recent years all around the world. Due of its high population in a short space, India is the most afflicted and terrible situation. This study presents a technique for detecting whether or not a face mask is used in offices or any other workplace with a large number of individuals. For this, we employed a convolutional neural network. The model was trained on a real-world dataset and successfully tested using live video streaming. The model's accuracy is further tested using several hyper parameters and many persons at various distances and locations inside the picture”. Keywords— Face Mask Detection, Convolutional NeuralNetwork, MobileNetV2, Corona virus Precaution.

Face Mask Detection Using Lightweight Deep Learning Architecture and Raspberry Pi Hardware: An Approach to Reduce Risk of Coronavirus Spread While Entrance to Indoor Spaces

The COVID-19 pandemic continues to spread around the world at full speed, threatening public health. In response, the World Health Organization recommends various preventive measures to reduce the spread of the COVID-19 virus. Wearing a mask is one of the preventive measures to reduce the contagion of the disease, and many governments around the world advise people to wear masks. One of the prominent symptoms of coronavirus is high fever. A person with a fever above normal is likely to have contracted the corona virus. This requires the identification of people with a high fever in order to prevent the epidemic in the public arena. This situation has caused people who want to enter public places to need masks and officers who control their body temperature. The aim of this study is to detect people who do not wear masks or do not wear them properly, and also to detect people with high fever through a system. The proposed system is designed as a system that can be integrated into automatic door systems. The system was basically implemented by running Mobile Net, one of the deep learning models, on the Raspberry Pi card. In the proposed method, 97.0% accuracy was obtained. Experimental results show that the proposed method can effectively recognize face masks and whether people have a high fever. This work is necessary for many closed areas that will make masks and fever control in public areas.

Face Mask Detection with Automated Door Entry Control Using Convolutional Neural Network

COVID-19, the coronavirus infection of 2019, has infected over 172 million people globally and killed over 3.7 million. One of the most prevalent ways for people to protect themselves in public areas is to wear masks. Face mask detection has become a critical computer vision task to control the infection, yet mask detection research is limited. So, real-time detection of the mask is proposed using a deep learning approach with an automated door entry control system. Deep learning plays a major role in Natural Language Processing, object identification, and facial recognition. The proposed system with the face net model incorporates mask detection efficiently and accurately. To detect the presence of a face mask, the trained face mask classifier is applied to the live video stream. The data from the detection phase is passed to an automated door entry control mechanism to control the entry of the people. The experiment is conducted, and the results are obtained. From the results, it is inferred that the system recognizes the presence of a mask and ensures safety through the automated door entry mechanism.

Face Mask Detection for Real Time Video Streams

The Face Mask Detection model is used to make sure a person is wearing a mask or not. This model results from the grappling situation presented by COVID-19, resulting in the mandatory use of masks at public places. Security agencies need to plant actual personnel to make sure all the people in public are wearing ‘masks’, this model will lessen the risk of people being contacted by COVID-19. This research helps us understand a broader perspective about the Face Mask Detection models by comparing different state of the art models. The model uses MobileNetV2 architecture that has inverted bottlenecks and depth-wise convolutions to filter features. The complete model is built in two phases, the first one consisting of making a Face Mask Detection model trained to detect the face and mask, and then placing it in the Real Time environment by using the OpenCV for actually predicting the usage of face mask.

Face mask detection using OpenCV

This paper proposes a Face Mask Detection Using OpenCV. This pandemic is causing an overall crisis in medical services. This infection especially spreads through beads which rise out of somebody contaminated with Covid and represents a danger to other people. The danger of transference is most elevated in gathering. one of the agreeable lifestyle choices protected from getting kindled is conveying a facial covering in open domains as proposed by world heath association on this pandemic. We will fabricate a continuous framework to distinguish regardless of whether the individual on the webcam is wearing a mask. we will prepare the facial covering finder model utilizing keras and openCv. A boundary container drawn over the face of the individual portrays weather the man or lady is conveying a mask or not. Assuming an individual's face is saved inside the data set, it distinguishes the name of the individual that isn't conveying facial coverings and an email may be shipped off that singular wariness them that they are not wearing a veils as a method for avoiding potential risk.

Real time COVID-19 facemask detection using deep learning

The COVID-19 pandemic is producing a global health pandemic. According to the World Health Organization (WHO), the utmost effective protection is to wear a face mask in crowded regions/areas. During this pandemic, it is compulsory for every person to wear a mask and maintain social distancing. In the field of Image Processing, Convolutional Neural Networks (CNNs) have risen to prominence as the most common type of image realization/recognition model. Our project's purpose is to research and assess Machine Learning (ML) technologies for identification and recognition of people wearing face masks in any pre-recorded videos, photos, or in actual-time (real- time) circumstances. Our project aims to create a real-time Graphics User Interface based Automated Facial Recognition as well as Mask Detection System. The algorithms used in the proposed methodology are Principal Component Analysis (PCA) and the HAAR Cascade Algorithm. Finally, the result is indicated by a “GREEN” color rectangle box, which would be drawn around the section of the face, which indicates that the person on the camera is wearing a mask, or a “RED” color rectangle box, which indicates that the person on the camera is not wearing a mask. This model achieves 99% accuracy.

Real-Time Face Mask Detection to Ensure COVID-19 Precautionary Measures in the Developing Countries

Recently, the rapid transmission of Coronavirus 2019 (COVID-19) is causing a significant health crisis worldwide. The World Health Organization (WHO) issued several guidelines for protection against the spreading of COVID-19. According to the WHO, the most effective preventive measure against COVID-19 is wearing a mask in public and crowded areas. It is quite difficult to manually monitor and determine people with masks and no masks. In this paper, different deep learning architectures were used for better results evaluations. After extensive experimentation, we selected a custom model having the best performance to identify whether people wear a face mask or not and allowing an easy deployment on a small device such as a Jetson Nano. The experimental evaluation is performed on the custom dataset that is developed from the website (See data collection section) after applying different masks on those images. The proposed model in comparison with other methods produced higher accuracy (99% for training accuracy and 99% for validation accuracy). Moreover, the proposed method can be deployed on resource-constrained devices.

Covid-19 Monitoring System Using Social Distancing Face Mask Detection and Classification on Live Video

Abstract: The spread of the Coronavirus has prompted individuals to remain indoors and adhere to COVID- appropriate practices, which include social distance, the use of face masks, hand sanitizers, and other measures to protectthemselves against infection. In heavily populated locations with limited resources, it is impossible to physically supervise compliance with these standards. As a result, an automated, lightweight, and powerful video monitoring system is required to make the process more efficient. This paper proposes an extensive and productive solution for performing person detection, social distance detection, face mask detection, and face mask classification using object detection, clustering, and Convolution Neural Networks (CNN) On video datasets, in addition to YOLOv3, density-based spatial clustering of applications with noise (DBSCAN), Dual Shot Face Detector (DSFD), and MobileNetV2 based binary classifier other techniques have also been used to achieve the predicted outcomes. This study also provides parallels for numerous face mask detection and classification models. Keywords: COVID-19, DBSCAN, DSFD, YOLOv3, MobileNetV2, CNN, clustering, Social Distance, Face Masks

Face Mask Detection in Crowd Areas using Deep Learning for Avoid Spreading of Covid-19 Corona Virus

Object discovery, focused on robotically noting device factors on snap shots or movies, expands the photo kind. In the previous couple of years, smart traveler manipulate has been appreciably utilized in small matters detection methods made use of in website vacationer manage, shrewd radar to manipulate structures, armed forces matters detection tools in the Navy. Great for beneficial treatments made use of to discover. Positioning of scientific tools made use of within the navy, medical navigating, operations, and so forth. COVID-19, a special corona virus dispersing in overdue 2019, stands for a opportunity for public fitness. Numerous international locations demand that everybody wear a public masks to keep away from the unfold of the Corona virus. To prevent the unfold of the Corona virus, an item discovery method just like a unmarried shot detector focuses on specific in addition to real-time popularity of face mask in supermarkets. Contributions are made with 3 parts: 1) to attract out the functions used by the lightweight spine network, that's primarily based upon SSD and additionally exquisite separable convection, which enhances discovery charge and Satisfies real-time detection wishes. 2) Suggest Feature Improvement Module (FEM) to strengthen the CNN fashions' deeper capabilities to beautify tiny things feature examples. 3) Develop a COVID-19-Mask, a big facts set to decide if customers are sporting mask, by gathering snap shots in 2 supermarkets. The corporation goals to reap high detection accuracy and also real-time efficiency of the proposed components.

Automatic Face Mask Detection System in Public Transportation in Smart Cities Using IoT and Deep Learning

The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources.

Ensure the Proper Wearing of Face Masks Using Machine Learning to Fight Covid-19 Virus

In this pandemic time, wearing face masks is mandatory to all because of the possibility that a person can get COVID-19 virus through their mouth, nose or eyes, which could possibly happen when a person has a direct or close contact to a person with that virus. But, despite the strict implementation, some people disregard the proper wearing of face masks and unaware the risks of possible virus transmission for such negligence. In this paper, it will demonstrate how a Convolutional Neural Network (CNN) can detect if a person is wearing a face mask or not and the additional parameter to support to detect if the face mask is properly worn by a person by considering the facial landmarks thru face recognition using Histogram of Oriented Gradients (HOG) feature descriptor with a linear SVM machine learning algorithm. Two (2) processes are involved in proper wearing of face masks detection. It needs to pass in Face Mask Detection to proceed to the next process which is the Face detection wherein the result of checking should return false to confirm the proper wearing of the face mask of a person.

Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic.

Deep neural networks have made tremendous strides in the categorization of facial photos in the last several years. Due to the complexity of features, the enormous size of the picture/frame, and the severe inhomogeneity of image data, efficient face image classification using deep convolutional neural networks remains a challenge. Therefore, as data volumes continue to grow, the effective categorization of face photos in a mobile context utilizing advanced deep learning techniques is becoming increasingly important. In the recent past, some Deep Learning (DL) approaches for learning to identify face images have been designed; many of them use convolutional neural networks (CNNs). To address the problem of face mask recognition in facial images, we propose to use a Depthwise Separable Convolution Neural Network based on MobileNet (DWS-based MobileNet). The proposed network utilizes depth-wise separable convolution layers instead of 2D convolution layers. With limited datasets, the DWS-based MobileNet performs exceptionally well. DWS-based MobileNet decreases the number of trainable parameters while enhancing learning performance by adopting a lightweight network. Our technique outperformed the existing state of the art when tested on benchmark datasets. When compared to Full Convolution MobileNet and baseline methods, the results of this study reveal that adopting Depthwise Separable Convolution-based MobileNet significantly improves performance (Acc. = 93.14, Pre. = 92, recall = 92, F-score = 92).

Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model.

This paper employs a unique sensor fusion (SF) approach to detect a COVID-19 suspect and the enhanced MobileNetV2 model is used for face mask detection on an Internet-of-Things (IoT) platform. The SF algorithm avoids incorrect predictions of the suspect. Health data are continuously monitored and recorded on the ThingSpeak cloud server. When a COVID-19 suspect is detected, an emergency email is sent to healthcare personnel with the GPS position of the suspect. A lightweight and fast deep learning model is used to recognize appropriate mask positioning; this restricts virus transmission. When tested with the real-world masked face dataset (RMFD) dataset, the enhanced MobileNetV2 neural network is optimal for Raspberry Pi. Our IoT device and deep learning model are 98.50% (compared to commercial devices) and 99.26% accurate, respectively, and the time required for face mask evaluation is 31.1 milliseconds. The proposed device is useful for remote monitoring of covid patients. Thus, the method will find medical application in the detection of COVID-19-positive patients. The device is also wearable.

CNFMD: Convolutional Network-based Face Mask Detection

CNFMD: Convolutional Network-based Face Mask Detection

CNFMD: Convolutional Network-based Face Mask Detection

After the rise of the coronavirus, every country has made a compulsion on wearing face masks in public places. Even though a few countries have reached a good number of vaccinations to date, the human body is still not immune to the new variants of the virus. It will take a few more years till everyone becomes mask-free. Thus, authorities need a system to keep a proper check on the discipline whether everyone is wearing a mask in a public place or not. This paper proposes a model with 99.5% accuracy which can be deployed and thus can monitor public places. This will help the authorities to control the spread of the virus. The model proposed is a fast model which took only 38.03 seconds on average per epoch while training. Using the Kaggle dataset, the CNFMD model is trained and tested. The dataset, pre-split into three parts is used from training, validation as well as testing of the model.

HRNET: AI-on-Edge for Mask Detection and Social Distancing Calculation.

The purpose of the paper is to provide innovative emerging technology framework for community to combat epidemic situations. The paper proposes a unique outbreak response system framework based on artificial intelligence and edge computing for citizen centric services to help track and trace people eluding safety policies like mask detection and social distancing measure in public or workplace setup. The framework further provides implementation guideline in industrial setup as well for governance and contact tracing tasks. The adoption will thus lead in smart city planning and development focusing on citizen health systems contributing to improved quality of life. The conceptual framework presented is validated through quantitative data analysis via secondary data collection from researcher’s public websites, GitHub repositories and renowned journals and further benchmarking were conducted for experimental results in Microsoft Azure cloud environment. The study includes selective AI models for benchmark analysis and were assessed on performance and accuracy in edge computing environment for large-scale societal setup. Overall YOLO model outperforms in object detection task and is faster enough for mask detection and HRNetV2 outperform semantic segmentation problem applied to solve social distancing task in AI-Edge inferencing environmental setup. The paper proposes new Edge-AI algorithm for building technology-oriented solutions for detecting mask in human movement and social distance. The paper enriches the technological advancement in artificial intelligence and edge computing applied to problems in society and healthcare systems. The framework further equips government agency, system providers to design and construct technology-oriented models in community setup to increase the quality of life using emerging technologies into smart urban environments.

Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm.

The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.