Face Mask detection using Mask R-CNN to control the spread of Covid-19

Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask identification software and applications have already been released by a few businesses, and others have promised to do the same for the service. The proposed work examines face mask detection accuracy using CNN networks. Mask wear is now required in many developed and developing countries worldwide when leaving the house or entering public spaces. It will be difficult to maintain touchless access control in buildings while recognising faces wearing masks on any surveillance systems. Masks covering faces has made face detection algorithms and performance difficult. The proposed work detect face mask labeled no mask or mask with detection accuracy. The work train the system to click images of a face and provide labeled data. The work is classified using Convolution Neural Network (CNN), a Deep learning technique, to classify the input image with the help of the classification algorithm MobileNetV2. The trained system shows whether a person in the video frame is wearing a mask or not. © 2023 IEEE.

Deep Learning-and Transfer Learning-based Models for COVID-19 Detection using Radiography Images

Image classification and segmentation techniques are still very popular in the medical field (for healthcare), in which the medical image plays an important role in the detection and screening of diseases. Recently, the spread of new viral diseases, namely Covid-19, requires powerful computer models and rich resources (datasets) to fight this phenomenon. In this study, we propose to examine the CNN Deep Learning algorithm and two Transfer Learning models, namely RestNet50 and MobileNetV2 using the pretrained model of the ImageNet database, experimented on the new dataset (COVID-QU-Ex Dataset 2022) offered by the University of Qatar. These models are tested to classify radiography images into two classes (Covid19 and Normal). The results achieved by CNN (Acc =95.97%), ResNet50 (Acc =95.53%) and MobileNetV2 (Acc =97.32%) show that these algorithms are promising in order to combat this Covid-19 disease by detecting it through thoracic images (Chest X-ray type). © 2023 IEEE.

DenseNet201 Model for Robust Detection on Incorrect Use of Mask

In 2022, the COVID-19 pandemic is still occurring. One of the optimal prevention efforts is to wear a mask properly. Several previous studies have classified the use of masks incorrectly. However, the accuracy resulting from the classification process is not optimal. This research aims to use the transfer learning method to achieve optimal accuracy. In this research, we used three classes, namely without a mask, incorrect mask, and with a mask. The use of these three classes is expected to be more detailed in detecting violations of the use of masks on the face. The classification method used in this research uses transfer learning as feature extraction and Global Average Pooling and Dense layers as classification layers. The transfer learning models used in this research are MobileNetV2, InceptionV3, and DenseNet201. We evaluate the three models' accuracy and processing time when using video data. The experimental results show that the DenseNet201 model achieves an accuracy of 93%, but the processing time per video frame is 0.291 s. In contrast to the MobileNetV2 model, which produces an accuracy of 89% and the processing speed of each video frame is 0.106 s. This result is inversely proportional to accuracy and speed. The DenseNet201 model produces high accuracy but slow processing time, while the MobileNetV2 model is less accurate but has faster processing. This research can be applied in the crowd center to monitor health protocols in the use of masks in the hope of inhibiting the transmission of the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Automatic approach for mask detection: effective for COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in-person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a deep learning-based system that can detect instances of improper use of face masks. A dual-stage convolutional neural network architecture is used in our system to recognize masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. In this paper, we propose a variant of a multi-face detection model which has the potential to target and identify a group of people whether they are wearing masks or not.

Application of AI techniques for Face Mask Detection

Covid-19 is a completely new problem, and we have seen it move to a brand new level. After the 3rd wave of Covid-19 in India and predictions of another wave this year it is a major concern and still many people are not following basic precautionary measures like wearing a mask in public locations this can be solved by our face mask detection program we want to be short a good way to respond to new facts, which they are all around us. Growing a secure environment can be paramount the human to make lifestyles as smooth as ever. Alternatives have to be taken to protect all who go back and to maintain them our loved ones who have no troubles. New era packages are being made each day to satisfy regulations and regulations but, the face mask becomes a new well known used for regular existence, but, to create a more secure surroundings that contributes to public protection, a want to be diagnosed at some stage in date and motion towards people who do not put on masks in public locations or offices. Many sections of the general public appear to simply accept Covid adherence protection gear. A face masks detector is among the most crucial equipment. This software allows one to find out who does not have the desired face masks. Those applications with them current tracking systems and neural network algorithm to see if an individual has put on a mask or not. About this, we'll do discussion in short the synthetic intelligence and its small additives specifically device gaining knowledge of and in-intensity analysing, in-intensity reading frameworks followed with the aid of the usage of simplicity implementation of face masks detection machine. © 2022 IEEE.

A Deep Learning Powered System to Lie Detection While Online Study

Many education facilities have recently switched to online learning due to the COVID-19 pandemic. The nature of online learning makes it easier for dishonest behaviors, such as cheating or lying during lessons. We propose a new artificial intelligence - powered solution to help educators solve this rising problem for a fairer learning environment. We created a visual representation contrastive learning method with the MobileNetV2 network as the backbone to improve predictability from an unlabeled dataset which can be deployed on low power consumption devices. The experiment shows an accuracy of up to 59%, better than several previous research, proving the usability of this approach.

Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Face Mask Detection Using Convolution Neural Network

Face recognition in the industry now is playing an important role in each sector. Each person has different type of features and face;therefore, each identity is unidentical. In this COVID outbreak, a major crisis has occurred due to which preventions are to be made. One such prevention is use of a face mask which is very much important. Nowadays, various firms and organizations are using facial recognition systems for their own general purpose. We all know that it has now been a crucial task to wear a mask every time, when we go somewhere. But as we know it is not possible to keep track of who wears a mask and who does not. We make the use of AI in our daily life. We achieve this with the help of a neural network system, which we train so that it can further describe people's features. Even though the original dataset was limited, the Convolutional Neural Network (CNN) model achieved exceptional accuracy utilizing the deep learning technique. With the use of a face mask detection dataset that contains both with and without face mask photographs, we are able to recognize faces in real-time from a live webcam stream using OpenCV. We will develop a COVID-19 face mask detection system using our dataset, along with Python, OpenCV, Tensor Flow, and Keras. © 2022 IEEE.

A Hybrid Deep Learning System to Detect Face-Mask and Monitor Social Distance

Coronavirus Disease 2019 (COVID-19) emerged towards the end of 2019, and it is still causing havoc on the lives and businesses of millions of people in 2022. As the globe recovers from the epidemic and intends to return to normalcy, there is a spike of anxiety among those who expect to resume their everyday routines in person.The biggest difficulty is that no effective therapeutics have yet been reported. According to the World Health Organization (WHO), wearing a face mask and keeping a social distance of at least 2 m can limit viral transmission from person to person. In this paper, a deep learning-based hybrid system for face mask identification and social distance monitoring is developed. In the OpenCV environment, MobileNetV2 is utilized to identify face masks, while YoLoV3 is used for social distance monitoring. The proposed system achieved an accuracy of 0.99. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covi-Gate: Face Mask and Temperature Detection System

This paper projects machine learning as a valuable tool for the restriction of the Covid-19 pandemic escalation in the global scenario. The proposed system involves detection of masked or unmasked people and a temperature sensing system for ensuring Covid-19 appropriate protocol is followed to allow only healthy person(s) in public/crowded places. The integration of Arduino Uno and MLX90614 non-contact temperature sensor, along with a MobileNetV2 machine learning model, is performed for complete execution. The system will classify a person as a masked or unmasked individual using ML techniques and detect their body temperature. If the individual meets the appropriate requirements, the system will enable them to access via the gate, which will be controlled by a servo motor in conjunction with a temperature sensor module. © 2022 IEEE.

Autonomous Medical Assistive Robot for Medical Camp

The outbreak of the Covid-19 epidemic has devastated the generation and impacted multiple layers of the healthcare sector. Resulting from this kind of exceptionally contagious virus and a shortfall of medical workers in the hospitals, front-line health workers, and patients are at risk. Thus, with an aim to diminish the risk of infections, a mobile robotic system is proposed that can autonomously ensure safety and protection in the hospital. The system can monitor the patients by moving autonomously and sanitizing the floor throughout the hospital, which is implemented by Robot Operating System (ROS), SLAM (Simultaneous Localization and Mapping) algorithm, and A∗ search algorithm, and then it uses the MobileNetV2 algorithm for safety mask detection and giving voice alert. The system also offers AI voice communication to assist and diagnose the patients, which can lessen person-to-person contact. The system has anticipated 89% accuracy for AI custom dataset, whereas the validation accuracy for face mask detection is 95%. © 2022 IEEE.

Incorrect facemask-wearing detection using image processing and deep learning

Now and in the future, a face mask is a very important strategy to protect people when a new contagious life threatens disease spread through the air appears. Currently, there is a serious health emergency because of the coronavirus disease 2019 (COVID-19) epidemic. The negative consequences of this pandemic need to be protected in public areas. Numerous methods are advised by the World Health Organization (WHO) to reduce infection rates and prevent depleting the available medical resources in the absence of efficient antivirals. Wearing masks is a non-pharmaceutical strategy to lessen the susceptibility to COVID-19 infection. This research aims to create a face mask identification system that is efficient and uses deep learning, which has proven to be beneficial in many real-world applications. This system has also used a transfer learning method with the MobileNetV2 model to classify people who wear face masks properly, wear face masks improperly, and are without masks. The results demonstrate that the proposed system has an accuracy of 99.4% which is higher than current systems. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

FACE MASK RECOGNITION SYSTEM ON RESTAURANT WITH SELF-PAYMENT SYSTEM USING CONVOLUTIONAL NEURAL NETWORK (CNN)

COVID-19 is a disease that affects many aspects of life transmitted by verbal interaction. Nowadays the rapid growth of COVID-19 has become an international issue due to violation of the face mask rules. This research will provide a comparison of the deep learning class, Convolutional Neural Network (CNN) which is used as the basis of the face mask recognition system and to adapt it into a payment verification system. This research will use MobileNetv2 and YOLO-v4 with its pretrained model using group of images composed of person using face mask and person not using face mask. Each model successfully performs the detection task. The result shows that MobileNetv2 has achieved a better overall percentage compared to the YOLO-v4 algorithm. Hence, MobileNetv2 has been chosen as the algorithm used for the payment verification system. © ICIC International.

Computer Vision-Based IoT Architecture for Post COVID-19 Preventive Measures

The COVID-19 pandemic has wreaked havoc on people all across the world. Even though the number of verified COVID-19 cases is steadily decreasing, the danger persists. Only societal awareness and preventative measures can assist to minimize the number of impacted patients in the work environment. People often forget to wear masks before entering the work premises or are not careful enough to wear masks correctly. Keeping this in mind, this paper proposes an IoT-based architecture for taking all essential steps to combat the COVID-19 pandemic. The proposed low-cost architecture is divided into three components: one to detect face masks by using deep learning technologies, another to monitor contactless body temperature and the other to dispense disinfectants to the visitors. At first, we review all the existing state-of-the-art technologies, then we design and develop a working prototype. Here, we present our results with the accuracy of 97.43% using a deep Convolutional Neural Network (CNN) and 99.88% accuracy using MobileNetV2 deep learning architecture for automatic face mask detection. © 2023 by the authors.

An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images.

In recent years, deep learning's identification of cancer, lung disease and heart disease, among others, has contributed to its rising popularity. Deep learning has also contributed to the examination of COVID-19, which is a subject that is currently the focus of considerable scientific debate. COVID-19 detection based on chest X-ray (CXR) images primarily depends on convolutional neural network transfer learning techniques. Moreover, the majority of these methods are evaluated by using CXR data from a single source, which makes them prohibitively expensive. On a variety of datasets, current methods for COVID-19 detection may not perform as well. Moreover, most current approaches focus on COVID-19 detection. This study introduces a rapid and lightweight MobileNetV2-based model for accurate recognition of COVID-19 based on CXR images; this is done by using machine vision algorithms that focused largely on robust and potent feature-learning capabilities. The proposed model is assessed by using a dataset obtained from various sources. In addition to COVID-19, the dataset includes bacterial and viral pneumonia. This model is capable of identifying COVID-19, as well as other lung disorders, including bacterial and viral pneumonia, among others. Experiments with each model were thoroughly analyzed. According to the findings of this investigation, MobileNetv2, with its 92% and 93% training validity and 88% precision, was the most applicable and reliable model for this diagnosis. As a result, one may infer that this study has practical value in terms of giving a reliable reference to the radiologist and theoretical significance in terms of establishing strategies for developing robust features with great presentation ability.

Artificial Intelligence Based Integrated and Distributed System for Preventing Covid-19 Spread Using Deep Learning

The COVID-19 pandemic is causing a worldwide emergency in healthcare. This virus mainly spreads through droplets which emerge from a person infected with coronavirus and poses a risk to others. The risk of transmission is highest in public places. Many measures have been suggested, such as maintaining a social distance, and wearing a face mask to avoid the spread of this virus. There are three modules in this work, in the first module a mask detection system which detects whether a person wears a mask or not using deep learning techniques such as MobileNet V2 architecture along with Facenet and Masknet. Accuracy of 98.6 percentage is achieved in this module with one or two people in the frame. Barricade has been set which does not allow people who does not wear mask and allows people who wears a mask. LED light indicators and LCD displays are used as alerts, and they are programmed to provide information that is both worn and not worn, depending on the output. In the second module, a system has been designed which detects the temperature of the person and detects whose temperature is above normal body temperature and alerts accordingly. In the third module a social distancing system has been designed which detects people who does not follow social distancing protocol and alerts them using deep learning techniques. The YOLOv3 algorithm is used which creates a square box around people that displays green or red color box according to the measurement output. The transfer learning methodology is also implemented to increase the accuracy of this module. The accuracy of 98.2 percentage is achieved for social distance detection module using YOLOv3 detection model with transfer learning. All three modules are integrated so it automatically monitors human body temperature, detects mask and social distancing at the barricade system.

Real-Time Mask Detection Based on SSD-MobileNetV2

After the outbreak of COVID-19, mask detection, as the most convenient and effective means of prevention, plays a crucial role in epidemic prevention and control. An excellent automatic real-time mask detection system can reduce a lot of work pressure for relevant staff. However, by analyzing the existing mask detection approaches, we find that they are mostly resource-intensive and do not achieve a good balance between speed and accuracy. And there is no perfect face mask dataset at present. In this paper, we propose a new architecture for mask detection. Our system uses SSD as the mask locator and classifier, and further replaces VGG-16 with MobileNetV2 to extract the features of the image and reduce a lot of parameters. Therefore, our system can be deployed on embedded devices. Transfer learning methods are used to transfer pre-trained models from other domains to our model. Data enhancement methods in our system such as MixUp effectively prevent overfitting. It also effectively reduces the dependence on large-scale datasets. By doing experiments in practical scenarios, the results demonstrate that our system performed well in real-time mask detection. © 2022 IEEE.

Real Time Web-based Facemask Detection

The widespread spread of the Covid-19 virus in 2020-2021 is very worrying for all people around the world, coupled with the spread of a new variant of the Covid-19 virus, which is more aggressive and easily transmitted, causing public unrest about when this pandemic will end. The policy of using masks to reduce the spread of the virus has been made to minimize the spread. But even if there is a policy, there are still people who don't want to wear masks. Therefore, a mask detection system is needed to help differentiate whether someone uses a mask or not by displaying alerts in a form of web application. This research was conducted using several data augmentation techniques to increase the variation of the data to be used before training the algorithm model using the Convolutional Neural Network (CNN) algorithm with MobileNetV2 and VGG19 architectures. Both models are then evaluated where the architecture with the best performance will be implemented in the form of a web application. The accuracy of both models was compared, with the result of MobileNetV2 being 99% accurate and VGG19 being 98%. MobileNetV2 as the model that has the best accuracy value will be implemented in the form of a web application using the Haar Feature-Based Cascade to detect masks. The web application will be publicly accessed local at Universitas Multimedia Nusantara. © 2022 IEEE.

Artificial Intelligence Framework for COVID Protocol Detection

Artificial intelligence is now penetrating into all the domains. Any domain can incorporate artificial intelligence to automate their process. In the outbreak of COVID pandemic, artificial intelligence has been very useful in many ways. artificial intelligence helps in automating process where it's not always possible for people to do and to reduce the wastage of human resource. Here we proposed a frame work to automate the detection of covid protocol violation in public places. Our work detecting people with & without masks and detects social distancing with a single model. The best performing model from the standard convolution neural network architectures namely VGG16 and MobileNetV2 are used in the present work, from the experiments it's found that MobileNetV2 outperformed VGG16. The developed system can easily be integrated/implemented on various embedded devices with limited computational capacity by using the MobileNetV2 architecture. Compared to other previous works, our work outstands by having good accuracy and compatible to use in real life application because of its requirement of less computational complexity. © 2022 IEEE.