Real-Time Face Mask Detection using Computer Vision and Machine Learning

Face mask detection is the process of identifying whether a person is wearing a face mask or not in real-time through the use of computer vision and machine learning algorithms. This technology can be used in various applications, such as security systems at public transportation hubs or in hospitals, to ensure compliance with health and safety regulations during a pandemic or other infectious disease outbreaks. The technology works by analyzing images or video streams from cameras and computer vision techniques are used to detect the presence of a face mask on a person's face. The output of the system is a binary result (i.e., mask detected or not detected) or a more detailed result that provides information about the type of mask and its location on the face. © 2023 IEEE.

Population Index and Analysis Based on Different Geographies, Using Distance Measurement, Social Distancing, and Deep Learning

Nowadays, every individual is familiar with the COVID-19 pandemic which has caused great turmoil in everyone's life. Also, they are aware that there is no medicine or drug to cure COVID immediately, and people are at the risk of losing their lives. Lack of vaccines or delay in vaccine production for mass results social distancing being the only measure to tackle this pandemic. As a result, social distancing has proven to be a very reliable and efficient way to diminish the growth of this disease;the reason why lockdowns are imposed, and people are asked to keep some distance from each other, for their safety as there will be minimal physical contact. Machine learning and artificial intelligence come into the picture in every solution to a generic problem the community faces nowadays like in medical, supply chain management, face detection, etc. Using the power of AI algorithms, the paper aims to develop a robust system to monitor and analyze social distance measurement protocols at public places during the COVID-19 pandemic with the help of CCTV feed and check whether they abide by the safety protocols or not by measuring the distance between them. The proposed approach is implemented to enumerate the number of violations at a popular public place to prevent massive crowds at particular periods. The proposed method is suitable to construct a scrutiny system at a public place to alert people and eschew mass gatherings that can be concluded using achieved results. The paper also has an analysis of the performance of different models of R-CNN, Fast R-CNN, and YOLO. YOLO architectures are validated based on object detection and object tracking rate in real time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Research on Mask Wearing Detection Based on Faster RCNN

In the context of the global raging of the new coronavirus (COVID-19), to effectively prevent the spread of the new coronavirus in the crowd, many places require the wearing of masks in public places. In response to this problem, this paper proposes a mask wearing detection based on the FasterRCNN algorithm. The method uses ResNet-50 to extract convolution features and selects high-quality suggestion boxes through NMS (non-maximum suppression), which increases the detection of incorrectly wearing masks, which can play a reminder role in practical applications and further improve the prevention of epidemics, and the final experiments show that the wearing of masks can be accurately and efficiently detected through the steps of feature extraction and prediction frame generation. © 2022 IEEE.

Social Distance Measurement and Face Mask Detection Using Deep Learning Models

The coronavirus disease 2019 has caused a worldwide catastrophe with its destructive spreading and causing death of more than 2.47 million people around the globe. In the current circumstance, most of the countries are trying to implement social distancing, wearing masks, extensive testing, and contact tracing strategies to curb the virus outbreaks. Maintaining adequate social or physical distance is believed to be a sufficient precautionary measure (standard) against the spread of the pandemic infection. This research paper has two different contributions of social distance measurement and face mask detection using various deep learning approaches. In the first section, we have monitored the social distance where we have detected people by examining a video feed with SSD-MobileNet and Faster R-CNN ResNet50 deep learning algorithms. Next, the image is converted into an overhead view to measure the specific distance among people to ensure safe physical distancing. In the second section, we have detected the face masks used by the people by implementing MobileNetV2 convolutional neural network architecture. Hence, we have used computer vision to find the region of interest of a face, and finally, we have found that the mask is in the face or not. Both of our social distance measurement and face mask detection systems offer high accuracy. As for the social distance monitoring, the accuracy greatly depends on the people detection, and the execution time is 30 ms and 89 ms for SSD-MobileNet and Faster R-CNN ResNet50, respectively. For the face mask detection, we obtained 99% accuracy, and it is checked in real-time so that we can prove that our model is not overfitting and it performs well outside our dataset in real-time camera. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN.

This study examines related literature to propose a model based on artificial intelligence (AI), that can assist in the diagnosis of depressive disorder. Depressive disorder can be diagnosed through a self-report questionnaire, but it is necessary to check the mood and confirm the consistency of subjective and objective descriptions. Smartphone-based assistance in diagnosing depressive disorders can quickly lead to their identification and provide data for intervention provision. Through fast region-based convolutional neural networks (R-CNN), a deep learning method that recognizes vector-based information, a model to assist in the diagnosis of depressive disorder can be devised by checking the position change of the eyes and lips, and guessing emotions based on accumulated photos of the participants who will repeatedly participate in the diagnosis of depressive disorder.

Deep Learning Algorithms based Vehicle Mobility Prediction from Satellite Imagery During Pandemic

The COVID-19 epidemic has made governments around the world to enforce lockdowns and isolations to stop the spread of virus. Both human and financial activities are affected throughout the globe. It takes time to recover from these losses. Financial actions influence social activities which incorporate signatures in satellite images that can be perceived and categorized. Satellite imagery aids in making decisions of predictors and decision makers by offering diverse types of perceptibility in the relating financial changes. In this paper, deep learning methods including Fast Region-based Convolutional Network (Fast R-CNN) and You Only Look Once (YOLO) are employed to identify the detailed elements in satellite images that can be used to find the financial indicators based on it. The proposed system uses Histogram Equalizer (HE) for enhancing the satellite pictures to provide accurate analysis about human movements. The system shows results on genuine instances of various destinations when COVID-19 flares up to delineate extraordinary quantifiable markers. The area is partitioned into different sections and the human and economic activities are identified. Mobility of people shows the spreading of COVID-19. YOLO offers the best performance in object (vehicle) identification from which the presence of economic downfall is predicted. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.