Drowsiness Detection System for Masked Face Based on Deep Neural Network and Haar Cascade

Nowadays, technology is growing rapidly followed by modernization. Face detection technology is one technology that has been developed and applied in various sectors such as biometrics recognition systems, retrieval systems, database indexing in digital video, security systems with restricted area access control, video conferencing, and human interaction systems. Eye detection is a further development of face detection in which the image of a human face was detected to be processed by detecting the location of both eyes on the face. Nowadays, the eye detection system can be used as a means of developing more complex applications and can be applied directly in the aspect of technology that uses eye detection like, eye state detection system, drowsiness and fatigue detection system, safety driving support systems or driver assistance system. In this study we propose drowsiness detection system utilizing current novel classification model such as Deep Neural Network (DNN), combined with Haar Cascade. The DNN is utilized to detect face, while Haar Cascade is utilized for detecting the eyes and its state on the detected face. In this study, due to Covid19 pandemic, we focused on developing the classifiers for detecting the face with mask. Apart from that, our proposed classifiers are also capable of identifying non-masked faces. The experimental result showed that by utilizing DNN and Haar Cascade, our proposed system could reach accuracy, precision, recall, and f1 measure as much as 81%, 88%, 80%, and 84%, respectively. © 2022 IEEE.

Research on Mask Wearing Recognition Based on Deep Learning

Since the outbreak of COVID-19 epidemic, research results have shown that the COVID-19 transmitted by droplets, and the most effective means of epidemic prevention is to wear masks. In public places where crowds gather, it is particularly important to use technical means to detect the situation of wearing masks, and remind people to wear masks in time to prevent cross-infection. This paper mainly starts with the target detection and tracking technology in the field of computer vision, and takes the recognition of whether to wear a mask as the entry point. Using python as the development tool, based on the convolutional neural network, the YOLOv2 algorithm is used as the core algorithm, and the ResNet50 network structure is built. Compared with other existing system test experiments, we can see that the system we built has better detection performance. © 2022 Association for Computing Machinery.

Indoor Safety Monitoring and Auto Temperature Detector for COVID-19

During the pandemic and endemic phase of COVID-19, prevention and precaution is one of the most important steps in order avoid the spread of the virus. This paper discusses about implementing a system to increase awareness of COVID-19. The system is a combination of body-temperature detector and building density detection technology. The purpose of this system is to count the total number of people population in a building and to measure their body temperature. The system is developed using Arduino UNO along with a number of sensors connected to it. © 2022 IEEE.

Indoor Safety Monitoring and Auto Temperature Detector for COVID-19

During the pandemic and endemic phase of COVID-19, prevention and precaution is one of the most important steps in order avoid the spread of the virus. This paper discusses about implementing a system to increase awareness of COVID-19. The system is a combination of body-temperature detector and building density detection technology. The purpose of this system is to count the total number of people population in a building and to measure their body temperature. The system is developed using Arduino UNO along with a number of sensors connected to it. © 2022 IEEE.

A Crowd Alert System for Pandemic Control using Yolov4

The Covid-19 pandemic in the late 2019 caused the world to shut down. Even though it is recommended to reduce overcrowding it still cannot be avoided. This can cause the pandemic to spread even more, especially since offices, schools and colleges are slowly reopening. With image detection making huge breakthroughs in the last decade, modern image detection technologies can now be combined with the current hardware to combat problems like overcrowding, which massively spreads the pandemic. In this paper, the YOLO v4 algorithm has been used, which greatly speeds up the process of detection and improves the overall accuracy of the system. © 2022 IEEE.

Face Recognition and Mask Detection Technology for Surveillance Systems

Corona pandemic has affected the daily routine of life disturbing the trade and economic globally. Wearing a mask has become compulsory and a new tradition. within the close to future, several suppliers can raise the shoppers to wear masks properly. Therefore, detection of face mask has become one of the important tasks to assist the international society. This paper provides a easy and simplified approach to detect the face masks using some of the important Machine Learning packages like TensorFlow, Keras, OpenCV and Scikit-Learn. The projected methodology detects the face from the image properly and so identifies if it's a mask thereon or not. As a police work task performing artist, it may detect a face together with a mask in motion. the tactic gives an accurate output with an accuracy of 96.77% on dataset. The model tendency to find the optimized values of parameters are employed using Convolutional Neural Network (CNN) model to identify whether the masks are worn properly or not while not inflicting over-fitting. © 2021 IEEE.

Pathogenesis of COVID-19 and the quality control of nucleic acid detection.

The new coronavirus pneumonia (COVID-19) epidemic spread rapidly throughout the world. Considering the strong infectivity and clustering of COVID-19, early detection of infectious cases is of great significance to control the epidemic. Nucleic acid testing (NAT) plays an important role in rapid laboratory diagnosis, treatment assessment, epidemic prevention and control of COVID-19. However, since COVID-19 is caused by a new emerging virus and NAT for COVID-19 has not been clinically applied before, false negative results inconsistent with clinical diagnosis have appeared in clinical practice. Therefore, it is urgent to improve the sensitivity of NAT for COVID-19. This study aimed to summarize the current situation and prospect of NAT based on the latest findings on COVID-19 infection. Also, the quality control of sample collection was discussed. Hopefully, this study could help to improve the effectiveness of NAT for COVID-19.