Face mask detection based on improved YOLOv3 algorithm

In the current situation of COVID-19 prevention and control, wearing masks remains an important way to prevent the transmission of the Novel Coronavirus. Aiming at the problem that the detection accuracy of the traditional YOLOv3 algorithm can still be improved, this paper proposes an improved yolov3 algorithm and applies it to the practical problem of detecting whether to wear a mask. Firstly, the algorithm introduces the residual structure of structural reparameterization in the feature extraction network named Darknet53 of YOLOv3 to obtain the input features;Secondly, the SimSPPF (Simplified Spatial Pyramid Pooling-Fast) is introduced to enhance feature extraction;Finally, an improved attention mechanism is introduced to make the model focus on regions with more prominent features. Besides, in order to ensure the accuracy of target detection, CIoU and Focal loss function was used in the training process. The results show that compared with the traditional YOLOv3, the detection accuracy of the improved algorithm for normal face and mask face is improved by 16.98% and 7.30% respectively, and the mAP is improved by 12.14%, which can meet the requirements of daily use and lay a foundation for rapid face recognition when wearing mask. () © 2023 IEEE.

Automated Face Mask Detection using Artificial Intelligence and Video Surveillance Management

Surveillance camera has become an essential, ubiquitous technology in people's daily lives, whether applicable for home surveillance or extended to public workplace detection. The importance of the camera is irreplaceable in terms of the agent for an enclosed system to function correctly. The goal of ubiquitous computing is to keep different devices or technology communicating seamlessly, allowing them to expand to other areas instead of limiting it to one device. However, many research papers have been released on how the camera can aid in the current situation where COVID-19 is still raging worldwide, especially in crowded places. This paper aims to suggest a method by which surveillance cameras on the university campus can automatically detect student face mask status and notify them. Alongside that, this concept of applying a video management system within the university campus will assist in the automation of invigilating the student's daily mask status from the number of embedded surveillance cameras around the campus. © 2023 IEEE.

Untargeted white-box adversarial attack to break into deep leaning based COVID-19 monitoring face mask detection system

The face mask detection system has been a valuable tool to combat COVID-19 by preventing its rapid transmission. This article demonstrated that the present deep learning-based face mask detection systems are vulnerable to adversarial attacks. We proposed a framework for a robust face mask detection system that is resistant to adversarial attacks. We first developed a face mask detection system by fine-tuning the MobileNetv2 model and training it on the custom-built dataset. The model performed exceptionally well, achieving 95.83% of accuracy on test data. Then, the model's performance is assessed using adversarial images calculated by the fast gradient sign method (FGSM). The FGSM attack reduced the model's classification accuracy from 95.83% to 14.53%, indicating that the adversarial attack on the proposed model severely damaged its performance. Finally, we illustrated that the proposed robust framework enhanced the model's resistance to adversarial attacks. Although there was a notable drop in the accuracy of the robust model on unseen clean data from 95.83% to 92.79%, the model performed exceptionally well, improving the accuracy from 14.53% to 92% on adversarial data. We expect our research to heighten awareness of adversarial attacks on COVID-19 monitoring systems and inspire others to protect healthcare systems from similar attacks. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Emotion Detection of People Wearing Face Mask

Though many facial emotion recognition models exist, after the Covid-19 pandemic, majority of such algorithms are rendered obsolete as everybody is compelled to wear a facemask to protect themselves against the deadly virus. Face masks can hinder emotion recognition systems, as crucial facial features are not visible in the image. This is because facemasks cover essential parts of the face such as the mouth, nose, and cheeks which play an important role in differentiating between various emotions. This study intends to recognize the emotional states of anger-disgust, neutral, surprise-fear, joy, sadness, of the person in the image with a face mask. In the proposed method, a CNN model is trained using images of people wearing masks. To achieve higher accuracy, the classes in the dataset are combined. Different combinations of clubbing are performed, and results are recorded. Images are taken from FER2013 dataset which consists of a huge number of manually annotated facial images of people. © 2023 IEEE.

Reutilizing Single-Use Surgical Face Masks to Improve the Mechanical Properties of Concrete: A Feasibility Study

The coronavirus outbreak (COVID-19) has caused a sharp increase in the use of Single-Use Surgical Face Masks (SUSFMs) as personal protective equipment. These eventually end up in waste disposal facilities causing environmental pollution. Those that end up in the water bodies fragment into microplastics that affect marine life. Since the SUSFM materials are made from polypropylene, a thermoplastic polymer material that takes a long time to degrade, it is important to develop sustainable mitigation measures to remove them from the environment. This study investigated the feasibility of reutilizing SUSFMs in concrete. SUSFMs were shredded and added to C30/37 grade concrete in various percentages, 0%, 0.5%, 1.0%, 1.5%, 2.0%, 2.5%, and 3.0%, by mass of cement content. The specimens were cured for 28 days before being tested for compressive strength, splitting tensile strength, and ultrasonic pulse velocity. The compressive strength decreased with an increase in the length and dosage content. The least decrease of 10.4% was observed at 0.5% content of 30mm length of SUSFM material. The results showed that concrete improved regarding splitting tensile strength, with the highest increase of 15.2% at 0.5% content of 30mm SUSFM. In addition, the overall quality of concrete remains at UPV values of more than 4000m/s registering good quality concrete. The results underscore the use SUSFM material in concrete in order to improve its quality while at the same time reducing waste.

Face Mask Classification based on Transfer Learning and MobileNet V2

Face mask image recognition can detect and monitor whether people wear the mask. Currently, the mask recognition model research mainly focuses on different mask detection systems. However, these methods have limited working datasets, do not give safety alerts, and do not work appropriately on masks. This paper aims to use the face mask recognition detection model in public places to monitor the people who do not wear the mask or the wrong mask to reduce the spread of Covid-19. The mask detection model supports transfer learning and image classification. Specifically, the collected data are first collected and then divided into two parts: with_mask and without_mask. Then authors build, implement the model, and obtain accurate mask recognition models. This paper uses and size of images datasets tested respectively. The experimental results show that the effect of the image size of was relatively better, and the training accuracy of different MobileNetV2 models is about 95%. Our analysis demonstrates that MobileNetV2 can correctly classify Covid-19. © 2022 ACM.

Human exposure against airborne pathogens in an office environment

Airborne exposure has been highlighted during the COVID-19 pandemic as a probable infection route. This experimental study investigates different protection methods at an office workstation, where the concentration characteristics are studied under the mixing ventilation conditions. The protection methods were the room air purifier, personal air purifier, face mask, and workstation partition panels. In experiments, the breathing machine, nebulizer, and syringe pump was used to generate an aerosol distribution of paraffin oil into the room. The breathing thermal manikin and the thermal dummy simulated the exposed and infected person, respectively. The concentration characteristics were measured from the manikin breathing zone. The temporal concentration characteristics were measured from zero concentration to steady-state conditions. The study provides insights into the effects of different protection methods for occupational health and safety decision-making for office indoor environments. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

An Improved Computer Vision based Face Mask Detection and Alerting System

A global healthcare crisis has been declared as a result of the covid-19 nandemic's extensive snread. The coronavirus spreads mostly by the release of droplets from an infected person's irritated nose and throat. The risk of spreading disease is highest in public gathering places. Wearing a facial mask in public is one of the greatest ways, according to the World Health Organization, to avoid getting an infectious disease. This research work proposes an approach to human face mask detection using TensorFlow and OpenCV. Whether or not a character is wearing a mask is indicated by an enclosing field drawn around their head. An alert email will be sent to a person whose face is in the database if they make a call without a mask worn. © 2023 IEEE.

Facemask detection using Deep Learning

COVID-19 was raised in the year 2020 which became more dangerous to society. According to the medical results, 100 million confirmed cases and 6 million deaths. This virus became an obstacle to gathering people in public places. This virus has spread all over the world. So, the Government has implemented a facemask policy to prevent the hazardous virus. It is a very difficult task to observe manually in crowded places. Most people are not wearing facemasks properly in public a place which causes the increase of the virus. So, the proposed model will detect the face mask whether the people are wearing it or not. By using, the HAAR-CASCADE technique we can able to detect whether the people are wearing the mask or not. By using this algorithm, we can able to prevent affecting of the virus to the person. This algorithm works effectively for detecting facemasks. The system compares faces with masks and faces without the mask. If people are not wearing a mask, the system detects through the camera and alerts by the alarm sound. The experiment results show the proposed technique achieves a 95% accuracy rate. © 2023 IEEE.

Zooming in and Zoning out: Remote Deliberation Impairs Team Decision Quality

The sudden shift to remote work offered a unique opportunity to investigate the effect of meeting modality on team decisions. We present data on classroom teams solving a classic team decision task type, the hidden profile, where members each have unique information that must be combined to arrive at the correct solution. Owing to the COVID-19 pandemic, we collected data on teams solving hidden profiles in-person, over Zoom, and then in-person while wearing face masks. We first demonstrate the efficacy of the decision task, a space-themed hidden profile where team members bring to bear data on exoplanets to choose which of three planets can best support human colonization. Once validated, the task was implemented as part of a team effectiveness course over four years: two years before the COVID-19 pandemic (2018-2020), one year of remote work (2020-2021), and one year of masked in-person work (2021-2022). Students were randomly assigned to teams and roles within each course and deliberated for 30 minutes to choose the best option. Examining the quality of team decisions shows marked differences based on the modality of team deliberations. Teams deliberating in-person had the greatest chance of solving the hidden profile, followed by teams meeting in-person with face masks;teams deliberating over Zoom were least likely to solve the hidden profile. Practical implications of team decision modalities for hybrid work design are discussed.

Performance of COVID Face Mask Detection Based on SVM Algorithm Using OpenCV DNN as Preprocessing Model

As COVID-19 became a pandemic in the world, wearing a mask has become one of the best measures to prevent the spread of the epidemic, so face mask recognition in public places has become a very important part of controlling the epidemic. This paper mainly tests the performance of the OpenCV DNN preprocessing model (OpenCV DNN + SVM) based on the SVM algorithm model in the face mask recognition dataset. The dataset I use is from Kaggle called COVID Face Mask Detection Dataset. This dataset contains 503 face images with masks and 503 face images without masks. I test the performance of using OpenCV DNN + SVM and using only the SVM algorithm to evaluate this study by setting a control experimental group. In this study, it was found that using OpenCV DNN + SVM, the accuracy of ROI parameters and SVM parameters can reach 93.06% and F1score can also reach 93.06% without a lot of adjustment. The accuracy rate can only reach 68.31%, and the F1score reaches 68.31%. Findings suggest that the method using OpenCV DNN + SVM can achieve slightly better results in the COVID Face Mask Detection Dataset, and can perform better than only using the SVM algorithm. In addition, using OpenCV DNN preprocessing model based on the SVM algorithm plays an important role in feature extraction in face mask recognition. If the developer does enough parameters tuning, the accuracy will also increase. © 2022 SPIE.

Social emotional processes during the third wave of COVID-19: Results from a close replication study in a Turkish sample.

Until recently, for almost 3 years, we used face masks to protect against COVID-19. Face masks disrupted our perception of socially relevant information, and impacted our social judgements as a result of the new social norms around wearing masks imposed by the pandemic. To shed light on such pandemic-induced changes in social emotional processes, Calbi et al. analysed data from an Italian sample collected in Spring 2020. They assessed valence, social distance and physical distance ratings for neutral, happy and angry male and female faces covered with a scarf or a mask. A year later, we used the same stimuli to investigate the same measures in a Turkish sample. We found that females attributed more negative valence ratings than males to angry faces, and that angry and neutral faces of females were rated more negatively than those of males. Scarf stimuli were evaluated more negatively in terms of valence. Participants attributed greater distance to more negative faces (angry > neutral > happy) and to scarf than the mask stimuli. Also, females attributed greater social and physical distance than males. These results may be explained by gender-stereotypic socialisation processes, and changes in people's perception of health behaviours during the pandemic.