Face mask detection and social distance monitoring system for COVID-19 pandemic

Abstract

Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learningbased visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillancebased dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10, 000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy 1 .

Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions

“…The YOLO-v5 algorithm, employed in this study for face detection with masks across different datasets, proves to be highly recommended based on the test results. These findings align with the research conducted by Javed et al [31], affirming the YOLO-v5 method's remarkable accuracy in detecting masked faces.…”

“…These results emphasize the importance of testing and evaluating the accuracy of mask detection, along with its suitability for various scenarios [20]. The YOLO-v5 method is an advanced algorithm known for its effectiveness in object detection [27], making it highly suitable for detecting masked faces in public spaces as a preventive measure against the spread of COVID-19 [31].…”

A Blockchain-Enabled Machine Learning Mask Detection method for Prevention of Pandemic Diseases

“…The CNN model trains the video data restoration and transformation for a facemask detector. This process can be refined and optimized based on the specific requirements and constraints of the video data restoration and transformation system [36,44,[51][52][53] . Additionally, it is essential to ensure that the [6,9,50,53,54] system complies with data privacy regulations and security standards; it involves the following phases :…”

“…The process of preparing and using training and testing data for a facemask detection CNN model typically involves the following steps [1,9,20,22,51]:…”

Face mask classification using convolutional neural networks with facial image regions and super resolution

“…One of computer vision tasks for identifying facial images of person wearing a mask is generally called as face mask detection. Many systems aim to accurately detect such masked face images, since it is necessary to prevent the spread of infectious diseases and to help us to easily manage security protocol of both private and public areas such as security camera system, surveillance CCTV during a COVID-19 outbreak, person authentication system, and so on [1]- [3]. Especially, during world public health emergency situation like COVID-19 pandemic, face mask detection system is one of many safety measures until today as a new normal way of life.…”