YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing

Wearing of surgical face masks has become the new norm of our daily life in the context of the COVID-19 pandemic. Under many conditions at various public places, it is necessary to check or monitor whether the face mask is worn properly. Manual judgement of mask wearing not only wastes manpower but also fails to monitor it in a way of all-time and real-time, posing the urge of an automatic mask wearing detection technology. Earlier automatic mask wearing methods uses a successive means in which the face is detected first and then the mask is determined and judged followingly. More recent methods take the end-to-end paradigm by utilizing successful and well-known CNN models from the field of object detection. However, these methods fail to consider the diversity of face mask wearing, such as different kinds of irregularity and spoofing. Thus, we in this study introduce a comprehensive mask wearing detection dataset (named as Diverse Masked Faces) by distinguishing a total of five different classes of mask wearing. We then adapt the YOLOX model for our specific task and further improve it using a new composite loss which merges the CIoU and the alpha-IoU losses and inherits both their advantages. The improved model is referred as YoloMask. Our proposed method was tested on the new dataset and has been proved to significantly outperform other SOTA methods in the literature that are either successive or end-to-end. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Face Mask Wearing Recognition System for Big Data Video Streaming

With the outbreak of the covid-19 pandemic in recent years, Video Stream Analytics technology quickly became a hot topic of discussion across technology forums. As it has appeared, in the pandemic situation in recent years, the use of masks when interacting with the community is a must, that's why the research works on mask identification today and more. receiving more and more attention. Understanding the situation, the team conducted facial recognition analysis inside the video to determine if the people appearing in the video were wearing masks. to then apply the trained model into practice. After a period of research, the team has also successfully built a mask recognition system that can generate images and can display the results as real-time video. Especially, the model is trained successful using systemml machine learning system. This is considered a positive result with real-time masked face recognition analysis. © 2022 IEEE.

An Efficient Face Mask Wearing Detection Algorithm Based on Improved YOLOv3

In recent years, the Corona Virus Disease 2019 (Covid-19) epidemic has raged around the world, with more than 500 million people diagnosed. Relevant medical research and analysis results on Covid-19 indicate that wearing masks is an effective method to prevent and restrain virus transmission. Mask detection stations have been set up in hospitals, railway stations, schools, where there is large crowd flow, but results are not as good as expected. In order to ameliorate pandemic preventing and control measures, a mask wearing detection algorithm YOLOv3-M3 was designed and proposed in this paper. The algorithm can effectively detect people without mask, while consequently reminding them. Firstly, we substituted the feature extraction network of YOLOv3 with MobileNetv3, a lightweight convolutional neural network. Secondly, we utilized K-Means++ to substitute the original ground truth clustering algorithm to improve prediction precision. In addition, the bounding box regression loss function was revised as CIoU loss function. This loss function solves the issues of overlapping between the ground truth and the anchor box, which has increased the training speed. After experiments, the precision of YOLOv3 algorithm on mAP 0.5 and mAP 0.75 is 93.5% and 71.9%, respectively. Elevating 3.1% and 2.6%, respectively, higher than that of YOLOv3 algorithm, and it was superior to SSD, SSD Lite, YOLOv3-Tiny and other one-stage object detection algorithms. The detection speed can reach 13.6 frame/s, which has met the requirements of pandemic prevention and control in most places and can be deployed on terminal devices for object detection. [ FROM AUTHOR]

FCOSMask: Fully Convolutional One-Stage Face MaskWearing Detection Based on MobileNetV3

method against the worldwide Coronavirus disease 2019 (COVID- 19). This paper proposes FCOSMask, a fully convolutional one-stage face mask wearing detector based on the lightweight network, for emergency epidemic control and long-term epidemic prevention work. MobileNetV3 is applied as the backbone network to reduce computational overhead. Thus, complex calculation related to anchor boxes is avoided in the anchor-free method, and Complete Intersection over Union (CIoU) loss is selected as the bounding box regression loss function to speed up model convergence. Experiments show that compared to other anchor-based methods, detection speed of FCOSMask is improved around 3 to 4 times on self-established datasets and mean average precision (mAP) achieves 92.4%, which meets the accuracy and real-time requirements of the face mask wearing detection task in most public areas. Finally, a Web-based face mask wearing system is developed that can support public epidemic prevention and control management.. © 2021 Association for Computing Machinery. All rights reserved.