Effective face mask and social distance detection with alert system for covid-19 using YOLOv5 model

Globally, numerous preventive measures were taken to treat the COVID-19 epidemic. Face masks and social distancing were two of the most crucial practices for limiting the spread of novel viruses. With YOLOv5 and a pre-Trained framework, we present a novel method of complex mask detection. The primary objective is to detect complex different face masks at higher rates and obtain accuracy of about 94% to 99% on real-Time video feeds. The proposed methodology also aims to implement a structure to detect social distance based on a YOLOv5 architecture for controlling, monitoring, accomplishing, and reducing the interaction of physical communication among people in the day-To-day environment. In order for the framework to be trained for the different crowd datasets from the top, it was trained for the human contrasts. Based on the pixel information and the violation threshold, the Euclidean distance between peoples is determined as soon as the people in the video are spotted. In the results, this social distance architecture is described as providing effective monitoring and alerting. © 2022 The authors and IOS Press.

Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal.

With the emergence of COVID-19, social distancing detection is a crucial technique for epidemic prevention and control. However, the current mainstream detection technology cannot obtain accurate social distance in real-time. To address this problem, this paper presents a first study on smartphone-based social distance detection technology based on near-ultrasonic signals. Firstly, according to auditory characteristics of the human ear and smartphone frequency response characteristics, a group of 18 kHz-23 kHz inaudible Chirp signals accompanied with single frequency signals are designed to complete ranging and ID identification in a short time. Secondly, an improved mutual ranging algorithm is proposed by combining the cubic spline interpolation and a two-stage search to obtain robust mutual ranging performance against multipath and NLoS affect. Thirdly, a hybrid channel access protocol is proposed consisting of Chirp BOK, FDMA, and CSMA/CA to increase the number of concurrencies and reduce the probability of collision. The results show that in our ranging algorithm, 95% of the mutual ranging error within 5 m is less than 10 cm and gets the best performance compared to the other traditional methods in both LoS and NLoS. The protocol can efficiently utilize the limited near-ultrasonic channel resources and achieve a high refresh rate ranging under the premise of reducing the collision probability. Our study can realize high-precision, high-refresh-rate social distance detection on smartphones and has significant application value during an epidemic.

Embedded Implementation of Social Distancing Detector Based on One Stage Convolutional Neural Network Detector

The recent COVID-19 is a very dangerous disease that intimidates humanity. It spreads very fast and many rules must be respected to reduce its prevalence. One of the most important rules is the social distance which means keeping a safe distance between two persons. A safe distance must be one meter or more. Respecting such rules in public spaces is a very challenging task that needs the assistance of artificial intelligence tools. In this paper, we propose a social distance detector using convolutional neural networks. The detector was based on the Yolo model with a custom-made backbone to guarantee real-time processing and embedded implementation. The backbone was optimized to make it suitable for embedded resources. The inference model was evaluated on the Pynq platform. The model was trained and fine-tuned using the MS COCO dataset. The evaluation of the proposed model proved its efficiency with a precision of 87.98% while running in real-time. The achieved results proved the efficiency of the proposed model and the proposed optimization for embedded implementation. © 2022 Lavoisier. All rights reserved.

Identification of Face Mask and Social Distancing using YOLO Algorithm based on Machine Learning Approach

An outbreak of the coronavirus disease due to occur in 2020 has already had a significant impact on the human race. Wearing a "Face Mask"and maintaining "Social Distancing"are the only means to protect ourselves from this pandemic. Several service providers, such as airlines, hotels, hospitals, and train stations, demand their customers to access the service only if the Mask is worn correctly and social distance is maintained. Manually checking to see if the rule of mask wearing and social distance is being observed is impossible due to the significant human resource consumption. As a one-stage detector, the COVID-19 Face Mask and Social Distancing Detector System uses an artificial neural network to combine high-level semantic information with various feature maps and a machine learning module to identify face masks and social distances at the same time. It will also be able to detect persons without masks and the violence of social separation by using existing IP cameras, CCTV cameras, and computer vision. This technology eliminates the need for a manual surveillance system by providing instruments for safety and security. The technology can be used in any type of infrastructure, including hospitals, government offices, schools, and construction sites. Therefore, the face mask and social distance detector system developed, could aid to secure the protection and security of ourselves and our loved ones. © 2022 IEEE.

An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic.

The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people's routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus's spread. In this paper, we developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, we collected and annotated 1000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1853 images and increased the dataset to 3300 images by data augmentation. Then, we trained and tested multiple Tensorflow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, we employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle's effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. We also used transfer learning for training the model. The final model was applied on four videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers' safety in the pandemic situation.