Multiple face mask wearer detection based on YOLOv3 approach

The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images.

Multiple face mask wearer detection based on YOLOv3 approach

The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images.

Real-time multiple face mask and fever detection using YOLOv3 and TensorFlow lite platforms

COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Camera-based Human Counting for COVID-19 Capacity Restriction

In this paper, a Human Counting system is implemented for COVID-19 capacity restrictions. It was implemented using the deep learning model You Only Look Once version 3(YOLOv3) to detect and count the people in a room. The system also can monitor the social distancing between the people in the room while labeling each person as 'safe' or 'unsafe' depending on whether they respect the social distancing protocols that the World Health Organization recommended or not. To make the project user friendly, a Graphical User Interface (GUI) was implemented to allow the user to choose the source of their images that will be used as input to be processed by the system. An experiment was carried out to evaluate the performance of the system under different conditions and in different scenarios where the evaluation was done according to some metrics such as accuracy, precision and recall. The output results from this experiment were demonstrated in details and compared to a similar algorithm as both algorithms focused on people detection using images from an inclined camera. The results show an accuracy of 96% for detection and the number of people counted. © 2022 IEEE.

A Review on Social Distancing Auto Detection techniques in perspective of COVID' 19

In late December 2019, a new strain of coronavirus disease was first discovered in Wuhan, China. In 2020, the virus became a global epidemic after just a few months. On May 2020, the World Health Organization (WHO) declared the outbreak as pandemic. The statistics by World Health Organization on 24th February 2021 confirm more than 115 million infected people and 2.5 million deaths in 200 countries. The scientists have developed the vaccines only a few months ago, before that the best way to avoid the spread ofCOVID-19 was to maintain a few feet physical distance from one another. Social distancing was one amongst the recommended remedies by the World Health Organization (WHO) to minimize the spread of COVID-19 in public areas. Government bodies and authorities have made the 6 feet social distance in public and enclosed areas such as school, shopping malls and transport facilities as mandatory. Even after so much effort from the government and World Health Organization, people do not maintain social distancing which leads to constant rise in the number of infected people. The models and methods proposed in this paper focus of automatic monitoring of social distancing, the models will be able to detect the distance between two people and raise the alarm if social distancing is not maintained.