Real Time Mask Detection using Convolutional Neural Network

With the advent of Convolutional Neural Networks, the field of image classification has seen tremendous growth, with various previously impossible applications now being pursued. One such application is face mask detection, which is an important problem to solve, considering recent pandemic. The novelty of this work is the training of YOLO (You Only Look Once) framework for custom object detection, which in this case is face mask, based on some empirical rules for fine-tuning the performance. Also, image classification is proposed to be combined with tracker, in order to implement real world access grant system based on compliance shown by mask wearer. © 2022 IEEE.

Real-Time Mask Detection Based on SSD-MobileNetV2

After the outbreak of COVID-19, mask detection, as the most convenient and effective means of prevention, plays a crucial role in epidemic prevention and control. An excellent automatic real-time mask detection system can reduce a lot of work pressure for relevant staff. However, by analyzing the existing mask detection approaches, we find that they are mostly resource-intensive and do not achieve a good balance between speed and accuracy. And there is no perfect face mask dataset at present. In this paper, we propose a new architecture for mask detection. Our system uses SSD as the mask locator and classifier, and further replaces VGG-16 with MobileNetV2 to extract the features of the image and reduce a lot of parameters. Therefore, our system can be deployed on embedded devices. Transfer learning methods are used to transfer pre-trained models from other domains to our model. Data enhancement methods in our system such as MixUp effectively prevent overfitting. It also effectively reduces the dependence on large-scale datasets. By doing experiments in practical scenarios, the results demonstrate that our system performed well in real-time mask detection. © 2022 IEEE.

Real-Time Mask Detection Based on SSD-MobileNetV2

After the outbreak of COVID-19, mask detection, as the most convenient and effective means of prevention, plays a crucial role in epidemic prevention and control. An excellent automatic real-time mask detection system can reduce a lot of work pressure for relevant staff. However, by analyzing the existing mask detection approaches, we find that they are mostly resource-intensive and do not achieve a good balance between speed and accuracy. And there is no perfect face mask dataset at present. In this paper, we propose a new architecture for mask detection. Our system uses SSD as the mask locator and classifier, and further replaces VGG-16 with MobileNetV2 to extract the features of the image and reduce a lot of parameters. Therefore, our system can be deployed on embedded devices. Transfer learning methods are used to transfer pre-trained models from other domains to our model. Data enhancement methods in our system such as MixUp effectively prevent overfitting. It also effectively reduces the dependence on large-scale datasets. By doing experiments in practical scenarios, the results demonstrate that our system performed well in real-time mask detection. © 2022 IEEE.

Accurate Real-Time Face Mask Detection Framework Using YOLOv5

After the COVID-19 pandemic, wearing a mask has become a must because it decreases the probability of infection by 68%. That is why a fast and accurate automatic mask detection is crucial to public institutions. In this paper, we present an accurate framework for real-time mask detection using YOLOv5 object detection algorithm. Our framework consists of four stages: image preprocessing by normalization and adding noise, adding negative samples and data augmentation then the detection core based on a modified version of YOLOv5. The proposed framework achieves 95.9% precision and 84.8% mean average precision using the Face Mask Detection dataset with a 10 milliseconds inference time. © 2022 IEEE.

Lightweight Real-Time Mask Wearing Detection Algorithm Based on YOLOv3

The novel coronavirus has broken out from 2019 and quickly become a global pandemic. It spreads rapidly from person to person through droplets, aerosols and other carriers. In order to prevent the spread of the virus, people must wear masks when entering and leaving public places and taking public transport to reduce the risk of virus transmission. How to detect the wearing of masks in public places and other natural environments has become a new research problem. This paper proposes a lightweight deep neural network (E-YOLO) to realize mask wearing detection in real-time scenarios. E-YOLO improves the general target detection YOLOv3 algorithm by the follows methods. Firstly, the Efficient-Net series B2 backbone feature extraction network is used to replace the original Darknet53 feature extraction network. Combined with the spatial pyramid pooling module and the bidirectional feature pyramid structure to enrich the semantic information of the feature layer, so as to achieve the balance between the speed and accuracy of target detection in real-time scenes. The experimental results show that, compared with the YOLOv3 algorithm, the E-YOLO algorithm possesses the same accuracy and speed. The network floating point calculation amount is one twelfth of YOLOv3, and the model size is only one quarter of YOLOv3, which is more suitable for resource-constrained platforms. © 2021 IEEE.

Real-Time Face Mask Detection and Analysis System

Due to COVID-19 situation, we need to wear face masks in public places. Reports say that wearing face mask at public places and at workspace reduces the transmission of virus as the SARS-CoV-2 spreads through atmosphere among people, at gathering in any environment. In this paper, a real-time face mask detection system is presented which will detect mask presence on the face using TensorFlow. We are using MobileNetV2 model to provide a greater accuracy in determining the mask presence. Accuracy obtained is 99%. Older systems do not provide a proper working system. A face mask detector has been designed with computer vision using Python, OpenCV, Keras, and TensorFlow. Video surveillance input can be given directly, and our primary purpose is to identify to check people are wearing masks on daily basis or not wearing masks and prepare a weekly and monthly report based on this observation and display the data on an interactive web application. System provides option to see the historical records, thereby reducing transmission. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.