Due to the epidemic of the coronavirus (Covid-19) and its rapid spread around the world, the world has faced an enormous crisis. To prevent the spread of the coronavirus, the World Health Organization (WHO) has introduced the use of masks and keeping social distance as the best preventive method. So, developing an automatic monitoring system for detecting facemasks in some crowded places is essential. To do this, we propose a mask recognition system based on transfer learning and Inception v3 architecture. In the proposed method, two datasets are used simultaneously for training including the Simulated Mask Face Dataset (SMFD) and MaskedFace-Net (MFN) This paper tries to increase the accuracy of the proposed system by optimally setting hyper-parameters and accurately designing the fully connected layers. The main advantage of the proposed method is that in addition to masked and unmasked faces, it can also detect cases of incorrect use of mask. Therefore, the proposed method classifies the input face images into three categories. Experimental results show the high accuracy and efficiency of the proposed method; so, this method has achieved an accuracy of 99.47% and 99.33% in training and test data respectively

During the COVID-19 pandemic, wearing a face mask has been known to be an effective way to prevent the spread of COVID-19. In lots of monitoring tasks, humans have been replaced with computers thanks to the outstanding performance of the deep learning models. Monitoring the wearing of a face mask is another task that can be done by deep learning models with acceptable accuracy. The main challenge of this task is the limited amount of data because of the quarantine. In this paper, we did an investigation on the capability of three state-of-the-art object detection neural networks on face mask detection for real-time applications. As mentioned, here are three models used, Single Shot Detector (SSD), two versions of You Only Look Once (YOLO) i.e., YOLOv4-tiny, and YOLOv4-tiny-3l from which the best was selected. In the proposed method, according to the performance of different models, the best model that can be suitable for use in real-world and mobile device applications in comparison to other recent studies was the YOLOv4-tiny model, with 85.31% and 50.66 for mean Average Precision (mAP) and Frames Per Second (FPS), respectively. These acceptable values were achieved using two datasets with only 1531 images in three separate classes.

Coronavirus 2019 has brought severe challenges to social stability and public health worldwide. One effective way of curbing the epidemic is to require people to wear masks in public places and monitor mask-wearing states by utilizing suitable automatic detectors. However, existing deep learning based models struggle to simultaneously achieve the requirements of both high precision and real-time performance. To solve this problem, we propose an improved lightweight face mask detector based on YOLOv5, which can achieve an excellent balance of precision and speed. Firstly, a novel backbone ShuffleCANet that combines ShuffleNetV2 network with Coordinate Attention mechanism is proposed as the backbone. Afterwards, an efficient path aggression network BiFPN is applied as the feature fusion neck. Furthermore, the localization loss is replaced with alpha-CIoU in model training phase to obtain higher-quality anchors. Some valuable strategies such as data augmentation, adaptive image scaling, and anchor cluster operation are also utilized. Experimental results on AIZOO face mask dataset show the superiority of the proposed model. Compared with the original YOLOv5, the proposed model increases the inference speed by 28.3% while still improving the precision by 0.58%. It achieves the best mean average precision of 95.2% compared with other seven existing models, which is 4.4% higher than the baseline.

Task 35: Using the trained mask detection model to predict face mask on images.

The data and targets are then split into training, and testing data be keeping 10% of data as testing and 90% as training data. A checkpoint is created, which will save the model, which will have the minimum validation loss. Then the training data is then fitted in the model so that predictions can be made in the future.

This blog post has been written with the collaboration of Marcos Toscano. In the new world of coronavirus, multidisciplinary efforts have been organized to slow the spread of the pandemic. The AI community has also been a part of these endeavors. In particular, developments for monitoring social distancing or identifying face masks have made-the-headlines. But all this hype and anxiety to show off results as fast as possible, added up to the usual AI overpromising factor (see AI winter), may be signaling the wrong idea that solving some of these use cases is almost trivial due to the mighty powers of AI. Without further ado, let's see some results so you can grasp an idea about what we're trying to solve here. These are compilations of many results from different cameras in short fragments, but there's also a complete 20 minutes video example at the end of this blog post. Note: the numbers above people are "votes" from a mask classifier: they should be positive when the person has a mask, negative if not, and 0 if undecided.

When a person without a mask is detected by RAD's autonomous robotic security device, the system can generate audible and visible alerts to remind people to mask up. RAD's AI-powered product lineup includes a stationary security tower, wall-mounted security devices, a self-contained security and communication device, and a roaming security robot. When a person without a mask is detected by the autonomous robotic security device, the system can generate, depending on customer preferences, audible and visible alerts to remind people to mask up, according to the company.

In this new era where we experiencing a pandemic and people all around are advised to wear masks, some people are not used to it and are avoiding to wear masks. The motivation behind this projects is that if we can take help of AI to detect people wearing or not wearing masks in public places, it would be helpful to increase our safety. If deployed correctly, the mask detector could potentially be used to help ensure our safety. Also, it is very depressing to be alive in this period, to witness so much happening in this world, I decided why not do something out of it i.e convert a real world problem in which we humans have to wear masks to go out, into a Machine Learning problem. The task here is to predict people wearing masks or not wearing them, given an image or a video.