Real-Time Mask Detection Based on SSD-MobileNetV2
This work addresses the need for efficient mask detection systems to reduce work pressure for staff during epidemics, but it is incremental as it adapts existing methods.
The paper tackled the problem of real-time mask detection for COVID-19 prevention by proposing an SSD-MobileNetV2 architecture, which achieved good performance in practical scenarios with reduced parameters for deployment on embedded devices.
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.