Real-Time Mask Detection Based on SSD-MobileNetV2

TL;DR

This paper presents a real-time mask detection system using SSD-MobileNetV2 that balances speed and accuracy, suitable for embedded devices, and employs transfer learning and data augmentation to improve performance.

Contribution

The authors propose a lightweight mask detection architecture based on SSD-MobileNetV2 with transfer learning and data augmentation, optimized for real-time deployment on embedded devices.

Findings

System achieves real-time detection with high accuracy

Reduces model size and computational requirements

Effective in practical scenarios for epidemic prevention

Abstract

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.