论文标题
基于SSD-Mobilenetv2的实时掩模检测
Real-Time Mask Detection Based on SSD-MobileNetV2
论文作者
论文摘要
Covid-19爆发后，作为最方便和最有效的预防手段，掩盖检测在流行病预防和控制中起着至关重要的作用。出色的自动实时面具检测系统可以减轻相关人员的大量工作压力。但是，通过分析现有的面具检测方法，我们发现它们大多是资源密集型的，并且在速度和准确性之间没有良好的平衡。目前还没有完美的面膜数据集。在本文中，我们提出了一种用于掩盖检测的新体系结构。我们的系统使用SSD作为掩码定位器和分类器，并用MobilenetV2进一步替换VGG-16来提取图像的功能并减少许多参数。因此，我们的系统可以部署在嵌入式设备上。转移学习方法用于将预训练的模型从其他域转移到我们的模型。我们系统中的数据增强方法，例如混合有效地防止过度拟合。它还有效地减少了对大规模数据集的依赖性。通过在实际情况下进行实验，结果表明我们的系统在实时掩模检测中的表现良好。
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.
