The World Health Organization (WHO) has recommended wearing face masks as one of the most effective measures to prevent COVID-19 transmission. In many countries, it is now mandatory to wear face masks, specially in public places. Since manual monitoring of face masks is often infeasible in the middle of the crowd, automatic detection can be beneficial. To facilitate that, we explored a number of deep learning models (i.e., VGG1, VGG19, ResNet50) for face-mask detection and evaluated them on two benchmark datasets. We also evaluated transfer learning (i.e., VGG19, ResNet50 pre-trained on ImageNet) in this context. We find that while the performances of all the models are quite good, transfer learning models achieve the best performance. Transfer learning improves the performance by 0.10\%--0.40\% with 30\% less training time. Our experiment also shows these high-performing models are not quite robust for real-world cases where the test dataset comes from a different distribution. Without any fine-tuning, the performance of these models drops by 47\% in cross-domain settings.
翻译:世界卫生组织(世卫组织)建议将戴面罩作为防止COVID-19传播的最有效措施之一,许多国家现在必须戴面罩,特别是在公共场所。由于对面罩的人工监测在人群中间往往不可行,因此自动检测是有益的。为了便利这项工作,我们探索了一些深层学习模式(如VGG1、VGG19、ResNet50),用于面部检测,并在两个基准数据集上评估这些模式。我们还评估了这方面的转移学习(如VGG19、ResNet50在图像网络上预先培训过的学习)。我们发现,虽然所有模型的性能都相当好,但转移学习模式取得最佳业绩。转移学习提高了0.10-0.40的性能,减少了30个培训时间。我们的实验还表明,在测试数据集来自不同分布的真实世界案例中,这些高性模型并不十分可靠。我们发现,这些模型在跨多环域环境中的性能下降了47 ⁇ 。