论文标题
使用Inceptionv3的转移学习面罩检测
Face Mask Detection using Transfer Learning of InceptionV3
论文作者
论文摘要
由于冠状病毒的迅速传播，世界正面临巨大的健康危机（Covid-19）。世界卫生组织（WHO）发布了一些准则，以防止冠状病毒的传播。根据WHO的说法，针对Covid-19的最有效的预防措施是在公共场所和拥挤的地区戴口罩。在这些领域手动监视人们非常困难。在本文中，提出了转移学习模型来自动化识别不戴口罩的人的过程。提出的模型是通过微调预先训练的最先进的深度学习模型InceptionV3构建的。在模拟的蒙版面部数据集（SMFD）上训练并测试了所提出的模型。采用图像增强技术来解决数据的有限可用性，以更好地培训模型和测试。该模型的表现优于其他最近提出的方法，方法在训练过程中的精度为99.9％，在测试过程中获得100％的精度。
The world is facing a huge health crisis due to the rapid transmission of coronavirus (COVID-19). Several guidelines were issued by the World Health Organization (WHO) for protection against the spread of coronavirus. According to WHO, the most effective preventive measure against COVID-19 is wearing a mask in public places and crowded areas. It is very difficult to monitor people manually in these areas. In this paper, a transfer learning model is proposed to automate the process of identifying the people who are not wearing mask. The proposed model is built by fine-tuning the pre-trained state-of-the-art deep learning model, InceptionV3. The proposed model is trained and tested on the Simulated Masked Face Dataset (SMFD). Image augmentation technique is adopted to address the limited availability of data for better training and testing of the model. The model outperformed the other recently proposed approaches by achieving an accuracy of 99.9% during training and 100% during testing.
