Face mask detection and classification via deep transfer learning.
Face mask detection and classification via deep transfer learning.
复制标题
通过深度迁移学习进行口罩检测和分类
DOI：
10.1007/s11042-021-11772-5
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发表时间：
2022
影响因子：
3.6
通讯作者：
Liu X
中科院分区：
文献类型：
--
作者：
Su X;Gao M;Ren J;Li Y;Dong M;Liu X
戴口罩是预防COVID-19传播和感染的重要方式。德国研究人员发现，戴口罩可以有效降低40%的COVID-19感染率。然而，在真实的世界中，戴面具的人脸检测受到诸如光照、遮挡和多目标等因素的影响。检测效果差，佩戴棉质口罩、海绵口罩、围巾等物品，大大降低了个人防护效果。因此，本文提出了一种融合迁移学习和深度学习的掩模检测和分类新算法。首先，本文提出了一种融合迁移学习和Efficient-Yolov 3的人脸检测算法，以EfficientNet为骨干特征提取网络，选择CIoU作为损失函数，减少网络参数个数，提高人脸检测的准确率。其次，本文将口罩分为合格口罩（N95口罩、一次性医用口罩）和不合格口罩（棉口罩、海绵口罩、围巾等）两大类，建立了一个掩码分类数据集，提出了一种新的掩码分类算法，该算法将迁移学习和MobileNet相结合，增强了模型的泛化能力，解决了数据量小、容易过拟合的问题。在公开人脸检测数据集上的实验表明，该算法比现有算法具有更好的检测性能。此外，在所创建的掩模分类数据集上进行实验。该算法的掩模分类准确率为97.84%，优于其他算法。
Wearing a mask is an important way of preventing COVID-19 transmission and infection. German researchers found that wearing masks can effectively reduce the infection rate of COVID-19 by 40%. However, the detection of face mask-wearing in the real world is affected by factors such as light, occlusion, and multi-object. The detection effect is poor, and the wearing of cotton masks, sponge masks, scarves and other items greatly reduces the personal protection effect. Therefore, this paper proposes a new algorithm for mask detection and classification that fuses transfer learning and deep learning. Firstly, this paper proposes a new algorithm for face mask detection that integrates transfer learning and Efficient-Yolov3, using EfficientNet as the backbone feature extraction network, and choosing CIoU as the loss function to reduce the number of network parameters and improve the accuracy of mask detection. Secondly, this paper divides the mask into two categories of qualified masks (N95 masks, disposable medical masks) and unqualified masks (cotton masks, sponge masks, scarves, etc.), creates a mask classification data set, and proposes a new mask classification algorithm that the combines transfer learning and MobileNet, enhances the generalization of the model and solves the problem of small data size and easy overfitting. Experiments on the public face mask detection data set show that the proposed algorithm has a better performance than existing algorithms. In addition, experiments are performed on the created mask classification data set. The mask classification accuracy of the proposed algorithm is 97.84%, which is better than other algorithms.
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影响因子：
3.1
作者：
Li, Yuanzhe;Guo, FeiFei;Guo, YanJun
通讯作者：
Guo, YanJun
DOI：
10.1073/pnas.2011674117
发表时间：
2020-09-08
影响因子：
11.1
作者：
Betsch, Cornelia;Korn, Lars;Boehm, Robert
通讯作者：
Boehm, Robert
影响因子：
19.7
作者：
Li, Lin;Qin, Lixin;Xia, Jun
通讯作者：
Xia, Jun
影响因子：
5.6
作者：
Loey, Mohamed;Manogaran, Gunasekaran;Khalifa, Nour Eldeen M.
通讯作者：
Khalifa, Nour Eldeen M.
影响因子：
2.9
作者：
MacIntyre CR;Seale H;Dung TC;Hien NT;Nga PT;Chughtai AA;Rahman B;Dwyer DE;Wang Q
通讯作者：
Wang Q