深度学习在口罩人脸检测领域的应用进展

Application progress of deep learning in mask face detection

新型冠状病毒主要以呼吸道飞沫的方式传播,正确佩戴口罩可以有效预防病毒传染,但口罩导致的部分遮挡问题使得人脸检测和识别算法的准确率大幅降低。为此,文章对深度学习基础理论及基于SSD,YOLO,RetinaNet等模型的人脸检测算法进行分析,探讨深度学习在该领域的应用进展,为进一步设计更有效口罩人脸算法提供方向性的指引。

The corona virus disease 2019 is mainly spread in the form of respiratory droplets.Wearing a mask correctly can effectively prevent the virus from spreading.However,the partial occlusion caused by the mask greatly reduces the accuracy of face detection and recognition algorithms.To this end,this article analyzes the basic theories of deep learning and face detection algorithms based on SSD,YOLO,RetinaNet and other models,discusses the application progress of deep learning in this field,and provides directional guidance for the further design of more effective mask face algorithms.