摘要
随着新型冠状肺炎疫情的爆发,佩戴口罩成为人们出行的必要条件,佩戴口罩可以有效地阻止疫情的传播,但也由于口罩的遮挡,对人脸检测和人脸识别造成了一定的影响。针对此问题,对深度学习理论和基于YOLO、SSD、RetinaNet、Fast R-CNN模型检测算法进行分析,并对实际模型的性能进行评估和思考。
With the outbreak of the new type of coronavirus pneumonia,wearing a face mask for travel has become a necessary condition for people to travel.Wearing a face mask can effectively prevent the spread of the epidemic,but it also has a certain impact on face detection and face recognition due to the occlusion of the mask.In response to this problem,this paper analyzes deep learning theory and model detection algorithms based on YOLO,SSD,Retina-Net,and Fast R-CNN,and evaluates and thinks about the performance of actual models.
出处
《工业控制计算机》
2022年第8期124-126,共3页
Industrial Control Computer
关键词
口罩
深度学习
算法
face mask
deep learning
algorithm
