Estimating the mask-wearing ratio in public places is important as it enables health authorities to promptly analyze and implement policies. Methods for estimating the mask-wearing ratio on the basis of image analysis have been reported. However, there is still a lack of comprehensive research on both methodologies and datasets. Most recent reports straightforwardly propose estimating the ratio by applying conventional object detection and classification methods. It is feasible to use regression-based approaches to estimate the number of people wearing masks, especially for congested scenes with tiny and occluded faces, but this has not been well studied. A large-scale and well-annotated dataset is still in demand. In this paper, we present two methods for ratio estimation that leverage either a detection-based or regression-based approach. For the detection-based approach, we improved the state-of-the-art face detector, RetinaFace, used to estimate the ratio. For the regression-based approach, we fine-tuned the baseline network, CSRNet, used to estimate the density maps for masked and unmasked faces. We also present the first large-scale dataset, the ``NFM dataset,'' which contains 581,108 face annotations extracted from 18,088 video frames in 17 street-view videos. Experiments demonstrated that the RetinaFace-based method has higher accuracy under various situations and that the CSRNet-based method has a shorter operation time thanks to its compactness.

翻译：估计公共场所戴面罩的比例十分重要,因为它使卫生当局能够迅速分析和执行政策。根据图像分析估算戴面罩的比例的方法已经得到报告。然而,仍然缺乏关于方法和数据集的全面研究。大多数最近的报告都直接建议采用常规物体探测和分类方法来估计这一比例。使用基于回归的方法估计戴面罩的人数是可行的,特别是针对面孔和隐蔽面孔的嵌入场景,但这一方法没有得到很好的研究。大规模和注解良好的数据集仍然有需求。在本文件中,我们提出了两种利用检测或回归方法估算比例的方法。对于基于检测的方法,我们改进了以常规物体探测和分类方法估算使用的最新脸孔探测器(Retina Face)的比例。对于基于回归的方法,我们调整了基础基线网络CSRNet(CSRNet),用于估算遮蔽和隐蔽面的密度地图。我们还介绍了第一种利用检测或回归方法估算比率的方法。关于基于检测方法的大规模数据估算方法,从第58/1985年的图像模型,从第58/1985年的图像模型中展示了第18/1985年版本。