用注意力InceptionV3模型实时检测口罩佩戴与社交距离，助力疫情防控。
A Real-time Face Mask Detection and Social Distancing System for COVID-19 using Attention-InceptionV3 Model
- 基于改进的注意力InceptionV3模型，同时识别口罩佩戴和社交距离
- 验证准确率达99.5%，每秒处理25帧，精度约98.2%
- 适合城市监控、交通枢纽等高风险区域的疫情预警使用
新冠疫情是当前全球最致命的流行病之一，其传播迅速。世界卫生组织（WHO）要求强制佩戴口罩并保持6英尺社交距离以降低传播风险。本文提出一个实时系统，可检测口罩佩戴情况与社交距离是否合规。采用定制的注意力-InceptionV3模型，在包含10,800张图像的双数据集上训练，达到98%训练准确率和99.5%验证准确率。系统在测试中实现约98.2%的精确度，帧率（FPS）达25.0。该系统能有效识别高风险传播区域，帮助管理部门及时发现隐患并发布预警，保障公众安全。
原文摘要 · Abstract (English)
One of the deadliest pandemics is now happening in the current world due to COVID-19. This contagious virus is spreading like wildfire around the whole world. To minimize the spreading of this virus, World Health Organization (WHO) has made protocols mandatory for wearing face masks and maintaining 6 feet physical distance. In this paper, we have developed a system that can detect the proper maintenance of that distance and people are properly using masks or not. We have used the customized attention-inceptionv3 model in this system for the identification of those two components. We have used two different datasets along with 10,800 images including both with and without Face Mask images. The training accuracy has been achieved 98% and validation accuracy 99.5%. The system can conduct a precision value of around 98.2% and the frame rate per second (FPS) was 25.0. So, with this system, we can identify high-risk areas with the highest possibility of the virus spreading zone. This may help authorities to take necessary steps to locate those risky areas and alert the local people to ensure proper precautions in no time.
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