基于YOLOv4的口罩规范检测算法研究

Research on Mask Specification Detection Algorithm Based on YOLOv4

新型冠状病毒传播方式主要为飞沫传播和近距离接触传播等,而正确佩戴口罩则是预防、隔离病毒的最方便有效的手段之一。但在公共场合下仍存在部分人群未佩戴口罩的情况,给公共安全带来了巨大威胁。从WIDER Face、MAFA、RMFD和MaskedFace-Net四个公开数据集中筛选7 240张图像,构成人脸口罩规范数据集用于算法训练与测试。结果表明,YOLOv4算法在检测精度和检测速度方面还不错,检测效果满足场景需求。基于YOLOv4的口罩规范检测算法可以有效减轻防疫人员的工作量,提高了人员密集公共场合的安全系数。

The transmission methods of the new coronavirus are mainly droplet transmission and close contact transmission,and wearing a mask correctly is one of the most convenient and effective means to prevent and isolate the virus.However,some people still do not wear masks in public places,posing a huge threat to public safety.7240 images are selected from four public datasets of WIDER Face,MAFA,RMFD and MaskedFace-Net to form a face mask specification dataset for algorithm training and testing.The results show that the YOLOv4 algorithm is good in terms of detection accuracy and detection speed,and the detection effect meets the needs of the scene.The mask specification detection algorithm based on YOLOv4 can effectively reduce the workload of epidemic prevention personnel and improve the safety factor in crowded public places.