Research on the use of YOLOv5 object detection algorithm in mask wearing recognition (WSRJ 2020)

Masks can help people to reduce inhalation of droplets and the risk of infection. Because of the COVID-19, many governments required people to wear marks to prevent virus spread. In some public places, there are tons of people going back and forth everyday so it’s impossible to settle a human monitor to identify whether everyone wears a mask. This work uses a different training version from YOLOv5 to train the dataset of mask wearing, and we use K-means to find the most appropriate anchors for datasets. Finally, by using data augmentation we get a more accurate model. Compared to human work, this model can be faster and more accurate to find a target and it can save countless money and time.

Recommended citation:
Download Paper | Download Slides