Mask Detection Based on Yolov5s

Abstract

Since the outbreak of the COVID-19 epidemic, wearing masks has become common sense and necessary protective equipment for go outside. The use of deep learning methods to detect whether a person is wearing a mask has also become a popular research direction in the field of computer vision. As an excellent object detection algorithm, Yolov5 is widely used in various fields. This article also applies the lightweight Yolov5s model for facial mask detection. Yolov5s uses a multi-scale detection method based on Feature Pyramid Network, which can effectively detect masks at different scales. This enables the model to obtain more accurate detection results on images of different scales. Yolov5s is a lightweight model with fewer parameters and faster detection speed compared to other Yolov5 models. The dataset in this article is from the Kaggle website. By preprocessing the dataset and training it on the Yolov5s network model, the trained model was tested and the effect of facial mask wearing detection was achieved.