Mask Wearing Detection Based on YOLOv5 Target Detection Algorithm under COVID-19

Abstract

Deep learning methods have been widely used in object detection in recent years as a result of advancements in artificial intelligence algorithms and hardware computing capacity. In light of the drawbacks of current manual testing mask wearing methods, this study offers a real-time detection method of mask wearing status based on the deep learning YOLOv5 algorithm to prevent COVID-19 and quicken the recovery of industrial production. The algorithm normalizes the original dataset, before connecting the data to the YOLOv5 network for iterative training, and saving the ideal weight data as a test set. The training and test results of the suggested approach are presented visually on a tensor board. With the help of cameras, this technique can collect faces, identify masked faces, and present prompts for mask use. According to experiment results, the suggested algorithm can match the requirements of real-world applications and has a high detection accuracy and good real-time performance.