Study on a mask-wearing recognition method based on YOLOv8

In order to detect the wearing of face masks in real time in the daily work of medical institutions and medical personnel, reduce the dependence on manual labor, and improve the detection efficiency, the object recognition algorithm based on YOLOv8 deep learning can be used to effectively identify and detect the wearing images of face masks. The experimental results show that compared with the recognition results based on the YOLOv5 algorithm, the recognition accuracy of the mask wearing detection based on the YOLOv8 algorithm is significantly improved, and the MAP (average accuracy value) reaches 95.3%, which meets the detection accuracy requirements of the mask detection task. From the perspective of application scenarios, masks will continue to play a key role in the healthcare sector. Medical institutions and medical staff need to wear masks in their daily work to prevent the cross-transmission of viruses and bacteria and protect the health of patients and themselves.