FACE MASK DETECTION USING YOLOV5

Abstract

Global pandemic COVID-19 circumstances emerged in an epidemic of dangerous disease in all over the world. Masks play a crucial role in protecting the health of individuals against respiratory diseases, as is one of the few precautions available for COVID-19 in the absence of immunization. Wearing a face mask will also help prevent the spread of infection . But most of the people are not wearing face masks in public places which increases the spread of viruses. Therefore, automated and efficient face detection methods are essential for such enforcement. In this paper, a face mask detection model has been presented which classifies the images as “with mask", “without mask” and ”incorrect mask”. The model is trained and evaluated using the Kaggle dataset. The object detection algorithm YOLOV5 based on deep learning is used for detecting mask. The experimental results show that the algorithm proposed in this paper can effectively recognize face masks and

realize the effective monitoring of personnel .The experimentation mAP of YOLOV5 exhibited average of 85.7% which proves the effectiveness of face mask detection.