Facemask detection and classification using you only look once version 7

Abstract

World Health Organization (WHO) suggests that wearing masks and keeping social distancing are the best ways to avoid infection transmission of communicable diseases. Consequently, most governments have forced people to wear masks in public areas to prevent communicable diseases such as COVID-19. Manual monitoring and surveillance are time-consuming and not always possible in crowded areas. Hence, object detection deep learning models can effectively handle these challenges. Therefore, this work aims to investigate the efficiency of different versions of the you only look once version 7 (YOLOv7) model in facemask detection and classification over the privately balanced dataset. The dataset comprises of 1,300 images with four novel classes; including no occlusion, correct mask, incorrect mask, and other use cases. Furthermore, the model’s performance was evaluated based on mean average precision (mAP), recall, precision, and inference time. Finally, a comparative result analysis has been reported to determine the best model for facemask detection and classification. YOLOv7 model versions exhibit widely various performances ranging from 20.7% mAP for YOLOv7-D6 to 95.5% for YOLOv7-tiny. In contrast, the inference time for all YOLOv7 versions covers a narrow range of 3 ms. In conclusion, the YOLOv7-tiny version outperforms other models, achieving a high detection performance and acceptable detection speed.