| Abstrakt: |
Facemasks are an essential tool for reducing the spread of respiratory viruses, including the COVID-19 virus. In the wake of the COVID-19 pandemic, the use of facemasks has become an important measure to prevent the spread of respiratory viruses. To monitor compliance with facemask-wearing regulations in public spaces, computer vision techniques can be used to detect whether individuals in images or videos are wearing facemasks. In this paper, we proposed facemask detection using R-CNN and Fast R-CNN techniques. We evaluate the performance of each technique using publicly available datasets and compare their accuracy and speed. Our results show that object detection using RNN and Fast RNN performs well as compared to the other techniques considering accuracy and speed. Overall, our study demonstrates the potential of computer vision techniques for facemask detection and required further research in this area to increase accuracy and robustness. [ABSTRACT FROM AUTHOR] |
