FMR-DBv2: an Improved Database for Mask and Respirator Type and FFP Protection Level Recognition Through Deep Learning

The widespread adoption of masks and respirators has significantly influenced various aspects of society, driving technological advances to improve comfort, efficiency, and sustainability. The COVID-19 pandemic underscored their essential role in the protection of public health, with continued relevance in the industrial, environmental, and hygiene-critical sectors. Recent developments in deep learning offer promising approaches for building automated systems that can detect mask and respirator usage. In this regard, this paper first aims to present an improved version of the Facial Masks and Respirators Database (FMR-DB), which can be used to create such systems. New features include a significant increase in available images, which has been expanded from 2565 to 4200 images, and the addition of You Only Look Once (YOLO), PASCAL Visual Object Classes (PascalVOC), and Common Objects in Context (COCO) labeling for image detection tasks. Furthermore, image classification and object detection tests were conducted using Convolutional Neural Networks (CNNs), Transformers, and YOLO to determine the types of masks and respirators accurately. Finally, to the best of the authors' knowledge, these tools were used for the first time to analyze the protection levels of respirators automatically. The results provide valuable insights for developing efficient and reliable automatic recognition systems.