Recognizing the Type of Mask or Respirator Worn Through a CNN Trained with a Novel Database

Since the onset of the coronavirus pandemic, researchers from all over the world have been working on projects aimed at countering its advance. The authors of this paper want to go in this direction through the study of a system capable of recognizing the type of mask or respirator worn by a person. It can be used to implement automatic entry controls in high protection areas, where people can feel comfortable and safe. It can also be used to make sure that people who work daily in contact with particles, chemicals, or other impurities wear appropriate respiratory protection. In this paper, a proof-of-concept of this system will be presented. It has been realized by using a state-of-the-art Convolutional Neural Network (CNN), EfficientNet, which was trained on a novel database, called the Facial Masks and Respirators Database (FMR-DB). Unlike other databases released so far, it has an accurate classification of the most important types of facial masks and respirators and their degree of protection. It is also at the complete disposal of the scientific community

Deep learning based masked face recognition in the era of the COVID-19 pandemic

During the coronavirus disease 2019 (COVID-19) pandemic, monitoring for wearing masks obtains a crucial attention due to the effect of wearing masks to prevent the spread of coronavirus. This work introduces two deep learning models, the former based on pre-trained convolutional neural network (CNN) which called MobileNetv2, and the latter is a new CNN architecture. These two models have been used to detect masked face with three classes (correct, not correct, and no mask). The experiments conducted on benchmark dataset which is face mask detection dataset from Kaggle. Moreover, the comparison between two models is driven to evaluate the results of these two proposed models

Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.EPSRC DTP PhD studentshi

Masked face recognition using deep learning: a review

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR