Automatic Face Mask Detection System in Public Transportation in Smart Cities Using IoT and Deep Learning

Abstract

The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources.

Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage

“…In recent years, the application of deep learning algorithms, such as convolutional neural networks, has improved the convenience and accuracy of image recognition and segmentation in different fields, such as face mask detection [ 33 ], classification of magnetic resonance images [ 34 ], X-ray images [ 35 ], and tree trunk identification [ 36 ]. The performance of deep learning has proven to be superior to other classical methods of computer vision [ 37 ].…”

A systematic review of COVID-19 transport policies and mitigation strategies around the globe

“… Bohman et al, (2021) propose the use of open spaces for holding events that require the attendance of many people. Other studies analyze the effect and compliance with government impositions in transportation systems such as the use of the mask ( Kumar et al, 2022 ) and the disinfection of units ( Kruszewska et al, 2022 ). F. Chen et al (2021) analyze alternatives for the boarding and disembarking of passengers, and propose an interdisciplinary framework to manage strategic visions, to implement systems to improve urban quality of life, and to inform managers and citizens about the spread of the virus.…”

“…While the first trend is fully within service operators’ reach, the second trend depends on industrial production operators of vehicles meeting biosecurity standards. Anandkumar et al (2022) suggest passenger temperature screenings before boarding as a way to reduce the need for restricted seating; for such screenings, Kumar et al (2022) recommend using IoT-enabled sensors, GSM and GPS modules with LCD display. These technological strategies are suggested in the above-mentioned literature because the risk of contagion has been shown high variation with respect to co-travel time and seat location.…”

A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask

“…Kumar et al [3] concentrated on the Internet of Things (IoT) and DL for the identification of FM in public transportation in smart cities. Teboulbi et al [4] focused on the DL framework for the categorization of FM.…”