Comparative Study Between MobilNet Face-Mask Detector and YOLOv3 Face-Mask Detector

Aadithya, V.; Balakumar, Sivanandan; Bavishprasath, M.; M, Raghul; Malathi, P.

doi:10.1007/978-981-16-6605-6_61

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…In [153], an FMD technique that employs MobileNetv2 as a basis and performs transfer learning has been proposed and tested on SMFD. In [154], a MobilNetv2-based FMD solution is developed and compared with YOLOv3.…”

Section: Lightweight Object Detectorsmentioning

confidence: 99%

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Himeur

Al-Maadeed

Varlamis

et al. 2023

Systems

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After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas of smart cities requires modern technologies, such as deep learning and deep transfer learning, and computer vision for automatic face mask detection and accurate control of whether people wear masks correctly. This paper reviews the progress in face mask detection research, emphasizing deep learning and deep transfer learning techniques. Existing face mask detection datasets are first described and discussed before presenting recent advances to all the related processing stages using a well-defined taxonomy, the nature of object detectors and Convolutional Neural Network architectures employed and their complexity, and the different deep learning techniques that have been applied so far. Moving on, benchmarking results are summarized, and discussions regarding the limitations of datasets and methodologies are provided. Last but not least, future research directions are discussed in detail.

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Section: Lightweight Object Detectorsmentioning

confidence: 99%

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Himeur

Al-Maadeed

Varlamis

et al. 2023

Systems

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mentioning

confidence: 93%

“…Iterations include the provision of test images to each model and the calculation of the average inference time over all iterations. As shown in Figure 9, the proposed system classifies the images in less time than MobiNet [29] and other models, which is a significant improvement. In order to evaluate the relationship between the predicted image complexity score and ground truth visual difficulty score, we compute Spearman's rank correlation coeffi- Electronics 2022, 11, x FOR PEER REVIEW cients between the two scores.…”

mentioning

confidence: 93%

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

Tamilarasan¹,

Rajmohan²,

Pavithra³

et al. 2022

Electronics

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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.

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Virtual Handwriting Based Smart Board Using Deep Learning

Malathi,

Gayathri,

Lakshmi

et al. 2023

2023 Innovations in Power and Advanced Computing Technologies (I-Pact)

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Comparative Study Between MobilNet Face-Mask Detector and YOLOv3 Face-Mask Detector

Cited by 7 publications

References 8 publications

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

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

Virtual Handwriting Based Smart Board Using Deep Learning

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