Comparative Analysis of Convolutional Neural Network Architectures for Real Time COVID-19 Facial Mask Detection

Lad, Akash; Mishra, Anubhav; Rajagopalan, Arul

doi:10.1088/1742-6596/1969/1/012037

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Even with insufficient training data, this strategy helps them perform well. This capability diminishes the likelihood of overfitting and enhances the effectiveness of processing extensive datasets [36] . The inherent characteristics of sequential CNN models motivate us to explore novel architectural designs.…”

Section: Methodsmentioning

confidence: 99%

Classification of COVID-19 X-ray images using transfer learning with visual geometrical groups and novel sequential convolutional neural networks

Kumar¹,

Kumar²

2023

MethodsX

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Section: Methodsmentioning

confidence: 99%

Classification of COVID-19 X-ray images using transfer learning with visual geometrical groups and novel sequential convolutional neural networks

Kumar¹,

Kumar²

2023

MethodsX

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“…obileNetv2: everal works employ MobileNetv2 [144,[147][148][149][150][151] and similar detectors such as NASNetMobile [152]. In [153], an FMD technique that employs MobileNetv2 as a basis and performs transfer learning has been proposed and tested on SMFD.…”

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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“…In 2020, the National Multimedia Software Engineering Technology Research Center of Wuhan University has open sourced the RMFD (Real-World Masked Face Dataset) and the Institute of Information Technology of the Chinese Academy of Sciences has also open sourced the MAFA dataset. At the same time, Baidu has also open sourced a mask face monitoring model based on its Paddle framework [3] . Theoretical analysis found that most of the general target detection methods are suitable for face mask wearing detection tasks.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of different scene for face mask detection based on YOLOv4

Cheng¹,

Zhang²,

Zhu³

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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The 2019-nCoV can be transmitted through respiratory droplets and other methods, which greatly endangers public health security. Wearing masks correctly has been proven to be one of the effective means to prevent virus infection, but limited by the complexity of practical application scenarios, the wearing of masks still relies heavily on manual supervision. Therefore, a fast and accurate face mask wearing detection method is urgently needed. In this paper, a mask detection algorithm based on improved YOLO-v4 is proposed as a solution to the problems of low accuracy, poor real-time performance, and poor robustness caused by complicated environments. In addition, a number of different training approaches, such as mosaic data augment, CIOU, label smoothing, cosine annealing, etc., are introduced. These techniques help to increase the training speed of the model as well as the accuracy of its detection. With a fast-training model, the model will be able to detect and compare the results of samples from different scenarios. The experiment will compare front and side faces, different colored masks, scenes of varying complexity and other perspectives in a systematic way. The experiment's result was able to reach 99.38 % accuracy after the model was trained using data from a variety of face masks being worn. Experiment results, both quantitative and qualitative, indicate that the method can be adapted to most scenarios and offers effective ideas for improvement.

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Comparative Analysis of Convolutional Neural Network Architectures for Real Time COVID-19 Facial Mask Detection

Cited by 4 publications

References 6 publications

Classification of COVID-19 X-ray images using transfer learning with visual geometrical groups and novel sequential convolutional neural networks

Classification of COVID-19 X-ray images using transfer learning with visual geometrical groups and novel sequential convolutional neural networks

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

Comparative analysis of different scene for face mask detection based on YOLOv4

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