Design Flow for Real-Time Face Mask Detection Using PYNQ System-on-Chip Platform

Fang, Tianyang; Huang, Xin; Saniie, Jafar

doi:10.1109/eit51626.2021.9491842

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…For example, no information about experimental environment and runtime is provided in methods [42], [77], [85], [120], [122]. The literatures [11], [47], [51], [60], [78]- [80] [72], [73], [75] achieve the real-time processing effects without GPU. In contrast, some CNNbased methods [37], [129] are time-consuming because of the operation of more than one networks.…”

Section: E Discussion On the Results Of Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

Wang¹,

Chen²,

Zheng³

2022

Preprint

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Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to the world since its outbreak. To fight against the disease, a series of artificial intelligence (AI) techniques are developed and applied to real-world scenarios such as safety monitoring, disease diagnosis, infection risk assessment, lesion segmentation of COVID-19 CT scans, etc. The coronavirus epidemics have forced people wear masks to counteract the transmission of virus, which also brings difficulties to monitor large groups of people wearing masks. In this paper, we primarily focus on the AI techniques of masked facial detection and related datasets. We survey the recent advances, beginning with the descriptions of masked facial detection datasets. Thirteen available datasets are described and discussed in details. Then, the methods are roughly categorized into two classes: conventional methods and neural network-based methods. Conventional methods are usually trained by boosting algorithms with handcrafted features, which accounts for a small proportion. Neural network-based methods are further classified as three parts according to the number of processing stages. Representative algorithms are described in detail, coupled with some typical techniques that are described briefly. Finally, we summarize the recent benchmarking results, give the discussions on the limitations of datasets and methods, and expand future research directions. To our knowledge, this is the first survey about masked facial detection methods and datasets. Hopefully our survey could provide some help to fight against epidemics. <br>

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Section: E Discussion On the Results Of Methodsmentioning

confidence: 99%

“…The process is illustrated in Fig. 7 Fang et al [75] developed a real-time system of masked facial detection that uses haar-like features for face detection and mouth detection, respectively. Similar with [73], face region is firstly located, then mouth detection is used to determine the mask-wearing conditions.…”

Section: A Conventional Methodsmentioning

confidence: 99%

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

Wang¹,

Chen²,

Zheng³

2022

Preprint

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“…Solutions, such as WearMask [71], or BinaryCoP [202] apply transfer learning to very fast and lightweight models such as YOLO-Fastest or adopt fast binarized NN inference frameworks such as Finn [225], and operate as mask-wearing sensors. They can be deployed as mobile apps or run on FPGA-powered hardware that allows high-speed face detection and classification in two stages [226].…”

Section: Federated Fmdmentioning

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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“…The data marked "-" in the table indicates that there is no corresponding description in the corresponding paper. Category Method Classes Datasets Accuracy&Precious mAP FPS Conventional Dewantara et al [ 12 ] 2 1000 images, self-built 86.9% – 25 Nieto-Rodriguez et al [ 55 ] 2 677 test cases, self-built 95% – 10 Petrovic et al [ 56 ] 3 – 84%–91% – 38 Fang et al [ 57 ] 2 6024 images, self-built 96.5% 46 Multi-Stage Cota et al [ 14 ] 2 2270 images, self-built – 85.92% 15 Lin et al [ 15 ] 2 992 images, self-built Daytime:95.8% Nighttime:94.6% – – Qin et al [ 58 ] 3 3835 images, self-built 98.7% – 33 Two-Stag...…”

Section: Materials and Experimentsmentioning

confidence: 99%

IYOLO-NL: An improved you only look once and none left object detector for real-time face mask detection

Zhou

2023

Heliyon

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Design Flow for Real-Time Face Mask Detection Using PYNQ System-on-Chip Platform

Cited by 12 publications

References 9 publications

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

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

IYOLO-NL: An improved you only look once and none left object detector for real-time face mask detection

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