ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic

Kumar, Akhil; Kalia, Arvind; Kalia, Aayushi

doi:10.1016/j.ijleo.2022.169051

Cited by 27 publications

(9 citation statements)

References 29 publications

(30 reference statements)

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“…In [165], the superiority of YOLOv4-tiny against YOLOv4 has been demonstrated regarding the recall and frame per second (FPS) processing. In [166], ETL-YOLOv4, an improved version of YOLOv4tiny, is introduced for FMD tasks. In [167], the performance of YOLOv4-Tiny for FMD is investigated and compared with YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x.…”

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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“…Wearing masks is a crucial way to avoid COVID-19 infection [7]. Recent research employs deep learning for face mask detection [8]. Many cutting-edge, pre-trained deep learning models, including you only look once (YOLO) and faster regions with convolutional neural networks (R-CNN) were utilized for transfer learning on new datasets [9].…”

Section: Literature Surveymentioning

confidence: 99%

“…The experimental findings demonstrate that the proposed algorithm in this research can successfully identify face masks in public areas. For mini YOLO v4, Kumar et al [8] suggested YOLO v4 with a revised and enhanced prediction network. By incorporating a modified-dense spatial pyramid pooling (SPP) that helps to improve the accurate prediction.…”

Section: Literature Surveymentioning

confidence: 99%

Incorrect facemask-wearing detection using image processing and deep learning

Habeeb

2023

Bulletin EEI

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Now and in the future, a face mask is a very important strategy to protect people when a new contagious life threatens disease spread through the air appears. Currently, there is a serious health emergency because of the coronavirus disease 2019 (COVID-19) epidemic. The negative consequences of this pandemic need to be protected in public areas. Numerous methods are advised by the World Health Organization (WHO) to reduce infection rates and prevent depleting the available medical resources in the absence of efficient antivirals. Wearing masks is a non-pharmaceutical strategy to lessen the susceptibility to COVID-19 infection. This research aims to create a face mask identification system that is efficient and uses deep learning, which has proven to be beneficial in many real-world applications. This system has also used a transfer learning method with the MobileNetV2 model to classify people who wear face masks properly, wear face masks improperly, and are without masks. The results demonstrate that the proposed system has an accuracy of 99.4% which is higher than current systems.

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“…The so-called computer vision is to program the computer to be able to think like the human brain, to recognize the environment in a picture or video, and to find the desired target. Kumar et al [11] proposed a mask recognition algorithm based on YOLO v4, which improved the mAp by 9.93% based on the previous study and played a very important role in monitoring the wearing of masks by outdoor people during the epidemic. M. Ramla et al [12] used an efficient deep neural network based on the YOLO detector to localize the fetal head to determine the developmental health and gestational safety of the fetus as well as to measure the fetal head at different gestational ages.…”

Section: Introductionmentioning

confidence: 99%