A Deep Learning Based Approach for Detection of Face Mask Wearing using YOLO V3-tiny Over CNN with Improved Accuracy

Nithin., A; Jaisharma, K.

doi:10.1109/icbats54253.2022.9758925

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…A sophisticated object identification method utilized by CNNs, YOLO is often used in combination with other YOLO algorithms. YOLO v2.0's average accuracy is just roughly 16% greater than that of faster r-CNN [22].…”

Section: You Only Look Once (Yolo)mentioning

confidence: 91%

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Uddin

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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Facial recognition research is one of the different types of research in this world today. In recent years, facial recognition in robots has attracted increased study interest. Robotic platforms now utilize a variety of object detection methods, with face detection being a viable use. Face detection in robotics is a computer technique that recognizes human faces in digital pictures and is used in a range of applications. Different authors have performed their research in different ways on the use of detection systems. This paper aims to give future researchers a better idea of using facial recognition systems in robotics. In this study, we reviewed research by various authors over recent years to facilitate future facial recognition research. In addition, scholars have addressed the topics, how they have done so, and the specifics of their approaches are described. This paper reviewed an overview of hardware implementation and software implementation by various authors. It can automatically focus cameras or count the number of people who have entered a location. Commercial applications of the method include displaying tailored advertisements in response to a recognized face along with the algorithms, functions and architectures used in facial recognition and giving the opinions of various authors mentioned. The comparative analysis of facial recognition and its architecture system is highlighted.

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Section: You Only Look Once (Yolo)mentioning

confidence: 91%

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Uddin

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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show abstract

“…YOLOv3-tiny: It is the real-time compressed version of YOLOv3; it has been pre-trained on the COCO dataset with 80 object classes. Many studies have considered YOLOv3-tiny as the core of their FMD systems, including [158,159]. For instance, in [159], the accuracy of 95% has been reached on a customized dataset of 135 images.…”

Section: Lightweight Object Detectorsmentioning

confidence: 99%

“…Many studies have considered YOLOv3-tiny as the core of their FMD systems, including [158,159]. For instance, in [159], the accuracy of 95% has been reached on a customized dataset of 135 images. Moving on, the FMD approach proposed in [160] is based on YOLOv3-tiny, which has been improved accordingly to solve the FMD task.…”

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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Crowd Abnormal Behaviour Detection and Comparative Analysis using YOLO Network

Lodha,

Kalamkar,

Heda

2024

2024 IEEE 9th International Conference for Convergence in Technology (I2CT)

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A Deep Learning Based Approach for Detection of Face Mask Wearing using YOLO V3-tiny Over CNN with Improved Accuracy

Cited by 4 publications

References 24 publications

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

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

Crowd Abnormal Behaviour Detection and Comparative Analysis using YOLO Network

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