Towards smart surveillance as an aftereffect of COVID-19 outbreak for recognition of face masked individuals using YOLOv3 algorithm

Kumar, Saurav; Yadav, Drishti; Gupta, Himanshu; Kumar, Mohit; Verma, Om Prakash

doi:10.1007/s11042-021-11560-1

Cited by 10 publications

(2 citation statements)

References 53 publications

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“…The study achieved high accuracy, but it was limited by the small size of the dataset. YOLOv3-tiny algorithm in a convolutional neural network (CNN) [19] is used for mask identification and the testing results indicated a mean average precision (mAP) of approximately 98.73% on custom dataset, outperforming YOLOv3-tiny by approximately 62%. One advantage of this study was using an augmented dataset, but it suffered from relatively low accuracy and required a long training time due to many iterations.…”

Section: Related Workmentioning

confidence: 99%

Real-time mask-wearing detection in video streams using deep convolutional neural networks for face recognition

Suhirman,

Saifullah,

Hidayat

et al. 2024

IJECE

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This research aims to develop a real-time mask-wearing detection system using deep convolutional neural networks (CNNs). This is crucial in the coronavirus disease 2019 (COVID-19) pandemic to alert individuals who are not wearing masks early on, thereby reducing the spread of the virus. Since COVID-19 primarily spreads through respiratory droplets and mask-wearing is recommended, our proposed study utilizes computer vision techniques, specifically image processing, to detect masked and unmasked faces. We employ a customized CNN architecture consisting of five convolutional layers, followed by max-pooling layers and fully connected (FC) layers. The final output layer utilizes softmax activation for classification. The model is updated with optimized layer configurations and parameter values. We are developing an application that uses a digital camera as an input device. The application utilizes a dataset comprising 11,792 image samples, which are used for training and testing purposes with the 80:20 ratio. Real-time testing is conducted using human subjects captured by the camera. The experimental results demonstrate that the CNN method achieves a classification accuracy of 99% on the training data and 98.83% during real-time video testing. These findings suggest that the real-time mask detection system using CNN performs effectively.

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Section: Related Workmentioning

confidence: 99%

Real-time mask-wearing detection in video streams using deep convolutional neural networks for face recognition

Suhirman,

Saifullah,

Hidayat

et al. 2024

IJECE

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“…YOLO will detect faces (masked or not) and then use a Convolutional Neural Network (CNN) algorithm to classify them into two categories [5]. There are many studies related to the use of the YOLO model for several different cases such as those conducted by the following researchers [7]- [14] II. RELATED WORKS…”

Section: Introductionmentioning

confidence: 99%

Implementation of Face Mask Detection Using Phyton Programming Language

Ceng Giap,

Erviana

2023

bit-Tech

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Since the beginning of the pandemic in 2019, people in Indonesia have been required to wear masks. Although until now the pandemic has ended, the need to use masks is still very much needed such as to maintain health, avoid air pollution and others. In detecting mask users, an application is needed that can help human work. Currently, the Python programming language is widely used to build applications in the field of computer vision, one of which is this face mask detection application. This application will detect mask users, whether they are wearing a mask or not. This developed application uses the Yolo model using the Face Mask Detection dataset developed by Larxel, where the Yolo model can work on the dataset provided. The test results show that the Yolo model can recognize mask users with an accuracy value above 90%. The second experiment was carried out to detect several faces of mask users, the Yolo model can recognize mask users or not with an average accuracy value of 91.75%. For future research, it is also expected to use other models besides Yolo and make comparisons of several models and make improvements to the problems that exist in each model and use real time data.

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