Real-Time Face Mask Detector Using YOLOv3 Algorithm and Haar Cascade Classifier

Vinh, Truong Quang; Anh, Nguyen Tran Ngoc

doi:10.1109/acomp50827.2020.00029

Cited by 61 publications

(20 citation statements)

References 11 publications

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“…This process achieved an accuracy of 100% on the validation dataset. Vinh and Anh [21] utilized the Haar Cascade classifier along with the YOLOv3 algorithm used to detect the face and detect the mask, respectively. This was a real-time detector and gave 90.1% accuracy on experimentation.…”

Section: Literature Surveymentioning

confidence: 99%

Detecting face mask using eigenfaces and vanilla neural networks

Sharma

Shridevi

Seshan

et al. 2022

IJEECS

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Coronavirus has <span>become one of the most deadly pandemics in 2021. Starting in 2019, this virus is now a significant medical issue all over the world. It is spreading extensively because of its modes of transmission. The virus spreads directly, indirectly, or through close contact with infected people. It is proclaimed that people should wear a mask in public areas as a counteraction measure, as it helps in suppressing transmission. A portion of the spaces, where the virus has broadly fanned out, is because of inappropriate wearing of facial cover. In crowded areas, keeping a check on facial masks manually is difficult. To automate this process, an effective and robust face mask detector is required. This paper discusses a hybrid approach using a machine learning technique called eigenfaces, along with vanilla neural networks. The accuracy was compared for three different values of principal components. The test accuracy achieved was 0.87 for 64 components, 0.987 for 512 components, and 0.989 for 1,000 components. Hence, this approach proved to be more promising and efficient than its counters.</span>

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Section: Literature Surveymentioning

confidence: 99%

Detecting face mask using eigenfaces and vanilla neural networks

Sharma

Shridevi

Seshan

et al. 2022

IJEECS

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show abstract

“…Be that as it may, the level of the exactness of all calculations step by step drops when observing real time because of elements like camera quality, enlightenment. Truong Quang Vinh et al [12] proposed a face mask detection tool using Yolov3 that utilizes Haar Cascade classifier for classifying the mask and face from various frames. The drawback to Haar cascades is that they will in general be inclined to false positive identifications, require boundary tuning when being applied for surmising/recognition, and just, as a rule, are not as precise as the more "present day" calculations.…”

Section: Related Workmentioning

confidence: 99%

Automatic Facial Mask Rule Violation Detection using Keras & Tensorflow to Limit COVID-19

Francis¹

2022

IJRASET

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COVID-19 is a pandemic disease that spread by itself coming in the contact of people. It was initially started from China and now it has been spread all over the world and many casualties have been occurred. Social distancing commonly known as physical distancing is a non-pharmaceutical approach through which it can be reduced. But social distancing only works when people started wearing mask because it can spread by sneezing even having distance among people. So wearing mask is mandatory to stop spreading this virus at its possible extent. In this paper, it has been intended to identify the people who are wearing mask or not. By the help of CCTV camera it can be recognized at the entrance of various public places such as mall, airport, railway station, mart and many more. If facial mask can be recognized effectively with high level of accuracy then it can become mandatory for people who are violating the rules. The proposed system uses Keras and Tensorflow model for identifying whether people are following the rule or not. Tensorflow is a deep learning methodology through which facial mask can be detected with all kind of situations. Proposed system is able to classify whether a person wear a mask or not, it is also able to identify whether people incorrectly wearing mask i.e. partial wearing. It is mandatory to identify whether people are properly using the mask or not. System identify this kind of situation and classified them accordingly. System uses hybrid technique by combining two algorithms i.e. keras and tensorflow. By combining both the systems it can be identified more precisely to identify the rule violations. Keywords: COVID-19, Facial Mask, Convolutional Neural Network, Classifiers, Machine Learning, Image Processing, Pattern Recognition.

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“…Another researcher proposed a model which is a combination with an inverse perspective mapping (IPM) technique with the YOLOv4-based Deep Neural Network (DNN) model, and it is applicable for different environments using CCTV cameras [6]. For face detection in real-time, the Haar Cascade classifier and YOLOv3 algorithm have been used as well [8]. A device like glasses was made for blind people with an alarm to maintain social distancing for the Covid-19 issue [11].…”

Section: Related Workmentioning

confidence: 99%

A Real-time Face Mask Detection and Social Distancing System for COVID-19 using Attention-InceptionV3 Model

Asif

Tisha

2022

J. eng. adv.

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One of the deadliest pandemics is now happening in the current world due to COVID-19. This contagious virus is spreading like wildfire around the whole world. To minimize the spreading of this virus, World Health Organization (WHO) has made protocols mandatory for wearing face masks and maintaining 6 feet physical distance. In this paper, we have developed a system that can detect the proper maintenance of that distance and people are properly using masks or not. We have used the customized attention-inceptionv3 model in this system for the identification of those two components. We have used two different datasets along with 10,800 images including both with and without Face Mask images. The training accuracy has been achieved 98% and validation accuracy 99.5%. The system can conduct a precision value of around 98.2% and the frame rate per second (FPS) was 25.0. So, with this system, we can identify high-risk areas with the highest possibility of the virus spreading zone. This may help authorities to take necessary steps to locate those risky areas and alert the local people to ensure proper precautions in no time.

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Real-Time Face Mask Detector Using YOLOv3 Algorithm and Haar Cascade Classifier

Cited by 61 publications

References 11 publications

Detecting face mask using eigenfaces and vanilla neural networks

Detecting face mask using eigenfaces and vanilla neural networks

Automatic Facial Mask Rule Violation Detection using Keras & Tensorflow to Limit COVID-19

A Real-time Face Mask Detection and Social Distancing System for COVID-19 using Attention-InceptionV3 Model

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