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    International Journal of Innovative Science and Research Technology
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    COVID-19 has affected the world badly. Studies have demonstrated that wearing a facial covering is one of the insurances to diminish the danger of viral transmission

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    COVID-19 has affected the world badly. Studies have demonstrated that wearing a facial covering is one of the insurances to diminish the danger of viral transmission

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    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
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    17 views2 pages

    Face Mask Detection and Social Distance Monitoring Using Machine Learning Architecture

    Uploaded by

    International Journal of Innovative Science and Research Technology
    COVID-19 has affected the world badly. Studies have demonstrated that wearing a facial covering is one of the insurances to diminish the danger of viral transmission

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Flag for inappropriate content
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    Volume 7, Issue 3, March – 2022 International Journal of Innovative Science and Research Technology

    ISSN No:-2456-2165

    Face Mask Detection and Social Distance Monitoring


    using Machine Learning Architecture
    Swapnaja Kalsait , Sayali Pote , Siddhi Muskande , Shivani Patil ,Prof. O. S. Dubal
    Department of Information Technology,
    Sinhgad Institutes SMT. Kashibai Navale College of Engineering Pune

    Abstract:- COVID-19 has affected the world badly. II. RELATED WORK
    Studies have demonstrated that wearing a facial
    covering is one of the insurances to diminish the danger Sahana Srinivasan et.al [1] provides a comparative
    of viral transmission. And many public places as well as study of different face detection and face mask classification
    public service providers require customers to use the models. Finally, a video dataset labelling method is
    service and place only if they wear mask correctly. proposed along with the labelled video dataset to
    Sometime it may not be easy to manually track the compensate for the lack of dataset in the community and is
    customer, whether they are wearing the mask or not. used for evaluation of the system. The system performance
    That’s why this technology holds the key here. In this is evaluated in terms of accuracy, F1 score as well as the
    system, we propose face mask detection using image prediction time, which has to be low for practical
    processing which is one of the high-accuracy and applicability. The system performs with an accuracy of
    efficient face mask detector. This proposed system is of 91.2% and F1 score of 90.79% on the labelled video dataset
    three stages i.e. 1. Image preprocessing 2. Face detection and has an average prediction time of 7.12 seconds for 78
    and crop 3. Face mask classifier. Our system is capable frames of a video.
    of detecting masked and unmasked faces and can be
    integrated with cameras and other hand detecting the In [2] proposed a system where For evaluation of the
    trained model, mAP (Mean Average Precision) was
    distance between two people whether they are maintain
    calculated for both the use cases (Social Distancing & Face
    distance or not .
    Mask Detection), it works by comparing the ground-truth
    Keywords:- Covid_19, Image Processing, Mask, CNN , bounding box vs the detected box and, in the end, returns the
    SSD. score. The higher the mAP score would be, the better model
    is in the detection of objects. Mean Average Precision was
    I. INTRODUCTION calculated for two different thresholds (0.25 % & 0.50 %)
    with 101 recall points. Three different classes were created
    In Wuhan, China at the end of 2019 Corona Virus was for classification those were Good, Bad & None, for which
    detected. From that time, it has been spreading like a wild True Positive & False Positive values were calculated with
    fire in a timber area. Millions have been affected and around ROC Curve for better understanding.
    have unfortunately passed away as on 30th of December
    2020, nearly a time since this contagion came to actuality. In [3] surveys various deep learning networks to
    People who have this illness can take up to 2 weeks to cure, develop such detectors. In this survey, the existing object
    with the threat of having to suffer fresh medical problems detection models used for surveillance and people detection
    caused by it. Kiddies and senior folks individualities have are analyzed. The one-stage and two-stage detectors along
    ended up being at the most elevated peril to get the with their applications and performance are outlined in a
    infection, which might indeed bring about death. Latterly, it comprehensive manner. Deep Learning models such as
    has been concentrated on to contain the infection than to fix AdaBoost, Voila-Jones, variants of CNN including ResNet,
    it. The infection spreads through the air, communicated by VGG-16, single-shot detectors MobileNet, and versions of
    one existent to another by contact, yet also by talking and YOLO are discussed and compared.
    playing. The solicitude was advanced to WHO (World
    Health Organization) which recommended that facial In[4] system focuses on a solution to help enforce
    coverings and social removing is the response to it, until a proper social distancing and wearing masks in public using
    fix is created. Putting a facial covering on can dwindle the YOLO object detection on video footage and images in real
    peril of getting tainted by an extraordinary degree, not time. The experimental results shown in the paper infer that
    simply to the one wearing it yet also to the others that he the detection of masked faces and human subjects based on
    interacts with. Wearing curtains each time we go out is YOLO has stronger robustness and faster detection speed as
    commodity we can do with little exertion that can compared to its competitors. Their proposed object detection
    adequately save lives, and that's definitively why it's in such model achieved a mean average precision score of 94.75%
    a lot of interest now of time. Hence we've proposed a system with an inference speed of 38 FPS on video.
    with two modules i.e. Face mask and social distancing
    In [5] proposed a system where they have taken one of
    the measures used to prevent COVID-19 spread and aimed
    to develop a deep learning model to categorize people with
    or without a mask at public places such as schools, colleges,
    and corporates. Developed algorithm using concepts of deep

    IJISRT22MAR666 www.ijisrt.com 1189


    Volume 7, Issue 3, March – 2022 International Journal of Innovative Science and Research Technology
    ISSN No:-2456-2165
    transfer learning and fine-tuning. The system developed on As well same will be for social distancing sensor module.
    the MobileNetv2 base model, the head of it was replaced by Alert is been generated consequently.
    the custom face mask detection algorithm and enabled the
    training of face masked and non-face masked images. The VII. CONCLUSION
    testing results have shown an accuracy of 98% on both
    categories with mask and without mask.. In this epidemic situation, where all people in countries
    are featuring to get back to normal routine, this system will
    III. OBJECTIVES OF SYSTEM play effective part in covering the use of face masks at
    workplaces. As the technology are blooming with arising
     To prevent the spread of Corona virus by promoting the trends the vacuity so we've new face mask sensor which can
    use of face masks with the help of effective technology to conceivably contribute to public healthcare. With the
    detect the face mask. increase and blooming technology and arising trends in
     To take necessary precautions for the safety of society by ways. We've proposed as small systemi.e new face mask
    predicting the future outbreaks of COVID-19. sensor which can conceivably contribute to
     To ensure a safe working environment. publichealthcare.We're using OpenCV, tensor inflow, keras
     To save the lives of people. and algorithm to descry whether people were wearing face
    masks or not. By the development of face mask discovery
    IV. MOTIVATION we can descry if the person is wearing a face mask and
    indeed the social distancing where we're calculating the
    Our main motive, Face mask discovery with Social distance and detecting whether the person is following the
    Distancing is the task of relating an formerly detected object social distancing or not.
    as that person wear mask or not and they're walking with
    maintaining Social Distance to each other. REFERENCES

    V. SYSTEM ARCHITECTURE [1.] Sahana Srinivasan and Ruchita R Biradar, “COVID-19


    Monitoring System using Social Distancing and Face
    Mask Detection on Surveillance video datasets”, IEEE
    2021
    [2.] Yash Indulkar, “Alleviation of COVID by means of
    Social Distancing & Face Mask Detection Using
    YOLO V4”,IEEE 2021
    [3.] S. Vijaya Shetty and Pooja S, “Social Distancing and
    Face Mask Detection using Deep Learning Models: A
    Survey”,IEEE 2021
    [4.] Krishna Bhambani and Tanmay Jain, “Real-time Face
    Mask and Social Distancing Violation Detection
    System using YOLO”,IEEE 2020
    [5.] Mayank Dev and Rajiv Dey, “Face Mask Detector
    using Deep Transfer Learning and Fine-Tuning”, IEEE
    2021
    [6.] Jiayan Ma and Jaideep Chakladar , “Using machine
    learning of clinical data to diagnose COVID-19: a
    systematic review and meta-analysis ”,Research
    Fig. 1: System Architecture Article 2020
    [7.] Krishna Kumar and Narendra Kumar, “COVID-19
    VI. METHODOLOGY Epidemic Analysis using Machine Learning and Deep
    Learning Algorithms ”,Journal 2020
    We're developing the design for detecting whether [8.] Alzubaidi MA and Banihani R,“An IoT-based
    person is wearing a mask or not and indeed for measuring Framework for Early Identification and Monitoring of
    the temperature of person. This system focuses on how to COVID-19 Cases ”, Journal Pre-proof, 2020
    identify a person wearing a mask on image or videotape [9.] Nadeem Ahmed and Wanli Xue , “A Survey of
    sluice with the help of Deep Learning and Machine COVID-19 Contact Tracing Apps ”,IEEE Access,2020
    Learning using Keras, TensorFlow, OpenCV and the Scikit- [10.] Ravi Pratap Singh and Mohd Javaid, “Internet of
    Learn library. We've used proposed armature which is an things (IoT) applications to fight against COVID-19
    accurate and effective and can be applied to bedded device. Pandemic ”,2020
    [11.] Michael. J. Horry and Subrata Chakraborty, “Role of
    For the determination the model designed calculates
    IoT to avoid spreading of COVID-19”, International
    ROI (Region of Interest), and latterly on cipher bounding
    Journal of Intelligent Networks,2020.
    box value for a particular face and insure that the box falls
    within the boundaries.. The “ Green” color box will be for
    with mask and “ Red” color box will be for without mask.
    Formerly all discovery is executed we will display the affair.

    IJISRT22MAR666 www.ijisrt.com 1190

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