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Robust Deep Learning Method to Detect Face Masks

Published: 26 October 2020 Publication History

Abstract

With the outbreak of novel coronavirus (2019-nCoV), wearing masks has become an effective way to prevent the transmission of the virus. But in public places, people are often reluctant to wear face masks and cause the virus to spread widely. This paper uses an efficient and robust object detection algorithm to automatically detect the faces with masks or without masks, making the epidemic prevention work more intelligent. Specifically, we collected an extensive database of 9886 images of people with and without face masks and manually labeled them, then use multi-scale training and image mixup methods to improve YOLOv3, an object detection algorithm, to automatically detect whether a face is wearing a mask. Our experiment results demonstrate that the mean Average Precision (mAP) of the improved YOLOv3 algorithm model reached 86.3%. This work can effectively and automatically detect whether people are wearing masks, which reduces the pressure of deploying human resources for checking masks in public places and has high practical application value.

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Cited By

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  • (2023)Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 PandemicSystems10.3390/systems1102010711:2(107)Online publication date: 17-Feb-2023
  • (2023)COVID-19 approved mask detection using mathematical morphologyAdvanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI10.1117/12.2642041(6)Online publication date: 2-Mar-2023
  • (2023)Aster: Encoding Data Augmentation Relations into Seed Test Suites for Robustness Assessment and Fuzzing of Data-Augmented Deep Learning Models2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS)10.1109/QRS60937.2023.00044(370-381)Online publication date: 22-Oct-2023
  • Show More Cited By

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cover image ACM Other conferences
AIAM2020: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture
October 2020
566 pages
ISBN:9781450375535
DOI:10.1145/3421766
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2020

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Author Tags

  1. YOLOv3
  2. deep learning
  3. face mask detection
  4. mixup
  5. multi-scale training

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  • Short-paper
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  • Refereed limited

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AIAM2020

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AIAM2020 Paper Acceptance Rate 100 of 285 submissions, 35%;
Overall Acceptance Rate 100 of 285 submissions, 35%

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Cited By

View all
  • (2023)Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 PandemicSystems10.3390/systems1102010711:2(107)Online publication date: 17-Feb-2023
  • (2023)COVID-19 approved mask detection using mathematical morphologyAdvanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI10.1117/12.2642041(6)Online publication date: 2-Mar-2023
  • (2023)Aster: Encoding Data Augmentation Relations into Seed Test Suites for Robustness Assessment and Fuzzing of Data-Augmented Deep Learning Models2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS)10.1109/QRS60937.2023.00044(370-381)Online publication date: 22-Oct-2023
  • (2023)Realtime Mask Detection of Kitchen Staff Using YOLOv5 and Edge Computing2023 3rd International Conference on Computer, Control and Robotics (ICCCR)10.1109/ICCCR56747.2023.10193943(33-40)Online publication date: 24-Mar-2023
  • (2023)Face Mask Detection: An Application of Artificial IntelligenceIntelligent Systems and Machine Learning10.1007/978-3-031-35081-8_16(193-201)Online publication date: 10-Jul-2023
  • (2023)Image Processing and Deep Neural Networks for Face Mask DetectionAdvancements in Smart Computing and Information Security10.1007/978-3-031-23095-0_14(187-200)Online publication date: 11-Jan-2023
  • (2022)Face Mask Detection Using Image Processing and Convolutional Neural Networks2022 IEEE 6th Conference on Information and Communication Technology (CICT)10.1109/CICT56698.2022.9997821(1-4)Online publication date: 18-Nov-2022
  • (2022)An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask DetectionArtificial Neural Networks and Machine Learning – ICANN 202210.1007/978-3-031-15934-3_44(531-543)Online publication date: 2022
  • (2021)Face mask recognition based on object detectionInternational Conference on Signal Image Processing and Communication (ICSIPC 2021)10.1117/12.2600460(69)Online publication date: 1-Jun-2021
  • (2021)RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC52423.2021.9659271(832-837)Online publication date: 17-Oct-2021
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