Deep Neural Architecture for Face mask Detection on Simulated Masked Face Dataset against Covid-19 Pandemic

Deep Neural Architecture for Face mask Detection on Simulated Masked Face Dataset against Covid-19 Pandemic

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Product Code: Python - Deep Learning
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Product Description

Aim:

              To apply the Deep Learning techniques based on convolution neural network improving the face mask detector accuracy.


Synopsis:

             The corona virus disease 2019 (COVID-19) has globally infected over 2.7million people and caused over 180,000 deaths. There are several similar large scale serious respiratory diseases, such as severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS), which occurred in the past few years. Therefore, more and more people are concerned about their health, and public health is considered as the top priority for governments. Furthermore, many public service providers require customers to use the service only if they wear masks. Face mask detection has become a crucial computer vision task to help the global society, but research related to face mask detection is limited.


Proposed System:

         We propose Retina Face Mask, which is a high-accuracy and efficient face mask detector. The proposed Retina Face Mask is a one-stage detector, which consists of a feature pyramid network to fuse high-level semantic information with multiple feature maps, and a novel context attention module to focus on detecting face masks. Face mask detection refers to detect whether a person wearing a mask or not.

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Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
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  10. Reference Papers
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The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.