Face mask detection
The sudden outbreak of the Covid-19 pandemic globally has disrupted the daily lives of millions of people, forcing them to adapt to the “new normal”, which includes mandatory wearing of facemasks and face shields in public areas. Amidst the new regulations, adapting to these new changes and attempti...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2023
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/166133 |
| _version_ | 1811678400486572032 |
|---|---|
| author | Chua, Delise Yun Jie |
| author2 | Fan Xiuyi |
| author_facet | Fan Xiuyi Chua, Delise Yun Jie |
| author_sort | Chua, Delise Yun Jie |
| collection | NTU |
| description | The sudden outbreak of the Covid-19 pandemic globally has disrupted the daily lives of millions of people, forcing them to adapt to the “new normal”, which includes mandatory wearing of facemasks and face shields in public areas. Amidst the new regulations, adapting to these new changes and attempting to form new habits still requires substantial time. Deployment of a face mask detector is thus essential to ensure compliance to the enforced guideline.
In this paper, we explore three different models with Convolutional Neural Network (CNN) based architecture for detecting facemasks. Supported by the Haar cascade classifier, the best performing model will then be integrated with the Raspberry Pi 4 and built in webcam to perform real time facemask detection.
Results from this study shows a promising outlook in terms of accuracy in detecting facemask with the 3 different Convolutional Neural Network architectures. Across the three models, it was observed that a trade-off occurs between accuracy and computation time. Performance of the system on the built-in webcam appears to be better than the Raspberry Pi in terms of accuracy and computation time.
However, further refinements and finetuning remains necessary to enhance the performance during the real time implementation. |
| first_indexed | 2024-10-01T02:52:40Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166133 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:52:40Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1661332023-04-21T15:37:53Z Face mask detection Chua, Delise Yun Jie Fan Xiuyi School of Computer Science and Engineering xyfan@ntu.edu.sg Engineering::Computer science and engineering The sudden outbreak of the Covid-19 pandemic globally has disrupted the daily lives of millions of people, forcing them to adapt to the “new normal”, which includes mandatory wearing of facemasks and face shields in public areas. Amidst the new regulations, adapting to these new changes and attempting to form new habits still requires substantial time. Deployment of a face mask detector is thus essential to ensure compliance to the enforced guideline. In this paper, we explore three different models with Convolutional Neural Network (CNN) based architecture for detecting facemasks. Supported by the Haar cascade classifier, the best performing model will then be integrated with the Raspberry Pi 4 and built in webcam to perform real time facemask detection. Results from this study shows a promising outlook in terms of accuracy in detecting facemask with the 3 different Convolutional Neural Network architectures. Across the three models, it was observed that a trade-off occurs between accuracy and computation time. Performance of the system on the built-in webcam appears to be better than the Raspberry Pi in terms of accuracy and computation time. However, further refinements and finetuning remains necessary to enhance the performance during the real time implementation. Bachelor of Science in Data Science and Artificial Intelligence 2023-04-18T02:30:20Z 2023-04-18T02:30:20Z 2023 Final Year Project (FYP) Chua, D. Y. J. (2023). Face mask detection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166133 https://hdl.handle.net/10356/166133 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Chua, Delise Yun Jie Face mask detection |
| title | Face mask detection |
| title_full | Face mask detection |
| title_fullStr | Face mask detection |
| title_full_unstemmed | Face mask detection |
| title_short | Face mask detection |
| title_sort | face mask detection |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/166133 |
| work_keys_str_mv | AT chuadeliseyunjie facemaskdetection |