Automatic Medical Face Mask Detection Based on Cross-Stage Partial Network to Combat COVID-19
According to the World Health Organization (WHO), the COVID-19 coronavirus pandemic has resulted in a worldwide public health crisis. One effective method of protection is to use a mask in public places. Recent advances in object detection, which are based on deep learning models, have yielded promi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/6/4/106 |
| _version_ | 1797461410081406976 |
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| author | Christine Dewi Rung-Ching Chen |
| author_facet | Christine Dewi Rung-Ching Chen |
| author_sort | Christine Dewi |
| collection | DOAJ |
| description | According to the World Health Organization (WHO), the COVID-19 coronavirus pandemic has resulted in a worldwide public health crisis. One effective method of protection is to use a mask in public places. Recent advances in object detection, which are based on deep learning models, have yielded promising results in terms of finding objects in images. Annotating and finding medical face mask objects in real-life images is the aim of this paper. While in public places, people can be protected from the transmission of COVID-19 between themselves by wearing medical masks made of medical materials. Our works employ Yolo V4 CSP SPP to identify the medical mask. Our experiment combined the Face Mask Dataset (FMD) and Medical Mask Dataset (MMD) into one dataset to investigate through this study. The proposed model improves the detection performance of the previous research study with FMD and MMD datasets from 81% to 99.26%. We have shown that our proposed Yolo V4 CSP SPP model scheme is an accurate mechanism for identifying medically masked faces. Each algorithm conducts a comprehensive analysis of, and provides a detailed description of, the benefits that come with using Cross Stage Partial (CSP) and Spatial Pyramid Pooling (SPP). Furthermore, after the study, a comparison between the findings and those of similar works has been provided. In terms of accuracy and precision, the suggested detector surpassed earlier works. |
| first_indexed | 2024-03-09T17:19:58Z |
| format | Article |
| id | doaj.art-3a07afc5fe6a46a1b6b2415fbc7529eb |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-09T17:19:58Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-3a07afc5fe6a46a1b6b2415fbc7529eb2023-11-24T13:17:23ZengMDPI AGBig Data and Cognitive Computing2504-22892022-09-016410610.3390/bdcc6040106Automatic Medical Face Mask Detection Based on Cross-Stage Partial Network to Combat COVID-19Christine Dewi0Rung-Ching Chen1Department of Information Technology, Satya Wacana Christian University, Salatiga 50711, IndonesiaDepartment of Information Management, Chaoyang University of Technology, Taichung 41349, TaiwanAccording to the World Health Organization (WHO), the COVID-19 coronavirus pandemic has resulted in a worldwide public health crisis. One effective method of protection is to use a mask in public places. Recent advances in object detection, which are based on deep learning models, have yielded promising results in terms of finding objects in images. Annotating and finding medical face mask objects in real-life images is the aim of this paper. While in public places, people can be protected from the transmission of COVID-19 between themselves by wearing medical masks made of medical materials. Our works employ Yolo V4 CSP SPP to identify the medical mask. Our experiment combined the Face Mask Dataset (FMD) and Medical Mask Dataset (MMD) into one dataset to investigate through this study. The proposed model improves the detection performance of the previous research study with FMD and MMD datasets from 81% to 99.26%. We have shown that our proposed Yolo V4 CSP SPP model scheme is an accurate mechanism for identifying medically masked faces. Each algorithm conducts a comprehensive analysis of, and provides a detailed description of, the benefits that come with using Cross Stage Partial (CSP) and Spatial Pyramid Pooling (SPP). Furthermore, after the study, a comparison between the findings and those of similar works has been provided. In terms of accuracy and precision, the suggested detector surpassed earlier works.https://www.mdpi.com/2504-2289/6/4/106object recognitionConvolutional Neural Network (CNN)COVID-19medical face maskYolodeep learning |
| spellingShingle | Christine Dewi Rung-Ching Chen Automatic Medical Face Mask Detection Based on Cross-Stage Partial Network to Combat COVID-19 Big Data and Cognitive Computing object recognition Convolutional Neural Network (CNN) COVID-19 medical face mask Yolo deep learning |
| title | Automatic Medical Face Mask Detection Based on Cross-Stage Partial Network to Combat COVID-19 |
| title_full | Automatic Medical Face Mask Detection Based on Cross-Stage Partial Network to Combat COVID-19 |
| title_fullStr | Automatic Medical Face Mask Detection Based on Cross-Stage Partial Network to Combat COVID-19 |
| title_full_unstemmed | Automatic Medical Face Mask Detection Based on Cross-Stage Partial Network to Combat COVID-19 |
| title_short | Automatic Medical Face Mask Detection Based on Cross-Stage Partial Network to Combat COVID-19 |
| title_sort | automatic medical face mask detection based on cross stage partial network to combat covid 19 |
| topic | object recognition Convolutional Neural Network (CNN) COVID-19 medical face mask Yolo deep learning |
| url | https://www.mdpi.com/2504-2289/6/4/106 |
| work_keys_str_mv | AT christinedewi automaticmedicalfacemaskdetectionbasedoncrossstagepartialnetworktocombatcovid19 AT rungchingchen automaticmedicalfacemaskdetectionbasedoncrossstagepartialnetworktocombatcovid19 |