TY - GEN
T1 - Multi-Class Image Classification Based on MobileNetV2 for Detecting the Proper Use of Face Mask
AU - Rokhana, Rika
AU - Herulambang, Wiwiet
AU - Indraswari, Rarasmaya
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/29
Y1 - 2021/9/29
N2 - Wearing face mask in public has become a health protocol standard during this pandemic to prevent further spread of COVID-19. Even though the detection of inappropriate use of face mask is important considering that people sometimes ignore the health protocols by lowering their face mask so it does not cover their nose, studies regarding automatic detection of proper use of face mask are still few. Therefore, in this research we propose a multi-class image classification for detecting the proper use of face mask based on MobileNetV2 architecture as the base model. We also propose a trainable head model for the network, consisting of a depthwise convolution layer and two fully-connected layers, that gives high classification performance. The experimental results show that the proposed system gives a high multi-class classification performance with an accuracy of 97%, precision of 97%, recall of 97%, and F1-score of 97%. The running time of the proposed method is 265.94 seconds which is considered efficient compared with other models. Because of its light-weight network architecture, the proposed method is suitable for further implementation towards a real-time application of surveillance systems. Therefore, in this research we present the results of an initial experiment of the proposed model on a real-time detection system by using a web camera.
AB - Wearing face mask in public has become a health protocol standard during this pandemic to prevent further spread of COVID-19. Even though the detection of inappropriate use of face mask is important considering that people sometimes ignore the health protocols by lowering their face mask so it does not cover their nose, studies regarding automatic detection of proper use of face mask are still few. Therefore, in this research we propose a multi-class image classification for detecting the proper use of face mask based on MobileNetV2 architecture as the base model. We also propose a trainable head model for the network, consisting of a depthwise convolution layer and two fully-connected layers, that gives high classification performance. The experimental results show that the proposed system gives a high multi-class classification performance with an accuracy of 97%, precision of 97%, recall of 97%, and F1-score of 97%. The running time of the proposed method is 265.94 seconds which is considered efficient compared with other models. Because of its light-weight network architecture, the proposed method is suitable for further implementation towards a real-time application of surveillance systems. Therefore, in this research we present the results of an initial experiment of the proposed model on a real-time detection system by using a web camera.
KW - MobileNetV2
KW - depthwise convolution
KW - face mask detection
KW - multi-class image classification
KW - real-time surveillance system
UR - http://www.scopus.com/inward/record.url?scp=85119960002&partnerID=8YFLogxK
U2 - 10.1109/IES53407.2021.9594022
DO - 10.1109/IES53407.2021.9594022
M3 - Conference contribution
AN - SCOPUS:85119960002
T3 - International Electronics Symposium 2021: Wireless Technologies and Intelligent Systems for Better Human Lives, IES 2021 - Proceedings
SP - 636
EP - 641
BT - International Electronics Symposium 2021
A2 - Yunanto, Andhik Ampuh
A2 - Kusuma N, Artiarini
A2 - Hermawan, Hendhi
A2 - Putra, Putu Agus Mahadi
A2 - Gamar, Farida
A2 - Ridwan, Mohamad
A2 - Prayogi, Yanuar Risah
A2 - Ruswiansari, Maretha
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Electronics Symposium, IES 2021
Y2 - 29 September 2021 through 30 September 2021
ER -