Realtime Facemask Detection using Deep Learning Framework TensorFlow-Keras - Erwan Eko Prasetiyo; Muhammad Luqman Bukhori; Gaguk Marausna; Iswanto Suwarno; Hendriana Helda Pratama (International Journal of Intelligent Systems and Applications in Engineering, 2022)
The spread of the Covid-19 virus can be reduced by implementing health protocols such as using masks properly. But there are still many people who ignore or even are reluctant to use masks when in public places. To increase public awareness and order in using masks, a system for detecting the use of masks is needed using image processing technology. The purpose of this research is to design and build a mask detection system using the deep learning framework TensorFlow. This mask detection system is to help monitor people using masks in implementing health protocols. The existence of this system is expected to help supervise people to comply with health protocols so that the transmission of the Covid-19 virus can be prevented. The proposed system test scenario uses face mask objects of various types of models and different colors. The test is applied to the condition of the object using a mask correctly or incorrectly. The conditions observed during the test included the proximity of the object to the camera, the lighting of the room, the number of people that could be detected, and the results of the decisions made by the mask detection system. The test results show that the mask detection system can function properly. The system developed has an accuracy rate of 93.2% for object detection capabilities with the use of the wrong mask. When detecting objects using masks correctly has an accuracy of 96.25%.