Identification of Face Mask With YOLOv4 Based on Outdoor Video - Harahap, Mawaddah; Kusuma, Leonardo; Suryani, Melva; Situmeang, Candra Ebenezer; Purba, Juniven Francisco (Politeknik Ganesha Medan, 2021)
The use of face masks in the current era is one of the special regulations in many countries including Indonesia to prevent the spread of coronavirus. However, not all people strongly agree to wear masks because they feel uncomfortable to wear even in crowded places require the use of masks such as shopping malls, hospitals, factories, stations and others by checking manually. Therefore, in the study proposed automatic detection of masks with YOLOv4 with the stage of data collection recording community activities in crowded places, labeling images of masks and non masks. The labelling results were conducted in training that resulted in 90.3% accuracy in the 2000 ierasi, the last of which was video testing in three different crowd locations: taxes, city parks and highways. Based on the test results, YOLOv4 can detect masks and non masks on videos with different obstruction conditions such as people wearing helmets, hand obstacles. However, for the detection of people with tissue obstruction conditions and improper position of wearing masks has not resulted in good detection.