Detection of Certain Objects Wearing Masks in Real Time To Prevent the Spread of the Virus (Yolov3) - Kusdarnowo Hantoro; Rusdianto Rustam; Amir Dahlan (Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi, 2022)
A significant increase in the spread of the corona virus (COVID-19) in the community is currently happening due to people not following the health protocol rules set by the ministry of health. One of the rules is to require people to wear masks while they are outside the home. Measures need to be implemented in anticipation of situations where people do not wear masks in public spaces. Therefore, the establishment of a mask detection system is chosen as a solution in order to solve the mentioned problem above. A real-time identification system for people wearing masks is proposed to be developed in this paper. The system utilizes Yolov3 with Darknet -53 as a deep learning mask detector and OpenCV as a real-time computer vision library, so that people doing activities in a public space captured by a video can be recognized and detected when they do not wear masks . In implementing deep learning, a data set of 4000 images is divided into two classes, i.e.,2000 images with masks f or data testing purposes and another 2000 images without masks for training custom objects. The Extreme Programming (XP) method as part of the Agile Process Model is adopted for system development. Computer language support and the latest system development tools have made it possible to utilize this method in an effort to develop this system rapidly. Requirement Analysis is conducted to obtain required processes before designing system. Writing code and testing the system will be the next step before the system is declared ready to be implemented in the public space. By adopting the XP development method, all of the above steps can be implemented repeatedly until the system delivers the expected results