Applying a Deep Learning Enhanced Public Warning System to Deal with COVID-19

Sunwoo Lee and Donghyeok An

116 350.350-359

10.23919/JCN.2021.000036

Abstract :In Korea, public warning systems are being actively used to provide COVID-19 information to people to avoid additional infections. The explosion of COVID-19 warning messages has caused redundant and unnecessary transmission of warning messages. This study propose an enhanced public warning system. First, a generation model based on deep learning is proposed for automatically generating the coordinates of the broadcast area. Second, the public warning system is modified to provide additional warning information to the users. Finally, a customization scheme for warning information is presented; therefore, the number of redundant and unnecessary warning messages decreases. The proposed generation model is evaluated by measuring the overshooting area and it is compared with the ground truth image. The output of the polygon generator and the circle generator show an image that is similar to the ground truth. The proposed public warning system was implemented, and a test scenario was conducted for the validation. The results demonstrate the feasibility of the proposed public warning system. 

Index terms :Alert area, broadcast area, COVID-19, deep learning, emergency message, public warning system.