###### Abstract
One study related to traffic, which is precedent research, is also the study using data about vehicle speed. In the precedent studies, using R-CNN gives high accuracy, but it is insufficient to deal with traffic accidents on the road in real-time. This is because R-CNN  
Figure 1: Traffic congestion costs by year. requires a lot of learning time and GPU calculation capacity due to learning progressing in several stages for real-time processing. YOLO is capable of real-time object detection that can handle larger amounts of images while having accuracy close to R-CNN, rather than seeking higher accuracy than existing models. This study has advantages due to using YOLO, which is characterized by its simple processing and extremely fast speed, because it is important to quickly detect accidents in real-time. Therefore, the goal of this study is to research a service that can detect abnormal situations on the roads without any additional devices through a model using YOLO and then convey information on the situation through notifications within seconds, which contributes to preventing secondary accidents and creating a safe traffic environment. Through the production of a deep learning-based model that can detect abnormal situations on the roads, a real-time notification application that can prevent secondary accidents, called DRAS (detecting road abnormal situations), was produced by implementing AI CCTV. Figure 2 shows that AI CCTV detects abnormal situations on roads and then conveys information to the users via FCM (Firebase Cloud Messaging). After detection, AI CCTV in Figure 2 delivers accident and location information within a 5 km radius and FCM (Firebase Cloud Messaging) to the user within 5 s as a web application. We expect positive effects from this service that can reduce secondary accidents and damage from obstacles on roads and prepare for traffic congestion through rapid information transmission. A comparison between DRAS and similar products is presented in Table 1. BATON SOS detects an accident using a 9-axis detection sensor attached through separate hardware rather than a web application, and users can notify undetectable accidents by pressing the button manually. If the users realize it is wrong after detection, its function has been developed to contact 911 or insurance companies after 30 s as a Grace period [7]. Unlike BATON SOS, Mapper's ATLAN navigation does not need individual hardware. However, to detect accidents and check for accidents by using VDS, workers from the traffic situation room check the CCTV and notification is sent. Figure 2: Service overview. However, DRAS detects abnormalities in real-time CCTV videos by using deep learning and delivers warning notifications and accident information to users within 5 km. It was confirmed that the information was transmitted to the user within 5 s by conducting a hypothetical experiment by selecting the location of the three regions. Detection and information transmission are fast, and users who received notifications can be aware of accidents and then prepare for traffic congestion and accidents, which helps traffic institutions to monitor lots of CCTV with fewer workers.