###### Abstract  
This research produced a model that detects abnormal phenomena on the road, based on deep learning, and proposes a service that can prevent accidents because of other cars and traffic congestion. After extracting accident images based on traffic accident video data by using FFmpeg for model production, car collision types are classified, and only the head-on collision types are processed by using the deep learning object-detection algorithm YOLO (You Only Look Once). Using the car accident detection model that we built and the provided road obstacle-detection model, we programmed, for when the model detects abnormalities on the road, warning notification and photos that captures the accidents or obstacles, which are then transferred to the application. The proposed service was verified through application notification simulations and virtual experiments using CCTVs in Daegu, Busan, and Gwangju. By providing services, the goal is to improve traffic safety and achieve the development of a self-driving vehicle sector. As a future research direction, it is suggested that an efficient CCTV control system be introduced for the transportation environment. AI CCTV; secondary accident; real-time notification; traffic congestion; deep learning; autonomous driving +
Footnote †: journal: Applied  
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arrive at the locations of accidents on the road with abnormalities. Secondary accidents and blocks on roads frequently cause traffic congestion. If an accident happened and a road is blocked or narrowed, it causes traffic congestion. Figure 1 shows an annual traffic congestion cost graph of Korea. Through the graphs showing traffic congestion, we can recognize that the situation is getting worse and that traffic congestion cost is increasing [2]. Traffic congestion costs mentioned here include the costs used to solve traffic accidents and the costs of environmental pollution caused by the congestion of many vehicles on the road. As such, secondary traffic accidents create problems in various ways, and we want to solve this problem with deep learning. Deep learning has the advantage of deriving the answer to problems without any intervention by humans, and it is being used exponentially in fusion industries, such as the automobile industry and the financial industry. Among deep learning technologies, there is object-detection technology. Object-detection is a computer technology related to computer vision and image processing that handles the detection of instances of a specific class of semantic objects in photos and videos. Using object-detection technology, we hope to solve traffic congestion and traffic accidents. CCTV is developing into AI CCTV with more advanced functions by adding recognition technology based on deep learning, while maintaining its original capabilities that monitor and capture something. Looking into previous studies that analyzed CCTV videos by deep learning, there was a study that analyzed a traffic situation through the estimation of vehicle type, vehicle traffic volume, and vehicle traffic speed by utilizing Faster R-CNN in CCTV for traffic control [3]; a study that estimated traffic volume by using stereo CCTV that detects vehicles in real-time in the left and right CCTV video [4]; a study that predicted the degree of danger according to vehicle speed by detecting vehicles approaching a crosswalk through the Nvidia Jetson Nano-class Edge device [5]; and a study that identified accidents on the water by analyzing data from the region in which many accidents occur and identifying objects by whether or not objects disappeared [6]. The purpose of the development of video analysis of AI CCTV is to detect vehicles, calculate traffic volume, and prevent accidents on the water. The research about AI CCTV has been conducted actively, but there is little related research on traffic accidents.