교통정보 CCTV를 활용한 인공지능 기반 포트홀 탐지 기술 개발

초록

As road infrastructure ages, potholes tend to form more frequently, resulting in increased vehicle damage and traffic accidents that pose significant societal challenges. Existing pothole detection methods, which use vehicle-mounted sensors or drones, suffer from limited coverage and high deployment costs. This study proposes an AI-based automated pothole detection model utilizing nationwide traffic surveillance CCTV. The model employs YOLOv8 with instance segmentation for precise pothole detection and integrates a vehicle tracking algorithm to automatically measure traffic volume. By combining data on pothole size, location, and traffic, the system quantitatively assesses road risk and supports maintenance prioritization. The proposed approach achieves high detection accuracy and real-time processing performance compared to existing methods, contributing to improved efficiency and safety in road infrastructure management.

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