Smart AI- Road Damage Detection Using Deep Learning

Abstract

Road infrastructure is a critical component of a nation’s transportation system, directly affecting safety, efficiency, and economic growth. Traditional road inspection techniques rely heavily on manual surveys and human observation, which are often time-consuming, labor-intensive, and inconsistent. This research presents an Artificial Intelligence (AI)-driven solution leveraging deep learning for real-time road damage detection and classification. The proposed system utilizes the YOLOv5s object detection framework, trained to identify cracks, potholes, and surface irregularities from images and videos captured through standard cameras. Comprehensive preprocessing, data augmentation, and real-time inference are applied to enhance model accuracy and robustness under varying lighting and environmental conditions. The model is implemented using OpenCV and PyTorch, offering a low-cost, scalable, and automated alternative for municipal authorities and transportation agencies. The results indicate high detection accuracy and real-time performance, making the system suitable for integration into smart city infrastructure monitoring, autonomous vehicle navigation, and drone-based road surveillance.