Revolutionizing Road Damage Assessment with YOLOv8-based Deep Learning and Computer Vision Techniques

Road surface damage detection and classification are fundamental areas of study, crucial for maintaining safe and efficient transportation infrastructure. Existing solutions have traditionally relied on manual inspections and assessments, resulting in time-consuming and resource-intensive processes. The study proposes an innovative and automated solution by leveraging deep learning and computer vision techniques for precise and efficient road damage identification and classification. This approach aims to revolutionize road damage assessment, optimizing maintenance planning, reducing costs, and enhancing road safety. This study enhances road maintenance strategies and effectively distributes resources for a safer future.