Efficient Road Navigation with Deep Learning on Damaged Routes

The increase of potholes and road surface damage poses important challenges to traffic safety and urban infrastructure management. Traditional detection methods rely on either manual inspection or cloud-based processing, which has more latency, cost, and scalability limitations. This paper proposes a real-time deep learning system for efficient road navigation using YOLOv4-tiny for pothole detection. The model classifies detected potholes based on its severity like small, medium, large and suggests the optimal navigation directions like left, center, right based on severity density in each region of the frame. The system integrates a web user interface built with Flask, which enables users to upload images or videos or use real-time webcam feeds for detection. Preprocessing techniques such as resizing, normalization, and data augmentation were applied to optimize model training. Evaluation metrics including Damage Density (DD), Route Efficiency Score (RES), and F1 Score validate the system's performance. The proposed solution achieves high detection precision and recall, ensures low-latency inference without requiring cloud connectivity, and demonstrates significant improvements in road safety decision-making. By integrating real-time detection with visual analytics and direction guidance, the system act as a tool for traffic management and preventive road maintenance.