Edge-Ready Road Damage Detection Using an Enhanced YOLO with Hyperparameter Tuning

Abstract

Maintaining road quality is a crucial aspect of smart city development and public safety, as damaged roads can lead to traffic congestion, accidents, and increased vehicle maintenance costs. Traditional road inspection methods rely heavily on manual surveys, which are not only time-consuming but also costly and prone to human error. With the rapid growth of urban infrastructure, the need for automated, accurate, and scalable road damage detection systems has become more urgent. Recent advances in deep learning and computer vision provide an opportunity to address these challenges by enabling real-time damage detection using high-performance object detection models. In this study, we present a road damage detection framework built on the YOLOv10n model, optimized specifically for real-time deployment on edge devices. The proposed system incorporates advanced hyperparameter tuning to achieve a balance between accuracy and computational efficiency. Unlike heavier detection models, YOLOv10n is lightweight yet powerful, making it suitable for resource-constrained environments. The framework demonstrates robust detection capabilities with a precision of 0.986, recall of 0.973, mean average precision (mAP@0.5) of 0.988, and an F1-score of 0.978, confirming its reliability in identifying different types of road damages such as cracks, potholes, and surface wear.