ROAD DAMAGE DETECTION

Efficient and accurate road damage detection is critical for the development of smart cities and maintaining safe transportation infrastructure. Manual inspection methods are slow, labor-intensive, and prone to errors, making automated detection necessary. In this project, we propose a YOLOv10n-based framework for real-time road damage detection optimized for edge devices. The system leverages advanced hyperparameter tuning to improve model performance while maintaining low computational requirements. The framework achieves high accuracy, with a precision of 0.986, recall of 0.973, mean average precision ([email protected]) of 0.988, and F1-score of 0.978. Deployment on NVIDIA Jetson Nano demonstrates an inference time of 0.13 seconds per frame at 7.5 FPS, while NVIDIA AGX Orin achieves 0.014 seconds per frame at 67 FPS, highlighting scalability and efficiency. This study demonstrates that YOLOv10n is highly effective for real-time, edge-based road damage recognition, enabling faster maintenance decisions and improved road safety.