Real-Time Road Surface Damage Detection Framework based on Mask R-CNN Model

Abstract

In the ever-evolving realm of infrastructure management, the timely and accurate detection of road surface damages is imperative for the longevity and safety of transportation networks. This research paper introduces a pioneering framework centered on the Mask R-CNN (Region-based Convolutional Neural Networks) model for real-time road surface damage detection. The overarching methodology encapsulates a deep learning-based approach to discern and classify various road aberrations such as potholes, cracks, and rutting. The chosen Mask R-CNN architecture, renowned for its proficiency in instance segmentation tasks, has been fine-tuned and optimized specifically for the unique challenges posed by road surfaces under diverse lighting and environmental conditions. A diverse dataset, amalgamating urban, suburban, and rural roadways under varied climatic conditions, served as the foundation for model training and validation. Preliminary results have not only underscored the model's robustness in real-time detection but also its superiority in terms of accuracy and computational efficiency when juxtaposed with extant methods. Concomitantly, the framework emphasizes scalability and adaptability, positing it as a frontrunner for potential integration into automated road maintenance systems and vehicular navigation aids. This trailblazing endeavor elucidates the potentialities of deep learning paradigms in revolutionizing road management systems, thus fostering safer and more efficient transportation environments.