Abstract
In the rapidly evolving sphere of infrastructure management, early detection of road damage stands paramount for ensuring both safety and longevity. This research introduces an innovative technique for real-time road damage detection by leveraging the Mask R-CNN (Region-based Convolutional Neural Networks) approach. The primary objective was to discern varied forms of damages – from cracks to potholes, ensuring timely interventions and repairs. Utilizing a robust dataset comprising images of multiple road surfaces under different environmental conditions, the Mask R-CNN model was trained exhaustively. Results reveal a commendable accuracy rate, with the model distinguishing between minor aberrations and significant damages adeptly. A distinctive feature was the model's capability to operate in real-time, aiding in instant damage reporting. Furthermore, a comparative analysis with existing methods demonstrated a marked improvement in terms of both detection speed and precision. The findings suggest promising implications for urban planning and road maintenance. The integration of such an approach can revolutionize the manner in which road monitoring is traditionally undertaken, potentially resulting in substantial economic savings and enhanced safety measures.