International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Keeping road in some good condition poses one of the most difficult and costly tasks, particularly with traditional methods that depend on manual surveys. These processes are time consuming, prone to error, and lead to late discovery of certain cracks and potholes that can drive up repair costs and threaten safety. To address this issue, our product is a mettalic roads damage automated system with machine learning. The system takes video pictures of roads, analyzes the data to find flaws, and categorizes issues according to how serious they are. It was created using MATLAB's machine learning capabilities and offers local governments immediate insights via an intuitive interface. By employing a prioritization framework based on the age of the roads, it guarantees that aging infrastructure is addressed quickly, improving resource distribution and repair timelines. This method seeks to reduce expenses, increase accuracy, and expedite road assessments. In addition to ensuring road safety and comfort while driving, this project supports sustainability in the management of municipal infrastructures by creating smarter, safer, and better-maintained roads.

Keywords: Road Damage Detection; Metallic road analysis; machine lesrning; image processing; road safety; aging infrastructure.

Downloads: | DOI: 10.17148/IJARCCE.2025.1411114