AUTOMATED ROAD DAMAGE DETECTION

Abstract

Automated Road Damage Detection is an intelligent system designed to identify and classify road surface defects such as potholes, cracks, and surface distortions using computer vision and deep learning techniques. Traditional manual inspection methods are time-consuming, costly, and often inaccurate, which highlights the need for automated solutions. This approach utilizes image processing algorithms and convolutional neural networks (CNNs) to analyze road images captured through cameras mounted on vehicles or smartphones. The system preprocesses input images, extracts relevant features, and applies trained machine learning models to detect and categorize different types of road damage in real time. By integrating GPS data, the solution can also map detected damages for efficient maintenance planning and smart city management. The proposed method improves detection accuracy, reduces human effort, and enables faster response from road maintenance authorities. Overall, automated road damage detection contributes to safer transportation, reduced accidents, and improved infrastructure monitoring through scalable and cost-effective technology.