Pothole Reporting System

Abstract

Potholes are a major challenge on urban roads, causing vehicle damage, traffic congestion, and safety hazards. This paper proposes a Pothole Detection and Reporting System that leverages computer vision, sensor data, and mobile connectivity to automatically identify and geo-tag potholes. The system uses image processing and machine learning techniques to detect road anomalies in real-time, then relays the location and severity of each pothole to a central database. This enables swift alerts to local authorities for targeted maintenance. Preliminary experiments show a high detection accuracy under varied road conditions, suggesting that widespread adoption of this system can significantly enhance road safety, reduce vehicle damage, and lower long-term infrastructure repair costs. Future work will focus on large-scale deployment and integration with smart city platforms to improve road monitoring and maintenance on a broader scale.