ADVANCED POTHOLE REPORTING AND MANAGEMENT SYSTEM USING MACHINE LEARNING

Abstract

otholes have become a significant issue that affect road safety and vehicle maintenance all around the world. They frequently lead to accidents and vehicle damage. Conventional pothole reporting techniques, which rely on government agencies involving engineers and laborers to locate and fix road damages, often prove to be ineffective, causing repairs to be delayed and raising the risk for both motorists and pedestrians. According to a recent study, potholes contribute to around 4,446 accidents annually. In this project, a novel pothole reporting and management system is proposed that makes use of modern technologies such as Firebase for real-time synchronization and image processing for pothole detection. Citizens can submit pothole reports by clicking images of potholes and providing geo-location via an easy-to-use interface, with the reports automatically reviewed for accuracy. Administrators have access to an extensive dashboard for effective report management. The suggested system intends to improve pothole management efficiency, accuracy, and responsiveness by using automatic detection, real-time updates, and user-friendly features. Adopting Firebase offers many benefits over standard SQL databases, such as real-time data management, scalability, and customization. This publication describes the architecture, features, and advantages of the system, demonstrating how it may greatly enhance public safety and road maintenance operations.