AI-Driven System For Detecting And Mapping Potholes

The increase in road accidents due to poor road conditions indicates the critical importance of timely maintenance to ensure public safety, reduce vehicle damage, and minimize costly repairs. The project presents an AI-driven system designed to detect and map road potholes, assess the severity of the pothole, and analyze road quality over time using video visuals. Utilizing advanced machine learning techniques and image processing algorithms, the system processes video data captured from cameras mounted on vehicles. The AI model accurately identifies potholes, classifies their severity, and geo-tags them on a digital map, providing real-time updates for drivers, public, and road maintenance authorities. By automating the detection and classification processes, the system significantly reduces the time and labor required for manual inspections. Extensive testing with real-world video footage highlights the system’s reliability and adaptability across various road environments, ensuring that it can be deployed in diverse geographic locations. By providing an understanding of road conditions, the system enables precise maintenance strategies and long-term planning, ultimately extending the lifespan of road infrastructure and enhancing overall transportation safety and efficiency.