AI-Driven Pothole Recognition and Autonomous Vehicle Alert System

The need for prompt and precise road distress assessment is increasing as the necessity of maintaining high-quality road assets rises. Being able to recognize potholes and different levels of road roughness is necessary for road condition monitoring, which has an impact on driving comfort and transportation safety. Potholes are often found by certified inspectors or structural engineers. However, this task requires a lot of time and poses a risk to the workforce. Road potholes can cause discomfort for passengers and potentially cause fatalities. On well-maintained roads, passengers can travel easily and comfortably. The ability of autonomous automobiles to precisely identify potholes on the road is essential for ensuring safe driving. The study proposes a way to road safety issues that makes use of new technologies for real-time pothole monitoring, with the goal of improving driving comfort and transit safety. Our system, which combines the YOLOv4 object detection framework with an ESP32 camera module and a cloud-based platform, enables the exact and fast identification of potholes, providing critical information to both drivers and road maintenance agencies. Our suggested system’s novel architecture ensures that the camera module, laptop server, and cloud platform communicate smoothly, allowing for effective alert production and delivery to users. This system represents a significant development in the domain of road safety by merging state-of-the-art technologies.