Fully Automated Pothole Detection and Repair System using Artificial Intelligence

India has many areas with poorly maintained roadways. In addition to causing physical problems like back discomfort and injured spines, potholes and uneven roadways can harm automobiles. This work demonstrates a completely autonomous robot that can identify and fix roads overnight at the lowest possible cost utilizing a high accuracy machine learning algorithm. The suggested model makes use of YOLO framework models, which have the highest (Placeholder1) accuracy according to MENG YANG (2020). The testing information is gathered from different automobiles across the nation. To increase the robot's accuracy, the hardware for the system makes use of various sensors that collect inputs and cross-validate the findings. The robot uses LIDAR, IMU, and ultrasonic sensors to determine the depth of potholes and identify structural problems on roads. The suggested model can reduce the cost of fixing a road by 60%.