AI- Based Road Pothole’s Detection System

Abstract

Road safety and maintenance have become critical concerns due to the growing number of accidents and vehicle damages caused by potholes. Manual inspection of roads is inefficient, time-consuming, and often inaccurate, especially under varying environmental conditions. To address these challenges, this project presents an AI-Based Road Pothole Detection System using the advanced YOLOv8 object detection algorithm for accurate, real-time identification of potholes from road videos.The system allows users to upload a video of a road segment, which is processed frame by frame to detect and localize potholes in both daytime and nighttime conditions, including rainy weather scenarios. Once a pothole is detected, the system calculates its approximate depth, captures the image, retrieves the GPS location, and automatically sends an email notification with these details to the concerned authority. Additionally, it provides an estimated cost for pothole repair, supporting smart decision-making in road maintenance management. By integrating computer vision, deep learning (YOLOv8), and automated alert systems, this model ensures faster, more reliable, and cost-effective pothole detection compared to traditional manual surveys. The proposed system contributes toward building smart city infrastructure, enhancing road safety, and optimizing maintenance efficiency under diverse environmental conditions.