Road Pothole Detection Using Deep Learning

Road maintenance is a critical aspect of urban infrastructure management, with potholes posingsignificant hazards to vehicle safety and driver comfort. Traditional methods of pothole detection are oftenlabor-intensive, time-consuming, and prone to human error. To address these challenges, we present anovel solution for road pothole detection leveraging deep learning techniques. This project employs theYOLOv8 architecture, a state-of-the-art object detection model known for its high speed and accuracy. The image-based mode allows users to upload static images, which are then analyzed for potholepresence. The video-based mode processes video files, enabling continuous monitoring of road conditions.The webcam mode provides real-time detection, making it suitable for integration into vehicle systems orroadside monitoring stations. Each mode leverages the YOLOv8 model to quickly and accurately identifypotholes, providing valuable data for timely road maintenance interventions.