Pothole Detection Using YOLO

Maintaining road infrastructure is essential for ensuring public safety and supporting efficient transportation networks. One persistent problem is the formation of potholes, which pose hazards to drivers, contribute to traffic delays, and cause vehicle damage. Conventional methods for detecting potholes typically rely on manual inspection, which can be time-consuming, labor-intensive, and difficult to scale. This project presents an automated solution for identifying potholes using cutting-edge computer vision and machine learning approaches. Leveraging the YOLOv8 object detection algorithm, the system analyzes images and video footage to pinpoint potholes with high accuracy in real time. Deep learning techniques enhance the system's performance by allowing it to recognize potholes of various shapes, sizes, and in different environmental settings. By streamlining the detection process, this solution aims to improve road safety through faster maintenance responses and reduced dependency on manual monitoring.