Advanced Pothole Detection and Repair Recommendation System Using Computer Vision Techniques

Potholes represent a persistent challenge for road infrastructure, leading to vehicle damage, compromised road safety, and increased maintenance expenditures. This research presents an advanced pothole detection and repair recommendation system leveraging state-of-the-art deep learning techniques[3]. The detection framework integrates YOLOv8 instance segmentation and the MIDAS depth estimation model alongside precise pixel-to-meter conversion methods to accurately identify and quantify pothole dimensions[1] [5]. Furthermore, the system encompasses automated and manual recommendation modules designed to deliver comprehensive repair solutions, specifying material selection, labor requirements, equipment utilization, as well as detailed cost and time estimates. By harnessing cutting-edge advancements in computer vision, the proposed system significantly enhances pothole detection accuracy and repair efficiency, representing a substantial improvement over conventional approaches and facilitating effective maintenance planning for road management authorities.