PRECISION ROAD DAMAGE DETECTION USING UAV IMAGING AND DEEP LEARNINGTECHNIQUES

Abstract

This paper presents an innovative approach to automated road damage detection using Unmanned Aerial Vehicle (UAV)
imagery and deep learning techniques. Conventional methods of identifying road damage are often laborious and hazardous for
human personnel, while UAVs and Artificial Intelligence (AI) technologies offer the potential to enhance efficiency and accuracy.
Leveraging YOLOv4, YOLOv5, YOLOv7 and YOLOv8 algorithms, the proposed methodology focuses on object detection and
localization within UAV images to identify various types of road damage. The ultimate goal is to augment the autonomous
maintenance system for roads by promptly detecting and notifying maintenance companies about road damage using drone-
captured images. Additionally, this project introduces novel classifications of pavement damage and proposes methodologies to
enhance object detection specifically tailored for UAV-captured scenarios, thereby laying the groundwork for further advancements
and research in this domain.