Next-Gen Remote Airport Maintenance: UAV-Guided Inspection and Maintenance Using Computer Vision

Abstract

This paper presents a novel system for the automated monitoring and maintenance of gravel runways in remote airports, particularly in Northern Canada, using Unmanned Aerial Vehicles (UAVs) and computer vision technologies. Due to the geographic isolation and harsh weather conditions, these airports face unique challenges in runway maintenance. Our approach integrates advanced deep learning algorithms and UAV technology to provide a cost-effective, efficient, and accurate means of detecting runway defects, such as water pooling, vegetation encroachment, and surface irregularities. We developed a hybrid approach combining the vision transformer model with image filtering and thresholding algorithms, applied on high-resolution UAV imagery. This system not only identifies various types of defects but also evaluates runway smoothness, contributing significantly to the safety and reliability of air transport in these areas. Our experiments, conducted across multiple remote airports, demonstrate the effectiveness of our approach in real-world scenarios, offering significant improvements over traditional manual inspection methods.