Development of Autonomous Monitoring Drones for Infrastructure Inspection

Abstract

Infrastructure assets such as bridges, roads, highways, tunnels, dams, power transmission lines, and high-rise buildings require regular inspection to ensure safety and reliability. Conventional visual inspection methods are time-consuming, expensive, and often expose inspectors to hazardous environments. Autonomous monitoring drones (UAVs) provide a faster, safer, and more cost-effective alternative by using advanced sensors, GPS, cameras, and AI-based technologies to collect and analyze structural data. This study proposes an AI-enabled autonomous drone framework for infrastructure inspection that integrates intelligent flight planning, automated data collection, computer vision-based defect detection, and condition assessment. The system accurately identifies cracks, corrosion, and other structural defects while optimizing flight paths for improved efficiency. The proposed framework enhances inspection accuracy, operational safety, and scalability compared to traditional methods, demonstrating significant potential for modern infrastructure management. Future developments may integrate swarm intelligence, edge computing, and digital twins to enable fully autonomous infrastructure monitoring and maintenance.