Realizing Rust Detection in Power Towers through YOLO Image Recognition and 5G Transmission with UAVs

As power transmission towers are increasingly affected by long-term exposure to natural disasters, structural collapse incidents have become more frequent, leading to large-scale power outages, public panic, and even casualties. To improve the efficiency and safety of tower inspections, this paper proposes a real-time monitoring and remote analysis system for rust damage detection based on 5G networks and Unmanned Aerial Vehicles (UAVs), integrated with the YOLOv11 object detection algorithm. The proposed system utilizes UAVs equipped with a YOLOv11-based image recognition model to detect and classify rust severity on power towers. Experimental results demonstrate that our system achieves high accuracy in rust identification and is capable of real-time severity assessment. The captured images are transmitted to a ground-based computer, while field personnel can connect through a 5G base station to the core network. A web-based interface provides backend maintenance teams with immediate access to rust condition data, enabling rapid decision-making. This system significantly reduces the risks and costs associated with traditional manual inspection methods.