Transmission Line Monitoring Using Computer Vision & AI

Abstract

Unmanned aerial vehicles (UAVs)with artificial intelligence (AI) can provide a revolutionary solution for the monitoring and inspection of power transmission lines. This study employs the YOLOv5 deep learning model to detect faults from custom datasets for the context of Nepal, where rugged terrains impede the feasibility of inspections. Some major faults we are proposing to inspect are broken insulators, vegetation encroachment, conductor sag, and corona losses. We used 950 bounding boxes in 400 images annotated manually, and augmented data was used for model robustness. The evaluation demonstrated the system was precise and accurate, demonstrating the system has the potential to reliably detect a fault. First, this research advances the state of the art in AI-driven infrastructure monitoring by proposing a scalable, efficient, and context-aware system for enhancing Nepal’s energy transmission reliability.