Research on Hidden Danger Recognition Algorithm for UAV Inspection Images of High-Voltage Cable Channels

Abstract

High-voltage cables represent a critical trend in future urban development, and the identification of potential hazards in cable channels has become a key focus of operational maintenance research. Traditional manual inspection methods are inefficient and struggle to comprehensively cover complex environments. This research leverages UAV inspection technology and image-based hazard identification algorithms, utilizing a dataset collected from real-world environments. By comparing mainstream object detection algorithms such as Cascade RCNN, Faster RCNN, YOLOv5, YOLOv7, and YOLOv8, the study ultimately adopts YOLOv8 and its segmentation variant—YOLOv8-seg—as the core models. These models achieve high average precision in detecting four major categories of hazards: construction machinery, ancillary facilities, cable terminal platform equipment, and vegetation encroachment. The proposed solution provides an efficient approach for enhancing power system maintenance.