SAF: A Structure-Aware Framework for Radial Ice Thickness Detection on Overhead Transmission Lines

Ice thickness estimation on overhead transmission lines (OHTL) is essential for mitigating icing-induced mechanical failures and ensuring safe grid operation. To address the challenges of detecting radial ice thickness in complex power line corridors, particularly geometric fragmentation of slender conductors and semantic ambiguity near occluded boundaries, this work proposes a structure-aware framework (SAF) based on 3-D point cloud segmentation and geometry-guided modeling. SAF introduces a structure-aware segmentation network, which integrates a cross-level spatial encoding module to preserve geometric continuity and a partition-aware loss to improve boundary localization under vegetation or tower occlusion. Building on accurate segmentation, a geometry-guided module performs centerline fitting and cross-sectional reconstruction to infer slice-level ice thickness. To support evaluation, a large-scale uncrewed aerial vehicle (UAV)-based point cloud dataset covering 32 OHTL is constructed, including six lines with ground-truth ice labels. Experimental results demonstrate that SAF achieves robust and accurate ice estimation across varied voltage levels and terrains, supporting its practical application in intelligent transmission line inspection and icing risk prevention.