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.

This paper focuses on the challenge of surface defect detection in manufacturing, particularly under conditions of background variation and noise interference. To tackle this issue, a novel Background-Adaptive Surface Defect Detection Network (BANet) is proposed. The BANet enhances the defect detection capabilities by improving generalization capacity through learning comparative abilities between positive samples and testing samples. In order to mitigate the impact of three types of noise (texture variation, translation, and rotation), a Foreground Edge Attention Mechanism (FEAM) and a Spatial Transformer Module (STM) are introduced. The FEAM enhances the model's ability to differentiate between foreground and background, thereby effectively reducing texture variation noise. The STM uses affine transformations to eliminate translation and rotation noise. The effectiveness of the proposed network is validated on Optical Communication Devices (OCDs) dataset, with results indicating superior performance compared to prevailing state-of-the-art methods. The findings of this study highlight the potential of our approach in effectively addressing surface defect detection in variable backgrounds and noisy conditions, thereby contributing significantly to the quality and reliability of manufacturing processes.