Semantic Segmentation of Key Categories in Transmission Line Corridor Point Clouds Based on EMAFL-PTv3
Li Lu, Linong Wang, Shaocheng Wu, Shengxuan Zu, Yanting Ai, Bin Song
Abstract
Accurate and efficient segmentation of key categories of transmission line corridor point clouds is one of the prerequisite technologies for the application of transmission line drone inspection. However, current semantic segmentation methods are limited to a few categories, involve cumbersome processes, and exhibit low accuracy. To address these issues, this paper proposes EMAFL-PTv3, a deep learning model for semantic segmentation of transmission line corridor point clouds. Built upon Point Transformer v3 (PTv3), EMAFL-PTv3 integrates Efficient Multi-Scale Attention (EMA) to enhance feature extraction at different scales, incorporates Focal Loss to mitigate class imbalance, and achieves accurate segmentation into five categories: ground, ground wire, insulator string, pylon, and transmission line. EMAFL-PTv3 is evaluated on a dataset of 40 spans of transmission line corridor point clouds collected by a drone in Wuhan and Xiangyang, Hubei Province. Experimental results demonstrate that EMAFL-PTv3 outperforms PTv3 in all categories, with notable improvements in the more challenging categories: insulator string (IoU 67.25%) and Pylon (IoU 91.77%), showing increases of 7.06% and 11.39%, respectively. The mIoU, mA, and OA scores reach 90.46%, 92.86%, and 98.07%, representing increases of 5.49%, 2.75%, and 2.44% over PTv3, respectively, proving its superior performance.