TLSNet: A semantic segmentation method for key point cloud regions of transmission lines

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2025-04-24 DOI:10.1016/j.isci.2025.112334

Shaotong Pei , Haichao Sun , Chenlong Hu , Weiqi Wang , Mianxiao Wu , Bo Lan

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引用次数: 0

Abstract

To address the limitations of current transmission line point cloud segmentation algorithms in accurately segmenting fine-grained structures, this paper proposes a model named TLSNet. The network addresses the issue of uneven point cloud distribution through a dynamic density adaptation mechanism (DALNF-Layer), achieves cross-scale context modeling with a hierarchical-offset transformer (DLCTransformer), and optimizes gradient propagation using the inverted residual module (InvResMLP). Together, these components form an efficient point cloud segmentation framework tailored for the complex scenarios of transmission lines. Experimental results demonstrate that TLSNet achieves precise segmentation of key regions in transmission line point clouds, outperforming existing algorithms in segmentation accuracy. This advancement provides a solid technical foundation for the digital operation and maintenance of transmission lines, as well as for autonomous unmanned aerial vehicle (UAV) inspection path planning.

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TLSNet：一种输电线路关键点云区域的语义分割方法

针对目前传输线点云分割算法在精确分割细粒度结构方面的局限性，本文提出了一种TLSNet模型。该网络通过动态密度自适应机制（DALNF-Layer）解决点云分布不均匀的问题，使用分层偏移变压器（DLCTransformer）实现跨尺度上下文建模，并使用反转残差模块（InvResMLP）优化梯度传播。总之，这些组件形成了一个高效的点云分割框架，为输电线路的复杂场景量身定制。实验结果表明，TLSNet能够实现对传输线点云关键区域的精确分割，在分割精度上优于现有算法。这一进展为输电线路数字化运维以及自主无人机（UAV）巡检路径规划提供了坚实的技术基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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