基于结构频谱感知框架的配电网点云语义分割

唐友源; 张辉; 杜瑞; 张恺宁; 曹云康; 别克扎提·巴合提; 陈厚权; 王耀南

doi:10.16383/j.aas.c250540

基于结构频谱感知框架的配电网点云语义分割

doi: 10.16383/j.aas.c250540 cstr: 32138.14.j.aas.c250540

唐友源^{1, 2,},
张辉^{2, 3,},
杜瑞^{2, 3,},
张恺宁^{2, 3,},
曹云康^{2, 3,},
别克扎提·巴合提^{2, 3,},
陈厚权^{2, 3,},
王耀南^{2, 3,}

1.
长沙理工大学人工智能学院长沙 410114
2.
机器人视觉感知与控制技术国家工程研究中心长沙 410012
3.
湖南大学人工智能与机器人学院长沙 410012

基金项目: 国家自然科学基金重大项目(62595801), 湖南省十大技术攻关项目(2024GK1010), 湖南省自然科学基金重点项目(2025JJ30024), 国网湖南省电力有限公司科技项目(5216A522001Y, 5216A5240003, 5216AJ250008)资助

详细信息

作者简介:
唐友源：长沙理工大学人工智能学院硕士研究生. 2023年获得湘南学院学士学位. 主要研究方向为点云智能感知. E-mail: tangyouyuan@stu.csust.edu.cn

张辉：湖南大学人工智能与机器人学院教授. 2007年获得湖南大学博士学位. 主要研究方向为数据分析, 图像处理和机器人控制. 本文通信作者. E-mail: zhanghui1983@hnu.edu.cn

杜瑞：湖南大学人工智能与机器人学院博士研究生. 2020年获得湘潭大学硕士学位. 主要研究方向为数据分析, 图像处理. E-mail: durui@hnu.edu.cn

张恺宁：湖南大学人工智能与机器人学院助理教授. 2025年获得武汉大学通信与信息系统专业博士学位. 主要研究方向为3D视觉和多模态感知. E-mail: carney@hnu.edu.cn

曹云康：湖南大学人工智能与机器人学院助理教授. 2025年获得华中科技大学机械工程专业博士学位. 主要研究方向为复杂场景下的工业视觉检测与识别, 工业基础模型及工业具身智能. E-mail: caoyunkang@hnu.edu.cn

别克扎提·巴合提：湖南大学人工智能与机器人学院博士研究生. 2020年获得英国克兰菲尔德大学硕士学位. 主要研究方向为信号分析, 时间序列分析. E-mail: bbiekezati@hnu.edu.cn

陈厚权：湖南大学人工智能与机器人学院博士研究生. 2022年获得中南林业科技大学计算机信息与工程学院学士学位. 主要研究方向为电力场景的多模态技术. E-mail: chq@hnu.edu.cn

王耀南：中国工程院院士, 湖南大学人工智能与机器人学院教授. 1995年获得湖南大学博士学位. 主要研究方向为数据分析, 智能控制和图像处理. E-mail: yaonan@hnu.edu.cn

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出版历程
- 收稿日期: 2025-10-13
- 录用日期: 2025-12-31
- 网络出版日期: 2026-03-14
- 刊出日期: 2026-04-20

Semantic Segmentation of Distribution Network Point Clouds Based on a Structure Spectrum-Aware Framework

TANG You-Yuan^{1, 2
,},
ZHANG Hui^{2, 3
,},
DU Rui^{2, 3
,},
ZHANG Kai-Ning^{2, 3
,},
CAO Yun-Kang^{2, 3
,},
BIEKEZATI Baheti^{2, 3
,},
CHEN Hou-Quan^{2, 3
,},
WANG Yao-Nan^{2, 3
,}

1.
School of Artificial Intelligence, Changsha University of Science and Technology, Changsha 410114
2.
National Engineering Research Center for Robot Visual Perception and Control Technology, Changsha 410012
3.
School of Artificial Intelligence and Robotics, Hunan University, Changsha 410012

Funds: Supported by Major Program of National Natural Science Foundation of China (62595801), Ten Technical Research Projects of Hunan Province (2024GK1010), Natural Science Foundation of Hunan Province (2025JJ30024), and Science and Technology Project of State Grid Hunan Electric Power Co., Ltd. (5216A522001Y, 5216A5240003, 5216AJ250008)

More Information

Author Bio:
TANG You-Yuan　Master student at the School of Artificial Intelligence, Changsha University of Science and Technology. He received his bachelor degree from Xiangnan University in 2023. His main research interest is point cloud intelligent perception

ZHANG Hui　Professor at the School of Artificial Intelligence and Robotics, Hunan University. He received his Ph.D. degree from Hunan University in 2007. His research interests include data analysis, image processing, and robot control. Corresponding author of this paper

DU Rui　Ph.D. candidate at the School of Artificial Intelligence and Robotics, Hunan University. He received his master degree from Xiangtan University in 2020. His research interests include data analysis and image processing

ZHANG Kai-Ning　Assistant professor at the School of Artificial Intelligence and Robotics, Hunan University. She received her Ph.D. degree in communication and information systems from Wuhan University in 2025. Her research interests include 3D vision and multimodal perception

CAO Yun-Kang　Assistant professor at the School of Artificial Intelligence and Robotics, Hunan University. He received his Ph.D. degree in mechanical engineering from Huazhong University of Science and Technology in 2025. His research interests include industrial vision detection and recognition in complex scenarios, industrial foundation models, and industrial embodied intelligence

BIEKEZATI Baheti　Ph.D. candidate at the School of Artificial Intelligence and Robotics, Hunan University. He received his master degree from Cranfield University in 2020. His research interests include signal analysis and time series analysis

CHEN Hou-Quan　Ph.D. candidate at the School of Artificial Intelligence and Robotics, Hunan University. He received his bachelor degree from the School of Computer and Information Engineering, Central South University of Forestry and Technology in 2022. His research interests include multimodal technologies for power-system applications

WANG Yao-Nan　Academician at Chinese Academy of Engineering, professor at the School of Artificial Intelligence and Robotics, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research interests include data analysis, intelligent control, and image processing

摘要

摘要: 配电网点云语义分割对于实现无人化巡检与智能电网运维具有重要意义. 尽管已有方法在空间建模与结构增强方面取得一定进展, 但在频谱特征挖掘与大规模点云处理效率上仍面临突出挑战. 为此, 提出一种结构频谱感知框架(SSAF), 以提升长距离配网场景下的点云表达能力. 在数据预处理阶段, 提出一种结合结构引导的层级滤波策略与结构感知的样本划分方法, 在压缩冗余背景点云的同时, 有效保持电力杆塔、电力线等关键目标的结构完整性与连续性. 在语义分割阶段, 构建空谱协同语义分割网络, 引入局部极坐标系以增强模型对方向特征的建模能力, 并设计基于注意力图的动态融合机制, 实现空间特征与频谱特征之间的自适应交互与信息增强. 实验结果表明, SSAF能在真实配电网场景点云数据集上实现更高的分割精度与推理效率, 在多个关键指标上优于现有代表性方法, 验证了其在复杂场景下的实用性和工程推广潜力.
- 配电网点云 /
- 语义分割 /
- 结构频谱感知 /
- 空谱融合 /
- 极坐标频谱变换
Abstract: The semantic segmentation of point clouds in power distribution networks is of great significance for enabling unmanned inspection and intelligent grid operation and maintenance. Although existing methods have made some progress in spatial modeling and structural enhancement, they still face prominent challenges in spectral feature extraction and the efficiency of large-scale point cloud processing. To address these issues, this paper proposes a structure spectrum-aware framework (SSAF) to enhance the expressive capability of point clouds in long-distance distribution network scenarios. In the data preprocessing stage, a structure-guided hierarchical filtering strategy and a structure-aware sample partitioning method are designed to reduce redundant background points while preserving the structural integrity and continuity of key objects such as poles and wires. During the semantic segmentation stage, a spatial-spectral collaborative semantic segmentation network is constructed, in which local polar coordinates are introduced to enhance direction-sensitive feature modeling. Furthermore, a dynamic fusion mechanism based on attention maps is employed to enable adaptive interaction and information enhancement between spatial and spectral features. Experimental results show that SSAF achieves higher segmentation accuracy and inference efficiency on real-world distribution network point cloud datasets. It outperforms existing representative methods across multiple key metrics, demonstrating its practicality and engineering generalization potential in complex scenarios.
- distribution network point cloud /
- semantic segmentation /
- structure spectrum awareness /
- spatial-spectral fusion /
- polar spectral transform

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图 1 结构频谱感知框架

Fig. 1 The structure spectrum-aware framework

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图 2 数据预处理

Fig. 2 Data preprocessing

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图 3 空谱协同语义分割网络

Fig. 3 Spectral-spatial collaborative semantic segmentation network

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图 4 双向门控融合

Fig. 4 Bidirectional gated fusion

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图 5 构建的配电网场景数据集可视化

Fig. 5 Visualization of the constructed distribution network scene dataset

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图 6 配电网场景点云语义分割结果可视化

Fig. 6 Visualization of cloud semantic segmentation results for distribution network point cloud

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图 7 不同处理阶段下分割精度与运行时间的对比

Fig. 7 Comparison of segmentation accuracy and runtime at different processing stages

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图 8 不同配置下的特征激活热图

Fig. 8 Feature activation heat maps under different configurations

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表 1 配电网场景点云语义分割实验(%)

Table 1 Experiment on semantic segmentation of distribution network point cloud (%)

方法	OA	mAcc	mIoU	背景IoU	电力线IoU	电力杆塔IoU	参数量(M)
DeepGCN^[19]	98.26	71.29	78.92	98.20	63.10	75.46	3.60
DGCNN^[20]	98.89	78.88	83.36	99.03	62.13	88.92	1.30
PointNet++^[14]	98.40	86.04	84.75	99.62	67.48	87.14	1.00
KPConv^[31]	98.90	87.91	85.51	99.90	65.71	90.91	15.00
PointNext-XL^[32]	98.27	91.40	88.54	98.65	74.72	92.25	41.60
PTv2^[22]	98.36	92.63	88.42	99.28	80.04	85.93	11.30
PointMetaBase-XL^[33]	98.38	94.21	93.62	99.11	92.35	89.40	15.30
DeLA^[34]	96.87	95.34	93.65	96.89	92.69	91.36	7.00
DeLA + X-3D^[35]	98.59	96.81	94.29	96.36	94.30	92.22	8.00
PTv3^[23]	98.64	97.11	94.37	96.56	94.42	92.12	—
PCM^[36]	98.79	97.27	95.69	96.96	96.17	93.93	34.20
SSCNet (本文)	98.74	97.99	96.20	97.85	94.36	96.40	12.54
注: 加粗字体表示各指标的最优结果.

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表 2 各场景滤波前后分类别点数统计(单位: 万点)

Table 2 Category-wise point cloud statistics before and after filtering (Unit: $ 10^4 $ points)

场景	原始点云			滤波后点云			背景点滤除率 (%)
场景	电力线点	电力杆塔点	背景点	电力线点	电力杆塔点	背景点	背景点滤除率 (%)
S1	86.9	16.4	4890.0	86.1	15.7	1415.0	71.1
S2	67.9	1625.0	4042.0	65.7	1622.1	45.9	98.9
S3	117.6	1090.4	3162.0	116.8	1087.5	397.0	87.5

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表 3 消融实验结果(%)

Table 3 The ablation experiment results (%)

方法	PRST	BGF	OA	mIoU	mAcc
1			98.12	94.13	91.74
2	$ \checkmark$		98.57	95.86	96.84
3		$ \checkmark$	98.78	95.47	93.06
4	$ \checkmark$	$ \checkmark$	98.74	96.20	97.99

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表 4 主流方法在S3DIS数据集上的对比结果

Table 4 Comparison of mainstream methods on the S3DIS dataset

方法	参数量 (M)	OA (%)	mAcc (%)	mIoU (%)
PointNet++^[14]	1.0	83.0	—	53.5
DGCNN^[20]	1.3	—	—	47.9
KPConv^[31]	15.0	—	72.8	67.1
PointNext-XL^[32]	41.0	91.0	77.2	71.1
PTv1^[37]	—	90.8	76.5	70.4
PTv2^[22]	11.3	91.6	78.0	72.7
PointMetaBase-XL^[33]	15.3	90.6	—	71.5
DeLA^[34]	7.0	92.2	80.0	74.1
DeLA + X-3D^[35]	8.0	92.2	80.1	74.3
PTv3^[23]	—	—	—	74.7
PCM^[36]	34.2	92.9	81.6	74.1
SSCNet	12.5	92.3	82.1	75.1

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