A Fast 3D Point Cloud Ｒecognition Method for Substation Equipment

NAVIGATE

Table of Contents

STATISTICS

Viewed1140

Downloads1044

A Fast 3D Point Cloud Ｒecognition Method for Substation Equipment

[HTML] PDF下载 (1044)

[1]LUO Yong,CHANG Jing,YUAN Qianjin,et al.A Fast 3D Point Cloud Ｒecognition Method for Substation Equipment[J].Journal of Zhengzhou University (Engineering Science),2023,44(03):64-70.[doi:10.13705/j.issn.1671-6833.2022.06.008]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44 Number of periods: 2023 03 Page number: 64-70 Column: Public date: 2023-04-30

Title:: A Fast 3D Point Cloud Ｒecognition Method for Substation Equipment

Author(s):: LUO Yong; CHANG Jing; YUAN Qianjin; WANG Yafei; School of Electrical and Information Engineering, Zhengzhou University

Keywords:: substation equipment; point cloud recognition; local coordinate system; feature descriptor; template matching

CLC:: TP391. 4；TG502. 34

DOI:: 10.13705/j.issn.1671-6833.2022.06.008

Abstract:: Aiming at the problem of substation equipment point cloud recognition, this study proposed a new recognition method. Firstly, a local coordinate system of equipment point cloud was established. The symmetry and distribution density of equipment point cloud were used to determine the x and y axes of the local coordinate system. The coordinate system was invariant to translate and rotate, and was robust to noise. Then, a new feature descriptor was defined based on the difference between the shape and view of substation equipment point cloud, which was used to describe and recognize the point cloud of equipment. Also, a template library containing 54 kinds of electrical equipment such as lightning arrester, circuit breaker, and disconnecting switch was established, which contained the information of type, number and feature descriptor of each template device. The feature descriptor of the equipment point cloud to be identified was calculated and was used for matching the most similar model in the template library to realize the recognition of equipment point cloud. Finally, the method proposed in this study and another two substation point cloud recognition algorithms were tested on 90 equipment point clouds to be identified. The results showed that our method could achieve 90% recognition accuracy, and the average time to identify a device was 3.2 s, which could balance the recognition accuracy and recognition efficiency. And our method slightly outperformed the other two methods when equipment point cloud with noise and occlusion. Moreover, when the density of the point cloud to be identified was not uniform, our method could still maintain the recognition accuracy of more than 70%.

References:: [1] HUANG L, LIANG Y, HUANG H, et al. Digital twin modeling and operating state assessment of substation equipment[C]∥4th International Conference on Energy, Electrical and Power Engineering (CEEPE). Piscataway: IEEE, 2021: 159-163.
[2] 陈义飞, 郭胜, 潘文安, 等. 基于多源传感器数据融合的三维场景重建[J]. 郑州大学学报(工学版), 2021, 42(2): 80-86.CHEN Y F, GUO S, PAN W A , et al. 3D scene reconstruction based on multi-source sensor data fusion[J]. Journal of Zhengzhou University (Engineering Science), 2021, 42(2): 80-86.
[3] GUO W Y, JI Y, LUO Y, et al. Substation equipment 3D identification based on KNN classification of subspace feature vector[J]. Journal of Intelligent Systems, 2019, 28(5): 807-819.
[4] 王菲, 王球, 任佳依, 等. 三维激光云扫描技术在变电站电气设备识别中的应用[J]. 佳木斯大学学报(自然科学版), 2020, 38(4): 124-128.WANG F, WANG Q, REN J Y, et al. Application of 3D laser cloud scanning technology in identification of electrical equipment in substation[J]. Journal of Jiamusi University (Natural Science Edition), 2020, 38(4): 124-128.
[5] 纪勇, 刘丹丹, 罗勇, 等. 基于霍夫投票的变电站设备三维点云识别算法[J]. 郑州大学学报(工学版), 2019, 40(3): 1-6, 12.JI Y, LIU D D, LUO Y, et al. Recognition of three-dimensional substation equipment based on Hough transform[J]. Journal of Zhengzhou University (Engineering Science), 2019, 40(3): 1-6, 12.
[6] WANG P S, LUO Y, GUO W Y. Study of base segmenting algorithm of substation equipment based on 3D point cloud[J]. Journal of Electrical Engineering &Technology, 2019, 14(1): 505-517.
[7] 鲜成,张欣,党晓婧. 基于深度学习的变电站设备自动识别方法[C]∥2019中国自动化大会.北京: 中国自动化学会,2019: 314-319.XIAN C, ZHANG X, DANG X J. Deep-learning based substation facilities automatic identification[C]∥The China Automation Congress 2019(CAC2019).Beijing:CAA,2019: 314-319.
[8] 王硕禾, 巩方超, 古晓东, 等. 基于特征融合的变电设备类型及故障识别算法研究[J]. 铁道学报, 2021, 43(4): 95-100.WANG S H, GONG F C, GU X D, et al. Research on types of substation equipment and fault identification algorithm based on feature fusion[J]. Journal of the China Railway Society, 2021, 43(4): 95-100.
[9] 李科. 基于PSO-KNN的变电站设备三维点云识别方法研究[J]. 电力系统保护与控制, 2021, 49(18): 182-187.LI K. 3D point cloud research on an identification method based on PSO-KNN substation equipment[J]. Power System Protection and Control, 2021, 49(18): 182-187.
[10] 袁千金. 基于三维点云的变电站设备附属电线分割及识别算法研究[D]. 郑州:郑州大学,2021.YUAN Q J. The study of substation equipment attached cable segmentation and identification algorithm based on 3D point cloud[D]. Zhengzhou: Zhengzhou University, 2021.
[11] ARASTOUNIA M, LICHTI D. Automatic object extraction from electrical substation point clouds[J]. Remote Sensing, 2015, 7(11): 15605-15629.
[12] ARASTOUNIA M, LICHTI D D. Automatic extraction of insulators from 3D LiDAR data of an electrical substation[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, II-5/W2: 19-24.
[13] ARASTOUNIA M, LICHTI D D. Segmentation of planar surfaces in LiDAR point clouds of an electrical substation by exploring the structure of points neighbourhood[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, XL-5: 55-62.
[14] 张子谦, 杨鸿斌, 陈俣, 等. 基于组件匹配的变电站快速建模方法[J]. 计算机应用与软件, 2018, 35(7): 69-75, 210.ZHANG Z Q, YANG H B, CHEN Y, et al. Rapid modeling method for substations based on component matching[J]. Computer Applications and Software, 2018, 35(7): 69-75, 210.
[15] WU Q Y, YANG H B, WEI M Q, et al. Automatic 3D reconstruction of electrical substation scene from LiDAR point cloud[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 143: 57-71.
[16] 崔天赢. 求取多边形最小面积外接矩形的计算机算法[J]. 科协论坛,2012(8): 78-79.CUI T Y. A computer algorithm for finding the minimum area circumscribed rectangle of a polygon[J]. Science &Technology Association Forum, 2012(8): 78-79.
[17] WAHL E, HILLENBRAND U, HIRZINGER G. Surflet-pair-relation histograms: a statistical 3D-shape representation for rapid classification[C]∥Fourth International Conference on 3D Digital Imaging and modeling, 2003 3DIM 2003 proceedings. Piscataway: IEEE, 2003: 474-481.

Similar References:

Memo

Last Update: 2023-05-08