A Fast 3D Point Cloud Ｒecognition Method for Substation Equipment

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A Fast 3D Point Cloud Ｒecognition Method for Substation Equipment

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[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]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 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%.

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Last Update: 2023-05-08