3D point cloud research on an identification method based on PSO-KNN substation equipment

被引：0

作者：

Li K. ^{[1
]}

机构：

[1] State Grid Sichuan Information & Telecommunication Company (Provincial Data Center), Chengdu

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2021年 / 49卷 / 18期

关键词：

3D point cloud recognition; 3D reconstruction; K-nearest neighbor classification algorithm; Particle swarm optimization; Substation equipment;

D O I：

10.19783/j.cnki.pspc.201461

中图分类号：

学科分类号：

摘要：

There are problems of poor accuracy and low efficiency caused by the lack of data in traditional three-dimensional reconstruction methods. Thus, based on a three-dimensional laser scanning point cloud, a recognition method for substation equipment is proposed. This combines the particle swarm optimization algorithm and the k-nearest neighbor classification algorithm. The particle population optimization algorithm is used to optimize the coefficient weight of each subspace feature, and the k-nearest neighbor classification method is used to classify the equipment. The influence of the size and loss rate of the point cloud subspace on the recognition effect is analyzed through experiment. It is compared with the improved iterative closest point algorithm to verify the superiority and accuracy of the method. Experimental results show that this method can effectively improve recognition accuracy and efficiency. The recognition accuracy can reach more than 95%, and the average recognition time is 0.19 seconds, which has application value. © 2021 Power System Protection and Control Press.

引用

页码：182 / 187

页数：5

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