Journal of Advanced Computational Intelligence and Intelligent Informatics
Online ISSN : 1883-8014
Print ISSN : 1343-0130
ISSN-L : 1883-8014
Regular Papers
Semantic Segmentation of Substation Site Cloud Based on Seg-PointNet
Wei Gao Lixia Zhang
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JOURNAL OPEN ACCESS

2022 Volume 26 Issue 6 Pages 1004-1012

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Abstract

3D point cloud semantic segmentation has been widely used in industrial scenes and has attracted continuous attention as a critical technology for understanding the intelligent robot scene. However, extracting visual semantics in complex environments remains a challenge. We propose the Seg-PointNet model based on multi-layer residual structure and feature pyramid for the LiDAR point cloud data semantic segmentation task in the complex substations scene. The model is based on the PointNet network and introduces a multi-scale residual structure. The residual structure multilayer perception (RES-MLP) model is proposed to fully excavate features at different levels and improve the characterization capabilities of complex features. Moreover, the 3D point cloud feature pyramid module is proposed to characterize the substation scene’s semantic features. We tested and verified the Seg-PointNet model on a self-built substation cloud point (SCP) dataset. The results show that the proposed Seg-PointNet model effectively improves the point cloud data segmentation accuracy, with an accuracy of 89.23% and mean intersection over union (mIoU) of 63.57%. This shows that the model can be applied to substation scenarios and provide technical support to intelligent robots in complex substation environments.

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