东北电力技术2025，Vol.46Issue(1)：30-34,52,6.
基于注意力权重PointNet++的电力走廊点云语义分割研究
Research on Power Corridor Point Cloud Semantic Segmentation Based on Attention Weighted PointNet++
摘要
Abstract
The traditional segmentation of point cloud data in power corridors can suffer from problems such as low accuracy and limita-tions in capturing local features of the data.For this reason,a PointNet++network scene segmentation model based on attention weights is proposed.It uses the PointNet++algorithm from deep learning for scene segmentation of power corridors,and then intro-duces a spatial attention mechanism to help the model focus on important spatial regions more effectively.For this purpose,a home-made dataset is used and an inverted bottleneck design is added to the MLP in each SA module based on the classical structure of the PointNet++network model to improve the processing efficiency and accuracy of the point cloud data.The results show that the im-proved PointNet++network has a 6.3%higher average convergence ratio(mIoU).The improved model with spatial attention mecha-nism shows better segmentation effect on self-made data set,especially in boundary division,which verifies the effectiveness of this method in point cloud semantic segmentation.关键词
点云语义分割/输电通道/PointNet++/注意力机制Key words
point cloud semantic segmentation/transmission channels/PointNet++/attention mechanism分类
信息技术与安全科学引用本文复制引用
鲍万轲,姜媛媛..基于注意力权重PointNet++的电力走廊点云语义分割研究[J].东北电力技术,2025,46(1):30-34,52,6.基金项目
安徽省重点研究与开发计划项目(202104g01020012) （202104g01020012）
安徽理工大学环境友好材料与职业健康研究院研发专项基金资助项目(ALW2020YF18) （ALW2020YF18）
