We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
基于 PointECA 网络的无序工件点云分割算法.
- Authors
梁艳阳; 周集华; 叶达游; 石峰; 黄子健; 孙伟霖; 王琼瑶; 曹梓涵; 何春燕
- Abstract
To address the problems of disorder, uneven sampling, and the poor segmentation of workpiece point clouds with mutual occlusion, a multiscale adaptive channel attention point cloud segmentation network (PointECA) was proposed. In this algorithm, multi-scale feature extraction module was used to better fuse the local neighborhood features of different scales and richer global feature information was obtained; the adaptive channel attention module was used to interactively learn the channel dimensions of local features at different scales to achieve a better semantic segmentation effect. In addition, the Workpieces dataset for semantic segmentation experiments was produced. A large amount of experimental data shows that PointECA achieves 95.42% mean intersection over union for work- piece part segmentation in disordered and mutually occluded scenes, which can provide better conditions for the fast sorting disordered workpieces.
- Publication
Machine Tool & Hydraulics, 2024, Vol 52, Issue 1, p87
- ISSN
1001-3881
- Publication type
Article
- DOI
10.3969/j.issn.1001-3881.01.013