AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Chinese Journal of Ship Research Article
PDF (2.4 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Publishing Language: Chinese

Point cloud segmentation of flange laser scanning for ship shafting intelligent installation

Pan CHEN1,2,3, Baoyou SHANG1,2,3, Tianyun LI1,2,3( ), Weijia LI1,2,3, Xiang ZHU1,2,3
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, Shanghai 200240, China
Hubei Key Laboratory of Naval Architecture & Ocean Engineering Hydrodynamics, Wuhan 430074, China
Show Author Information

Abstract

Objectives

Laser scanning technology used in the intelligent installation of ship shafting has such advantages as non-contact, high-speed scanning and high-precision imaging. The laser point cloud data includes the size, position and direction information of space objects. Point cloud segmentation can greatly reduce the calculation scale of the data and improve the measurement efficiency of the relative pose of the butt flange.

Methods

In this paper, deep learning theory is used to study point cloud segmentation and obtain a point cloud dataset of flange parts. The PointNet model is used for training. Optimization strategies are formulated in three aspects, namely dropout regularization, learning rate attenuation and point cloud data enhancement, then tested on a ship shafting intelligent installation platform.

Results

The convergence results of the model tend to be stable, with the accuracy of the training set reaching 0.88 and that of the verification set reaching 0.65. The flange point cloud segmentation experiment shows clear contour edges.

Conclusion

The results of this study show that the proposed method has good convergence and generalization performance, and can improve the efficiency of ship shafting intelligent installation.

Keywords

ship shaftingintelligent installationdeep learningpoint cloud segmentation
CLC number: U665.12 Document code: A

References

【1】
【1】
 
 
Crossref Google Scholar
Chinese Journal of Ship Research
Volume 18 Issue 6,
December 2023
Pages 268-274
DOI: 10.19693/j.issn.1673-3185.03114

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Cite this Report
Close
Close
Cite this article:
CHEN P, SHANG B, LI T, et al. Point cloud segmentation of flange laser scanning for ship shafting intelligent installation. Chinese Journal of Ship Research, 2023, 18(6): 268-274. https://doi.org/10.19693/j.issn.1673-3185.03114

571

Views

11

Downloads

0

Crossref

1

Scopus

2

CSCD
Received: 29 September 2022
Revised: 05 December 2022
Published: 14 December 2023
© 2023 Chinese Journal of Ship Research.