Impact Factor:  5.3

DOI number:  10.1109/TIM.2023.3269119

Affiliation of Author(s):  北京科技大学自动化学院

Teaching and Research Group:  仪器科学与技术系

Journal:  IEEE Transactions on Instrumentation and Measurement

Key Words:  point cloud, semantic segmentation, 3D

Abstract:  Detecting free-bent pipelines and analyzing the global-range clearance in cluttered industrial scenarios are a significant yet challenging task in pipeline inspection. Most of the previous work focused on local ranges or specific pipeline components instead of covering a wide length range. This article proposes a comprehensive pipeline segmentation and clearance analysis framework that combines a deep pipeline feature learning module for semantic pipeline segmentation and a pipeline primitive growing strategy for measuring the clearance along the length range in a point-cloud scenario. We propose to learn enhanced high-level pipeline features by incorporating an important geometric cue concerning cylindrical axial consistency (CAC), which benefits better pipeline segmentation from the point-cloud background. Then, each pipeline instance is modeled as a sequence of 3-D cylinder primitives. We establish a geometric model to measure the minimum panoramic clearance of each cylinder segment in the scene. Then, the pipeline clearance is tracked along the centerline in a segment sequential growing mode. Experiments were carried out on real-world industrial point-cloud scenarios. The proposed pipeline semantic segmentation achieves the state-of-the-art performance. The clearance analysis module reaches a mean accuracy of 0.018 mm. Our method is applicable to various free-bent pipelines and robust to cluttered backgrounds.

Co-author:  Zitai Zhou,Junhua Sun

First Author:  Jie Zhang

Indexed by:  Journal paper

Document Code:  2511712

Discipline:  Engineering

Document Type:  J

Volume:  72

Issue:  2511712

Page Number:  1-11

ISSN No.:  1557-9662

Translation or Not:  no

Date of Publication:  2023-01-01

Included Journals:  SCI

Links to published journals:  https://ieeexplore.ieee.org/document/10106290