影响因子：5.3
DOI码：10.1109/TIM.2023.3269119
所属单位：北京科技大学自动化学院
教研室：仪器科学与技术系
发表刊物：IEEE Transactions on Instrumentation and Measurement
关键字：point cloud, semantic segmentation, 3D
摘要：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.
合写作者：Zitai Zhou,Junhua Sun
第一作者：Jie Zhang
论文类型：期刊论文
论文编号：2511712
学科门类：工学
文献类型：J
卷号：72
期号：2511712
页面范围：1-11
ISSN号：1557-9662
是否译文：否
发表时间：2023-01-01
收录刊物：SCI
发布期刊链接：https://ieeexplore.ieee.org/document/10106290