포인트 클라우드 기반 선체 구조 변형 탐지 알고리즘 적용 연구

As ship condition inspection technology has been developed, research on collecting, analyzing, and diagnosing condition information has become active. In ships, related research has been conducted, such as analyzing, detecting, and classifying major hull failures such as cracks and corrosion using 2...

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Published in大韓造船學會 論文集 Vol. 59; no. 4; pp. 235 - 242
Main Authors 송상호, 이갑헌, 한기민, 장화섭, Song, Sang-ho, Lee, Gap-heon, Han, Ki-min, Jang, Hwa-sup
Format Journal Article
LanguageEnglish
Korean
Published 대한조선학회 01.08.2022
Subjects
조선공학
3
Online AccessGet full text
ISSN1225-1143
2287-7355
DOI10.3744/SNAK.2022.59.4.235

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Abstract As ship condition inspection technology has been developed, research on collecting, analyzing, and diagnosing condition information has become active. In ships, related research has been conducted, such as analyzing, detecting, and classifying major hull failures such as cracks and corrosion using 2D and 3D data information. However, for geometric deformation such as indents and bulges, 2D data has limitations in detection, so 3D data is needed to utilize spatial feature information. In this study, we aim to detect hull structural deformation positions. It builds a specimen based on actual hull structure deformation and acquires a point cloud from a model scanned with a 3D scanner. In the obtained point cloud, deformation(outliers) is found with a combination of RANSAC algorithms that find the best matching model in the Octree data structure and dataset.
AbstractList As ship condition inspection technology has been developed, research on collecting, analyzing, and diagnosing condition information has become active. In ships, related research has been conducted, such as analyzing, detecting, and classifying major hull failures such as cracks and corrosion using 2D and 3D data information. However, for geometric deformation such as indents and bulges, 2D data has limitations in detection, so 3D data is needed to utilize spatial feature information. In this study, we aim to detect hull structural deformation positions. It builds a specimen based on actual hull structure deformation and acquires a point cloud from a model scanned with a 3D scanner. In the obtained point cloud, deformation(outliers) is found with a combination of RANSAC algorithms that find the best matching model in the Octree data structure and dataset. KCI Citation Count: 0
As ship condition inspection technology has been developed, research on collecting, analyzing, and diagnosing condition information has become active. In ships, related research has been conducted, such as analyzing, detecting, and classifying major hull failures such as cracks and corrosion using 2D and 3D data information. However, for geometric deformation such as indents and bulges, 2D data has limitations in detection, so 3D data is needed to utilize spatial feature information. In this study, we aim to detect hull structural deformation positions. It builds a specimen based on actual hull structure deformation and acquires a point cloud from a model scanned with a 3D scanner. In the obtained point cloud, deformation(outliers) is found with a combination of RANSAC algorithms that find the best matching model in the Octree data structure and dataset.
Author Song, Sang-ho
장화섭
송상호
Jang, Hwa-sup
이갑헌
한기민
Lee, Gap-heon
Han, Ki-min
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Title 포인트 클라우드 기반 선체 구조 변형 탐지 알고리즘 적용 연구
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