Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Ship Hull Offset Feature Recognition Functionality Exploration
Based on Convolutional Neural Networks
Lin Du1, Guangnian Li1, Pengjun Zheng1, Yuehui Shu1, Torgeir Moan2
1Maritime and Transportation College, Ningbo University.

Ningbo, Zhejiang Province, China
2Centre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology.

Trondheim, Trøndelag, Norwaycurve (SAC) that affects hydro statistics (Pérez & Clemente, 2011).
Koelman and Veelo pointed out the limitation of NURBS on ship hullmodeling (Koelman & Veelo, 2013). Yang and Huang applied asimulation-based hydrodynamic design optimization of ship hull forms
at the early design stage, and reduced the total drag compared to theoriginal design (Yang & Huang, 2016). Yu and Lee indicated a design
method for the efficient gooseneck bulbous bow for the middle-sized
ferry (Yu & Lee, 2017). Greshake and Bronsart solved the restriction oftensor-product B-splines to regular control meshes with a subdivision
method to refine polygon meshes (Greshake & Bronsart, 2018). He andhis team established a gradient-based optimization framework, including
a discrete adjoint method for efficient derivative computations (He et al.,2019). Katsoulis et al presented a T-splines-based parametric modeler(TshipPM) for complex ship forms, capable to provide smoothgeometries at low computational modeling cost (Katsoulis et al., 2019).
Wei et al illustrated a reliability-based robust design optimization(RBRDO) framework for ship hull form design (Wei et al., 2019). Bašić
et al introduced an improved linear theory by considering the boundary
layer effects through the tangency correction that can handle flow
separation (Bašić et al., 2020). Can et al investigated the wake factor ofa self-propelled container ship by Telfer’s GEOSIM method based on
CFD (Can et al., 2020). Matveev applied numerical simulation on theship resistance reduction with air injecting (Matveev, 2020). Miao andWan illustrated a hydrodynamic optimization tool, OPTShip-SJTU,
containing four components, a hull form modifier, performanceevaluator, surrogate model building, and optimizer module (Miao &Wan, 2020). Moreira and Soares used an artificial neural network (ANN)to estimate wave-induced ship hull bending moment and shear force
from ship motions (Moreira & Soares, 2020). Ni et al applied an SBD(simulation-based design) optimization framework to optimize thedesign of a SWATH (small water area twin-hull) (Ni et al., 2020). Zhang
et al optimized the DTMB5415 with the ISIGHT platform (Zhang et al.,2020).

ABSTRACT ：At the same as pictures, the ship hull offset data is sequential in space,which indicates that the geometry information carried by the data is
associated with their sequence of values. Theoretically, the convolutional
neural network (CNN), which has been widely applied in modern daily
life, could also identify the ship hull's geometric features. This paper
initially demonstrated that computer vision techniques (such as CNN orVGG) can recognize the ship hull offset features like human designers,
and provided some support for further ship modeling investigations andresearch.

KEY WORDS: Ship Hull Offset; Convolutional Neural Networks;
Computer Vision; Feature Learning.

INTRODUCTION
The geometry of hull form affects the hydrodynamic performance, safety
and economics of ships significantly. To reduce the emission of ships,
the research on ship hull optimization methods is concentrated in recent
years, and there are increasing investigators trying to apply artificial
intelligence, machine learning, and deep learning to ship design. The
computer vision (CV) technology could import the recognition
functionally of geometry to programs, and there are many successful
engineering applications already. As the pictures and voices, the ship
offset table is a matrix carrying the information associated with certainsequences. In this case, it is possible to use CV to train a neural network
to recognize the ship hull form features.
Ship hull form design and optimization is always a critical challenge toresearchers. Pérez and Suárez introduced a method to create a quasidevelopable B-spline surface between two limit curves is presented(Pérez & Suárez, 2007). Pérez and Clemente followed the traditional
design principles of naval architecture, starting with a sectional area3501