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Ship Type Recognition via a Coarse-to-Fine Cascaded Convolution Neural Network

Published online by Cambridge University Press:  28 February 2020

Xinqiang Chen
Affiliation:
(Institute of Logistics Science and Engineering, Shanghai Maritime University)
Yongsheng Yang
Affiliation:
(Institute of Logistics Science and Engineering, Shanghai Maritime University)
Shengzheng Wang
Affiliation:
(Merchant Marine College, Shanghai Maritime University)
Huafeng Wu
Affiliation:
(Merchant Marine College, Shanghai Maritime University)
Jinjun Tang*
Affiliation:
(School of Traffic and Transportation Engineering, Central South University, Changsha)
Jiansen Zhao
Affiliation:
(Merchant Marine College, Shanghai Maritime University)
Zhihuan Wang
Affiliation:
(Institute of Logistics Science and Engineering, Shanghai Maritime University)

Abstract

Most previous research has handled the task of ship type recognition by exploring hand-craft ship features, which may fail to distinguish ships with similar visual appearances. This situation motivates us to propose a novel deep learning based ship type recognition framework which we have named coarse-to-fine cascaded convolution neural network (CFCCNN). First, the proposed CFCCNN framework formats the input training ship images and data, and provides trainable input data for the hidden layers of the CFCCNN. Second, the coarse and fine steps are run in a nesting manner to explore discriminative features for different ship types. More specifically, the coarse step is trained in a similar manner to the traditional convolution neural network, while the fine step introduces regularisation mechanisms to extract more intrinsic ship features, and fine tunes parameter settings to obtain better recognition performance. Finally, we evaluate the performance of the CFCCNN model for recognising the most common types of merchant ship (oil tanker, container, LNG tanker, chemical carrier, general cargo, bulk carrier, etc.). The experimental results show that the proposed framework obtains better recognition performance than the conventional methods of ship type recognition.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2020

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