Accurate ship segmentation via ship contour prediction

Xiaowu Xiao, Changjun Ai, Weishen Wang, Zhiqiang Zhou, Linhao Li, Jun Chu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Accurate ship segmentation in optical remote sensing images is challenging. In this paper, ship contour prediction network is introduced to improve the performance of ship segmentation network. Ship contour prediction network predicts the ship contour, which can promote the learning of ship segmentation network, producing more accurate segmentation results. The ship contour prediction network can be naturally embedded into any ship segmentation network with encoder-decoder architecture. Moreover, ship contour prediction network is only used in the training process, and thus the network not only can improve segmentation accuracy, but also does not increase computational cost in the testing process.

Original languageEnglish
Title of host publicationProceedings of the 39th Chinese Control Conference, CCC 2020
EditorsJun Fu, Jian Sun
PublisherIEEE Computer Society
Pages7402-7405
Number of pages4
ISBN (Electronic)9789881563903
DOIs
Publication statusPublished - Jul 2020
Event39th Chinese Control Conference, CCC 2020 - Shenyang, China
Duration: 27 Jul 202029 Jul 2020

Publication series

NameChinese Control Conference, CCC
Volume2020-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference39th Chinese Control Conference, CCC 2020
Country/TerritoryChina
CityShenyang
Period27/07/2029/07/20

Keywords

  • Convolutional neural network
  • Fully convolutional network
  • Semantic segmentation
  • Ship contour prediction
  • Ship segmentation

Fingerprint

Dive into the research topics of 'Accurate ship segmentation via ship contour prediction'. Together they form a unique fingerprint.

Cite this