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Fast ship detection in optical remote sensing images based on sparse mobilenetv2 network

  • Jinxiang Yu
  • , Tong Yin
  • , Shaoli Li
  • , Shuo Hong
  • , Yu Peng*
  • *Corresponding author for this work
  • School of Electronics and Information Engineering, Harbin Institute of Technology

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

Abstract

Ship detection in optical remote sensing images (ORSIs) has drawn lots of attention because of its extensive potential in maritime applications. Although many methods have been proposed in recent years, there are still great challenges for improving the detection accuracy and detection speed. In this paper, a fast ship detection method in optical remote sensing images based on sparse MobileNetV2 network is proposed, which has high accuracy and fast detection speed. Ship detection problem is turned into a sub-image classification one, which successfully avoids the massive computation caused by the region proposal stage in previous methods. The sparse MobileNetV2 network has high detection accuracy and less computation benefited from the convolutional neural networks and the depth separable convolution. Furthermore, the pruning method is used to compress the network to decrease model complexity and prevent overfitting. Several experiments are conducted based on some optical remote sensing images from Google Earth. The results demonstrate that the proposed method achieves over 5x speed enhancement compared with several mainstream ship detection methods, while the accuracy is competitive.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computing - Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019
EditorsJeng-Shyang Pan, Yongquan Liang, Jerry Chun-Wei Lin, Shu-Chuan Chu
PublisherSpringer
Pages262-269
Number of pages8
ISBN (Print)9789811533075
DOIs
StatePublished - 2020
Externally publishedYes
Event13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019 - Qingdao, China
Duration: 1 Nov 20193 Nov 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1107 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019
Country/TerritoryChina
CityQingdao
Period1/11/193/11/19

Keywords

  • Model compression
  • Optical remote sensing image
  • Ship detection
  • Sparse MobileNetV2 network

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