Review of deep learning-based algorithms for ship target detection from remote sensing images

被引：2
|
作者：
Huang Z. [1 ,2 ]
Wu F. [1 ]
Fu Y. [1 ]
Zhang Y. [1 ]
Jiang X. [1 ]
机构：
[1] Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun
[2] University of Chinese Academy of Sciences, Beijing
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2023年 / 31卷 / 15期
关键词：
convolutional neural networks; image dataset; remote sensing imagery; ship target detection;
D O I：
10.37188/OPE.20233115.2295
中图分类号：
学科分类号：
摘要：
The detection of naval targets is a key area of research interest in the field of remote sensing im⁃ age processing and pattern recognition. Moreover，the automatic detection of naval targets is crucial to both civil and military applications. In this study，we discuss and analyze the advantages and disadvantages of typical deep-learning-based target-detection algorithms，compare and summarize them，and summarize state-of-the-art deep-learning-based ship target detection methods. We also provide a detailed introduction to five aspects of state-of-the-art ship target detection methods，including multi-scale detection，multi-an⁃ gle detection，small target detection，model light-weighting，and large-format wide remote sensing imag⁃ ing. We also introduce the common evaluation criteria of ship target recognition algorithms and existing ship image datasets，and discuss the current problems faced by ship target detection algorithms using re⁃ mote sensing images and future development trends in the field. © 2023 Chinese Academy of Sciences. All rights reserved.
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收藏
页码：2295 / 2318
页数：23
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