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

文献类型：期刊论文

作者Z. Huang; F. Wu; Y. Fu; Y. Zhang and X. Jiang
刊名Guangxue Jingmi Gongcheng/Optics and Precision Engineering
出版日期2023
卷号31期号:15页码:2295-2318
ISSN号1004924X
DOI10.37188/OPE.20233115.2295
英文摘要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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源URL[http://ir.ciomp.ac.cn/handle/181722/67542]  
专题中国科学院长春光学精密机械与物理研究所
推荐引用方式
GB/T 7714		 Z. Huang,F. Wu,Y. Fu,et al. Review of deep learning-based algorithms for ship target detection from remote sensing images[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2023,31(15):2295-2318.
APA Z. Huang,F. Wu,Y. Fu,&Y. Zhang and X. Jiang.(2023).Review of deep learning-based algorithms for ship target detection from remote sensing images.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,31(15),2295-2318.
MLA Z. Huang,et al."Review of deep learning-based algorithms for ship target detection from remote sensing images".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 31.15(2023):2295-2318.

入库方式： OAI收割

来源：长春光学精密机械与物理研究所

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