Identifying Ships From Radar Blips Like Humans Using a Customized Neural Network-Reference-Cited by-同舟云学术

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Identifying Ships From Radar Blips Like Humans Using a Customized Neural Network

Published:2024-07 Issue:7 Volume:25 Page:7187-7205
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Ma Feng¹,Kang Zhe¹^ORCID,Chen Chen²^ORCID,Sun Jie³,Xu Xiao-Bin⁴^ORCID,Wang Jin⁵^ORCID

Affiliation:

1. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China

2. School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China

3. Nanjing Smart Water Transportation Technology Company Ltd., Nanjing, China

4. School of Automation, Hangzhou Dianzi University, Hangzhou, China

5. Liverpool, Logistics, Offshore and Marine (LOOM) Research Institute, Liverpool John Moores University, Liverpool, U.K.

Funder

Funds for National Key Research and Development Program of China

National Natural Science Foundation of China

Zhejiang Province Key Research and Development Projects

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10582787/10401028.pdf?arnumber=10401028

Reference51 articles.

1. An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system

2. Artificial Intelligence Methods in Safe Ship Control Based on Marine Environment Remote Sensing

3. Rapid detection to long ship wake in synthetic aperture radar satellite imagery

4. Coastal Navigation with Marine Radar for USV Operation in GPS-Restricted Situations

5. Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

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