Research on Detection Methods for Dynamic Ship Targets in Complex Marine Environment From Visible Light Images
ABSTRACT The detection of distant small ship targets in the marine environment is a critical and challenging issue that urgently needs to be addressed in the realization of accurate marine information control in the complex environment. It is of great significance for monitoring Marine environment a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.70000 |
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| _version_ | 1850191295277957120 |
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| author | Yao Wang Yi Jiang Weigui Zeng Silei Cao |
| author_facet | Yao Wang Yi Jiang Weigui Zeng Silei Cao |
| author_sort | Yao Wang |
| collection | DOAJ |
| description | ABSTRACT The detection of distant small ship targets in the marine environment is a critical and challenging issue that urgently needs to be addressed in the realization of accurate marine information control in the complex environment. It is of great significance for monitoring Marine environment and safeguarding maritime sovereignty. In the process of acquiring target information on ships at sea, the images captured typically contain information of dynamic targets within dynamic scenes. Traditional, singular methods are inadequate for obtaining complete information on these dynamic targets. Based on this, the article proposes an integrated method combining dynamic target detection algorithms, edge detection operators, and deep learning‐based target detection algorithms. This method constructs an improved dynamic target detection algorithm to achieve comprehensive information acquisition and detection of the position, size, and type of moving ship targets in complex marine environments. Experimental simulation has validated the network performance and practical value. The network has been deployed on an Nvidia Jetson TX2 development board for real‐world testing, confirming its performance in detecting dynamic ship targets in actual marine environments, and providing a viable technical approach and theoretical support for enhancing the refined target selection capability. |
| format | Article |
| id | doaj-art-e71d6dfc53e1473184d0924d2867ffa3 |
| institution | OA Journals |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-e71d6dfc53e1473184d0924d2867ffa32025-08-20T02:14:58ZengWileyEngineering Reports2577-81962025-04-0174n/an/a10.1002/eng2.70000Research on Detection Methods for Dynamic Ship Targets in Complex Marine Environment From Visible Light ImagesYao Wang0Yi Jiang1Weigui Zeng2Silei Cao3Naval Aviation University Yantai ChinaNaval Aviation University Yantai ChinaNaval Aviation University Yantai ChinaNaval Aviation University Yantai ChinaABSTRACT The detection of distant small ship targets in the marine environment is a critical and challenging issue that urgently needs to be addressed in the realization of accurate marine information control in the complex environment. It is of great significance for monitoring Marine environment and safeguarding maritime sovereignty. In the process of acquiring target information on ships at sea, the images captured typically contain information of dynamic targets within dynamic scenes. Traditional, singular methods are inadequate for obtaining complete information on these dynamic targets. Based on this, the article proposes an integrated method combining dynamic target detection algorithms, edge detection operators, and deep learning‐based target detection algorithms. This method constructs an improved dynamic target detection algorithm to achieve comprehensive information acquisition and detection of the position, size, and type of moving ship targets in complex marine environments. Experimental simulation has validated the network performance and practical value. The network has been deployed on an Nvidia Jetson TX2 development board for real‐world testing, confirming its performance in detecting dynamic ship targets in actual marine environments, and providing a viable technical approach and theoretical support for enhancing the refined target selection capability.https://doi.org/10.1002/eng2.70000deep learningdynamic targetedge detectionmarine environmentship target |
| spellingShingle | Yao Wang Yi Jiang Weigui Zeng Silei Cao Research on Detection Methods for Dynamic Ship Targets in Complex Marine Environment From Visible Light Images Engineering Reports deep learning dynamic target edge detection marine environment ship target |
| title | Research on Detection Methods for Dynamic Ship Targets in Complex Marine Environment From Visible Light Images |
| title_full | Research on Detection Methods for Dynamic Ship Targets in Complex Marine Environment From Visible Light Images |
| title_fullStr | Research on Detection Methods for Dynamic Ship Targets in Complex Marine Environment From Visible Light Images |
| title_full_unstemmed | Research on Detection Methods for Dynamic Ship Targets in Complex Marine Environment From Visible Light Images |
| title_short | Research on Detection Methods for Dynamic Ship Targets in Complex Marine Environment From Visible Light Images |
| title_sort | research on detection methods for dynamic ship targets in complex marine environment from visible light images |
| topic | deep learning dynamic target edge detection marine environment ship target |
| url | https://doi.org/10.1002/eng2.70000 |
| work_keys_str_mv | AT yaowang researchondetectionmethodsfordynamicshiptargetsincomplexmarineenvironmentfromvisiblelightimages AT yijiang researchondetectionmethodsfordynamicshiptargetsincomplexmarineenvironmentfromvisiblelightimages AT weiguizeng researchondetectionmethodsfordynamicshiptargetsincomplexmarineenvironmentfromvisiblelightimages AT sileicao researchondetectionmethodsfordynamicshiptargetsincomplexmarineenvironmentfromvisiblelightimages |