Loading…
Real-Time Long-Distance Ship Detection Architecture Based on YOLOv8
Long-distance detection of maritime ships is pivotal for the development of intelligent collision avoidance systems. Despite significant advancements in target detection achieved through deep learning, the identification of long-distance ships poses a substantial challenge due to their small pixel s...
Saved in:
| Published in: | IEEE access 2024, Vol.12, p.116086-116104 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | cdi_FETCH-LOGICAL-c2044-dce2e5f4bc2b9bece5ee40547c5c2eeddec5a1d560de4652a3e6b1f9af7ff84d3 |
| container_end_page | 116104 |
| container_issue | |
| container_start_page | 116086 |
| container_title | IEEE access |
| container_volume | 12 |
| creator | Gong, Yanfeng Chen, Zihao Deng, Wen Tan, Jiawan Li, Yabin |
| description | Long-distance detection of maritime ships is pivotal for the development of intelligent collision avoidance systems. Despite significant advancements in target detection achieved through deep learning, the identification of long-distance ships poses a substantial challenge due to their small pixel size in images. Consequently, the recognition of long-distance ships essentially amounts to small object detection. In response to these challenges in small object detection, this paper proposes Ship-YOLOv8, a modified architecture derived from You Only Look Once version 8 (YOLOv8). First, we developed the C-Bottleneck Transformer neural network (C-BoTNet), which is integrated at the end of the backbone, to enhance the global receptive field and facilitate feature fusion. Additionally, we incorporated shallow features with deep features and introduced a dedicated detection layer for small objects into the original structure. Furthermore, we optimized the C2f in the neck using the cross stage partial network (VoVGSCSP) based on GSConv. Finally, we conducted optimization using the Wise-IoU loss function. Extensive experiments conducted on a self-created dataset of long-distance ships demonstrate the remarkable capabilities of Ship-YOLOv8. The proposed method achieves an AP0.5 of 91.8%, significantly outperforming YOLOv8's AP0.5 of 70.6%. Moreover, our method attains a detection speed of 4.8 ms per image during inference, showcasing its efficiency in real-time applications. To validate the algorithm's broad applicability, comparative experiments were conducted on a public maritime dataset SeaShips. Ship-YOLOv8 achieved an AP0.5 score of 99.3%, surpassing YOLOv8's 98.6%. Code is available at https://github.com/zihaohao123/Ship-YOLOv8 . |
| doi_str_mv | 10.1109/ACCESS.2024.3445154 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3098888131</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10638050</ieee_id><doaj_id>oai_doaj_org_article_5948342586b84f9b9767bde17f238af1</doaj_id><sourcerecordid>3098888131</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2044-dce2e5f4bc2b9bece5ee40547c5c2eeddec5a1d560de4652a3e6b1f9af7ff84d3</originalsourceid><addsrcrecordid>eNpNUE1PwkAQbYwmEuQX6KGJ5-J-trtHLKgkTUgED542291ZWAIUt8XEf29riWEuM_Py3pvJi6J7jMYYI_k0yfPZcjkmiLAxZYxjzq6iAcGpTCin6fXFfBuN6nqL2hItxLNBlL-D3iUrv4e4qA7rZOrrRh8MxMuNP8ZTaMA0vjrEk2A2vltOAeJnXYONW_RzUSy-xV104_SuhtG5D6OPl9kqf0uKxes8nxSJIYixxBogwB0rDSllCQY4AEOcZYYbAmAtGK6x5SmywFJONIW0xE5qlzknmKXDaN772kpv1TH4vQ4_qtJe_QFVWCsdGm92oLhkgjLCRVoK5mQpszQrLeDMESq0w63XY-91DNXXCepGbatTOLTvK4qkaAvTjkV7lglVXQdw_1cxUl34qg9fdeGrc_it6qFXeQC4UKRUII7oL8aNfz4</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3098888131</pqid></control><display><type>article</type><title>Real-Time Long-Distance Ship Detection Architecture Based on YOLOv8</title><source>IEEE Open Access Journals</source><creator>Gong, Yanfeng ; Chen, Zihao ; Deng, Wen ; Tan, Jiawan ; Li, Yabin</creator><creatorcontrib>Gong, Yanfeng ; Chen, Zihao ; Deng, Wen ; Tan, Jiawan ; Li, Yabin</creatorcontrib><description>Long-distance detection of maritime ships is pivotal for the development of intelligent collision avoidance systems. Despite significant advancements in target detection achieved through deep learning, the identification of long-distance ships poses a substantial challenge due to their small pixel size in images. Consequently, the recognition of long-distance ships essentially amounts to small object detection. In response to these challenges in small object detection, this paper proposes Ship-YOLOv8, a modified architecture derived from You Only Look Once version 8 (YOLOv8). First, we developed the C-Bottleneck Transformer neural network (C-BoTNet), which is integrated at the end of the backbone, to enhance the global receptive field and facilitate feature fusion. Additionally, we incorporated shallow features with deep features and introduced a dedicated detection layer for small objects into the original structure. Furthermore, we optimized the C2f in the neck using the cross stage partial network (VoVGSCSP) based on GSConv. Finally, we conducted optimization using the Wise-IoU loss function. Extensive experiments conducted on a self-created dataset of long-distance ships demonstrate the remarkable capabilities of Ship-YOLOv8. The proposed method achieves an AP0.5 of 91.8%, significantly outperforming YOLOv8's AP0.5 of 70.6%. Moreover, our method attains a detection speed of 4.8 ms per image during inference, showcasing its efficiency in real-time applications. To validate the algorithm's broad applicability, comparative experiments were conducted on a public maritime dataset SeaShips. Ship-YOLOv8 achieved an AP0.5 score of 99.3%, surpassing YOLOv8's 98.6%. Code is available at https://github.com/zihaohao123/Ship-YOLOv8 .</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2024.3445154</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Accuracy ; Algorithms ; Collision avoidance ; Datasets ; Deep learning ; Feature extraction ; Long-distance ship ; Machine learning ; Marine vehicles ; Maritime communications ; Neural networks ; Object recognition ; Real time ; Real-time systems ; Ships ; small object detection ; Target detection ; Transformers ; YOLO ; YOLOv8</subject><ispartof>IEEE access, 2024, Vol.12, p.116086-116104</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2044-dce2e5f4bc2b9bece5ee40547c5c2eeddec5a1d560de4652a3e6b1f9af7ff84d3</cites><orcidid>0000-0001-5985-2521 ; 0000-0002-1965-857X ; 0009-0004-2871-5283</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10638050$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,786,790,4043,27666,27956,27957,27958,55284</link.rule.ids></links><search><creatorcontrib>Gong, Yanfeng</creatorcontrib><creatorcontrib>Chen, Zihao</creatorcontrib><creatorcontrib>Deng, Wen</creatorcontrib><creatorcontrib>Tan, Jiawan</creatorcontrib><creatorcontrib>Li, Yabin</creatorcontrib><title>Real-Time Long-Distance Ship Detection Architecture Based on YOLOv8</title><title>IEEE access</title><addtitle>Access</addtitle><description>Long-distance detection of maritime ships is pivotal for the development of intelligent collision avoidance systems. Despite significant advancements in target detection achieved through deep learning, the identification of long-distance ships poses a substantial challenge due to their small pixel size in images. Consequently, the recognition of long-distance ships essentially amounts to small object detection. In response to these challenges in small object detection, this paper proposes Ship-YOLOv8, a modified architecture derived from You Only Look Once version 8 (YOLOv8). First, we developed the C-Bottleneck Transformer neural network (C-BoTNet), which is integrated at the end of the backbone, to enhance the global receptive field and facilitate feature fusion. Additionally, we incorporated shallow features with deep features and introduced a dedicated detection layer for small objects into the original structure. Furthermore, we optimized the C2f in the neck using the cross stage partial network (VoVGSCSP) based on GSConv. Finally, we conducted optimization using the Wise-IoU loss function. Extensive experiments conducted on a self-created dataset of long-distance ships demonstrate the remarkable capabilities of Ship-YOLOv8. The proposed method achieves an AP0.5 of 91.8%, significantly outperforming YOLOv8's AP0.5 of 70.6%. Moreover, our method attains a detection speed of 4.8 ms per image during inference, showcasing its efficiency in real-time applications. To validate the algorithm's broad applicability, comparative experiments were conducted on a public maritime dataset SeaShips. Ship-YOLOv8 achieved an AP0.5 score of 99.3%, surpassing YOLOv8's 98.6%. Code is available at https://github.com/zihaohao123/Ship-YOLOv8 .</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Collision avoidance</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Feature extraction</subject><subject>Long-distance ship</subject><subject>Machine learning</subject><subject>Marine vehicles</subject><subject>Maritime communications</subject><subject>Neural networks</subject><subject>Object recognition</subject><subject>Real time</subject><subject>Real-time systems</subject><subject>Ships</subject><subject>small object detection</subject><subject>Target detection</subject><subject>Transformers</subject><subject>YOLO</subject><subject>YOLOv8</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>DOA</sourceid><recordid>eNpNUE1PwkAQbYwmEuQX6KGJ5-J-trtHLKgkTUgED542291ZWAIUt8XEf29riWEuM_Py3pvJi6J7jMYYI_k0yfPZcjkmiLAxZYxjzq6iAcGpTCin6fXFfBuN6nqL2hItxLNBlL-D3iUrv4e4qA7rZOrrRh8MxMuNP8ZTaMA0vjrEk2A2vltOAeJnXYONW_RzUSy-xV104_SuhtG5D6OPl9kqf0uKxes8nxSJIYixxBogwB0rDSllCQY4AEOcZYYbAmAtGK6x5SmywFJONIW0xE5qlzknmKXDaN772kpv1TH4vQ4_qtJe_QFVWCsdGm92oLhkgjLCRVoK5mQpszQrLeDMESq0w63XY-91DNXXCepGbatTOLTvK4qkaAvTjkV7lglVXQdw_1cxUl34qg9fdeGrc_it6qFXeQC4UKRUII7oL8aNfz4</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Gong, Yanfeng</creator><creator>Chen, Zihao</creator><creator>Deng, Wen</creator><creator>Tan, Jiawan</creator><creator>Li, Yabin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-5985-2521</orcidid><orcidid>https://orcid.org/0000-0002-1965-857X</orcidid><orcidid>https://orcid.org/0009-0004-2871-5283</orcidid></search><sort><creationdate>2024</creationdate><title>Real-Time Long-Distance Ship Detection Architecture Based on YOLOv8</title><author>Gong, Yanfeng ; Chen, Zihao ; Deng, Wen ; Tan, Jiawan ; Li, Yabin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2044-dce2e5f4bc2b9bece5ee40547c5c2eeddec5a1d560de4652a3e6b1f9af7ff84d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Collision avoidance</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Feature extraction</topic><topic>Long-distance ship</topic><topic>Machine learning</topic><topic>Marine vehicles</topic><topic>Maritime communications</topic><topic>Neural networks</topic><topic>Object recognition</topic><topic>Real time</topic><topic>Real-time systems</topic><topic>Ships</topic><topic>small object detection</topic><topic>Target detection</topic><topic>Transformers</topic><topic>YOLO</topic><topic>YOLOv8</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gong, Yanfeng</creatorcontrib><creatorcontrib>Chen, Zihao</creatorcontrib><creatorcontrib>Deng, Wen</creatorcontrib><creatorcontrib>Tan, Jiawan</creatorcontrib><creatorcontrib>Li, Yabin</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Open Access: DOAJ - Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gong, Yanfeng</au><au>Chen, Zihao</au><au>Deng, Wen</au><au>Tan, Jiawan</au><au>Li, Yabin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-Time Long-Distance Ship Detection Architecture Based on YOLOv8</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2024</date><risdate>2024</risdate><volume>12</volume><spage>116086</spage><epage>116104</epage><pages>116086-116104</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Long-distance detection of maritime ships is pivotal for the development of intelligent collision avoidance systems. Despite significant advancements in target detection achieved through deep learning, the identification of long-distance ships poses a substantial challenge due to their small pixel size in images. Consequently, the recognition of long-distance ships essentially amounts to small object detection. In response to these challenges in small object detection, this paper proposes Ship-YOLOv8, a modified architecture derived from You Only Look Once version 8 (YOLOv8). First, we developed the C-Bottleneck Transformer neural network (C-BoTNet), which is integrated at the end of the backbone, to enhance the global receptive field and facilitate feature fusion. Additionally, we incorporated shallow features with deep features and introduced a dedicated detection layer for small objects into the original structure. Furthermore, we optimized the C2f in the neck using the cross stage partial network (VoVGSCSP) based on GSConv. Finally, we conducted optimization using the Wise-IoU loss function. Extensive experiments conducted on a self-created dataset of long-distance ships demonstrate the remarkable capabilities of Ship-YOLOv8. The proposed method achieves an AP0.5 of 91.8%, significantly outperforming YOLOv8's AP0.5 of 70.6%. Moreover, our method attains a detection speed of 4.8 ms per image during inference, showcasing its efficiency in real-time applications. To validate the algorithm's broad applicability, comparative experiments were conducted on a public maritime dataset SeaShips. Ship-YOLOv8 achieved an AP0.5 score of 99.3%, surpassing YOLOv8's 98.6%. Code is available at https://github.com/zihaohao123/Ship-YOLOv8 .</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2024.3445154</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0001-5985-2521</orcidid><orcidid>https://orcid.org/0000-0002-1965-857X</orcidid><orcidid>https://orcid.org/0009-0004-2871-5283</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2169-3536 |
| ispartof | IEEE access, 2024, Vol.12, p.116086-116104 |
| issn | 2169-3536 2169-3536 |
| language | eng |
| recordid | cdi_proquest_journals_3098888131 |
| source | IEEE Open Access Journals |
| subjects | Accuracy Algorithms Collision avoidance Datasets Deep learning Feature extraction Long-distance ship Machine learning Marine vehicles Maritime communications Neural networks Object recognition Real time Real-time systems Ships small object detection Target detection Transformers YOLO YOLOv8 |
| title | Real-Time Long-Distance Ship Detection Architecture Based on YOLOv8 |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-21T04%3A36%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Real-Time%20Long-Distance%20Ship%20Detection%20Architecture%20Based%20on%20YOLOv8&rft.jtitle=IEEE%20access&rft.au=Gong,%20Yanfeng&rft.date=2024&rft.volume=12&rft.spage=116086&rft.epage=116104&rft.pages=116086-116104&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2024.3445154&rft_dat=%3Cproquest_cross%3E3098888131%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c2044-dce2e5f4bc2b9bece5ee40547c5c2eeddec5a1d560de4652a3e6b1f9af7ff84d3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3098888131&rft_id=info:pmid/&rft_ieee_id=10638050&rfr_iscdi=true |