The number of various types in the DOTA.
<div><p>Ship object detection and fine-grained recognition of remote sensing images are hot topics in remote sensing image processing, with applications in fishing vessel operation command, merchant ship navigation route planning, and other fields. In order to improve the detection accur...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1852017402493009920 |
|---|---|
| author | Xuhui Liu (546025) |
| author2 | Chi Feng (7863566) Shuran Zi (22103924) Zhengkun Qin (16850100) Qinghe Guan (22103927) |
| author2_role | author author author author |
| author_facet | Xuhui Liu (546025) Chi Feng (7863566) Shuran Zi (22103924) Zhengkun Qin (16850100) Qinghe Guan (22103927) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xuhui Liu (546025) Chi Feng (7863566) Shuran Zi (22103924) Zhengkun Qin (16850100) Qinghe Guan (22103927) |
| dc.date.none.fl_str_mv | 2025-08-21T17:35:23Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0330485.g008 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_number_of_various_types_in_the_DOTA_/29961404 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> 0 remote sensing images experimental results show 09 %, respectively 0 visible light 5 </ sub ship object perception fuse different scales spatial position information div >< p ship object detection ship object fine ship ’ ship objects different types different categories unique features sub xmlns sfrm ), selective memory regression loss paper proposes paper designs hot topics grained recognition grained features focal loss detection accuracy classification loss based method |
| dc.title.none.fl_str_mv | The number of various types in the DOTA. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Ship object detection and fine-grained recognition of remote sensing images are hot topics in remote sensing image processing, with applications in fishing vessel operation command, merchant ship navigation route planning, and other fields. In order to improve the detection accuracy for different types of remote sensing ship objects, this paper proposes a ship object perception and feature refinement method based on the improved ReDet, called Mamba-ReDet (M-ReDet). First, this paper designs a ship object fine-grained feature extraction backbone (Mamba-ReResNet, M-ReResNet), which selects and reconstructs the unique features of different types of ship objects through the Mamba’s selective memory to improve the algorithm’s ability to extract fine-grained features. Secondly, the M-ReDet consists of the Ship Object Perception Module (SOPM) and the Ship Feature Refinement Module (SFRM), which can extract the ship’s spatial position information from the feature map, fuse different scales of spatial position information and use this information to refine the fine-grained features to improve the detection accuracy of the algorithm for different categories of ships. Finally, we use the KFIoU and Focal Loss as the regression loss and classification loss of the algorithm to improve the accuracy of the training. The experimental results show that the mAP<sub>0.5</sub> of the M-ReDet algorithm on the FAIR1M(ship) and DOTAv1.0 visible light (RGB) remote sensing image datasets are 43.29% and 82.09%, respectively, which is 2.78% and 3.34% higher than that of the ReDet.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_e22cdfd846db5bdaa802b980d010f28b |
| identifier_str_mv | 10.1371/journal.pone.0330485.g008 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29961404 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The number of various types in the DOTA.Xuhui Liu (546025)Chi Feng (7863566)Shuran Zi (22103924)Zhengkun Qin (16850100)Qinghe Guan (22103927)NeuroscienceBiotechnologySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxlink "> 0remote sensing imagesexperimental results show09 %, respectively0 visible light5 </ subship object perceptionfuse different scalesspatial position informationdiv >< pship object detectionship object fineship ’ship objectsdifferent typesdifferent categoriesunique featuressub xmlnssfrm ),selective memoryregression losspaper proposespaper designshot topicsgrained recognitiongrained featuresfocal lossdetection accuracyclassification lossbased method<div><p>Ship object detection and fine-grained recognition of remote sensing images are hot topics in remote sensing image processing, with applications in fishing vessel operation command, merchant ship navigation route planning, and other fields. In order to improve the detection accuracy for different types of remote sensing ship objects, this paper proposes a ship object perception and feature refinement method based on the improved ReDet, called Mamba-ReDet (M-ReDet). First, this paper designs a ship object fine-grained feature extraction backbone (Mamba-ReResNet, M-ReResNet), which selects and reconstructs the unique features of different types of ship objects through the Mamba’s selective memory to improve the algorithm’s ability to extract fine-grained features. Secondly, the M-ReDet consists of the Ship Object Perception Module (SOPM) and the Ship Feature Refinement Module (SFRM), which can extract the ship’s spatial position information from the feature map, fuse different scales of spatial position information and use this information to refine the fine-grained features to improve the detection accuracy of the algorithm for different categories of ships. Finally, we use the KFIoU and Focal Loss as the regression loss and classification loss of the algorithm to improve the accuracy of the training. The experimental results show that the mAP<sub>0.5</sub> of the M-ReDet algorithm on the FAIR1M(ship) and DOTAv1.0 visible light (RGB) remote sensing image datasets are 43.29% and 82.09%, respectively, which is 2.78% and 3.34% higher than that of the ReDet.</p></div>2025-08-21T17:35:23ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0330485.g008https://figshare.com/articles/figure/The_number_of_various_types_in_the_DOTA_/29961404CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299614042025-08-21T17:35:23Z |
| spellingShingle | The number of various types in the DOTA. Xuhui Liu (546025) Neuroscience Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> 0 remote sensing images experimental results show 09 %, respectively 0 visible light 5 </ sub ship object perception fuse different scales spatial position information div >< p ship object detection ship object fine ship ’ ship objects different types different categories unique features sub xmlns sfrm ), selective memory regression loss paper proposes paper designs hot topics grained recognition grained features focal loss detection accuracy classification loss based method |
| status_str | publishedVersion |
| title | The number of various types in the DOTA. |
| title_full | The number of various types in the DOTA. |
| title_fullStr | The number of various types in the DOTA. |
| title_full_unstemmed | The number of various types in the DOTA. |
| title_short | The number of various types in the DOTA. |
| title_sort | The number of various types in the DOTA. |
| topic | Neuroscience Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> 0 remote sensing images experimental results show 09 %, respectively 0 visible light 5 </ sub ship object perception fuse different scales spatial position information div >< p ship object detection ship object fine ship ’ ship objects different types different categories unique features sub xmlns sfrm ), selective memory regression loss paper proposes paper designs hot topics grained recognition grained features focal loss detection accuracy classification loss based method |