{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Liu X"],"funding":["Department of Science and Technology of Jilin Province"],"pagination":["e0330485"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12370123"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(8)"],"pubmed_abstract":["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 consi"],"journal":["PloS one"],"pubmed_title":["M-ReDet: A mamba-based method for remote sensing ship object detection and fine-grained recognition."],"pmcid":["PMC12370123"],"funding_grant_id":["YDZJ202501ZYTS600"],"pubmed_authors":["Liu X","Qin Z","Zi S","Guan Q","Feng C"],"additional_accession":[]},"is_claimable":false,"name":"M-ReDet: A mamba-based method for remote sensing ship object detection and fine-grained recognition.","description":"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 consi","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-05-07T04:59:58.405Z","creation":"2026-04-07T22:33:25.08Z"},"accession":"S-EPMC12370123","cross_references":{"pubmed":["40839647"],"doi":["10.1371/journal.pone.0330485"]}}