Assessment of YOLO11 for Ship Detection in SAR Imagery under Open Ocean and Coastal Challenges

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dc.contributor.authorBakirci, Murat
dc.contributor.authorBayraktar, Irem
dc.date.accessioned2025-03-17T12:22:47Z
dc.date.available2025-03-17T12:22:47Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.descriptionCentro de Investigacion y de Estudios Avanzados del Instituto Politenico Nacional (Cinvestav); Centro de Investigacion y de Estudios Avanzados del Instituto Politenico Nacional (Cinvestav), Electrical Engineering Department; Electron Devices Society (EDS); IEEE
dc.description21st International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2024 -- 23 October 2024 through 25 October 2024 -- Hybrid, Mexico City -- 204854
dc.description.abstractShip detection from Synthetic Aperture Radar (SAR) images plays a crucial role in maritime surveillance and safety. This study focuses on evaluating the performance of the latest state-of-The-Art YOLO algorithm, YOLO11, for ship detection, particularly because it has not been tested on SAR images prior to this research. YOLO11 was selected for its recent release and potential improvements over previous iterations. To assess its effectiveness, the algorithm is compared with earlier YOLO versions using SAR imagery. The dataset is categorized into two subsets: open ocean images and coastal images, where distinguishing ships from coastal structures presents a significant challenge. The advantages and limitations of YOLO11 are thoroughly examined through a comparative analysis with its predecessors. Results indicate that YOLO11 outperforms earlier versions in most scenarios, particularly excelling in open ocean environments. Although ship detection from SAR images is inherently difficult, YOLO11 achieves promising Precision, Recall, and mAP values of 0.865, 0.813, and 0.792, respectively. Its performance in open ocean images exceeds these average values, highlighting YOLO11's efficacy in maritime surveillance. However, performance in coastal images is lower, with YOLOv10 performing closely to YOLO11 in these cases. The findings underscore YOLO11's effectiveness for ship detection from SAR images, showcasing its enhanced ability to detect ships in challenging environments and emphasizing its relevance for future maritime applications. © 2024 IEEE.
dc.identifier.doi10.1109/CCE62852.2024.10770926
dc.identifier.isbn979-835037754-5
dc.identifier.scopus2-s2.0-85214934458
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/CCE62852.2024.10770926
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1390
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2024 21st International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectCNN
dc.subjectdeep learning
dc.subjectobject detection
dc.subjectSAR imagery
dc.subjectship detection
dc.subjectYOLO11
dc.subjectYOLOv10
dc.subjectYOLOv9
dc.titleAssessment of YOLO11 for Ship Detection in SAR Imagery under Open Ocean and Coastal Challenges
dc.typeConference Object

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