Bakirci, MuratBayraktar, Irem2025-03-172025-03-172024979-835037754-5https://doi.org/10.1109/CCE62852.2024.10770926https://hdl.handle.net/20.500.13099/1390Centro 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); IEEE21st International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2024 -- 23 October 2024 through 25 October 2024 -- Hybrid, Mexico City -- 204854Ship 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.eninfo:eu-repo/semantics/closedAccessCNNdeep learningobject detectionSAR imageryship detectionYOLO11YOLOv10YOLOv9Conference Object10.1109/CCE62852.2024.107709262-s2.0-85214934458N/A