YOLO-BDM: An Improved Ship Detection Algorithm Based on YOLOv11n

Fangyuan Xiong¹ , Dezhi Han² , Xiang Shen³ , Manlin Zhu¹

School Of Information Engineering, Shanghai Maritime University, 1550 Haigang Avenue, Shanghai, 201306, Shanghai, China
xiongfangyuan@stu.shmtu.edu.cn (corresponding author)
Shanghai Ship and Shipping Research Institute Co., Ltd., Shanghai, 200135, China
dzhan@shmtu.edu.cn
School of Computer Science, The University of Sydney, Sydney, New South Wales, Australia
shenxiang1107@163.com

Abstract

Synthetic Aperture Radar (SAR) ship detection is crucial for maritime traffic management, search and rescue, and environmental monitoring but remains challenging due to small targets, blurred contours, and complex ocean backgrounds. To address these issues, this paper proposes YOLO-BDM, an improved detector based on YOLOv11. The Diverse Branch Block (DBB) is introduced into the backbone to enhance feature representation through multi-branch training and reparameterized inference. A Multi-scale Contextual Attention (MCA) mechanism is integrated into the backbone and neck to strengthen multi-scale semantic modeling and background discrimination. Additionally, a four-layer Bidirectional Feature Pyramid Network (BiFPN) is employed for efficient multi-scale feature fusion. Experiments on the SAR-Ship dataset show YOLO-BDM achieves 97.27% mAP, 94.11% Precision, and 93.07% Recall, surpassing the baseline and validating its effectiveness.

Key words

Ship detection; BiFPN module; YOLOv11n; DBB module; MCA attention mechanism

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS251018019X

Publication information

Volume 23, Issue 2, (April 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

Download Available in PDF
Portable Document Format

How to cite

Xiong, F., Han, D., Shen, X., Zhu, M.: YOLO-BDM: An Improved Ship Detection Algorithm Based on YOLOv11n. Computer Science and Information Systems, 23(2), 729–756 (2026). https://doi.org/10.2298/CSIS251018019X