Work

A Multi-Modal Approach for Robust Oriented Ship Detection: Dataset and Methodology

Year: 2026

Type: article

Abstract: Maritime ship detection is a critical task for security and traffic management. To advance research in this area, we constructed a new high-resolution, spatially aligned optical-SAR dataset, named MOS-Ship. Building on this, we propose MOS-DETR, a novel query-based framework. This model incorporates an innovative multi-modal Swin Transformer backbone to extract unified feature pyramids from both RGB and SAR images. This design allows the model to jointly exploit optical textures and SAR scattering signatures for precise, oriented bounding box prediction. We also introduce an adaptive probabilistic fusion mechanism. This post-processing module dynamically integrates the detection results generated by our model from the optical and SAR inputs, synergistically combining their complementary strengths. Experiments validate that MOS-DETR achieves highly competitive accuracy and significantly outperforms unimodal baselines, demonstrating superior robustness across diverse conditions. This work provides a robust framework and methodology for advancing multimodal maritime surveillance.

Source: Remote Sensing

DOI: 10.3390/rs18020274

Authors: Jianing You, Yixuan Lv, Shengyang Li, Silei Liu, Kailun Zhang, Yuxuan Liu

Institutions: Chinese Academy of Sciences, Technology and Engineering Center for Space Utilization, University of Chinese Academy of Sciences

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: Chinese Academy of Sciences