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.

Remote sensing images exhibit arbitrary orientations, uneven sample distributions, and large scale variations. Existing semi-supervised detectors mainly handle rotation yet neglect how distribution and scale impair pseudo-label quality and positive–negative sample assignment. These limitations manifest in three ways: (1) Inconsistent features. Classification and regression features often mismatch, leading pseudo-labels selected based on classification scores to poorly align with ground truths. (2) Inconsistent sample distributions. A fixed selection threshold yields unstable pseudo-labels due to uneven object distributions, injecting noise into training. (3) Inconsistent discriminative regions. Objects with extreme aspect ratios often have key features at the periphery, causing them to be mislabeled as background. To tackle these issues, we propose CDNet, a novel Consistency-Driven semi-supervised object detection network for remote sensing with four designs: (i) A task-feature alignment module align classification and regression features, allowing classification scores to better reflect pseudo-label quality. (ii) A GMM-based adaptive thresholding strategy that adjusts pseudo-label selection criteria across training stages to cope with uneven distributions. (iii) A sample assignment consistency constraint method and (iv) a scale consistency loss to regularize high-aspect-ratio and scale-varied objects. Extensive experiments on DOTAv1.5 and DIOR-R demonstrate CDNet's superiority over state-of-the-art methods.