The advent of deep learning has significantly propelled the utilization of neural networks for Synthetic Aperture Radar (SAR) ship detection in recent years. However, there are two main obstacles in SAR detection. Challenge 1: The multiscale nature of SAR ships. Challenge 2: The influence of intricate near-shore environments and the interference of clutter noise in offshore areas, especially affecting small-ship detection. Existing neural network-based approaches attempt to tackle these challenges, yet they often fall short in effectively addressing small-ship detection across multiple scales and complex backgrounds simultaneously. To overcome these challenges, we propose a novel network called SwinT-FRM-ShipNet. Our method introduces an integrated feature extractor, Swin-T-YOLOv5l, which combines Swin Transformer and YOLOv5l. The extractor is designed to highlight the differences between the complex background and the target by encoding both local and global information. Additionally, a feature pyramid IEFR-FPN, consisting of the Information Enhancement Module (IEM) and the Feature Refinement Module (FRM), is proposed to enrich the flow of spatial contextual information, fuse multiresolution features, and refine representations of small and multiscale ships. Furthermore, we introduce recursive gated convolutional prediction heads (GCPH) to explore the potential of high-order spatial interactions and add a larger-sized prediction head to focus on small ships. Experimental results demonstrate the superior performance of our method compared to mainstream approaches on the SSDD and SAR-Ship-Dataset. Our method achieves an F1 score, mAP_0.5, and mAP_0.5:0.95 of 96.5% (+0.9), 98.2% (+1.0%), and 75.4% (+3.3%), respectively, surpassing the most competitive algorithms.

Foundation models have achieved remarkable breakthroughs across various domains, with the widely use of Masked Image Modeling (MIM) and Self-Supervised Learning (SSL). However, these models lack language comprehension, which limits their ability to generalize in downstream tasks and zero-shot applications. The CLIP (Contrastive Language-Image Pretraining) model overcomes these challenges by integrating textual information and performs exceptionally in remote sensing. However, no such multimodal model exists for the SAR (Synthetic Aperture Radar) domain. To bridge this gap, we propose SARCLIP, a vision-language model specially designed for SAR, which leverages textual supervision to enhance visual representation and achieves fine-grained vision-language alignment through modality alignment. Firstly, to overcome the lack of SAR image-text datasets, we propose the Detection-to-Caption (D2C) algorithm, which transforms heterogeneous format of object detection into diverse captions, ultimately creating the vision-language dataset, MMSAR. Additionally, to address the distortion and information loss caused by downsampling in high-resolution SAR images, we propose AnyReader, a dynamic module capable of processing images of any size through a low-resolution encoder. We validate SARCLIP’s generalization and performance on five downstream tasks: image-text retrieval, zero-shot classification, few-shot classification, linear probing and object counting. Especially, we introduce a new counting benchmark, SARCount. Our results demonstrate that SARCLIP consistently outperforms the CLIP baseline across various model sizes. Impressively, SARCLIP achieved a 7.07% higher average recall on the retrieval task compared to the largest CLIP model. In zero-shot classification, SARCLIP surpasses CLIP by 7.8% in average accuracy across three downstream datasets.