DWTF-DETR: A DETR-Based Model for Inshore Ship Detection in SAR Imagery via Dynamically Weighted Joint Time–Frequency Feature Fusion

Highlights: What are the main findings? A Dual-Domain Feature Fusion Module (DDFM) is proposed to jointly extract spatial and frequency-domain features, enhancing sensitivity to ship backscatter in cluttered inshore environments. A Dual-Path Attention Fusion Module (DPAFM) combines shallow detail and deep semantic features via attention-based reweighting, improving robustness against blurred boundaries. What is the implication of the main finding? The dual-domain and dual-path fusion strategies validate the effectiveness of combining time–frequency information with attention-guided enhancement for SAR ship detection. The findings provide insights for transformer-based detection models, with potential applications in real-time monitoring of harbors and nearshore maritime surveillance. Inshore ship detection in synthetic aperture radar (SAR) imagery poses significant challenges due to the high density and diversity of ships. However, low inter-object backscatter contrast and blurred boundaries of docked ships often result in performance degradation for traditional object detection methods, especially under complex backgrounds and low signal-to-noise ratio (SNR) conditions. To address these issues, this paper proposes a novel detection framework, the Dynamic Weighted Joint Time–Frequency Feature Fusion DEtection TRansformer (DETR) Model (DWTF-DETR), specifically designed for SAR-based ship detection in inshore areas. The proposed model integrates a Dual-Domain Feature Fusion Module (DDFM) to extract and fuse features from both SAR images and their frequency-domain representations, enhancing sensitivity to both high- and low-frequency target features. Subsequently, a Dual-Path Attention Fusion Module (DPAFM) is introduced to dynamically weight and fuse shallow detail features with deep semantic representations. By leveraging an attention mechanism, the module adaptively adjusts the importance of different feature paths, thereby enhancing the model’s ability to perceive targets with ambiguous structural characteristics. Experiments conducted on a self-constructed inshore SAR ship detection dataset and the public HRSID dataset demonstrate that DWTF-DETR achieves superior performance compared to the baseline RT-DETR. Specifically, the proposed method improves mAP@50 by 1.60% and 0.72%, and F1-score by 0.58% and 1.40%, respectively. Moreover, comparative experiments show that the proposed approach outperforms several state-of-the-art SAR ship detection methods. The results confirm that DWTF-DETR is capable of achieving accurate and robust detection in diverse and complex maritime environments.