StarRoute-DBNet: A Novel Multi-Modal Framework for Advanced Target Detection in Dynamic Environments Using SAR and Optical Image Fusion with FocusGraph and PhaseRoute

Multimodal object detection based on synthetic aperture radar (SAR) and optical imagery is of great significance in remote sensing, particularly under adverse weather conditions, nighttime environments, and complex background scenarios. Although SAR imagery has unique advantages under all-weather conditions, its object detection performance still faces challenges in low-texture regions and cluttered scenes. Optical imagery provides rich spatial and texture information, but its applicability is limited in harsh environments. To overcome the limitations of unimodal SAR object detection, this paper proposes a novel multimodal object detection framework, termed StarRoute-DBNet, to improve detection accuracy and robustness through multimodal data fusion and efficient feature interaction. Specifically, a FocusGraph (Graph Convolution-Based Feature Relationship Modeling) module is first designed to adaptively model the spatial relationships between optical and SAR features via graph convolutional networks (GCNs), thereby capturing complex cross-modal spatial dependencies. This module enhances feature interaction across modalities, improves the localization accuracy of oriented targets, and shows clear advantages for small-object detection in complex backgrounds. Second, to alleviate the loss of critical information during downsampling, a PhaseRoute (Sparse Routing Polyphase Downsampling Module) is introduced, which combines multi-phase decomposition with a Top-2 sparse routing strategy to preserve informative spatial cues. By incorporating Gumbel noise into the routing process, the proposed module further improves routing flexibility, detection accuracy, and model robustness. In addition, a Multi-Scale Shuffle-Gated Fusion (MSSGF) module is proposed to address the multi-scale issue in multimodal feature fusion. This module integrates multi-scale convolutional branches, channel shuffles, and dual-attention mechanisms to enhance feature interaction across scales, while an adaptive weighted fusion strategy is employed to dynamically adjust the fusion weights of multimodal features. As a result, the proposed method significantly improves detection accuracy and robustness, especially in complex scenes. Extensive experiments conducted on the MVSDA dataset and the M4-SAR dataset demonstrate that the proposed StarRoute-DBNet consistently outperforms existing state-of-the-art methods under complex backgrounds and adverse conditions. In particular, it achieves clear advantages in oriented object detection and small-object detection, verifying its effectiveness and robustness for cross-modal remote sensing object detection.