PCFD-Net: A Parallel Collaborative Fusion-Detection Network for SAR and Optical Imagery

Synthetic aperture radar (SAR)–optical image fusion and object detection are two closely related tasks in remote sensing. Fusion can provide richer texture and structural cues for downstream detection, while detection can, in turn, provide object-level location and semantic information to improve fusion. However, effectively integrating these two tasks within a unified training framework remains challenging. Their optimization objectives are inherently different: fusion emphasizes cross-modal information preservation and structural fidelity, whereas detection focuses more on discriminative target representation. As a result, direct joint training often leads to mutual interference rather than mutual reinforcement. In addition, most existing joint frameworks remain serial or unidirectional, limiting effective bidirectional knowledge transfer between fusion and detection. To address these issues, we propose PCFD-Net (Parallel Collaborative Fusion-Detection Network), which consists of a fusion branch, a detection branch, and a bidirectional interaction branch, and unifies fused image generation and oriented object detection within a single training framework through explicit bidirectional interaction. The fusion branch employs dual ResNet-50 encoders, a multi-scale attention fusion module, and a progressive decoder, while the detection branch is built on YOLOv8. The key component of the proposed framework is the bidirectional interaction branch. On the one hand, the multi-scale fused features generated by the fusion branch are injected into the detection backbone to enhance the exploitation of cross-modal intermediate representations. On the other hand, we develop CSMDE (Category Semantic–Modality Disentangled Embedding), which disentangles category-discriminative and modality-preference semantics to map detector category outputs into instance-level semantic embeddings. These embeddings, together with object locations, are further fed into a dual-discriminator mechanism to reversely constrain the fusion branch, thereby strengthening SAR-discriminative target preservation and optical background structure consistency. Experiments on the M4-SAR and OGSOD1.0 datasets demonstrate that PCFD-Net consistently outperforms representative fusion and detection methods, achieving superior fusion quality and stronger downstream detection performance.