SGNet: A Structure-Guided Lightweight Network for VDT Salient Object Detection

Visible–depth–thermal (VDT) salient object detection (SOD) aims to jointly exploit RGB, depth and thermal cues to segment the most visually significant regions. However, most existing VDT SOD models are heavy in parameters and computational cost, limiting their deployment on real-world and edge devices. To tackle this, we propose structure-guided network (SGNet), a structure-guided lightweight network for efficient VDT SOD. Specifically, we design a lightweight tri-modal fusion module (TFM) to integrate three modalities at the semantic level, and a shared structure extraction module (SSEM) to extract common structural information from depth and thermal modalities. A structure refine module (SRM) further injects the extracted structure into the deepest semantic features, while a multiscale feature refinement module (MFRM) progressively decodes multilevel features under deep supervision to produce saliency maps with clear boundaries. Benefiting from these modules, SGNet achieves competitive performance on the VDT2048 benchmark with only 5.51M parameters and a real-time speed of 120 frames/s (FPS) at $320\times 320$ resolution, surpassing state-of-the-art methods while remaining deployment-friendly.