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

Visual-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 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 multi-level 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 5.51 M parameters and a real-time speed of 120 FPS at 320 × 320 resolution, surpassing state-of-the-art methods while remaining deployment-friendly.