Asymmetric Feature Consistency Reinforcement Network for Visual-Depth-Thermal Salient Object Detection and a New Benchmark.

Abstract

Fusing Visual-Depth-Thermal (VDT) data holds immense potential for robust Salient Object Detection (SOD) in complex environments. However, current research is constrained by dataset scarcity and the limitations of symmetric direct fusion strategies. To address these gaps, we first construct a comprehensive benchmark named LiTR-2654, comprising 2,654 spatially aligned VDT image triplets captured via LiDAR and dual-modality cameras. This dataset features high diversity and reduced center bias, designed to advance practical applications. With this benchmark, we propose the Asymmetric Feature Consistency Reinforcement Network (AFCRNet), effectively utilizing triple-modality cues to achieve accurate SOD. AFCRNet comprises mainly two core technical innovations: "Unify-then-Integrate" fusion strategy investigates modality-complementary information and context-guided decoder module enables the common focus of multi-level features. Specifically, cross-level thermal and visual features are densely interacted to obtain consistent feature representations. Meanwhile, taking depth features as supplements, same-level triple-modality features are integrated with the attention mechanism, significantly suppressing complex background interference and highlighting salient objects. To further improve the segmentation accuracy, high-level contextual information is introduced into multi-level features to accurately distinguish salient objects, and edge supervision is also utilized to optimize the object contour. Comprehensive analysis of different methods is conducted on published and self-built datasets, demonstrating the superiority of the proposed method. The constructed novel benchmark will be made publicly available at: github.com/215HH/LiTR-2654.