Asymmetric light-aware progressive decoding network for RGB-thermal salient object detection

Abstract

In recent years, RGB-T salient object detection (SOD) technology has attracted increasing attention. By incorporating thermal images, it can identify salient objects under challenging scenes such as low illumination. However, due to the inherent modality differences between RGB and thermal images, the interaction and fusion of multi-modal features become a critical challenge. To address this challenge, we propose a novel asymmetric light-aware progressive decoding network (ALPD-Net) for RGB-T SOD. Specifically, we develop an asymmetric light-aware interaction (ALI) module that facilitates effective interaction between the two modalities in an asymmetric manner, reducing interference information. In addition, we propose a Channel-Space Feature Fusion module to select and fuse information from different modalities in both channel and spatial dimensions. Finally, we design a phased progressive decoding strategy that divides the decoding process into two stages, gradually refining features to generate high-quality saliency maps. Extensive experiments conducted on three publicly available RGB-T SOD datasets demonstrate that the proposed ALPD-Net achieves outstanding performance against the state-of-the-art RGB-T SOD methods.