Adaptive Depth Enhancement Network for RGB-D Salient Object Detection

Abstract

RGB-D Salient Object Detection (SOD) aims to identify and highlight the most visually prominent objects from complex backgrounds by leveraging both RGB and depth information. However, depth maps often suffer from noise and inconsistencies due to the imaging modalities and sensor limitations. Additionally, the low-level spatial details and high-level semantic information from multiple levels pose another complexity layer. These issues result in depth maps that may not align well with the corresponding RGB images, causing incorrect foreground and background segmentation. To address these issues, we propose a novel adaptive depth enhancement network (ADENet), which adopts the Depth Feature Refinement (DFR) module to mitigate the negative impact of low-quality depth data and improve the synergy between multi-modal features. We also design a simple yet effective Cross Modality Fusion (CMF) module that combines the spatial and channel attention mechanisms to calibrate single modality features and boost the fusion. The Progressive Multiscale Aggregation (PMA) decoder has also been introduced to integrate multiscale features, promoting more globally retained information. Extensive experiments illustrate that our proposed ADENet is superior to the other 10 state-of-the-art methods on four benchmark datasets.