Making BBS-Net More Efficiency for RGB-D Salient Object Detection

Abstract

BBS-Net is a novel bifurcated backbone strategy network based on cascaded refinement, which achieves the most advanced performance in RGB-D salient object detection models. However, the performance of the model in multi-scale feature extraction and depth feature enhancement is not strong enough, and the generated saliency maps contain a lot of background distraction and blurred edges. In this paper, based on BBS-Net framework, we adopt more effective feature extraction network and depth feature enhancement module. Specifically, 1) Use the Res2Net-50 structure that constructs hierarchical residual-like connections within a single residual block as a feature extraction network; 2) Use ECA module as channel attention to extract depth information and enhance cross-modal compatibility. The comprehensive experiments of our method on 6 datasets show that it outperforms the original BBS-Net and 7 current state-of-the-art methods in 4 evaluation metrics.