Underwater image saliency detection based on refined attentional feedback mechanism

Abstract

Underwater image saliency detection makes the terrestrial saliency detection model less effective in the application of underwater vehicles due to factors such as turbid water and unstable light, resulting in a degradation of the model's performance. We offer a model for underwater saliency detection based on an improved attentional feedback mechanism to overcome the aforementioned issues. The features at the top and bottom levels are effectively fused by forming a cascaded feedback decoder through the cross feature module and adding channel space attention, after which the residual refinement module is added for further refinement. The training and testing process uses underwater open datasets. The experimental findings demonstrate that our method is superior to other ways in comparative analysis with four general saliency detection methods and two underwater saliency detection methods on four underwater image datasets, proving the model's viability and efficacy.