A novel embedded cross framework for high-resolution salient object detection

Abstract

Salient object detection (SOD) is a fundamental research topic in computer vision and has attracted significant interest from various fields, it has revealed two issues while driving the rapid development of salient detection. (1) The salient regions in high-resolution images exhibit significant differences in location, structure, and edge details, which makes them difficult to recognize and depict. (2) The traditional salient detection architecture is insensitive to detecting targets in high-resolution feature spaces, which leads to incomplete saliency predictions. To address these limitations, this paper proposes a novel embedded cross framework with a dual-path transformer (ECF-DT) for high-resolution SOD. The framework consists of a dual-path transformer and a unit fusion module for partitioning the salient targets. Specifically, we first design a cross network as a baseline model for salient object detection. Then, the dual-path transformer is embedded into the cross network with the objective of integrating fine-grained visual contextual information and target details while suppressing the disparity of the feature space. To generate more robust feature representations, we also introduce a unit fusion module, which highlights the positive information in the feature channels and encourages saliency prediction. Extensive experiments are conducted on nine benchmark databases, and the performance of the ECF-DT is compared with that of other existing state-of-the-art methods. The results indicate that our method outperforms its competitors and accurately detects the targets in high-resolution images with large objects, cluttered backgrounds, and complex scenes. It achieves MAEs of 0.017, 0.026, and 0.031 on three high-resolution public databases. Moreover, it reaches S-measure rates of 0.909, 0.876, 0.936, 0.854, 0.929, and 0.826 on six low-resolution public databases.