Dual-Branch Feature Fusion Network for Salient Object Detection

Abstract

Proper features matter for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present the dual-branch feature fusion network (DBFFNet), a simple effective framework mainly composed of three modules: global information perception module, local information concatenation module and refinement fusion module. The local information of a salient object is extracted from the local information concatenation module. The global information perception module exploits the U-Net structure to transmit the global information layer by layer. By employing the refinement fusion module, our approach is able to refine features from two branches and detect salient objects with final details without any post-processing. Experiments on standard benchmarks demonstrate that our method outperforms almost all of the state-of-the-art methods in terms of accuracy, and achieves the best performance in terms of speed under fair settings. Moreover, we design a wide-field optical system and combine with DBFFNet to achieve salient object detection with large field of view.