Dual-path multi-branch feature residual network for salient object detection

Abstract

Salient object detection involves identifying visually distinctive objects within an image. However, the effective integration of long-range dependencies and local feature information in search for detailed segmentation remains a challenging task for established methods. In this paper, a novel dual-path multi-branch feature residual network (DMFRN) is proposed for salient object detection. First, we design a dual-path encoder consisting of a convolutional network and a lightweight vision transformer, which ensures the capture of local features and long-range dependencies while balancing efficiency. Secondly, a multi-scale feature interaction module (MFIM) is designed to further extract global feature information. It utilizes skip connections to facilitate interaction between multi-scale information, thereby enhancing feature representation. Finally, a multi-branch feature residual module (MFRM) is designed in the decoder to more effectively fuse the features provided by skip connections with the features in the encoder path. The residual structure is employed to refine the features. Our method is compared with competitors on five benchmark datasets, with average MAE and Fmax of 0.042 and 0.893 respectively, both outperforming other competing methods.