BENet: Boundary Enhance Network for Salient Object Detection

Zhiqi Yan, Shuang Liang

Abstract: Although deep convolutional networks have achieved good results in the field of salient object detection, most of these methods can not work well near the boundary. This results in poor boundary quality of network predictions, accompanied by a large number of blurred contours and hollow objects. To solve this problem, this paper proposes a Boundary Enhance Network (BENet) for salient object detection, which makes the network pay more attention to salient edge features by fusing auxiliary boundary information of objects. We adopt the Progressive Feature Extraction Module (PFEM) to obtain multi-scale edge and object features of salient objects. In response to the semantic gap problem in feature fusion, we propose an Adaptive Edge Fusion Module (AEFM) to allow the network to adaptively and complementarily fuse edge features and salient object features. The Self Refinement (SR) module further repairs and enhances edge features. Moreover, in order to make the network pay more attention to the boundary, we design an edge enhance loss function, which uses the additional boundary maps to guide the network to learn rich boundary features at the pixel level. Experimental results show that our proposed method outperforms state-of-the-art methods on five benchmark datasets.