Salient Object Detection with Boundary Information

Abstract

How to distinguish the low-contrast area near boundaries is a basic challenge in salient object detection. Most of recent state-of-the-art methods can achieve a good performance but still can't work well near boundaries. In this paper, we propose a novel network based on multi-level feature fusion with boundary information to solve this problem. Our model includes two separate decoding sub-networks, one is object sub-network to detect salient objects and another is boundary sub-network which outputs error maps to get boundary information by boundary maps. Moreover, we design a connection and fusion module to exchange and fuse information of objects and boundaries. In addition, to balance the two subnetworks, the optimal weight of loss function is obtained by experiments. The experimental results show that our model can distinguish the low-contrast area near boundaries well by boundary information and achieves the state-of-the-art performance on five common datasets.