Salient object detection with boundary information

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 sub-networks, 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.