  Fully convolutional neural networks (FCNs) have shown their advantages in the
salient object detection task. However, most existing FCNs-based methods still
suffer from coarse object boundaries. In this paper, to solve this problem, we
focus on the complementarity between salient edge information and salient
object information. Accordingly, we present an edge guidance network (EGNet)
for salient object detection with three steps to simultaneously model these two
kinds of complementary information in a single network. In the first step, we
extract the salient object features by a progressive fusion way. In the second
step, we integrate the local edge information and global location information
to obtain the salient edge features. Finally, to sufficiently leverage these
complementary features, we couple the same salient edge features with salient
object features at various resolutions. Benefiting from the rich edge
information and location information in salient edge features, the fused
features can help locate salient objects, especially their boundaries more
accurately. Experimental results demonstrate that the proposed method performs
favorably against the state-of-the-art methods on six widely used datasets
without any pre-processing and post-processing. The source code is available at
http: //mmcheng.net/egnet/.
