  Fully convolutional neural networks (FCNs) have shown outstanding performance
in many computer vision tasks including salient object detection. However,
there still remains two issues needed to be addressed in deep learning based
saliency detection. One is the lack of tremendous amount of annotated data to
train a network. The other is the lack of robustness for extracting salient
objects in images containing complex scenes. In this paper, we present a new
architecture$ - $PDNet, a robust prior-model guided depth-enhanced network for
RGB-D salient object detection. In contrast to existing works, in which RGB-D
values of image pixels are fed directly to a network, the proposed architecture
is composed of a master network for processing RGB values, and a sub-network
making full use of depth cues and incorporate depth-based features into the
master network. To overcome the limited size of the labeled RGB-D dataset for
training, we employ a large conventional RGB dataset to pre-train the master
network, which proves to contribute largely to the final accuracy. Extensive
evaluations over five benchmark datasets demonstrate that our proposed method
performs favorably against the state-of-the-art approaches.
