  Recent Salient Object Detection (SOD) systems are mostly based on
Convolutional Neural Networks (CNNs). Specifically, Deeply Supervised Saliency
(DSS) system has shown it is very useful to add short connections to the
network and supervising on the side output. In this work, we propose a new SOD
system which aims at designing a more efficient and effective way to pass back
global information. Richer and Deeper Supervision (RDS) is applied to better
combine features from each side output without demanding much extra
computational space. Meanwhile, the backbone network used for SOD is normally
pre-trained on the object classification dataset, ImageNet. But the pre-trained
model has been trained on cropped images in order to only focus on
distinguishing features within the region of the object. But the ignored
background information is also significant in the task of SOD. We try to solve
this problem by introducing the training data designed for object detection. A
coarse global information is learned based on an entire image with its bounding
box before training on the SOD dataset. The large-scale of object images can
slightly improve the performance of SOD. Our experiment shows the proposed RDS
network achieves the state-of-the-art results on five public SOD datasets.
