RGB-D Salient Object Detection by a CNN With Multiple Layers Fusion

Abstract

Recent saliency detectors use depth to improve the precision of the results. However, most of the existing RGB-D saliency detectors only treat depth as an additional feature, which cannot explore the distinguishing ability of the depth map. In this letter, we propose a deep convolutional neural network for RGB-D saliency detection. The network takes RGB-D as inputs and produces a saliency prediction in an end-to-end manner. To solve the scale problem, we fuse the features of higher layers to the features of lower layers gradually. Our method not only fully explores the information of the depth, but also makes the RGB and depth tightly coupled. Compared with the state-of-the-art RGB saliency detectors and depth-aware saliency detectors on two benchmark datasets, our method outperforms the competitors on mean absolute error and F-measure by large margins.