Abstract: Depth information has been proved to be practical information for salient object detection, but it is still a matter worth exploring that how can the depth information and RGB information complement each other better so as to achieve higher performance. To this end, this paper proposes a RGB-D salient object detection method based on depth image gain. A gain subnet is added to the double-branch network structure, and the gain of the depth image for saliency detection is obtained by the method of saliency map difference, which is used as a pseudo GT for the gain subnet pre-training. The three-branch network separately obtains RGB features, depth features, and depth gain information, and finally fuses the features of the three branches to obtain the final salient object detection result, the gain information provides the fusion basis for the two branch feature fusion. The experimental results of salient object detection based on depth image gain show that the salient object foreground object obtained by this method is more prominent, and it has better performance on multiple experimental datasets.

Key words: depth information, salient object detection, image gain