Depth Enhanced Cross-Modal Cascaded Network for RGB-D Salient Object Detection

Deep modal can provide supplementary features for RGB images, which deeply improves the performance of salient object detection (SOD). However, depth images are disturbed by external factors during the acquisition process, resulting in low-quality acquisitions. Moreover, there are differences between the RGB and depth modals, so simply fusing the two modals cannot fully complement the depth information into the RGB modal. To enhance the quality of the depth image and integrate the cross-modal information effectively, we propose a depth enhanced cross-modal cascaded network (DCCNet) for RGB-D SOD. The entire cascaded network includes a depth cascaded branch, a RGB cascaded branch and a cross-modal fusion strategy. In the depth cascaded branch, we design a depth preprocessing algorithm to enhance the quality of the depth image. And in the process of depth feature extraction, we adopt four cascaded cross-modal guided modules to guide the RGB feature extraction process. In the RGB cascaded branch, we design five cascaded residual adaptive selection modules to output the RGB image feature extraction in each stage. In the cross-modal fusion strategy, a cross-modal channel-wise refinement is adopted to fuse the top-level features of the different modal feature branches. Finally, the multiscale loss is adopted to optimize the network training. Experimental results on six common RGB-D SOD datasets show that the performance of the proposed DCCNet is comparable to that of the state-of-the-art RGB-D SOD methods.