Abstract: In order to solve the problem that saliency detection based on RGB images cannot accurately detect sa-lient object in scenes such as multiple targets or small targets,a novel saliency detection network based on RGB-D cross-modal feature fusion is proposed.The network takes the improved fully convolutional network(FCN)as the dual-stream backbone network,extracts the color and depth features and makes predictions respec-tively,and finally uses the inception structure fusion to generate the final saliency map.Aiming at the problem that the actual receptive field of the original FCN is far lower than the theoretical receptive field,and the global image information is not really used,a dual-branch structure global and local feature extraction block is designed.The global feature branch is used to extract global information and guide local feature extrac-tion.This builds an improved FCN.In addition,considering the differences between color and depth features at different levels,a cross-modal feature fusion module is proposed,which uses dot product to selectively fuse color and depth features.Compared with addition and cascade,it uses dot multiplication.It can effec-tively reduce noise and redundant information.Comprehensive experiments on three benchmark datasets demonstrate that compared with 21 mainstream networks,this model is basically in the top three levels in terms of S value,F value and MAE.At the same time,the size of the model is analyzed.In comparison,its size is only 4.7%of MMCI,which has a decrease of 22.8%compared with the existing smallest model A2dele.