Abstract: ‍ ‍This paper aims to propose a co-salient object detection model on RGBD images based on deep learning algorithm. Firstly， this paper constructs a multi-stream encoder structure which can be effectively employed to extract deep convolution features of RGBD images. Then， a multi-modal feature fusion module is used to sufficiently integrate the deep features from the encoder. Finally， a decoder equipped with the residual connection and deep supervision is designed to generate saliency maps. The experimental results on two public datasets show that the performance of our model is superior to the six state-of-the-art models， where the saliency map of our model presents more precise boundary details.