The invention discloses a salient object image detection method based on double-flow communication and global information guidance. The method includes: inputting an original color information image and an original depth information image into the convolutional neural network classification training model for training to obtain a salient object detection prediction graph; obtaining an optimal weight vector and a bias term of a convolutional neural network classification training model through a loss function between a set formed by the salient object detection prediction graph and a set formedby the real salient detection image; and inputting the three-dimensional image in the selected data set into the trained convolutional neural network model to obtain a saliency detection result. According to the invention, the salient object detection efficiency and accuracy of the image are improved, and easy migration of parameters is reduced.
Salient object image detection method based on double-flow communication and global information guidance