A saliency detection method is proposed based on a cascaded full convolutional neural network. This network is mainly composed of two full convolutional neural networks. In the first stage, a full-convolutional neural network with a pyramid pooling module encoding and decoding architecture is constructed, and the pyramid pooling module can be used to effectively suppress the interference of background noises. In the second stage, an edge detection network is designed to learn the edge information of a salient region, and the accurate boundary saliency map is obtained by the fusion of two-stage saliency maps. The experimental results show that the proposed method has high accuracy, high recall rate, and low average absolute error in image significance detection dataset ECSSD and SED2, which provides the reliable pretreatment results for target recognition, machine vision and other applications.