High-level features can help low-level features eliminate semantic ambiguity, which is crucial for obtaining the precise salient object. Some methods use high-level features to pro vide global guidance for some layers of the network. However, there remain several problems: (1) the global guidance has not been fully mined, which leads to its limited capacity; (2) the semantic gap between global guidance and lowlevel features is ignored, and simple merging methods will cause feature aliasing. To remedy the problems, we propose a dual-stream network based on global guidance with two plug-ins, global attention based multi-scale high-level feature extraction module (GAMS) to mine global guidance and scale adaptive global guidance module (SAGG) to seamlessly integrate the global guidance into each decoding layer. Comprehensive experiments on the five largest benchmark datasets demonstrate our method outperforms previous state-of-the-art methods by a large margin. Code is available at https://github.com/shuyonggao/DSGGN.