Abstract: Effectively capturing and utilizing the features and complementary potential between different feature layers of an image is of great significance for accurately locating salient objects and preserving their detailed contours. Aiming at the problem of generalization ability caused by the fact that most existing RGB-T saliency detection models input the extracted image features directly into the aggregation modules, and use a simple recursive structure to locate the saliency object, a new method saliency enhancement and global awareness for RGB-T salient object detection is proposed. Firstly, the pre-trained model is used to extract the original features of the image. Secondly, a noise reduction module (NRM) is proposed, which acts as a bridge between encoding blocks and cross-modal decoding blocks to purify the features of salient objects. The high-level semantic guidance module (HSGB) is proposed to combine the high-level semantic information of different modes to preserve the location information of salient objects. Finally, a multi-modal interaction module (MMIB) is designed to retain the location information of salient objects and guide the aggregation of multi-level features to obtain the final salient map. Experimental results on VT821, VT1000 and VT5000 datasets show that the MAE index of the proposed method reaches 3.0%, 1.8% and 3.0%, respectively, which is superior to most existing methods.