RGB-Thermal Salient Object Detection(SOD) aims to identify common salient regions or objects from both the visible and thermal infrared modalities. Existing methods usually based on the hierarchical interactions within the same modality or between different modalities at the same level. However, this approach may lead to a situation where one modality or one level of features dominates the fusion result during the fusion process, failing to fully utilize the complementary information of the two modalities. Additionally, these methods usually overlooking the potential for the network to extract specific information in each modality. To address these issues, we propose a Bidirectional Alternating Fusion Network (BAFNet) consisting of three modules for RGB-T salient object detection. In particular, we design a Global Information Enhancement Module(GIEM) for improving the information representation of high-level features. Then we propose a novel bidirectional alternating fusion strategy which is applied during decoding, and we design a Multi-modal Multi-level Fusion Module(MMFM) for collaborating mulit-modal mulit-level information. Furthermore, we embed the proposed Modal Erase Module (MEM) into both GIEM and MMFM to extract the inherent specific information in each modality. Our extensive experiments on three public benchmark datasets show that our method achieves outstanding performance compared to state-of-the-art methods.