In addition to RGB images, thermal IR images can be used to extract salient information, which is crucial for salient object detection. With the development and popularization of IR sensing equipment, thermal IR images have become readily available, and RGB-T salient object detection has become a popular research topic. However, there is currently a lack of comprehensive surveys on the existing methods. First, we briefly introduce machine learning-based RGB-T salient object detection methods and then focus on two types of deep learning methods based on CNNs and vision transformers. Subsequently, relevant datasets and evaluation metrics are introduced, and both qualitative and quantitative comparative analyses are conducted on representative methods using these datasets. Finally, challenges and future development directions for RGB-T salient object detection are summarized and discussed.