Salient object detection has been widely used in computer vision tasks such as image understanding， semantic segmentation， and object tracking by simulating the human visual system to find the most attractive targets for visual attention. With the rapid development of deep learning technology， salient object detection research has made great breakthroughs. This paper presents a comprehensive and systematic survey of salient object detection based on RGB images， RGB-D/T （Depth/Thermal） images， and light field images in the past five years. Firstly， the task characteristics and research difficulties of the three research branches are analyzed. Then the research technical route of each branch is expounded and the advantages and disadvantages are analyzed. At the same time， the mainstream datasets and common performance evaluation indexes of three kinds of research branches are introduced. Finally， possible future research trends are prospected.