Aiming at the problem that the existing salient object detection algorithms suffers from the similar background interference, the detection accuracy of the target is low and the stability is poor. We propose a salient object detection method based on binocular vision. Firstly, inspired by the visual characteristics of the human eye, we consider the depth information acquired by binocular vision model as the salient features based on human visual characteristics. Secondly, we use the depth information and the result of region segmentation based on multi-feature fusion clustering to analyze the regional level depth saliency of image quantitatively. Thirdly, we make the weighted fusion of the global saliency map and regional level depth saliency map to highlight the objection area. Finally, we suppress the background to complete salient object detection based on the regional distribution of fusion results. The results show that compared with the existing methods, the proposed method can effectively suppress the interference of similar background with high accuracy and stability simultaneously.