**5. Conclusions**

We propose HANet, a hybrid network based on an attention mechanism for stereoscopic salient object detection. HANet uses a novel attention method that fuses RGB and depth attention maps to filter the original saliency features. Combined with an encoder–decoder network, HANet provides higher performance on the NJUD and NLPR datasets. Furthermore, an ablation study confirms that the HANet performance decreases when removing the RGB attention map, indicating the effectiveness of the proposed hybrid attention mechanism. The RGB attention map helps solving interference caused by the depth principle error, which occurs when non-salient objects are close to the depth sensor. Moreover, HANet provides high performance in scenes containing multiple objects, large objects, and other complex information.

**Author Contributions:** Y.C. conceived and designed the experiments, analyzed the data and wrote the paper. W.Z. supervised the work, helped with designing the conceptual framework, and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Natural Science Foundation of China (Grant Nos. 61502429), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY18F020012), and the China Postdoctoral Science Foundation (Grant No. 2015M581932).

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


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