SIHENet: Semantic Interaction and Hierarchical Embedding Network for 360° Salient Object Detection

With the application of panoramic imaging instruments in environmental perception and measurement, 360 degrees salient object detection (SOD) has become a concern. However, how to reduce the serious distortion of 360 degrees images is still an open question. In this article, we fully exploit the intrinsic information complementarity of cross-projection and cross-level features and innovatively propose a semantic interaction and hierarchical embedding network (SIHENet) to realize 360 degrees SOD. In terms of fusion of different projection features, considering that the distortion of cubemap projection (CMP) images is relatively small, we propose a multiscale hierarchical embedding (MSHE) module to establish the context of CMP images and embed equirectangular projection (ERP) images in it to reduce distortion. In terms of cross-level feature fusion, in order to solve the feature dilution problem in feature transfer, we propose a multiprojection semantic interaction (MPSI) module to integrate the high-level information of ERP and CMP images. In order to solve the edge ambiguity problem, we propose a multilevel semantic matching (MLSM) module to enhance the edge information at different levels of ERP image. Extensive experiments on three public 360 degrees datasets demonstrate the competitive performance of the proposed model in comparison to state-of-the-art (SOTA) 360 degrees SOD models.