PRNet: Parallel Refinement Network with Group Feature Learning for Salient Object Detection in Optical Remote Sensing Images

Recent years have witnessed many research efforts for addressing the challenging difficulties for salient object detection in optical remote sensing images (ORSI-SOD). However, due to irregular imaging mechanism and complex scene properties, existing models suffer from a disproportion of performance and efficiency, yet remain much exploration room. We propose the parallel refinement network (PRNet) with group feature learning (GFL) framework for ORSI-SOD. Specifically, we propose a parallel refinement module (PRM) with three parallel and same blocks in which two proposed different branches aggregate features in a GFL strategy, one for fine-grained features' aggregation from up to down and another for reversal features' aggregation from down to up. Benefiting from the novel and efficient framework, PRNet outperforms 15 state-of-the-art models on three public benchmark datasets (an average S-measure, mean E-measure, and MAE of 91.95%, 96.85%, and 1.25%, respectively) and runs up to real-time detection performance (36 FPS) on a single NVIDIA 2080Ti GPU, achieving a better tradeoff between performance and efficiency among deep comparison models. The project will be available at https://github.com/ BIT-GuSY/PRNet-ORSI.