PRNet: Parallel Refinement Network With Group Feature Learning for Salient Object Detection in Optical Remote Sensing Images
Shengyu Gu, Yong Song, Ya Zhou, Yashuo Bai, Xin Hui Yang, Yuxin He · IEEE Geoscience and Remote Sensing Letters · 2024
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 with group feature learning (PRNet) framework for ORSI-SOD. Specifically, we propose a parallel refinement module with three parallel and same blocks in which two proposed different branches aggregating features in a group feature learning strategy, one for fine-grained features aggregation from up to down, another for reversal features aggregation from down to up. Benefiting from the novel and efficient framework, PRNet outperforms over 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%), runs up to real-time detection performance (36 FPS) on a single NIVIDIA 2080Ti GPU, achieving a better trade-off between performance and efficiency among deep comparison models. Project will be available at https://github.com/BIT-GuSY/PRNet-ORSI.