Exploring a Lightweight and Efficient Network for Salient Object Detection in ORSI

In recent years, Optical Remote Sensing Image Salient Object Detection (ORSI-SOD) has made substantial progress. Nevertheless, it remains an open-ended research area with complex challenges. Most existing ORSI-SOD methods, aiming for high-performance detection, demand large-scale parameters and high computational costs. This significantly restricts their application on resource-constrained devices, which have limited computing power and memory capacity. To tackle this issue, we propose a lightweight and highly efficient ORSI-SOD network, termed RAMENet. With only 5.18M parameters and 8.72G FLOPs, RAMENet can achieve competitive detection accuracy compared to state-of-the-art methods. Specifically, we devise a Dynamic Region-aware Block (DRB) that can be nested within the encoder to realize plug-and-play functionality. This enables the network to learn ORSI domain-specific feature representations, thus more effectively locating salient object regions. Furthermore, we present a novel Multi-path Enhanced M-shaped Decoder (MED), which integrates both bottom-up and top-down paradigms. Comprising two feature extraction sub-branches and a master feature refinement branch, this architecture achieves multi-granularity feature aggregation via cross-level feature interaction. Consequently, it significantly improves the detailed representation capability while maintaining the integrity of the object structure. Extensive experimental results indicate that the RAMENet outperforms 5 state-of-the-art lightweight methods in terms of S_α, F_β^mean, MAE on EORSSD and ORSSD datasets, with improvement reaching 0.68%, 0.92%, 0.13%, 0.60%, 1.13%, and 0.07%, respectively. The code and results are available at https://github.com/hjy0518/RAMENet/.