Exploring a Lightweight and Efficient Network for Salient Object Detection in ORSI
Jinyu Han, Fuming Sun, Yaoyao Hou, Jing Sun, Haojie Li
Abstract
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sub>, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β</sub><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>mean</i></sup>, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAE</i> 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/.