A novel multi-scale salient object detection framework utilizing nonlinear spiking neural P systems

Abstract

Salient object detection (SOD) is fundamental to computer vision applications ranging from autonomous driving and surveillance to medical imaging. Despite significant progress, existing methods struggle to effectively model multi-scale features and their complex interdependencies, particularly in challenging real-world scenarios with complex backgrounds and varying scales. To address these limitations, this paper proposes a novel detection framework that leverages the hierarchical processing capabilities of nonlinear spiking neural P (NSNP) systems. The proposed framework introduces three key innovations: a bio-inspired convolution mechanism that captures fine-grained local features with neural dynamics; a semantic learning module enhanced by Contextual Transformer Attention for comprehensive global context understanding; and an adaptive mixed attention-based fusion strategy that optimizes cross-scale feature integration. The experimental results on four challenging benchmark datasets demonstrate that the proposed method outperforms fourteen other state-of-the-art methods, achieving average improvements of 1.02%, 1.3%, 2.3%, and 0.1% on the four evaluation metrics ($S_m$, $E_{\xi }^m$, $F_{\beta }^w$, and $MAE$), respectively. These advances validate the potential of spiking neural P systems in salient object detection, while opening new possibilities for bio-inspired approaches in visual computing.