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

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.