The parallel visual perception network based on nonlinear spiking neural P systems for camouflaged object detection

Abstract

Numerous species have evolved camouflage through morphological adaptations that mimic environmental colors and textures, posing significant challenges for visual detection systems. Current camouflaged object detection (COD) methods remain limited in simulating biological visual mechanisms due to inadequate multi-stage cognitive modeling and weak biological correspondence in neural computations. To address these limitations, a parallel visual perception network (NSNPVPNet) based on nonlinear spiking neural P (NSNP) systems is proposed, simulating biological visual processes through three core modules: scene perception, cognitive reasoning, and decision inference module. A bio-inspired convolutional block reconstructed through NSNP systems enhances biological-computational mapping relationships. Experimental evaluations across four benchmark datasets demonstrate superior performance over twenty state-of-the-art COD methods, achieving average metric improvements of 3.2% ($S_m$), 2.5% ($a{E}_m$), 5.4% ($F_{\beta }^w$), and 1.2% ($M$). These advancements validate NSNP systems’ potential in COD applications and pioneer new bio-inspired approaches for bionic visual computing. The implementation is available at: https://github.com/Williamzhounan/NSNPVPNet.