A ultra fusion network model based on nonlinear spiking neural P systems for infrared small target detection

Abstract

Infrared small target detection (IRSTD) has become a significant challenge due to the weak target, complex and variable backgrounds, and harsh imaging environments. Currently, most models are based on the U-net framework or its improved versions, but they often overlook the in-depth optimization of the output features. In response to this situation, this paper innovatively proposes a network specifically designed for this task, called UFNet-NSNP. The core of this network is its unique Ultra Fusion Net (UFNet), which efficiently integrates multiscale and multilevel features to significantly enhance the detail expressiveness of images. In addition, UFNet-NSNP introduces a novel Convolutional Attention Fusion Module (CAFM-SNP), which processes and further fuses the output features of the UFNet through cascaded Dilated convolutions and attention mechanisms to comprehensively enhance the global information capture capability of images. At the same time, the network has designed a nonlinear attention mechanism (NA) based on the nonlinear spiking mechanism, and by combining this mechanism with the spatial attention mechanism (SA), it has created a new attention module(NSNP-A). This module can enhance the nonlinear features of images. Experiments were conducted on the NUAA-SIRST and IRSTD-1k datasets. The experimental results show that UFNet-NSNP achieved an $IoU$ of 77.46% on the NUAA-SIRST dataset and 69.21% on the IRSTD-1k dataset, it demonstrates superior comprehensive performance compared to existing state-of-the-art (SOTA) methods. The code will be available at https://github.com/ZYX-111222/UFNet-NSNP.