MSGEGA: Multiscale Gaussian Enhancement and Global-Aware Network for Infrared Small Target Detection

In recent years, the development of deep learning has significantly improved the performance of infrared small targets detection (IRSTD), particularly in addressing the issue of detecting small infrared targets with high signal-to-clutter ratio (SCR). However, detecting infrared small targets in complex backgrounds with low SCR and poor contrast with the background still faces challenges such as low detection rate and high false alarm rate. In this paper, we propose a novel network called MSGEGA: Multi-Scale Gaussian Enhancement and Global-Aware Network. MSGEGA combines Multi-Scale Gaussian Enhancement with Global-Aware to strengthen the ability to perceive global background features and capture local detailed features of the target. We design infrared small targets feature enhancement module based on multi-scale gaussian templates, which is applied to the head and skip connections to further improve the distinguishability between IRSTs with low-SCR and background. Meanwhile, Global-Aware leverages its global receptive field to suppress background and clutter components with high mutual similarity in images, integrates fine structures and boundary information from different layers, thereby enhancing the local feature representation of IRSTs. To validate the effectiveness of the proposed method, publicly available datasets are filtered to form a specialized low SCR dataset named IRST-LSCR. Extensive experiments show that MSGEGA achieves outstanding performance in detecting small targets with low SCR (SCR < 6). Specifically, the proposed method demonstrates significant advantages on the screened dataset, achieving an AUC of 0.992. At a detection rate of 0.871, it maintains a false alarm rate of 0.9e-5, outperforming all comparison algorithms. The model has 1.17 M parameters and 2.50 GFLOPs, achieving 39.80 FPS on a single RTX 4090.