Single-Frame Infrared Small Target Detection via Gaussian Curvature Inspired Network

Mingjin Zhang, Ke Yue, Boyang Li, Jie Guo, Yunsong Li, Xinbo Gao

Abstract: Single-frame infrared small target detection (SIRSTD) is in urgent demand for many practical tasks, such as fire rescue and urban management systems, benefiting from the excellent performance of infrared (IR) imaging in harsh climates and low-light environments. SIRSTD strives to segment small targets from the background as accurately as possible. However, in a real-world application, complex background environments with high brightness and strong edges have similar physical characteristics to small IR targets, which makes it extremely difficult to separate small targets. To address this challenge, we propose a novel Gaussian Curvature Inspired Network (GCI-Net). Inspired by the well-known Gaussian curvature, we develop a Gaussian curvature-based branch (GCB) to eliminate the smoothing noise and preserve the target structure texture information. In addition, we design a complementary patch-group attention (PGA) module that relies on the complementary relationship between low-level and high-level features to provide accurate guidance for GCB. The curvature information generated by the GCB is continuously optimized under the constraint of the curvature information of the ground truth. The proposed GCI-Net provides a reliable guarantee for accurate separation of small targets from the background. We conduct extensive experiments on the public IRSTD-1k and SIRST datasets. The experimental results demonstrate that the proposed GCI-Net outperforms the state-of-the-art (SOTA) methods.