Adaptive Frequency Separation Enhancement Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) aims to accurately extract small targets from complex backgrounds. It plays a crucial role in applications such as military reconnaissance and drone detection. Existing studies predominantly focus on spatial-domain feature extraction. However, they neglect to fully exploit frequency-domain information, which may contain critical patterns for comprehensive signal characterization. Consequently, this limitation results in suboptimal detection accuracy. To effectively capture the structural characteristics of small targets, frequency-domain adaptive decomposition module (FDADM) is embedded into the spatial-domain encoder of the proposed network. The proposed FDADM effectively improves the accuracy of capturing target details and enhances structural feature consistency. Furthermore, considering that small targets may have blurred edges in complex backgrounds, gradient localization enhancement module (GLEM) is proposed. Gradient direction and gradient magnitude consistency functions are defined to precisely characterize target edges and contours, which improves detection accuracy. Experimental results demonstrate that the proposed method can effectively extract infrared small target under complex background conditions.