FDDBA-NET: Frequency Domain Decoupling Bidirectional Interactive Attention Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) involves determining the coordinate position of the target in complex infrared images. However, challenges arise due to the absence of internal texture structure, edge dispersion, weak energy characteristics of the target, and a significant amount of background clutter resembling the target’s morphology, impeding precise target location. To address these challenges, we propose an IRSTD network, named frequency domain decoupling bidirectional interactive attention network (FDDBA-NET), designed from the perspective of frequency domain decoupling (FDD). To suppress backgrounds that are similar in shape and structure to the target, exploiting the spectral differences between the target and background in the frequency domain, we adopt two learnable masks to extract the target-specific spectrum and the target-background-consistent spectrum detrimental to detection. The specific spectrum aids in target detection. A target-level contrast loss is designed to maximize the disparity between these two spectra, ensuring optimal detection results. In addition, to preserve target details in high-level semantic information, we introduce a bidirectional interactive attention module that leverages mutual modulation of deep global and shallow local features, facilitating deep and shallow feature fusion. To validate our approach, we conduct experiments comparing our proposed network with state-of-the-art conventional methods and deep learning methods on public datasets. The results demonstrate the superior performance of our method.