Triple-Domain Feature Learning With Frequency-Aware Memory Enhancement for Moving Infrared Small Target Detection

As a subfield of object detection, moving infrared small target detection (ISTD) presents significant challenges due to tiny target sizes and low contrast against backgrounds. Currently existing methods primarily rely on the features extracted only from spatiotemporal domain. Frequency domain has hardly been concerned yet, although it has been widely applied in image processing. To extend feature source domains and enhance feature representation, we propose a new triple-domain strategy (Tridos) with the frequency-aware memory enhancement on spatiotemporal domain for ISTD. In this scheme, it effectively detaches and enhances frequency features by a local-global frequency-aware module (LGFM) with Fourier transform (FT). Inspired by human visual system (HVS), our memory enhancement is designed to capture the spatial relationships of infrared targets among video frames. Furthermore, it encodes temporal dynamics motion features via differential learning and residual enhancing. In addition, we further design a residual compensation to reconcile possible cross-domain feature mismatches. To our best knowledge, proposed Tridos is the first work to explore infrared target feature learning comprehensively in spatiotemporal-frequency domains. The extensive experiments on three datasets (i.e., DAUB, ITSDT-15K, and IRDST) validate that our triple-domain infrared feature learning scheme could often be obviously superior to state-of-the-art (SOTA) ones. Source codes are available at
https://github.com/UESTC-nnLab/Tridos
.