Triple-domain Feature Learning with Frequency-aware Memory Enhancement for Moving Infrared Small Target Detection
CoRR(2024)
摘要
Moving infrared small target detection presents significant challenges due to
tiny target sizes and low contrast against backgrounds. Currently-existing
methods primarily focus on extracting target features only from the
spatial-temporal domain. For further enhancing feature representation, more
information domains such as frequency are believed to be potentially valuable.
To extend target feature learning, we propose a new Triple-domain Strategy
(Tridos) with the frequency-aware memory enhancement on the spatial-temporal
domain. In our scheme, it effectively detaches and enhances frequency features
by a local-global frequency-aware module with Fourier transform. Inspired by
the human visual system, our memory enhancement aims to capture the target
spatial relations between video frames. Furthermore, it encodes temporal
dynamics motion features via differential learning and residual enhancing.
Additionally, we further design a residual compensation unit to reconcile
possible cross-domain feature mismatches. To our best knowledge, our Tridos is
the first work to explore target feature learning comprehensively in
spatial-temporal-frequency domains. The extensive experiments on three datasets
(DAUB, ITSDT-15K, and IRDST) validate that our triple-domain learning scheme
could be obviously superior to state-of-the-art ones. Source codes are
available at https://github.com/UESTC-nnLab/Tridos.
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