  Camouflaged object detection (COD) aims to accurately detect objects hidden
in the surrounding environment. However, the existing COD methods mainly locate
camouflaged objects in the RGB domain, their performance has not been fully
exploited in many challenging scenarios. Considering that the features of the
camouflaged object and the background are more discriminative in the frequency
domain, we propose a novel learnable and separable frequency perception
mechanism driven by the semantic hierarchy in the frequency domain. Our entire
network adopts a two-stage model, including a frequency-guided coarse
localization stage and a detail-preserving fine localization stage. With the
multi-level features extracted by the backbone, we design a flexible frequency
perception module based on octave convolution for coarse positioning. Then, we
design the correction fusion module to step-by-step integrate the high-level
features through the prior-guided correction and cross-layer feature channel
association, and finally combine them with the shallow features to achieve the
detailed correction of the camouflaged objects. Compared with the currently
existing models, our proposed method achieves competitive performance in three
popular benchmark datasets both qualitatively and quantitatively.
