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.

通过在频域中在频域中使用可学习和可分离的频率感知机制，我们提出了一种能够在众多具有挑战性场景中准确检测到隐藏对象的方法，该方法包括频率引导的粗定位阶段和保持细节的精细定位阶段，并通过多级特征提取、优先引导校正和跨层特征通道关联等步骤实现校正和定位。与现有模型相比，我们提出的方法在三个流行的基准数据集上在定性和定量方面取得了有竞争力的性能。