Spatial-Frequency Collaborative Learning for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task that struggles to accurately detect the objects concealed in the surrounding environment. This is largely attributed to the intrinsic similarity of the camouflaged objects with the surrounding environment. To address this challenge, we propose a Spatial-Frequency Collaborative Learning network for COD (SFCNet). Specifically, we propose a Domain Transformation Fusion (DTF) module to handle the similarity between the camouflaged objects and the background, because when processed in the frequency domain, the features of the camouflaged object and the background become easy to discriminate. Then, we design a Cross-domain Integration Unit (CIU) to integrate the high-level features progressively through a Spatial-Frequency Coordinated Fusion (SFCF) module and a Multi-scale Feature Enhancement (MFE) module. Finally, the low-level features are combined with the high-level features from different decoding stages to correct the camouflaged objects in detail. In addition, an Edge Amplification (EA) module is designed to enable the model to pay attention to the global contour of the camouflaged object. It can facilitate the generation of prediction maps with accurate object boundaries. Extensive experiments on four benchmark COD datasets show that SFCNet outperforms state-of-the-art (SOTA) COD models. Meanwhile, it also has the characteristics of low parameters (21.01 M) and low computational complexity (24.14 G). Codes and results are released on https://github.com/Zhaorui328/SFCNet.