Efficient Camouflaged Object Detection Network Based on Channel Reconstruction and Hybrid Attention
Kuan Wang, Xiuhong Li, Songlin Li, Yulong Bai, Boyuan Li, Ming Chih Lu, Zhenhong Jia · 2025
Camouflaged object detection is designed to segment objects that are highly integrated into the background and is highly challenging. At present, the main problem is how to effectively maintain the consistency of object semantics and avoid the loss of object information in the process of multi-scale feature fusion. To address this issue, we propose an efficient camouflaged object detection network (CHNet) based on channel reconstruction and hybrid attention. In our method, the feature channel is reconstructed using the strategy of Fusion-Separation-Transform-Fusion, and the adjacent features with strong semantic correlation are fused. Then, the global and local hybrid attention mechanism is used for layer-by-layer refinement, to maintain the consistency of object semantics to the largest extent. Experimental results on three large benchmark datasets show that our CHNet outperforms 11 state-of-the-art (SOTA) models, making it the most advanced COD model with 11.19M parameters. Our code is available at https://github.com/akuan1234/CHNet