Assisted Refinement Network Based on Channel Information Interaction for Camouflaged Object Detection
Kuan Wang, Xiuhong Li, Yulong Bai, Songlin Li, Ming Chih Lu, Zhenhong Jia · 2025
Current camouflaged object detection (COD) methods predominantly focus on cross-layer feature fusion while neglecting cross-channel information interaction within the same layer. To address this limitation, we propose ARNet, a novel channel-aware refinement network featuring three key innovations. First, our Channel Information Interaction Module (CIIM) enables bidirectional horizontal-vertical integration of split-channel features, effectively capturing complementary cross-channel information through channel dimension fusion. Second, the Assisted Guidance Module (AGM) generates semantic-aware guidance maps from backbone features to dynamically regulate feature decoding and enhance spatial localization. Third, a Multi-scale Enhancement (MSE) module employs multi-scale convolution and residual-connected strategies to refine feature representations and expand contextual perception. Extensive experiments on COD10K, CAMO, and NC4K datasets demonstrate ARNet's superiority over 12 state-of-the-art methods. Code and results are available at: https://github.com/akuan1234/ARNet