Bilateral Enhanced Complementary Network for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task aimed at identifying and segmenting camouflaged targets that are difficult to distinguish from complex backgrounds. To address the issues of incomplete detection and missing edges in camouflaged targets, this paper proposes a Bilateral Enhanced Complementary Network (BECNet) for COD. The network adopts a two-branch detection method, which is used for object recognition, edge recognition, and texture supervision respectively, to alleviate the feature ambiguity of features extracted from a single branch. Additionally, we introduce a Semantic Amplification Module (SAM) to further extract multi-scale semantic features. To effectively aggregate the discriminative features generated by both branches, we designed a Semantic-Texture Interaction Module (SIM). Finally, we incorporate an Edge Complementary Dual Attention Module (ECDA) during the decoding process to refine the model using edge information. Extensive experiments demonstrate the effectiveness and robustness of BECNet.