Boosting Foreground-Background Disentanglement for Camouflaged Object Detection

In nature, certain objects exhibit patterns that closely resemble their backgrounds, a phenomenon commonly referred to as Camouflaged Object Detection (COD). We argue that existing COD approaches often suffer from insufficient discriminability for these objects, which we attribute to a lack of effective disentangling of foreground and background representations. To address this, we propose a novel Foreground-Background Disentanglement Network (FBD-Net) that enhances foreground-background disentanglement learning to improve discriminability. Specifically, we design an Edge-guided Foreground-Background Decoupling (EFBD) module, which facilitates the separated learning of foreground and background representations. Additionally, we introduce the Foreground-Background Representation Disentangling Head (DisHead) to further boost the discriminative power of the model. The DisHead consists of two objectives: the Edge Objective and the FoBa Objective. Furthermore, we propose three complementary modules: the Context Aggregation Module (CAM) for initial coarse object detection, the Scale-Interaction Enhanced Pyramid (SIEP) for multi-scale information extraction, and the Cross-Stage Adaptive Fusion (CSAF) module for subtle clue accumulation. Extensive experiments demonstrate that both our CNN-based and Transformer-based FBD-Nets outperform 26 state-of-the-art COD methods across four public datasets. Codes will be released on https://github.com/TomorrowJW/FBD-Net-COD .