Skeleton-Boundary Guided Network for Camouflaged Object Detection

Camouflaged object detection (COD) aims to resolve the tough issue of accurately segmenting objects hidden in the surroundings. However, the existing methods suffer from two major problems: the incomplete interior and the inaccurate boundary of the object. To address these difficulties, we propose a three-stage skeleton-boundary guided network (SBGNet) for the COD task. Specifically, we design a novel skeleton-boundary label to be a complementary to the typical pixel-wise mask annotation, emphasizing the interior skeleton and the boundary of the camouflaged object. Furthermore, the proposed feature guidance module (FGM) leverages the skeleton-boundary feature to guide the model to focus on both the interior and boundary of the camouflaged object. Besides, we design a bidirectional feature flow path with the information interaction module (IIM) to propagate and integrate the semantic and texture information. Finally, we propose the dual feature distillation module (DFDM) to progressively refine the segmentation results in a fine-grained manner. Comprehensive experiments demonstrate that our SBGNet outperforms 20 state-of-the-art methods on three benchmarks in both qualitative and quantitative comparisons.