The Devil Is in the Masks: Label Disentangling Learning Network for Camouflaged Object Detection

Camouflaged object detection involves identifying objects that closely resemble their background. Despite the significant progress made by existing methods, we observe that detecting camouflaged objectboundariesremains more challenging than predicting central regions. This difficulty primarily arises from the use of binary mask supervision during training, which treats all pixels uniformly and neglects the varying importance of different pixels. To address this problem, we first employ distance transformation to disentangle the mask into a body map and a detail map. The body map focuses on the central region of the object, while the detail map concentrates on boundary areas, capturing more comprehensive pixel information than traditional boundary supervision methods. Building on this, we propose a label disentangling learning network, LDLNet, which consists of a body branch for body map learning, a detail branch for detail map capturing, and an iterative interaction branch to enhance collaboration between body and detail features. Specifically, to alleviate redundant information generated during the fusion process at different levels, body branch introduces a multi-dimensional denoising fusion block, whereas detail branch uses a multi-view context awareness block to capture the fragile boundaries of camouflaged objects. The iterative interaction branch fuses body and detail features and feeds them back to refine feature extraction in each branch. Extensive experimental results on four widely used public datasets demonstrate that LDLNet outperforms the state-of-the-art approaches. Code is available athttps://github.com/XDWang97/LDLNet